Nom Schoenn

Anchling Prénom

Nicolas Organisme D'accueil

Varotto Silvia

Notice analytique AUTEUR

Keywords: français, 5 maxi) Choix modale, Préférences déclarées, modèles de régression logistique, Prévision de la demande Mots-clés (anglais, 5 maxi) Mode choice, Stated preferences, Logistic regression models, Forecasting Termes géographique

Titre (français)

Modélisation du choix modal comprenant les nouvelles formes de mobilités Titre (anglais) Mode choice modeling including new forms of mobility Résumé (français) Ce travail a pour objectif de modéliser le choix modal pour un trajet aller de manière à déceler les facteurs clés des nouvelles formes de mobilités tout en rendant compte de la corrélation qui existe entre ce choix et les informations que l'on possède sur le trajet retour. Pour ce faire, a été conduit un sondage préférences déclarées pour les membres de l'ENTPE. L'analyse des données récoltées et les estimations de modèles logit ont permis de montrer que le temps hors véhicules et le temps de recherche d'un parking au retour sont des facteurs essentiels dans le choix de mode de transport tout comme la météo.

Les différents modèles développés ont aussi permis de mettre en valeur l'existence d'une hétérogénéité dans les préférences modales entre différents groupes de personnes qui ne pouvaient être décrit à partir des variables socioéconomiques récoltés.

Résumé (anglais)

This work aims to model mode choice for an inward trip in order to understand the key factors of new forms of mobility while accounting for the correlation that exists between this choice and the information that we have on the return trip. To do this, a stated preference experiment was conducted for ENTPE members. The analysis of the collected data and the estimations of the logit models made it possible to show that the out-of-vehicle time and the parking search time on the way back have a negative impact on the shares of the new mobilities. The models also showed that the weather had a negative impact on bike and bike-sharing system. The various models developed have also made it possible to highlight the existence of heterogeneity in mode preferences between different groups of people that could not be described from the socioeconomic variables collected.

Liste des illustrations

Liste des tables

Introduction

In recent years, new forms of mobility have emerged in urban environments. These new forms are the result of technical innovations, the use of certain traditional modes of transport which have been redesigned or with the emergence of new technologies and what came out of it. These changes have brought modifications to the original transport systems, either by adding new services that can results in the saturation of that system or by transforming people behavior within that system. To observe these changes and predict them in order to ensure the resilience of transport networks, experts in the field have developed transport models. These models can help decision-makers to design policies in the context of urban planning.

Transports models have two main components: supply and demand. In transport planning it is common to use the so-called four steps model that was first introduced by the Chicago Area Transportation Study in the 50's (Hensher and Button, 2007). Historically, this is a static macroscopic trip-based model. The term static refers to the demand side of the model and it means that the model can give a picture of the level of service of a network at a certain time t because the OD matrices are supposed constant. Macroscopic refers to the scale of the model and means that the data is aggregated which shortens computation time, and hence facilitates the analysis of large-scale networks. Lastly, trip-based refers to the demand component, and it means that all simulated trips are supposed to be independent from one another. To summarize, this model can give a complete vision of a largescale network at a certain time t with a simplified modelling of congestion phenomenon and users behavior. However, as mentioned before, the urban environment tends to be more complex than when this model was developed. Congestion phenomenon are more frequent and people's behavior becomes more complex because of the wide range of available services. Hence, as the four-steps model is static it does not capture the propagation of congestion phenomenon in time and space and the correlation between trips within a day, other models have been developed to better represent urban environments.

To refine the modeling of the level of service, dynamic traffic models have been developed [START_REF] Johari | Macroscopic networklevel traffic models: Bridging fifty years of development toward the next era[END_REF]. These models acknowledge the impact of the state of the network at time t-1 on the level of service of the network at time t. Hence, they better represent the state of the network because they capture congestion propagation in time and space. Nonetheless, for many years those models only existed at the micro or mesoscopic scale making them time consuming when implemented for large-scale networks. Recently, the LICIT-Eco7 lab developed a dynamic traffic simulation platform, MnMs [START_REF] Becarie | GitHub -licit-lab/MnMS: Multimodal simulator for DIT4TraM project[END_REF], that works at the macroscopic level allowing the analysis of large-scale networks such as urban environments. It captures the spread of congestion phenomenon in time and space but also the multimodal compound of such transport systems. To do so the model uses the macroscopic fundamental diagram [START_REF] Daganzo | Urban gridlock: Macroscopic modeling and mitigation approaches[END_REF] to which has been added a third dimension to acknowledge multimodal traffic [START_REF] Geroliminis | A three-dimensional macroscopic fundamental diagram for mixed bi-modal urban networks[END_REF].

Alongside the efforts made to better represent the supply, researchers developed tools to enrich the estimation of the demand. To achieve that, activity-based models have been developed. Those models consider that the trip demand is not inherent, but it is the result of the need to perform activities along the day. Therefore, trips are not independent as they are connected through a schedule. Moreover, if trip-based models often consider that user always choose the shortest path, activity-based models use more parameters to better capture the decision mechanism of users as the cost of the trip or the comfort of the chosen mode. Furthermore, those models, because they better represent the process of choosing a certain trip and allow the forecast of more complex scenarios by playing with more variables, are more suited to analyze transport policies than trip-based model [START_REF] Zargayouna | Modèles multi-agents de déplacement à base d'activités : Etat de l'art[END_REF].

Dynamic traffic simulations and activity-based modeling have been investigated rather separately for a long time.

Research have been led to integrate one with another, but some gaps are still to be field. Section 1.1 presents some of those integrated models, section 1.2 highlights the limitations in the research field and section 1.3 depicts the problem of the current study.

Integration of activity-based modeling and dynamic simulations

In this section I review different techniques that have been used to integrate activity-based modeling with traffic simulation. Section 1.1.1 presents the sequential integration of schedule choice and route choice. Section 1.1.2 introduce the use of a supernetwork. Section 1.1.3 is about departure time module. Section 1.1.4 introduces several equilibria that have been developed to solve those models. Finally, section 1.1.5 depicts how to extend those models.

Sequential integration of schedule choice and route choice

The first one, and the most direct one, consists in having a module that create schedules for people from disaggregated data [START_REF] Bekhor | Integration of Activity-Based and Agent-Based Models: Case of Tel Aviv, Israel[END_REF][START_REF] Halat | Activity Cancellation and Rescheduling by Stressed Households: Improving Convergence in Integrated Activity-Based and Dynamic Traffic Assignment Models[END_REF][START_REF] Javanmardi | Integration of TRANSIMS with the ADAPTS Activity-based Model[END_REF][START_REF] Lin | Integration of Activity-Based Modeling and Dynamic Traffic Assignment[END_REF]Xu et al., 2017bXu et al., , 2017a)). Those schedules are then the core that generates trip demand, meaning that the route choice and the traffic simulation are done in a second module. Listed in The fact that there are two units in the computation process means that they are two criteria for the convergence of the model or that there are two convergences in the process. The route choice can be represented through several calculations : shortest path (which is not the real process when looking at the data) [START_REF] Bekhor | Integration of Activity-Based and Agent-Based Models: Case of Tel Aviv, Israel[END_REF][START_REF] Javanmardi | Integration of TRANSIMS with the ADAPTS Activity-based Model[END_REF], least generalized cost path (travel time, gas price, toll, comfort…) [START_REF] Halat | Dynamic network equilibrium for daily activity-trip chains of heterogeneous travelers: application to large-scale networks[END_REF][START_REF] Lin | Integration of Activity-Based Modeling and Dynamic Traffic Assignment[END_REF][START_REF] Zhang | An Integrated, Validated, and Applied Activity-Based Dynamic Traffic Assignment Model for the Baltimore-Washington Region[END_REF] or using the concept of hyperpath (least generalized perceived cost1 ) [START_REF] Halat | Activity Cancellation and Rescheduling by Stressed Households: Improving Convergence in Integrated Activity-Based and Dynamic Traffic Assignment Models[END_REF][START_REF] Verbas | Integrated Mode Choice and Dynamic Traveler Assignment in Multimodal Transit Networks: Mathematical Formulation, Solution Procedure, and Large-Scale Application[END_REF]Xu et al., 2017bXu et al., , 2017a)). The advantage of such models is that most of them are easy to manipulate, therefore they can model complex behavior such as the correlation between modes within a day, or the interaction between several household members. Nevertheless, they have limitations. The first one would be that in order to keep such level of detail ones need a lot of disaggregate data and a lot of information about the characteristics of the population, those data not always exist depending on the area we are working with because of anonymization policies. The second would be that because the route choice and the traffic simulation are done in a second phase, there can be inconsistencies in the produced schedules. Specifically, in each iteration we first fix travel time to choose a schedule and then we fix the schedules in order to choose a route, hence being late or early at an activity is seen as an inconsistency because it does not follow the chosen schedule. Thirdly, in order to reduce those inconsistencies and because of the convergence issues of the two modules, the models would need a lot of iterations making them computationally burdensome, and in the end some inconsistencies are never solved. Fourthly, this type of integrations has been only implemented in microscopic and mesoscopic simulations to keep a high-medium level of detail and because aggregating the data to have a macroscopic simulation would increase the computation time.

Supernetwork to jointly choose schedule and route

The second method is based on the way one represents the transportation system in the network. Specifically, the system is converted into a network in which the nodes represent activities, and the links represent either a route (if connecting two nodes) or the duration of an activity (if connecting one node with itself). Each user chooses a set of activities in a global set of all feasible sets of activities. Each link generates either a utility, if related to an activity, or a disutility, if related to a trip. Therefore, when a user chooses a certain route, it naturally produces a schedule to which a utility is related. The aim of the model is to maximize this utility. References can be found in One advantage of those types of models is that users only choose a set of activities, time is flexible, meaning that inconsistencies as described in section 1.4.1 does not exist. The preferred arrival time and duration of an activity are modelled by the utility related to the performance of that activity. This modelling is called supernetwork [START_REF] Ramadurai | Dynamic User Equilibrium Model for Combined Activity-Travel Choices Using Activity-Travel Supernetwork Representation[END_REF]. This natural integration allows to acknowledge both the utility of performing an activity and the disutility of traveling at the same time which includes exogenous factors such as gas price and tolls [START_REF] Vo | A household optimum utility approach for modeling joint activity-travel choices in congested road networks[END_REF]. Again, the simplicity of such models allows the modeling of correlation between modes within a day and leverage between household utility and individual needs [START_REF] Fu | Modelling joint activity-travel pattern scheduling problem in multi-modal transit networks[END_REF][START_REF] Vo | A mixed-equilibrium model of individual and household activity-travel choices in multimodal transportation networks[END_REF][START_REF] Vo | A household optimum utility approach for modeling joint activity-travel choices in congested road networks[END_REF]. However, because of the fact that each activity has to be represented with a node and that each activity is linked to another through several links the level of knowledge needed to represent the network is huge and the data might not be available. It also induces an important computation time. To reduce the computation burden, researchers converted the dynamic problem into a static equivalent which does not correctly model the congestion phenomenon. Moreover, this kind of network hasn't been implemented in large-scale networks, most recent studies have only been implemented for the Sioux Falls network (Fig 1). Lastly, only few modes are acknowledged in those supernetwork (car, bus and metro). [START_REF] Vo | A mixed-equilibrium model of individual and household activity-travel choices in multimodal transportation networks[END_REF][START_REF] Vo | A household optimum utility approach for modeling joint activity-travel choices in congested road networks[END_REF] 1.1.3.

Departure time module within the dynamic assignment

The last method uses a departure time module that is directly integrated in the simulation [START_REF] Cantelmo | A utility-based dynamic demand estimation model that explicitly accounts for activity scheduling and duration[END_REF]. This module can model departure time choice, route choice and mode choice at the same time. Both the utility of performing an activity and the disutility of traveling are acknowledged in a unique utility function which is the core of the module allowing a unique convergence criterion. In addition, the utility of the activity can model complex phenomena such as clock-based (changing over time) and duration-based (dependent of how long one performs the activity) utilities [START_REF] Cantelmo | Incorporating activity duration and scheduling utility into equilibrium-based Dynamic Traffic Assignment[END_REF]. Literature can be found in Table 3. Here schedule conflicts can arise but are seen as an additional disutility for being early or late rather than an inconsistency as discussed in section 1.4.1. Another advantage is the fact that those models have been developed for macroscopic simulations making them more efficient for urban environments. The integration between those modules uses the extension of the bottleneck proposed in [START_REF] Li | Bottleneck model revisited: An activity-based perspective[END_REF]. The issue with those integrations is that only few decision variables are in the models (departure time, route, mode) and the disutility of travelling only includes travel time and being late or early. Also, heterogeneity between users is not acknowledge, parameters for both the utility function of an activity and the disutility of travel are only trip/purpose dependent. Moreover, if the correlation between trips is correctly captured thanks to the activity duration, there can be inconsistencies in the mode choice within a day and there is no representation of the household members' interactions. Finally, exogenous factors such as the weather are not acknowledged in this model.

Article

Developed equilibria

About the equilibria formulated in those integrations, in the first two types of integration it is mainly fixed-point problem (Flötteröd et al., 2012;[START_REF] Halat | Activity Cancellation and Rescheduling by Stressed Households: Improving Convergence in Integrated Activity-Based and Dynamic Traffic Assignment Models[END_REF][START_REF] Javanmardi | Integration of TRANSIMS with the ADAPTS Activity-based Model[END_REF][START_REF] Verbas | Integrated Mode Choice and Dynamic Traveler Assignment in Multimodal Transit Networks: Mathematical Formulation, Solution Procedure, and Large-Scale Application[END_REF]Xu et al., 2017b) or variational inequality [START_REF] Fu | A network equilibrium approach for modelling activity-travel pattern scheduling problems in multi-modal transit networks with uncertainty[END_REF][START_REF] Halat | Dynamic network equilibrium for daily activity-trip chains of heterogeneous travelers: application to large-scale networks[END_REF][START_REF] Lin | Integration of Activity-Based Modeling and Dynamic Traffic Assignment[END_REF][START_REF] Ramadurai | Dynamic User Equilibrium Model for Combined Activity-Travel Choices Using Activity-Travel Supernetwork Representation[END_REF][START_REF] Vo | A mixed-equilibrium model of individual and household activity-travel choices in multimodal transportation networks[END_REF][START_REF] Vo | A household optimum utility approach for modeling joint activity-travel choices in congested road networks[END_REF], which might be converted into a fixed-point problem thanks to a GAP function. Those equilibria mainly focus on satisfying Wardrop's first principle [START_REF] Wardrop | SOME THEORETICAL ASPECTS OF ROAD TRAFFIC RESEARCH[END_REF]. Concerning the third type the formulation is a complementarity problem [START_REF] Cantelmo | Incorporating activity duration and scheduling utility into equilibrium-based Dynamic Traffic Assignment[END_REF] which aims to satisfy the Nash equilibrium [START_REF] Nash | Equilibrium points in n -person games[END_REF] For solving those equilibria, the most common solver is the method of successive average [START_REF] Cantelmo | A utility-based dynamic demand estimation model that explicitly accounts for activity scheduling and duration[END_REF][START_REF] Cantelmo | Incorporating activity duration and scheduling utility into equilibrium-based Dynamic Traffic Assignment[END_REF][START_REF] Halat | Activity Cancellation and Rescheduling by Stressed Households: Improving Convergence in Integrated Activity-Based and Dynamic Traffic Assignment Models[END_REF][START_REF] Halat | Dynamic network equilibrium for daily activity-trip chains of heterogeneous travelers: application to large-scale networks[END_REF][START_REF] Lin | Integration of Activity-Based Modeling and Dynamic Traffic Assignment[END_REF][START_REF] Vo | A household optimum utility approach for modeling joint activity-travel choices in congested road networks[END_REF]Xu et al., 2017b). It might be paired with a quasi-Newton algorithm [START_REF] Halat | Dynamic network equilibrium for daily activity-trip chains of heterogeneous travelers: application to large-scale networks[END_REF], a diagonalization [START_REF] Vo | A mixed-equilibrium model of individual and household activity-travel choices in multimodal transportation networks[END_REF][START_REF] Vo | A household optimum utility approach for modeling joint activity-travel choices in congested road networks[END_REF] or a gradient projection algorithm [START_REF] Cantelmo | A utility-based dynamic demand estimation model that explicitly accounts for activity scheduling and duration[END_REF] which can also be used alone [START_REF] Ramadurai | Dynamic User Equilibrium Model for Combined Activity-Travel Choices Using Activity-Travel Supernetwork Representation[END_REF]. Others use systematic relaxation algorithm [START_REF] Bekhor | Integration of Activity-Based and Agent-Based Models: Case of Tel Aviv, Israel[END_REF].

Extending the models

As mentioned earlier, transport models aim to ensure the resilience of transportation systems, assist decisionmakers in creating new public policies, or even help them for urban planning. To achieve this, these models must be robust and therefore be regularly updated, for example, by incorporating the new forms of mobility we have already discussed. In section 1.2.5.1, I provide more detailed information about these new mobility forms. Section 1.2.5.2 introduces the existence of two types of data and explains how combining the two allows for the inclusion of these new ways of getting around.

Extension of dataset and reduction of data needs

The new forms of mobility are diverse. As previously mentioned, they can manifest either as new modes of transportation or as new uses of traditional modes of transportation. Research has been conducted to understand the impact of these forms on the current modal split to see their share but also to understand from which conventional modes sharing system users are coming from [START_REF] Manout | Implications of pricing and fleet size strategies on shared bikes and e-scooters: a case study from Lyon, France[END_REF]. Others worked on understanding how those new services are used [START_REF] Reck | Explaining shared micromobility usage, competition and mode choice by modelling empirical data from Zurich, Switzerland[END_REF]. In the meantime, some studies have been led to highlight the key factors that are influencing the utility functions of those alternative in mode choice modeling [START_REF] Aubagna | Electric scooter sharing: How do people value it as a last-mile transportation mode?[END_REF][START_REF] Krauss | What drives the utility of shared transport services for urban travellers? A stated preference survey in German cities[END_REF]). When used in the context of mode choice models, shared mobility systems are often considered modes used for short distances or as secondary options for first or last-mile connectivity. However, shared bicycles are used for a multitude of distances, serving as a possible alternative to more traditional modes even for medium-distance trips [START_REF] Reck | Explaining shared micromobility usage, competition and mode choice by modelling empirical data from Zurich, Switzerland[END_REF]. As for electric scooters, on their own, they may not be a competitive alternative for such distances [START_REF] Reck | Explaining shared micromobility usage, competition and mode choice by modelling empirical data from Zurich, Switzerland[END_REF]. Nevertheless, with the emergence of Mobility as a Service platforms, electric scooters from shared services could very well be used for mediumdistance trips [START_REF] Meng | Policy implementation of multi-modal (shared) mobility: review of a supply-demand value proposition canvas[END_REF]. However, to my knowledge, no study has been conducted in this direction.

1.1.5.2.

Revealed-preferences and stated-preferences Revealed-preferences data refers to dataset that include data about trips that have been done. Surveys to obtain such data often contain a set of questions to report the trips a household did during a determined period (in general households are asked to report the trips they made the day before the survey). The survey often includes questions about the purpose of the trip and socio-economic characteristics of each member of the household.

Stated-preferences data refers to dataset that include preference over several alternatives that don't necessarily exist. Surveys to obtain such data often contain a set of different situations and propose different alternatives in each situation (e.g., a person is proposed different transportation option to make a trip and depending on the information they have on each option they must choose which one suits them best).

The studies mentioned before include revealed-preferences dataset as inputs to estimate the current demand on the current network. However, activity-based modeling can also be used to estimate the impact of a new service on the network. Because revealed-preferences dataset don't include information about a service that does not exist yet, stated-preferences are preferred to estimate new modal shares. The issue with stated preferences is that they are biased. Studies show that there is a difference between stated-preferences and revealedpreferences. To overcome this issue it is common to use both dataset and to link them using a scale parameter for their standard deviations [START_REF] Dissanayake | Investigating household vehicle ownership, mode choice and trip sharing decisions using a combined revealed preference/stated preference Nested Logit model: case study in Bangkok Metropolitan Region[END_REF][START_REF] Dosman | Combining Stated and Revealed Preference Data to Construct an Empirical Examination of Intrahousehold Bargaining[END_REF][START_REF] Kim | Exploring Heterogeneous Structural Relationships Between E-Shopping, Local Accessibility, and Car-Based Travel: An Application of Enriched National Household Travel Survey Add-on Data[END_REF][START_REF] Van Essen | Travelers' compliance with social routing advice: evidence from SP and RP experiments[END_REF][START_REF] Weis | Surveying and analysing mode and route choices in Switzerland 2010-2015[END_REF]. In most recent studies, researchers proposed to build the statedpreferences survey based on the revealed-preferences answers of people to facilitate the response and to reduce bias from the beginning [START_REF] Rudloff | Mobility surveys beyond stated preference: introducing MyTrips, an SP-off-RP survey tool, and results of two case studies[END_REF][START_REF] Weis | Surveying and analysing mode and route choices in Switzerland 2010-2015[END_REF]. However, some recent studies tried to use revealed preferences to estimate the new modal shares [START_REF] De Clercq | Estimating the Potential Modal Split of Any Future Mode Using Revealed Preference Data[END_REF]. To do so, they first estimate the modal share for the alternatives that are present in the current revealed preference dataset and then they define the attributes for the new modes. Next, they compute how close the new modes of transportation are to the current modes. After, they use this closeness to define scale parameters and compute the new utility functions using those scale parameters. The issue with that methodology is that it hasn't been compared with a classic methodology using stated preferences data so it's hard to tell if the estimates are reasonable. In conclusion, to the state-of-the-art, the use of stated preferences data is still the best way to implement new modes in transport models.

Research gap

Although some major advances have been made in the integration of activity-based model and dynamic traffic simulation some limitations remain:

▪ Research Gap 1: Bike sharing system and e-scooter sharing system are not included together with conventional modes in mode choice modeling for medium-trip distance. ▪ Research Gap 2: Few studies acknowledge the existence of new mobilities in the mode choice process. ▪ Research Gap 3: Models that have been implemented in macroscopic simulation do not include mode consistencies within a day. ▪ Research Gap 4: If some studies solved modes consistencies, to my knowledge, none focused one the correlations between mode choice within a day. ▪ Research Gap 5: Integrated models at a macroscopic scale only acknowledge a few decision variables (departure time, route choice and mode choice). ▪ Research Gap 6: Exogenous factors, such as the weather or the weekday, are rarely acknowledged in the models; only gas price and tolls are considered in supernetworks and schedule module. ▪ Research Gap 7: Because of their complexity, recent models are still computationally burdensome, which might discourage policymakers that manage large-scale network from using them.

Research objective

This study aims to address research gap 1. The idea is that in order to integrate an activity-based model with dynamic traffic simulation we first need to have a robust mode choice model in which new mobility is included. I will benefit from this work to also address research gap 2, research gap 4 and research gap 6 by adding these factors in the choice model for future integration with traffic simulation. This means that the research objective is to model mode choice with new mobilities for medium-distance trip while acknowledging potential obstacles and constraints from the return trip. The model will be used to observe changes in the modal split and to see what are the key factors that could support an increase in the shares of emerging forms of mobility. To do so I intend to create a model by using an SP-experiment which will help by bringing new forms of mobilities and new variables that aims to represent obstacles and correlation related to each alternative. The SP-study will be restricted to home-work mid distance trip for people that work or study at the ENTPE. Mid-distance means that all origins will be in the Grand Lyon area and the only destination is the ENTPE. Hence the questions I will try to answer are:

Research question 1: Which factors influence the mode choice with emerging forms of mobility?

Research question 2: Do the information that one possesses on the return trip influence the inward mode choice?

The methodology for this study is as follows:

Fig 2.

Methodology for the present study

The remaining of this report is organized as follows: Section 2 details the methodology that allowed me to design an efficient SP-experiment. Section 3 analyses the collected answers. Section 4 focuses on the mode choice model and the estimation of their parameters. Section 5 presents some scenarios to understand how new mobility could be more attractive. Finally, section 6 presents my conclusion on this study, its limitations and future work.

Design of an SP experiment

Data collection Data Analysis

Estimation of the choice model Forecasting

Material and methods

The population whose mobility habits I wish to model is the population of the Lyon metropolitan area. The most recent data concerning this population are those from the 2015 household travel survey. Unfortunately, this "revealed preferences" data does not contain all the forms of mobility I wish to represent, in particular, it does not include self-service electric scooters. What's more, this data is 8 years old, and the habits of the people of Lyon have probably changed since then.

One solution would be to collect a stated preferences (SP) survey containing the alternatives I want to model on a smaller sample such as the ENTPE population. Section 2.1. presents the trips chosen for the questionnaire. Section 2.2. introduce the different attributes that will be used to describe these trips. Section 2.3 details the socioeconomic variables that will be collected. Finally, section 2.4. describe the method and tools used for the design of the survey.

Origin, destination and modes

To carry out this survey, I need to describe the situations I am going to present in it. First, I chose the trip I wanted to represent in our questions. I chose the Home-ENTPE trip. To facilitate the respondents thought process, I chose to design the survey using realistic trips. I used Google Maps to obtain different levels for each attribute. To ensure a certain variability in attribute levels, I chose 3 possible home locations:

Villeurbanne (53 rue Anatole France, 69100 Villeurbanne)

Lyon -Centre-Ville (69-59 Bd des Canuts, 69004 Lyon)

Banlieue (36 Rue Romain Rolland Bis, 69200 Vénissieux)

In order to obtain levels as close to reality as possible, I took the attributes of the home -ENTPE routes for an arrival at 9 am on a Thursday morning according to Google Maps forecasts. Once I had the trips, I had to choose which transport alternatives would be available for each trip.

In Lyon, the available modes are walking, private car, public transportation, personal bicycle (electric or not), personal scooter (electric or not), other private micro-mobility (roller skates, skateboard...), bike sharing-system (Vélo'v) (electric or not), e-scooter sharing system (Dott or Tier) and car sharing system.

Because of the distance between origin and destination (3.97 km, 7.37 km and 9.37 km as the crow flies), walking and e-scooter sharing-system have been excluded from the options as stand-alone modes. In order to include them, they are integrated with the public transport mode, giving us the first two alternatives in the survey: Walking/Public Transport/Walking (PT) and shared e-scooter/Public Transport/Walking (ESSPT). To simplify the questionnaire, I assumed that e-scooters were available at each origin, which is not the case [START_REF] Dott | Plaquette de présentation du cursus ingénieur de l'ENTPE[END_REF][START_REF] Tier | Application mobile Tier[END_REF]. I have ruled out the possibility of completing the trip by shared e-scooter, given the short distance between the nearest stop and ENTPE (450m).

The other three alternatives are the private car (PC), the personal bicycle (PB) and the bike sharing-system (BSS).

The combination of bike sharing-system and public transport is not considered, as the distance between the starting point and the nearest metro station, or the distance between the starting point and the start of the second section of the public transport route (around 1.5 km), is too short for this option to be relevant [START_REF] Reck | Explaining shared micromobility usage, competition and mode choice by modelling empirical data from Zurich, Switzerland[END_REF].

I have chosen to exclude the car sharing-system alternative. First, because I need to minimize the number of alternatives in order to optimize the number of responses required. Second, I decided to exclude this one, on the assumption that this service is used more when there are loads to carry or strong time constraints, and therefore it is used on an occasional basis in the context of a home -ENTPE trip.

Attributes of the alternatives

Once the alternatives had been chosen, I had to determine the attributes (i.e., characteristics) of each alternative.

Based on previous studies, I assumed that mode choice is influenced by in-vehicle travel time2 (TT), out-of-vehicle time (walking and waiting combined) (OT), number of transfers (NbT) and cost 1 .

In addition, I investigated potential obstacles to mobility services: Availability at Arrival (Avail) for the BSS option and Rain on Arrival (RA) for the PT option. To select these variables, I drew on empirical research conducted on obstacles to shared mobility [START_REF] Shui | A review of bicycle-sharing service planning problems[END_REF]. I considered three other variables to understand whether the information about the return trip can influence the mode chosen for the inward trip. These variables were the following ones: weather forecast for the return trip (WR), parking search time for the return trip (PST), and additional time spent in traffic jams by car during the return trip the day before (CT).

All times are expressed in minutes and the costs in euros. The number of changes is an integer number, and availability at arrival is expressed as the number of spots remaining at the "VAULX-Hôtel de Ville" Velo'v station at the time the decision is made. Rain during the inward journey is a binary variable equal to one when it rains, weather forecast on the return trip is an integer between 0 and 100 expressing the percentage of the rain probability on the return trip, and extra time due to traffic jams the day before is a number between 0 and TT*0.5.

To define the levels of these attributes, I mostly used Google Maps, following the methodology described earlier.

For the ESSPT alternative, I had to build several alternatives in order to have variability. For the departure from Vénissieux, there are 2 possible stops to start the PT trip. To calculate 2 alternatives, I took the distance between these 2 stops and the origin. For Lyon and Villeurbanne, I took the distance to the stop closest to the origin and the distance to the first transfer (Hôtel de Ville for Lyon and Laurent-Bonnevay for Villeurbanne) to calculate our 2 alternatives. I then calculated the travel time using a commercial speed of 15km/h [START_REF] Manout | Implications of pricing and fleet size strategies on shared bikes and e-scooters: a case study from Lyon, France[END_REF], to which I added the characteristics of the public transport routes departing from each of these points.

Costs were calculated using the fares available on the transport providers' websites [START_REF] Dott | Plaquette de présentation du cursus ingénieur de l'ENTPE[END_REF][START_REF] Jcdecaux | Grille tarifaire Vélo'V[END_REF]SYTRAL, 2023;[START_REF] Tier | Application mobile Tier[END_REF]. In order to have several levels, I considered prices with and without subscriptions, using the corresponding fares which are also available on the websites of these services 3 . For cars, I took the distance available on Google Maps, the average consumption of diesel (resp. petrol) for French vehicles (Statista Research Department, 2023) and multiplied it by the average price of diesel (resp. petrol) (Global Petrol Prices, 2023) to obtain two price levels for each origin. Availability at arrival varies between 0 and 30, 30 being the maximum number of free spots at the station. The possible parking search times are 0, 6 and 12 min. 0 corresponding to having a parking at home or an available spot at the nearest Vélo'V station for the BSS option, and 6 and 12 min corresponding to the search time according to the density of the zone considered.

Attributs Units Levels

𝑅𝐼

Socio-economic characteristics of the respondents

Collecting socio-economic data from respondents is important for several reasons. Firstly, these data enable us to check that our sample is representative of the population surveyed, and in the event that it is not, to correct the results using weights. Secondly, these characteristics enable the study oof differences in the decision-making mechanisms of different groups in the sample. To allow a direct comparison with the EMD 2015 survey, I have used the socio-economic variables they used. Thus, I collected information regarding age, last diploma obtained4 , employment 3 , possession of a driving license, vehicle ownership, type of dwelling occupation, number of adults and minors in the household, respondent's main modes of transport 3 , respondent's secondary modes of transport 3 if any, area of residence5 and possession of a transport season ticket.

-

Survey design

Once the attributes and socio-economic variables had been determined, the choice situations had to be created. My aim was to obtain the maximum amount of information with the minimum number of answers. To do this, I decided to adopt an efficient design for the survey, and to find an efficient design I used Ngene software (ChoiceMetrics, n.d.). Ngene is a software package which, starting from a model specification, selects the attribute levels of each alternative in such a way as to minimize the chosen criterion. In this case, I chose the "S-optimality" criterion (Eq. 1) which is a necessary condition to have significant parameters (unfortunately it is not sufficient).

𝑆 -𝑜𝑝𝑡𝑖𝑚𝑎𝑙𝑖𝑡𝑦 = max 𝑘∈𝐾 (( 𝑠𝑒 1 (𝛽 𝑘 )𝑡 𝛼 𝛽 𝑘 ) 2 )
Eq. 1 Equation of the S-optimality used in Ngene [START_REF] Rose | Sample size requirements for stated choice experiments[END_REF] Where K is the set of parameters' names, 𝑠𝑒 1,𝑘 is the asymptotic standard error of parameter k with a sample size of 1, X is the vector of attributes, 𝛽 is the vector of parameters, 𝑡 𝛼 is the t-value corresponding to the (1 -𝛼) confidence interval.

To operate, Ngene needs the type of model, the number of choice situations to be generated, the number of situation blocks, the model's utility functions, the possible levels for each attribute, and an estimate of the model's parameters, called the priors.

Regarding the type of model, I will use logit models. Those models provide a framework that aims at reproducing people's choice mechanics. To do so logit models use utility functions (Eq. 2) which are supposed to describe the different factors that influence those behaviors using explanatory variables [START_REF] Ben-Akiva | Discrete choice analysis: theory and application to travel demand[END_REF]. In our case the possible explanatory variables are the trip attributes, as described in section 2.2., and the socio-economic variables, as described in section 2.3. Because there is multiple alternatives the logit model used is a multinominal logit model (MNL).

∀𝑖 ∈ 𝐼, ∀𝑗 ∈ 𝐽, ∀𝑘 ∈ 𝐾 𝑈 𝑖𝑗𝑘 = 𝐴𝑆𝐶 𝑗 + 𝛽 𝑗 * 𝑋 𝑖𝑗𝑘 + 𝜖 𝑖𝑗𝑘

Eq. 2 Utility function for logit models

where I is the set of individuals, J is the set of alternatives, K is the set of choice situations, 𝑈 𝑖𝑗𝑘 is the utility function of individual i for alternative j and choice situation k, 𝐴𝑆𝐶 𝑗 is the alternative specific constant of j, 𝛽 𝑗 is the vector of parameters to be estimated, 𝑋 𝑖𝑗𝑘 is the vector of explanatory variables of individual i and alternative j in choice situation k and 𝜖 𝑖𝑗𝑘 is the logistic-distributed error term.

Once the parameters are estimated, it is possible to compute the probability of choosing a certain alternative (Eq. 3).

∀𝑖 ∈ 𝐼, ∀𝑗 ∈ 𝐽, ∀𝑘 ∈ 𝐾 𝑃 𝑖𝑗 = 𝑒 𝐴𝑆𝐶 𝑗 +𝛽 𝑗 * 𝑋 𝑖𝑗𝑘 ∑ 𝑒 𝐴𝑆𝐶 𝑚 +𝛽 𝑚 * 𝑋 𝑖𝑚𝑘 𝑚∈𝐽 Eq. 3 Formula for computing the probability of choosing an alternative with a logit model

22

where I is the set of individuals, J is the set of alternatives, 𝑃 𝑖𝑗 is the probability that individual i choose alternative j, 𝐴𝑆𝐶 𝑗 is the alternative specific constant of j, 𝛽 𝑗 is the vector of parameters to be estimated, 𝑋 𝑖𝑗 is the vector of explanatory variables of individual i and alternative j.

There is a possibility that when choosing the explanatory variables, I didn't think of every factor that actually influence people's choice and so that it exists a correlation between repeated observations over time that cannot be captured with an MNL. Hopefully, advanced choice modeling techniques such as the mixed-logit model can capture these correlations using individual specific error [START_REF] Train | Discrete choice methods with simulation[END_REF]. In classic logit models the preference of individuals regarding the different alternatives is captured by the alternative specific constant (ASC) which is the same for every individual. In the mixed logit model with individual specific error, the error components aim at allowing the ASC to be individual specific meaning that each individual can have their own mode preference. When using that choice model, I will use Eq. 4.

∀𝑖 where I is the set of individuals, J is the set of alternatives, 𝑈 𝑖𝑗 is the utility function of individual i for alternative j, 𝐴𝑆𝐶 𝑗 is the alternative specific constant of j, 𝛽 𝑗 is the vector of parameters to be estimated, 𝑋 𝑖𝑗 is the vector of explanatory variables of individual i and alternative j for choice situation k, 𝛾 𝑗𝑚 is the parameter of alternative j that captures the impact of the error term 𝜎 𝑚𝑖 , 𝜎 𝑚𝑖 is the individual specific error components of alternative m that aims at representing the distribution of individual preferences for that alternative and 𝜖 𝑖𝑗𝑘 is the logisticdistributed error term.

In Ngene, we first estimated a MNL and then the mixed-logit model. However, the number of necessary responses estimated by Ngene for the ML was greater than the number of people at ENTPE. I therefore considered the MNL specification for the design of the study. I chose to generate 30 choice situations (minimum possible for the specified model) and to have 5 blocks of 6 questions. Ngene does not allow for socio-economic variables. Utility functions (Eq. 5) are available below. To construct the priors, I had the choice between using assumed value or a distribution with a MonteCarlo simulation but to keep it simple I have chosen to use the values found in the literature. The issue is that it was quite hard finding one that could be use. As mentioned in section 1.1.5.1. most studies conducted on BSS and ESS consider them as a full option for a short distance trip or as a first or last mile option and I couldn't find a study in which one of them was combined with PT (Baek et al., 2021;[START_REF] Krauss | What drives the utility of shared transport services for urban travellers? A stated preference survey in German cities[END_REF][START_REF] Reck | Mode choice, substitution patterns and environmental impacts of shared and personal micro-mobility[END_REF][START_REF] Reck | Explaining shared micromobility usage, competition and mode choice by modelling empirical data from Zurich, Switzerland[END_REF]. I finally chose to use the values of [START_REF] Krauss | What drives the utility of shared transport services for urban travellers? A stated preference survey in German cities[END_REF] (except for RI, WR and CT) because it was the closest to my own in terms of alternatives and in variables. However, there are still some differences with the current study. First, their survey was conducted on a different population. Here I focused on members of the ENTPE so mostly students, researchers, and people from administration, in their study they interrogated people from several cities of Germany and did not focus on a specific part of the population. Then, their experiment was divided in two, one for short distance (0.5km to 4km) and another for medium distance (2 to 20km), population was randomly divided in two so that each groups answer only for short distance or medium distance. Finally, they have more alternatives; for short distance they had e-scooter sharing-system, bike sharing-system, walk and private car; for medium distance they had car sharing-system, ride pooling, transit and private car, meaning that it also did not have a private bike option in both cases. Also, I used their parking search time estimate even though it was for the inward trip and mine is for the return trip because I couldn't find any studies that integrates variables of the return trip in the inward trip utility function. RI was taken from [START_REF] Reck | Mode choice, substitution patterns and environmental impacts of shared and personal micro-mobility[END_REF] but I transformed distance*precipitation in TT*precipitation. For WR and CT I just assumed an estimate based on the other because I could not find any studies with variables that were like those ones. All priors can be found in Table 7. The highest S-optimality criterion is 32.15, which means I need a minimum of 33 respondents per block, or 165 respondents in total. The complete table of choice situations is available in Appendix 1. For the distribution of the survey, I used the Qualtrics Metrics tool ("Qualtrics," n.d.). The questionnaire comprises two parts. The first collects the respondent's socio-economic data. The second is the SP manual users, and the SP experiment. An example of a choice situation is available in Appendix 2.

In order to obtain a homogeneous number of responses per block, a counter was set up to generate an equal number of blocks across the entire sample. To force respondents to read the question, and thus avoid them clicking in the same spot throughout the survey (in the event that they always wish to use the same mode of transport), the order of the mobility options was randomized for each choice situation. However, for reasons of readability and due to the limitations of the software used, the order of questions within each block could not be randomized. Thus, there may be a bias due to the user's fatigue when completing the survey.

The questionnaire was sent to all members of the ENTPE via internal e-mail addresses. To reduce the number of responses required, I decided to have each participant complete 2 blocks of 6 questions. In total, 398 respondents participated in the survey. 292 responses were 100% complete, and 33 contained only the person's socioeconomic information (these will not be considered as they contain no information on the SP experiment), giving a response rate of around 33%, which is close to the average response rate for surveys sent by e-mail [START_REF] Aubagna | Electric scooter sharing: How do people value it as a last-mile transportation mode?[END_REF]. Finally, each block obtained at least 143 responses (complete or incomplete), which is well above the minimum requirement of 33 (+300%).

Results and statistics

Once the data has been cleaned up, we obtained 3,619 observations from 332 separate respondents. In this section, we present the descriptive statistics of the survey. Section 3.1. describes explains mobility habits of ENTPE members for the home-work trip. Section 3.2. compares mobility habits and survey answers to show rationality in people's answers. Section 3.3. presents the descriptive statistics regarding the socio-economic variables. Finally, section 3.4. highlights the fact the sample I obtain was not representative and shows how adjustments were made.

Mobility habits for the Home -ENTPE trips

In the socio-economic parts of the survey question 36 was: "In your everyday life when you need to go from your home to the ENTPE which combination of modes do you mostly take" and question 37 was about the second mostly taken combination. I thought it could be interesting to look at those data to see if people kept on with their habits or if they tried new things during the experiment. Results are displayed in Fig 3. First, what we see is that people that do have a secondary combination of modes have something that is really similar to their main one. Then, we note that there is a strong tendency to include walk to the combination. The main reason of that is that a lot of people leave near the ENTPE (see Fig 4). Another reason is that when taking another mode such as transit or sharing-system you must combine it with walk in order to get in the network.

Then we have transit which again makes sense because there is a bus stop which is 500m away from the school. Moreover, the two other main residency area are Villeurbanne (3.6 km in Fig 4 ) and Lyon (6.9 km in Fig 4) which are well connected through the TCL network. After is private micro-mobility which can be explained by the fact that there are secured infrastructure for bike on almost all trip from Villeurbanne or Lyon, also there is a lot of parking infrastructure in the campus to encourage members to use their bike. Next, we have car which can be justified by the fact that most answers are from students (see Table 10) which mostly don't own a car compared to staff members (see Fig 5 and6). Then come sharing-systems which tells that today for a home -work trip this option is not really considered. However, if we look at the number of subscriptions per modes (see Fig 7) we see that almost 1/3 of the respondents actually have a pass for bike sharing-system meaning that they might use them for other purposes. So, we can expect that in the SP-experiment there is a pattern due to people's practice in their 6 I stopped at 10km to make the graph readable but in reality, 18 other respondents live between 10km and 100km away from the school. When we look at this graph, we note that the hierarchy between chosen mode and main/secondary is the same modes which might means that people just chose what they were accustomed to and went with it during the whole experiment. In order to check if they did, we can check if their decision seems rational. For that I will display the distribution of each chosen variable and the mean of the unchosen ones (fig. 9 -18) I also added the proportion of chosen values according to weather attributes. ) is that for all variables it seems like the peak of chosen values is always lower or similar to the peak of the mean of the unchosen values on the abscise, which means that even if people chose something close to what they actually do, there is still some rationality in their decision making. Of course, there are still peaks of chosen value that are higher than the mean of the unchosen one, those are due to trade-off between variables and will allow the estimation of our parameters when doing choice modeling. When looking at the chosen modes according to weather attributes' values we see that there is a change in shares depending on those variables, this change seems logic (less bike and less BSS when it's raining) meaning that individuals took those factors into account when making their decision revealing, again, a sense of rationality.

Descriptive statistics

I calculated some descriptive statistics in order to investigate the impact of the socio-economic variables on the mode choice. For the categorical variables, I did either a Chi² test, when all groups of that variable had at least 5 observations, or a Fisher test otherwise (Table 8). For the continuous variables I wanted to do a T-Test but unfortunately the variables did not follow a normal distribution, so I had to use a Shapiro-Wilk test ( In Table 8 and Table 9 I put significant variables in bold. We see that we have a lot of them meaning that the study captured some heterogeneity between groups which I will be able to model when doing the choice modeling.

Sample adjustments

To complete the analysis of the dataset I wanted to check if the sample was representative of the whole population of the ENTPE. To do so I went on the website of the school and look for reference values that could describe the population. Combining data from the social report (ENTPE, 2021), the description of the different labs that exist on campus (ENTPE, n.d.), and the school presentation booklet for potential new students (ENTPE, 2023), I found reference values to compare our sample with the whole population.

To compute the adjustment factors I used Eq. 6 for the lines that have nothing in other count (see Table 10) for the others the issue is that I was missing data for one of the categories (for example: I don't know how many non-binary people there is in the school) so I used Eq. 7. Results are displayed in .

We notice that the collected sample was not representative of the whole population. Specifically, women staff members that are between 25 and 45 years old were overrepresented such as civil-servant women and nonbinary students (adjustment factors below 1). On the other hand, men staff members who are between 36 and 45 years old were highly underrepresented and those who are between 18 and 35 years old are not represented at all (adjustment factors above 1).

Model specifications

Once I had these data, I set out to find the best possible specifications for the mode choice model. In this section, I present 5 of them, representing the main stages of the specification finding. Section 4.1 describes the estimate for the Ngene specification. Section 4.2 presents a much simpler model that uses only trip attributes collected during the survey (ATA model). Section 4.3 shed light on the facts that there is heterogeneity between groups (SEV model). Section 4.4 introduce the impact of environmental characteristics (WA model). Finally, section 4.5 highlights heterogeneity between individual in the decision making (ECP model). The models were estimated using PythonBiogeme [START_REF] Bierlaire | PythonBiogeme: a short introduction[END_REF]. To compare those models, I used likelihood ratio tests that are summarized in In order to compare the alternative specific constant, the continuous variables have been centered, and the alternative specific constant for the private car alternative has been normalized to 0.

Ngene model statistics

First, let's look at the Ngene Specification. For this first specification, I have adopted the utility functions (UF) used for the design of the SP survey (Eq. 5). The table below summarizes the estimation of the parameters and their pvalue.

Parameters

Number of parameters: 30

Log likelihood: -3987.322 First let's look at 𝛽 𝑐𝑜𝑠𝑡 , this parameter is significant, and its estimate is negative. This parameter is also generic which means that if any cost of any alternative increases while other parameters remain the same, then people are less likely to choose that alternative, which makes sense.

Regarding the private car utility function, we see that 𝛽 𝑇𝑇 𝑃𝐶 is negative which means that if the travel time of car increases while all other parameters remain the same, people are less likely to choose car, which seems rational.

Unfortunately, 𝛽 𝑃𝑆𝑇 𝑃𝐶 , 𝛽 𝑅𝐼 𝑃𝐶 and 𝛽 𝐶𝑇 𝑃𝐶 are not significant which means that the specification should be change or that the variables should be dropped.

Concerning the transit alternative, we see that 𝐴𝑆𝐶 𝑃𝑇 is significant and positive which means that PT is preferred to car when all variables are equal to 0. 𝛽 𝑇𝑇 𝑃𝑇 and 𝛽 𝑂𝑇 𝑃𝑇 are both significant and both of their estimate is negative which means that if the travel time or the out-of-vehicle time of PT should increase while all other parameters remain constant, the decision makers will be less attracted by that option. 𝛽 𝑅𝐼 𝑃𝑇 is also significant and negative meaning that when it rains the longest the trip is, the less people are likely to choose that option which does not really makes sense since PT is safe from the rain in theory. 𝛽 𝑁𝑏𝑇 𝑃𝑇 and 𝛽 𝑊𝑅 𝑃𝑇 are not significant.

Now for the private bike option, 𝐴𝑆𝐶 𝑃𝐵 is significant and positive meaning that the bike alternative is preferred to the car alternative when all variables are null. 𝛽 𝑊𝑅 𝑃𝐵 is significant and negative meaning that when the probability of rain in the outward trip, people will less likely choose the PB alternative. 𝛽 𝑇𝑇 𝑃𝐵 , 𝛽 𝑃𝑆𝑇 𝑃𝐵 and 𝛽 𝑅𝐼 𝑃𝐵 are not significant.

Regarding the bike sharing system alternative, 𝐴𝑆𝐶 𝐵𝑆𝑆 is significant and negative meaning that when all variables are equal to zero car is preferred to BSS. Unfortunately, all other parameters involved in the utility function of bike sharing-system are not significant which is an issue especially regarding 𝛽 𝑇𝑇 𝐵𝐿𝑆 because it would mean that the travel time has not impact on the utility function is not rational.

Finally, for the ESSPT utility function, we have 𝐴𝑆𝐶 𝐸𝑆𝑆𝑃𝑇 which is significant and negative meaning that car is preferred to ESSPT when all variables are null. 𝛽 𝑂𝑇 𝐸𝑆𝑆𝑃𝑇 is also negative and significant meaning that when all variables are fixed, and out-of-vehicle time is increased the probability of choosing ESSPT will decrease. All other parameters are non-significant.

What we see here is that a lot of parameters are not significant. The plan is then to work on the specification to have more significant parameters. To do that I'll begin with a simpler model and augment progressively the number of variables. In the following sections, non-significant parameters will be excluded from the model specification.

A simpler model

For this model, I put all trip attributes, dropped all environmental variables and all non-significant attributes. There are the classic variables (TT, OT, COST, NbT) but also the new one (PST and CT). Utility functions are detailed in Eq. 8, parameters estimate, and p-values are available in

Number of parameters: 12

Log likelihood: -4039.073

First, we see that 𝐴𝑆𝐶 𝑃𝑇 , 𝐴𝑆𝐶 𝑃𝐵 , 𝐴𝑆𝐶 𝐵𝑆𝑆 and 𝐴𝑆𝐶 𝐸𝑆𝑆𝑃𝑇 are still significant and goes in the same direction as in the Ngene model meaning that the interpretation still holds. Same goes for 𝛽 𝑐𝑜𝑠𝑡 , 𝛽 𝑇𝑇 𝑃𝐶 , 𝛽 𝑇𝑇 𝑃𝑇 and 𝛽 𝑂𝑇 𝐸𝑆𝑆𝑃𝑇 except that now 𝛽 𝑇𝑇 𝑃𝑇 is the parameter of both 𝑇𝑇 𝑃𝑇 and 𝑂𝑇 𝑃𝑇 because when they were separated they were not significantly different.

Regarding the utility function of the car, we see that there is only one parameter for both additional return times which is 𝛽 𝐴𝑑𝑇 . Its value is negative meaning that an increase in 𝐶𝑇 𝑃𝐶 or 𝑃𝑆𝑇 𝑃𝐶 while keeping all other parameters constant will make people less likely to choose the car option.

Now for the PT alternative we observe that reducing the number of variables, and so potentially retrieving correlation between some of them, allowed 𝛽 𝑁𝑏𝑇 𝑃𝑇 to be significant. Its value is negative meaning that an augmentation of the number of transfers for the transit option will result in making that alternative less attractive.

On the private bike utility function, we now have 𝛽 𝑇𝑇 𝐵 that is significant which is good. It is shared with the bike sharing-system alternative, meaning that an increase of travel time for PB (resp. BSS) while having all other parameters fixed will make the decision makers less likely to choose the bike (resp. BSS) option. 𝛽 𝐴𝑑𝑇 is also in both utility functions. In the bike it is the parameter for the parking search time, and in bike sharing-system it holds for out-of-vehicle time and parking search time. Again, its sign is negative meaning that an increase in one those variables will make its alternative less likely to be chosen. Now looking at magnitude we see that 𝛽 𝐴𝑑𝑇 is higher than 𝛽 𝑇𝑇 𝐵 meaning that in order to make the BSS option more competitive there is a need to reduce the out-of-vehicle time and the parking search time meaning that people want to have a the insurance that if they choose the bike sharing-system option they will have one next to their origin and that they will have a free spot when coming back in order to optimize their walking time.

Concerning the e-scooter combined with transit alternative, 𝛽 𝑇𝑇 𝐸𝑆𝑆𝑃𝑇 is significant and shared with both travel times of the ESS leg and the PT leg. Its sign is negative meaning that if the travel time of one leg increase it will have the same negative impact on the attractivity of the ESSPT option. 𝛽 𝑂𝑇 𝐸𝑆𝑆𝑃𝑇 is also significant and significantly different than 𝛽 𝑇𝑇 𝐸𝑆𝑆𝑃𝑇 , meaning that for the same increase but in the out-of-vehicle time the effect while be worst on the attractivity of that alternative, probably because as PT is combined with ESS creating an extra fare, people expect that the ESS leg is enough to reduce significantly the out-of-vehicle time of that option, so that it can be competitive to the PT alternative. The interpretation can go further by acknowledging the fact that 𝛽 𝑁𝑏𝑇 𝐸𝑆𝑆𝑃𝑇 is not significant meaning that what bothers people is the walking time more than the waiting time of the out-of-vehicle time otherwise 𝛽 𝑁𝑏𝑇 𝐸𝑆𝑆𝑃𝑇 would have been significant and negative.

So, this specification gives us a model where all parameters are significant and in which all important variables (travel time and cost) are, meaning we can move further and try capturing more complex behavior such as heterogeneity between groups.

Capturing groups heterogeneity

The next step was to add socio-economic variables to our model. These variables allow the acknowledgment of differences in decision mechanics among different groups of people. Due to the large number of socio-economic variables and the large number of classes in some of these groups, I can't test every possibility. As a guide, I used the descriptive statistics (Table 8 and9) to find out which are the most relevant and in which utility functions it is most suitable to include them. The final model contains a total of 24 socio-economic variables (counting each class of an included variable as a variable) that can be used in several utility functions (see Eq. 9) Parameters estimation and their p-values are available in Table 14. Except for 𝐴𝑆𝐶 𝑃𝐵 and 𝛽 𝑇𝑇 𝐵 all variables from model ATA are still significant and goes in the same direction meaning that the interpretation still holds.

𝑈
Regarding 𝐴𝑆𝐶 𝑃𝐵 we see that it is not significant anymore meaning that the bike option is neither preferred nor overlooked compared to the private car alternative.

Concerning 𝛽 𝑇𝑇 𝐵 we see that is has been separated in two parameters 𝛽 𝑇𝑇 𝑃𝐵 and 𝛽 𝑇𝑇 𝐵𝑆𝑆 that are both significant and negative meaning that the direction is the same only that the impact of travel time is higher in bike than in bike sharing-system. Now for the socio-economic variables, we note that 𝛽 𝐸𝐷𝑈𝐶.𝐿𝐸𝑉 0 is negative and in the PC and PB utility functions meaning that people who have the Bac as their last diploma are less likely to choose the private car or the private bike alternative compared to the other options when all other variables are null. 𝛽 𝐸𝐷𝑈𝐶.𝐿𝐸𝑉 1 is positive and only in the PT UF meaning that people whose last degree is a DUT or a BTS are more likely to choose the transit alternative compared to the other ones. 𝛽 𝐸𝐷𝑈𝐶.𝐿𝐸𝑉 2 is negative and in the utility function of the private car alternative meaning that people who have a master's degree, an engineer diploma or a maitrise as their last diploma are more likely to overlook the car alternative when all other variables are equal to 0. 𝐸𝐷𝑈𝐶. 𝐿𝐸𝑉 4 is present in the PC, PT and PB alternatives. It has one parameter for both the bike and transit option and another one for the private car alternative. All parameters are negative and the magnitude of the car's UF is higher than the other which means that people who have a PhD or an aggregation are less likely to choose PT or PB compared to BSS and ESSPT and even less likely to choose the car. 𝛽 𝐽𝑂𝐵 0 𝑂𝑅 1 is negative and in the utility functions of car and bike. The variable JOB is equal to 0 or 1 when the person is a student and student of the ENTPE have as a last diploma a bac and this variable is also present in the PC and PB option meaning that people who have the bac are even less likely to choose one of these alternatives if they also are a student.

𝛽 𝐺𝐸𝑁𝐷𝐸𝑅 0 is positive and in the bike UF meaning that men are more likely to choose this option than the others when all variables are null.

𝛽 𝑀𝐼𝑁𝑂𝑅 is negative in the bike UF meaning that people who have minors in their household are less likely to take the bike, which makes sense because they might have to bring their children to school which is harder with a bike than with a car or transit.

𝛽 𝑀𝑀 𝑃𝑀𝑀 is positive and in the bike alternative meaning that people who have a private non-motorized mean of transport are more likely to choose the private bike option. In the PT alternatives there is 𝑀𝑀 𝑊 and 𝑀𝑀 𝑇𝐶 , those two variables have two different parameters the one of 𝑀𝑀 𝑊 is positive and the one of 𝑀𝑀 𝑇𝐶 is negative which seems to not makes sense because people who use PT in their main modes of transportation should be more likely to choose the PT option. The issue is that the two variables are correlated because when you have PT in your main modes of transportation you need to have walk too. So actually what the value of these parameters tell us is that if you have walk in your main modes of transportation you are more likely to choose PT than any other alternative, and if you have PT in your main modes of transportation you also are more likely to choose PT (because 1.88-0.701=1.179 which is positive) but less than if you only have walk as main modes of transportation.

And after that there are 𝑆𝑀 𝑊 and 𝑆𝑀 𝑃𝑇 which are opposite signs. As said in section 3.1. if you have one mode as main modes there is a high probability that you also have it in your secondary modes (Fig 3 𝑆𝑀 𝑃𝑇 is also in the bike option and its parameter is negative which means that if you have transit in your secondary modes then you are less likely to choose the private bike option. Then there is 𝛽 𝑀 𝐶 is the private car utility function and that is positive which means that if a respondent has car in their main modes or their secondary modes, they are more likely to choose the car alternative when all other parameters are null. 𝛽 𝑀 𝑆𝑆 tells us that if you have sharing system as one of your main modes of transportation.

𝛽 𝑁𝐵.𝑀𝑉 is in the car utility function and is positive which means that the more you motorized vehicles in your household the more likely you are to choose car alternative, which looks rational. 𝛽 𝑁𝐵.𝑉𝐸𝐿𝑂 is the UF of bike and is positive which means than the more bike you access to, the more attractive will be the private bike alternative, which seems reasonable.

𝛽 𝑅𝑇 0 𝑂𝑅 1 tells us that if the respondent is an owner or the renter of social housing or a student's residence, then you are less likely to use public transportation which is hard to interpret.

Regarding the subscription, if you have one for the Vélo'V then you are more likely to choose BSS compared to the other alternative, less likely to choose transit and even less likely to choose car compared to bike, BSS and ESSPT. If you have a pass for the e-scooter sharing system, then you are more likely to choose ESSPT. And if you have a transit pass then you are less likely to choose car, bike or bike sharing-system compared to transit and ESSPT which makes sense.

Finally, 𝛽 𝑇𝑌𝑃𝐸 2 𝑂𝑅 3 tells that if you live in the city center or in the suburb you are less likely to choose the car alternative and 𝛽 𝑇𝑌𝑃𝐸 1 𝑂𝑅 2 says that if you live in the suburbs or the countryside you are more likely to choose the bike sharing-system alternative compared to the other one when all parameters are null.

Impact of the weather

After, the socio-economic variables I added the variables related to the rain. Rain on the way in was not significant when interacted with the time of travel so I used it as a binary variable, rain on the way out was divided by a 100 in order to be able to compare the parameters of rain on the way in with the ones of the way out. As they are exterior factors, I normalized the parameter of the car alternative to 0. For the elucidation of the specifications (Eq. 10) I only showed where the rain factors were added to make it clearer (the rest of the utility functions was the same as in the SEV model).

𝑈 We note that 𝐴𝑆𝐶 𝑃𝐵 is significant again and that is still positive meaning that when all variables are equal to 0, private bike is preferred to car. All parameters that were in the SEV model are still significant and in the same directions meaning that the interpretation still holds except for the magnitude of 𝛽 𝑇𝑇 𝐵𝑆𝑆 and 𝛽 𝐴𝑑𝑇 because the weather attributes reverse their order of magnitude.

Regarding the weather attributes, rain on the inward trip is significant in the utility functions of private bike, bike sharing-system and e-scooter combined with transit, it was not in the transit UF, meaning that when it rains respondent are equally attracted to public transportation and car which seems correct as both options are protected from the rain. In the PB and BSS options they have a shared parameter 𝛽 𝑅𝐼 𝐵 which is negative, that means that when it rains people are less likely to choose private bike or bike sharing-system than car or PT which seems correct. On the other hand, 𝛽 𝑅𝐼 𝐸𝑆𝑆𝑃𝑇 is positive which means that when it rains people are more likely to choose that alternative than the other ones. This might be due to the fact that in the PT option the first leg is walk while here it is e-scooter, meaning that this first leg should be shorter in the ESSPT option than the PT option and so that people will be safe from the rain sooner. For the rain in the outward trip only one parameter is significant in the bike utility functions, this makes sense cause if it rains on the way out the car alternative is not impacted, and for transit, bike sharing-system and ESSPT you're not constrained for your choice in the way out so you can choose an option that is protected from the rain. 𝛽 𝑅𝑂 𝑃𝐵 is negative meaning that when the probability of rain on way out increases the likelihood to choose the private bike alternative decreases which, again, makes a lot of sense.

Capturing heterogeneity between individuals

In order to fully capture heterogeneity between individuals I changed the choice model by adding individual specific random error components. Socio-economic variables aimed at capturing heterogeneity between groups and with the error components the model will be able to capture heterogeneity between individuals. The error components are here to capture what could not be captured with attributes or the variables that were used in the study, hence capturing heterogeneity between individuals without regarding the socio-economic group they're in.

For the error components I used a normal distribution and to estimate their parameters I used five hundred draws. Utility functions can be found in Eq. 11.

𝑈 Regarding the alternative specific constants and the trip attributes, they are all still significant and in the same direction except for 𝑅𝐼 𝐸𝑆𝑆𝑃𝑇 which is not significant anymore, but otherwise interpretation still holds.

Concerning the socio-economic variables, a lot of their parameters were not significant anymore (𝛽 ) for some of them, this means that the differences between individuals are more important that differences between groups. The parameters related to socio-economic variables that are still significant are in the same directions than before meaning that their interpretation still hold.

Now for the parameters related to the error components. I made three different specifications before coming with this one. The first one was one error component in each alternative (except car for normalization issues) and one generic parameter. The parameter was not significant, meaning that the people who had a preference did not have the same level of preference for each alternative. So, then I tried specific parameters and the one of transit and bike were significant. Which means that one group of individuals like public transportation and bike more than the others. Finally, I implemented four different error components in each alternative and the goodness of fit was way better according to the likelihood ratio test. The one of ESSPT was not significant so I dropped it. The other ones were significant meaning that there are some groups of people that like PT, bike and bike-sharing system more than the others and that those groups are made of different people, otherwise the specification with one error component would have had a better goodness of fit and the parameter of BSS would have been significant. Then I added parameters to cross the error components with other alternatives to see if the group of people who had a preferred option had an opinion on another one. Three of those parameters were significant: 𝛾 𝑃𝐵𝑃𝑇 , 𝛾 𝐵𝑆𝑆𝑃𝐵 and 𝛾 𝑃𝑇𝐵𝑆𝑆 . 𝛾 𝑃𝐵𝑃𝑇 shares the sign of 𝛾 𝑃𝑇 , 𝛾 𝐵𝑆𝑆𝑃𝐵 shares the sign of 𝛾 𝑃𝐵 and 𝛾 𝑃𝑇𝐵𝑆𝑆 shares the sign of 𝛾 𝐵𝑆𝑆 which means that the group of people that likes PT, also likes bike, which could make sense if we suppose that it is because of environmental consciousness; the group of people that prefers bike also likes BSS, if we suppose that there are people who just like biking; and the group of people that like BSS also like PT, which makes sense if we suppose that it's people who likes shared modes of transportation.

sense. If the objective is to compute the value of time the SEV model seem way more pertinent. The second thing is that since the issue seems to come from the variables related to the weather there might an issue related to how I constructed them.

In the remaining of this section will keep the WA model as reference because due to time constraints I cannot forecast with the ECP model however I'll keep in mind that the values-of-time are higher than expectations and that it can have an impact on the forecast.

Cost Elasticities of the probabilities

Cost elasticities are derived from the cost of an alternative and aims at computing how the probability of an alternative will react to a variation of the cost of that alternative (direct point elasticity) or another (cross point elasticity). The cost elasticities on the alternatives' probability are available on Table 21 for the EC model. Lines correspond to the alternatives from which we took the cost and columns represent the alternative that we took the probability from, so line Car, column Bike is the cross elasticity of cost car on the probability of choosing bike. Here we see that all direct point cost elasticities are negative meaning that an increase in the cost of an alternative will decrease the probability of that alternative which makes sense, the magnitude of those elasticities tells us that the bigger variation will happen in the e-scooter sharing-system combined with transit, with its cost, indeed if the cost of ESSPT increase by 10% then the probability will decrease by 1.5%. On the contrary, the cross-point elasticities are all positive meaning that an increase of the cost of an alternative will increase the probability of choosing any other alternative. In both model when ranking the elasticities, we clearly see that the cost of PT is the one that have the higher impact on the alternatives. Again all direct point elasticities are negative and all cross elasticities are negative meaning that when the time of one alternative increases the probability of choosing that alternative decreases and the probability of choosing the other increases which makes sense, the direct elasticity that is the highest in absolute value is the BSS alternative with an elasticity of -1.84 meaning that an increase of 10% in 𝑇𝑇 𝐵𝑆𝑆 will reduce the probability of choosing that alternative by 18.4%. For the cross elasticities Time PT is still the variables that has the most impact on the other alternatives.

Alternative

Travel Time Elasticities of the probabilities

Those elasticities help us to understand what the impact of the cost and the travel time of each alternative have on the probabilities of all alternatives however if we want to have the impact of other variables, we would need to compute other elasticities. Instead of doing that I focused on specific variables by creating scenarios that directly gives the change in the modal split that is induced by the variation of one variable. But first let's look at our modal split.

Modal split

Concerning the modal split, we can refer to the article [START_REF] Manout | Implications of pricing and fleet size strategies on shared bikes and e-scooters: a case study from Lyon, France[END_REF]. This article presents a modal split calculation for the city of Lyon. However, there are a number of differences between this study and theirs. Firstly, the data used includes information on the entire population of Lyon, not just ENTPE members. Secondly, the data are RP part of it is trip information for car, PT, bike and walk is from the EMD 2015 and the BSS and ESS trip records are from 2022 and this study uses SP data that is from 2023. Also, all trip types are included in the data of this article, not just home-work trips. Finally, the alternatives considered in their survey are the private car (driver or passenger), public transport, shared bicycle, shared electric scooter and walking. Thus, I merged the alternatives personal car driver and personal car and the alternative walking to have the most comparable modal split Fig 21. We see that the modal split is very different from that of the city of Lyon. The hierarchy is completely different, and the orders of magnitude are not respected. This is due to the fact that the population surveyed in the RP data they used is not the same as that of this survey. First, students are overrepresented compared to the Lyon metropolitan area. Other factors could be put forth, the infrastructures available are not necessarily the same. For example, parking spaces are limited at ENTPE, which may explain the car's low share of the modal split. On the other hand, there are plenty of bicycle facilities on campus, which may encourage people to use them, especially as civil servants, who make up a large if not majority of ENTPE members, can get a bonus if they use this mode of transport for the majority of their home-to-work commute. The social aspect must also be taken into account, particularly the difference in social class, which is higher at the ENTPE than in the overall population of the Lyon metropolitan area. Finally, the ENTPE presents itself as a school involved in the ecological transition, and we can therefore imagine that its members share a certain number of common values, notably ecological values, which may explain the very high share of public transport and cycling compared to the shares of the Grand Lyon. This means that, as expected, this model cannot be used as such for the Lyon metropolitan area and further work would be needed to do so. When looking at the resulting split we don't observe a huge change in the repartition of the shares for both scenarios but the second one seems to be better.

Indeed, in the first scenario, the share of ESSPT increases by 8.30%, 11.90% of the new users come from the car alternative, 69.79% comes from transit, 14.95% from bike, and 3.37% from BSS. In the second scenario, the increase in the modal share is of 20.73%. 14.70% of the transfer comes from car, 63.01% comes from public transportation users, 18.00% are from the private bike and 4.28% are from bike sharing-system.

Again, scenario 1 is best because it's the one that increases most the share of ESSPT and decrease less the shares of BSS.

Improving both shares'

In this last section I present two scenarios that aims at enhancing the shares of bike sharing-system and e-scooter sharing system combined with PT.

Scenario 1

In this first proposition I wanted to see what the effects of combining the first scenarios of the two other sections would be, meaning having more spots for the bike-sharing option and increase the number of e-scooters available. Modal split is available in Fig 29.

Scenario 2

In the last scenario I wanted to see if a negative policy on another alternative than BBS and ESSPT could have a positive and significant impact on the shares of those alternatives and thus reinforce the potential of those new mobility. The most effective way to that would be to simulate damage in the transit system because it the one that have the higher share, however, due to the cost of such system this doesn't seem realistic. The second share in ranking in the one of bike but due to environmental issues it seems unreasonable to damage the bike option nowadays. So, I turned to the car alternative and investigated what would happen if there were fewer parking slots available for the car option resulting in an increase of the parking search time of 30%. Resulting modal shares are displayed in Fig 30. From what we see scenario 1 improved both the share of BSS and ESSPT while scenario two seems to make no difference compared to reference in those shares.

In numbers we have that for the first scenario, the increase in the modal share is of 7.68% for BSS and of 20.42% for ESSPT. 13.58% of the transfer comes from car, 59.35% comes from public transportation user and, 27.01% are from the private bike. For the other scenario, the shares of bike sharing-system is heightened by 0.37% and by 0.23% for ESSP. Of course, a 100% comes from the car option.

Here we can say with confidence that the first scenario is better meaning that in this case, improving a service is better than damaging one. However, even if the best model makes no doubt there is a second question which is where are this people coming from? In the first scenario only 13.58% come from the car against a 100% for the other and even if the main objective is two improves the shares of new mobilities decreasing the shares of the car should be an objective as well meaning that is could be interesting to go further in order to find a better trade-off.

So, what we see here is that there is some potential that can be exploited in order to enhance the shares of the new forms of mobility. However, this potential is limited, as mentioned in section 3.1. shared mobility might be not adapted for this kind of trip.

Discussion and conclusion

This section concludes this report. During this internship I designed an SP survey, analyzed the data obtained and created a mode choice model for which I tested numerous specifications with the aim of finding the most suitable one, starting with a logit model and ending with a mixed logit model incorporating error components panel. These different specifications enabled me to test the impact of new variables on demand estimation. Section 6.1. presents the main research findings. Section 6.2. recognizes the limitations of the current study. Finally, section 6.3. introduce some recommendation for future research.

Main research findings

In summary, the results show that adding additional information to the ones used in the classical models can contribute to explain mode choice. For example, the information that users have about the return trips, in particular the parking search time of the return trip, the extra time spent in traffic jams the day before and the probability of rain on the way back have an impact on the mode choice on the outward trip. Rain on the inward trip also has a significant impact on the utility functions of bike and bike sharingsystem and with probability of rain on the return trip, has a large impact on the estimate of values of time. On the other hand, available spots at arrival are never significant.

I showed that the out-of-vehicle time of BSS and ESSPT and the parking search time on the way back of BSS were the most important time of those alternatives meaning that people need a high availability of these modes to consider them for a Work-Home trip which makes sense as being late could have bad consequences for them. Now comparing our results with the priors, we see that the results are not completely as expected. First, the specification for rain on the way in was changed cause not significant, it was supposed to be interacted with travel time, but it didn't work. Still with the rain, we expected it to be significant for all alternative whether it was for the inward or backward trip, however it was only significant for bike for the way out, and bike and BSS for the way in. Otherwise, the direction of every parameter is as expected but the magnitude of the ratios is different than expected. This can be explained by the fact that the parameters used come from a survey [START_REF] Krauss | What drives the utility of shared transport services for urban travellers? A stated preference survey in German cities[END_REF] that has many differences from our own. First, the geographical area. I focused on a very small and specific part of the Lyon metropolitan area in France, whereas the other survey was carried out in several German cities. Secondly, the alternatives proposed were not the same: PC, PT, PB, BSS, ESSPT for this study and in the other: self-service electric scooter, self-service bike, walking and private car for short distances and carpooling, self-service car, public transport and private car for medium distances [START_REF] Krauss | What drives the utility of shared transport services for urban travellers? A stated preference survey in German cities[END_REF]. In addition, the alternatives offered differed according to the distance travelled and people only had short distance our medium distance in their survey.

Also, the use of advance choice models allows to reveal that there was a correlation between answers in the survey that could be shown using only explanatory variables. Adding random error components showed that there were several groups of respondents not belonging in a certain group that could have been represented using the socio-economic variables collected that preferred one option over the others.

Then when computing indicators such as the value of time. For some models, I obtain a value that is very close to the one recommended by the French Ministry of Ecology, Energy and Territory, which reassures me on the validity of those models and the quality of the data.

Finally, by computing the model split with the developed model I was able to show that today the forms of mobility have a low share in the modal split. However, using forecasting methods I was able to create scenarios that could support the emergence of new forms of mobility. More specifically, I was able to support the idea that it is important to have a strong availability for sharing-systems to insure reliability for the decision makers. Nevertheless, the increase of the modal split seemed limited.

Limitations

This study has several limitations. Firstly, the survey was carried out on a very specific and very small sample of the Lyon population and is, a priori, not representative of the latter, which prevents us from using this data alone to predict the overall behavior of the Lyon population. Specifically, the modal split, I obtain quite different results from the modal split I was able to find for the city of Lyon. This result can be explained by the fact that the populations surveyed are not the same and that ENTPE employees and students are not representative of the Lyon population.

Secondly, further analysis is needed to understand if the random error components showed a real heterogeneity among individual's preference or that people only considered part of the alternative available. Indeed, several things could support that assumption. Firstly, the instructions might haven't been clear enough about the fact that, in choice situations, respondents had access to all the proposed transport options, and not just those to which the respondent actually has access in everyday life (particularly for the private car). It is also possible that, in trying to represent as accurately as possible, through the choice situations, the transport options actually available for a home -ENTPE journey, respondents got confused between hypothetical choices and reports of the mode they actually use, placing the survey in an in-between RP and SP. Similarly, some choice situations may not make sense for the respondent. Indeed, if an individual is accustomed to using public transport and has the means to do so, he will choose his home in such a way as to optimize his public transport travel time, making choice situations presenting very long travel times for this alternative of little relevance. Finally, not all potential decision-making factors have been taken into account, particularly the environmental factor, which could have a major impact at the ENTPE.

In addition some results are hard to interpret, for example parking search time on the way back should have not be significant for the bike sharing-system because of the fact that people don't have a mode constraints for the way back when choosing that alternative for the inward trip.

There were also some simplifications made when creating the survey, which may have complicated the estimation of certain parameters. For example, in real life, in terms of vehicle travel time, the bike sharing-system and bicycle options are very similar. However, bike sharing system has a cost, whereas a bicycle alone does not, which puts the BSS option at a major disadvantage and complicates the estimation of the parameters of the utility function of this alternative. In view of the number of respondents obtained, I could have imagined two different surveys, one with the Car, Public Transport, Bicycle and Electric Scooter Self-Service and Public Transport modes, and another with the Car, Bike Sharing-system, Bicycle and Electric Scooter Self-Service and Public Transport options depending on whether or not the respondent owned a bicycle, so as not to have to confront these two options and thus facilitate the estimation of the parameters of the two utility functions. Also, the non-in-vehicle trip times could have been better constructed, for example by having different levels for each alternative, and in particular for the alternative of e-scooter sharing system and public transport, so as to make this option more competitive, given that it is very expensive compared with the others. I could have added more variability to our variables, and in particular our cost variable, even if this meant departing a little from reality, in order to make it easier to estimate the parameters of our model. Finally, the parameters used in the construction of the design with Ngene were very far from the estimates we obtained, even in terms of ratios, which certainly distorted the efficiency of our design.

Also, there is definitely something happening with the weather attributes. When I add these factors, I obtain values-of-time that are higher than the expectations and are not realistic. One hypothesis could be that the construction of those variables (binary variable for the rain in, and probability of having rain on the way out) was not optimal.

Finally, the implementation of random error components in the model is not optimal. Indeed, I tried to have only one with a generic parameter or with specific parameters, but the model had a higher goodness of fit with three random errors. The problem is that if we use each of them for a single alternative, we can't account for exchanges between the different alternatives, i.e. when the respondent prefer an alternative do they have another preference. To overcome this problem, I have added

Fig 1 .

 1 Fig 1. Sioux Fall Network from (source : Vo et al., 2021, 2020) ................................................................................................. Fig 2. Methodology for the present study ................................................................................................................................... Fig 3. Main modes and secondary modes of surveyed individuals .............................................................................................. Fig 4. Distance Home-ENTPE for the people who answered the survey ..................................................................................... Fig 5. Proportion of student and civil-servant student per NB_CAR ........................................................................................... Fig 6. Proportion of staff per NB_CAR ..................................................................................................................................... Fig 7. Number of subscriptions per mode................................................................................................................................... Fig 8. Number of observation per mode in the SP-dataset .......................................................................................................... Fig 9. Number of observations for each chosen time of the experiment ...................................................................................... Fig 10. Number of observations for each chosen out-of-vehicle time of the experiment .............................................................. Fig 11. Number of observations for each chosen parking search time of the experiment ............................................................. Fig 12. Number of observations for each mean of unchosen time of the experiment.................................................................... Fig 13. Number of observations for each mean of unchosen out-of-vehicle time of the experiment ............................................. Fig 14. Number of observations for each mean of unchosen parking search time of the experiment ............................................ Fig 15. Number of observations for each mean of chosen number of transfer of the experiment .................................................. Fig 16. Number of observations for each mean of chosen cost of the experiment ........................................................................ Fig 17. Number of observations for each mean of unchosen number of transfer of the experiment .............................................. Fig 18. Number of observations for each mean of unchosen cost of the experiment ..................................................................... Fig 19. Proportion of chosen alternative when it is rain in the inward trip and when it is not ....................................................... Fig 20. Proportion of chosen alternative according to the probability of rain on the way back ..................................................... Fig 21. Modal split for the city of Lyon (Manout et al., 2023) .................................................................................................... Fig 22. Modal split resulting from the WA model ...................................................................................................................... Fig 23. Reference modal split .................................................................................................................................................... Fig 24. Modal split resulting from scenario 1 ............................................................................................................................. Fig 25. Modal split resulting from scenario 2 ............................................................................................................................. Fig 26. Modal split resulting from scenario 3 ............................................................................................................................. Fig 27. Modal split with the EC model when more e-scooter are available ................................................................................. Fig 28. Modal split with the EC model when e-scooter are free for students ............................................................................... Fig 29. Reference modal split .................................................................................................................................................... Fig 30. Modal split with EC model when the availability of e-scooters and bike sharing-system is increased .............................. Fig 31. Modal split with EC model when there are less parking spot available for the car alternative ..........................................

Table 1 .

 1 Schedule module and Route Choice Module Integration ............................................................................................... Table 2. Supernetwork Integration ............................................................................................................................................. Table 3. Departure Time Module ............................................................................................................................................... Table 4. Equilibria ....................................................................................................................................................................Table 5. Summary table of the trip-related attributes and their levels .......................................................................................... Table 6. List and description of the socio-economic variables .................................................................................................... Table 7. Summary table of parameters, their retained estimates and the S-optimality criterion related to each ............................. Table 8.p-values of the Chi² test and the Fisher test for the categorical socio-economic variables................................................ Table 9. p-value for the Shapiro-Wilk test for the continuous socio-economic variables ............................................................. Table 10. Number of people in the population and in the sample and their adjustment factors .................................................... Table 11. Likelihood ratio tests for the 6 models ........................................................................................................................ Table 12. Estimated parameter values of the model used for Ngene and corresponding robust p-value ........................................ Table 13. Estimated parameters of the model containing the new explanatory variables and their T-Test .................................... Table 14. Estimated parameters of the model containing socio-economic variables and their T-Test ........................................... Table 15. Estimation of model parameters and their T-Test ........................................................................................................ Table 16. Parameters estimate and their robust p-values for the EP model .................................................................................. Table 17. Value of Time for the ATA model ............................................................................................................................. Table 18. Value of Time for the SEV model .............................................................................................................................. Table 19. Time values for the WA model .................................................................................................................................. Table 20. Value of Time for the ECP model .............................................................................................................................. Table 21. Cost elasticities for the EC model ............................................................................................................................... Table 22. Time elasticities for the EC model .............................................................................................................................. Table 23. Reference modal split ................................................................................................................................................ Liste des abre viations ASC: alternative specific constant ATA model: model with all trip attributes Avail: number of spot available at arrival for bike sharing-system BSS: bike sharing-system CT: time spent in congestion the day before ECP model: model with error component panel ESS: e-scooter sharing system ESSPT: e-scooter combined with transit MNL: multinominal logit model ML: mixed logit model NbT: Number of transfer OT: out-of-vehicle time PC: private car PB: personal bike PST: parking search time on the way back PT: public transportation RI: rain on the way in RP: revealed preferences SEV model: model with socio-economic variables SP: stated preferences TT: travel time UF: utility function WA model: model with the weather attributes WR: probability of rain on the way back

Fig 1 .

 1 Fig 1. Sioux Fall Network from (source : Vo et al., 2021, 2020)

Fig 3 .

 3 Fig 3. Main modes and secondary modes of surveyed individuals

  who have these modes in their main modes or their second modesMain ModesSecondary Modes everyday lives. New forms of mobility might not have a strong share in the developed model because of the fact that we are investigating a home -work trip but hopefully I'll be able to capture why with the new variables and hence create scenarios to find policies that might change that.

Fig 4 .Fig 5 .

 45 Fig 4. Distance Home-ENTPE for the people who answered the survey 6

Fig

  Fig 6. Proportion of staff per NB_CAR

Fig 8 .

 8 Fig 8. Number of observation per mode in the SP-dataset

Fig 9 .

 9 Fig 9. Number of observations for each chosen time of the experiment

Fig 21 .

 21 Fig 21. Modal split for the city of Lyon (Manout et al., 2023) Fig 22. Modal split resulting from the WA model

Fig 29 .

 29 Fig 29. Reference modal split

Table 1 .

 1 Table 1 are articles in which the authors used that technic and some relevant characteristics of the model they developed. Schedule module and Route Choice Module Integration

	Article	Route choice	Convergence criteria	Simulation's scale
	(Lin et al., 2008)	Least cost path Generalized	OD matrices or travel times	Mesoscopic
	(Bekhor et al., 2011)	Shortest Path	Not Explicit	Mesoscopic
	(Javanmardi, 2012)	Shortest Path	Level of service and Input ABM	Microscopic
	(Halat et al., 2017)	Hyperpath	Penalty inconsistency schedule and Generalized path cost	Micro/Mesoscopic
	(Xu et al., 2017a)	Hyperpath	Generalized Cost Path	Mesoscopic
	(Xu et al., 2017b)	Hyperpath	Utility	Mesoscopic

Table 2 .

 2

	Article	Traffic Assignment	Convergence criteria	Added Value
	(Ramadurai and Ukkusuri, 2010)	Dynamic	Utility	Dynamic Supernetwork
	(Fu and Lam, 2018)	Static	Utility	Joint utility (homogenous household)
	(Vo et al., 2020)	Dynamic (static equivalent)	Travel Time	Joint utility (heterogenous household)
	(Vo et al., 2021)	Dynamic (static equivalent)	Travel Time	Mixed equilibrium (individual, household)

Table 2 .

 2 Supernetwork Integration

Table 3 .

 3 Departure Time Module

  . Comparison of those equilibria are available in Table4.

	(Ramadurai and Ukkusuri, 2010)	Variational Inequality	Gradient Projection Algorithm	Wardrop 1 st Principle -User
	(Fu and Lam, 2014)	Variational Inequality	Not Discussed	User
	(Halat et al., 2016)	Variational Inequality	Quasi-Newton Algorithm and Method of Successive Average	Wardrop 1 st Principle
	(Vo et al., 2020)	Variational Inequality	Diagonalization and Method of Successive Average	Household Optimum and Household System Optimum
	(Vo et al., 2021)	Variational Inequality	Diagonalization	Mixed Equilibrium (Individual, Household)
	(Cantelmo and Viti, 2019)	Complementarity problem	Method of Successive Average	Nash Equilibrium
			Table 4. Equilibria
	Article	Equilibrium	Solver	Type
	(Flötteröd et al., 2012)	Fixed-point	Not Explicit	Route
	(Javanmardi, 2012)	Fixed-point	Not Explicit	User
	(Verbas et al., 2016)	Fixed-point	Not Explicit	Wardrop's 1 st Principle
	(Halat et al., 2017)	Fixed-point	Method of Successive Average	Micro/Mesoscopic
	(Xu et al., 2017b)	Fixed-point	Method of Successive Average	Mesoscopic
	(Lin et al., 2008)	Variational Inequality	Method of Successive Average	Wardrop's 1 st Principle -User

Table 5

 5

. Summary table of the trip-related attributes and their levels

Table 6 .

 6 List and description of the socio-economic variables

		1 Missing value
		Socio-economic variable
	ID	Respondent's ID
	GENDER	Respondent's Gender
		0 Man
		1 Woman
		2 Non-Binary
	AGE	Respondent's age (years)
	EDUC_LEV	Last diploma
		0 BAC

  ∈ 𝐼, ∀𝑗 ∈ 𝐽, ∀𝑘 ∈ 𝐾 𝑈 𝑖𝑗𝑘 = 𝐴𝑆𝐶 𝑗 + 𝛽 𝑗 * 𝑋 𝑖𝑗𝑘 + ∑ 𝛾 𝑗𝑚 * 𝜎 𝑚𝑖

	+ 𝜖 𝑖𝑗𝑘
	𝑚∈𝐽
	Eq. 4. Utility function used for capturing individual's heterogeneity

  𝑈 𝑁𝑔𝑒𝑛𝑒 𝑃𝑉 = 𝛽 𝑇𝑇 𝑃𝑉 * 𝑇𝑇 𝑃𝑉 + 𝛽 𝑐𝑜𝑠𝑡 * 𝐶𝑂𝑆𝑇 𝑃𝑉 + 𝛽 𝑃𝑆𝑇 𝑃𝑉 * 𝑃𝑆𝑇 𝑃𝑉 + 𝛽 𝑅𝐼 𝑃𝑉 * 𝑅𝐼 * 𝑇𝑇 𝑃𝑉 + 𝛽 𝐶𝑇 𝑃𝑉 * 𝐶𝑇 𝑃𝑉 * 𝑇𝑇 𝑃𝑉 𝑈 𝑁𝑔𝑒𝑛𝑒 𝑃𝑇 = 𝐴𝑆𝐶 𝑃𝑇 + 𝛽 𝑇𝑇 𝑃𝑇 * 𝑇𝑇 𝑃𝑇 + 𝛽 𝑐𝑜𝑠𝑡 * 𝐶𝑂𝑆𝑇 𝑃𝑇 + 𝛽 𝑂𝑇 𝑃𝑇 * 𝑂𝑇 𝑃𝑇 + 𝛽 𝑁𝑏𝑇 𝑃𝑇 * 𝑁𝑏𝑇 𝑃𝑇 + 𝛽 𝑅𝐼 𝑃𝑇 * 𝑅𝐼 * 𝑇𝑇 𝑃𝑇 + 𝛽 𝑊𝑅 𝑃𝑇 * 𝑊𝑅 𝑈 𝑁𝑔𝑒𝑛𝑒 𝑃𝐵 = 𝐴𝑆𝐶 𝑃𝐵 + 𝛽 𝑇𝑇 𝑃𝐵 * 𝑇𝑇 𝑃𝐵 + 𝛽 𝑃𝑆𝑇 𝑃𝐵 * 𝑃𝑆𝑇 𝑃𝐵 + 𝛽 𝑅𝐼 𝑃𝐵 * 𝑅𝐼 * 𝑇𝑇 𝑃𝐵 + 𝛽 𝑊𝑅 𝑃𝐵 * 𝑊𝑅 𝑃𝐵 𝑈 𝑁𝑔𝑒𝑛𝑒 𝐵𝑆𝑆 = 𝐴𝑆𝐶 𝐵𝑆𝑆 + 𝛽 𝑇𝑇 𝐵𝑆𝑆 * 𝑇𝑇 𝑉𝐿𝑆 + 𝛽 𝑐𝑜𝑠𝑡 * 𝐶𝑂𝑆𝑇 𝐵𝑆𝑆 + 𝛽 𝑂𝑇 𝐵𝑆𝑆 * 𝑂𝑇 𝐵𝑆𝑆 + 𝛽 𝑃𝑆𝑇 𝐵𝑆𝑆 * 𝑃𝑆𝑇 𝐵𝑆𝑆 + 𝛽 𝐴𝑣𝑎𝑖𝑙 𝐵𝑆𝑆 * 𝐴𝑣𝑎𝑖𝑙 𝐵𝑆𝑆 + 𝛽 𝑅𝐼 𝐵𝑆𝑆 * 𝑅𝐼 * 𝑇𝑇 𝐵𝑆𝑆 + 𝛽 𝑊𝑅 𝐵𝑆𝑆 * 𝑊𝑅 𝑈 𝑁𝑔𝑒𝑛𝑒 𝐸𝑆𝑆𝑃𝑇 = 𝐴𝑆𝐶 𝐸𝑆𝑆𝑃𝑇 + 𝛽 𝑇𝑇 𝐸𝑆𝑆 * 𝑇𝑇 𝐸𝑆𝑆 + 𝛽 𝑇𝑇 𝐸𝑆𝑆𝑃𝑇 * 𝑇𝑇 𝐸𝑆𝑆𝑃𝑇 + 𝛽 𝑐𝑜𝑠𝑡 * (𝐶𝑂𝑆𝑇 𝐸𝑆𝑆 + 𝐶𝑂𝑆𝑇 𝑇𝐶 ) + 𝛽 𝑂𝑇 𝐸𝑆𝑆𝑃𝑇 * 𝑂𝑇 𝐸𝑆𝑆𝑃𝑇 + 𝛽 𝑁𝑏𝑇 𝑇𝐶 * 𝑁𝑏𝑇 𝐸𝑆𝑆𝑃𝑇 + 𝛽 𝑅𝐼 𝐸𝑆𝑆 * 𝑅𝐼 * 𝑇𝑇 𝐸𝑆𝑆 + 𝛽 𝑅𝐼 𝑇𝐶 * 𝑅𝐼 * 𝑇𝑇 𝐸𝑆𝑆𝑃𝑇 + 𝛽 𝑊𝑅 𝐸𝑆𝑆𝑃𝑇 * 𝑊𝑅

Eq. 5 Utility functions used for the design in NGENE

Table 9

 9

	Variable	Categorie	Test	CAR	PT	BIKE	BSS	ESSPT
	GENDER	Total	Chi2	0.764006 0.058796 0.004906 0.082464	1
	EDUC_LEV	0 Fisher	6.53E-11 9.12E-13 0.000239 0.120778	0.0425
	EDUC_LEV	1 Fisher	1.63E-21 3.62E-06 8.06E-05 0.118063 0.625574
	EDUC_LEV	2 Fisher	0.020608 0.038661 0.955098	1 0.113043
	EDUC_LEV	3 Fisher	5.16E-08 1.16E-06 0.116699 0.529181 0.018933
	EDUC_LEV	4 Fisher	0.334135 7.84E-05 7.57E-06 0.837981 0.006038
	EDUC_LEV	5 Fisher	0.012142 0.120894	3.4E-05	0.51789 0.572106
	JOB	0 Fisher	6.94E-19 6.63E-18 0.000446 4.05E-05 0.007766
	JOB	1 Fisher	9.59E-06 0.001254	1 0.457512 0.038536
	JOB	2 Fisher	1.91E-09 0.001046 0.056989 0.727441 0.540438
	JOB	3 Fisher	1.17E-57 1.25E-17 2.16E-05 1.31E-05 0.081068
	JOB	4 Fisher	0.330453 7.03E-14 5.52E-20 0.339265	0.05765
	DRIVER_LICENSE Total	Chi2	9.3E-08 0.001902 0.112215 0.229848 0.514691
	RESIDENCY_TYPE	1 Fisher	2.54E-06 6.01E-08 0.001313	0.08682 0.257673
	RESIDENCY_TYPE	2 Fisher	2.59E-12 0.331791	7.6E-06	0.23331 0.876373
	RESIDENCY_TYPE	3 Fisher	2.45E-42 1.99E-15 0.078183 4.74E-05 0.069359
	MM_W	Total	Chi2	4.52E-20	8E-13 1.67E-07 0.057071 0.034007
	MM_C	0 Fisher	1.49E-28 8.59E-39 1.91E-11 0.043547 0.028157
	MM_C	1 Fisher	1.49E-28 8.59E-39 1.91E-11 0.043547 0.028157
	MM_TC	Total	Chi2	5.91E-12 1.27E-15 1.37E-17	0.14112 0.970154
	MM_PMM	Total	Chi2	6.58E-07 3.17E-16 3.73E-60 0.227847 0.257575
	MM_SS	0 Fisher	0.000712 0.198827 0.365695 9.56E-08 0.518067
	MM_SS	1 Fisher	0.000712 0.198827 0.365695 9.56E-08 0.518067
	MM_O	0 Fisher	0.603051 0.304597	0.81369	1 0.002484
	MM_O	1 Fisher	0.603051 0.304597	0.81369	1 0.002484
	SM_W	Total	Chi2	4.77E-19 1.15E-11	8.6E-07 0.057112 0.020023
	SM_C	0 Fisher	1.1E-146	1.2E-49 1.21E-18 1.98E-05 0.122698
	SM_C	1 Fisher	1.1E-146	1.2E-49 1.21E-18 1.98E-05 0.122698
	SM_TC	Total	Chi2	4E-11 7.89E-16 1.38E-19 0.117579 0.764545
	SM_PMM	Total	Chi2	4.51E-06 6.22E-17 5.35E-59 0.184948 0.343834
	SM_SS	0 Fisher	1.1E-146	1.2E-49 1.21E-18 1.98E-05 0.122698
	SM_SS	1 Fisher	1.1E-146	1.2E-49 1.21E-18 1.98E-05 0.122698
	SM_O	0 Fisher	0.603051 0.304597	0.81369	1 0.002484
	SM_O	1 Fisher	0.603051 0.304597	0.81369	1 0.002484
	TYPE	1 Fisher	0.093192 0.599682 0.287762 0.293729 0.181246
	TYPE	2 Fisher	4.46E-23 1.59E-06 0.020725 0.001232	1
	TYPE	3 Fisher	1.51E-21 0.000212	1.41E-06 0.033581 0.645748
	SUB_TC	Total	Chi2	7.76E-23 3.06E-25 1.59E-21	0.0411 0.140087
	SUB_BSS	Total	Chi2	7.5E-14 0.099529 0.742947 4.09E-13 0.062054
	SUB_CSS	0 Fisher	0.141101 0.001979	1.22E-06	1	1
	SUB_CSS	1 Fisher	0.141101 0.001979	1.22E-06	1	1
	SUB_ESS	0 Fisher	0.141101	0.40328 0.315074	1 0.000297
	SUB_ESS	1 Fisher	0.141101	0.40328 0.315074	1 0.000297
	SUB_O	0 Fisher	0.904165 0.853354 0.191882 0.021505 0.401872
	SUB_O	1 Fisher	0.904165 0.853354 0.191882 0.021505 0.401872

), which test if the median of a group is significantly different from the one of the other groups. Table 8.p-values of the Chi² test and the Fisher test for the categorical socio-economic variables

Table 10 .

 10

	Count	Weight	Count	Other	Weight	Adj
	Population	population	Sample	Count	Sample	Factor
	ADMIN and OTHER

Table 10. Number of people in the population and in the sample and their adjustment factors

Table 11 ,

 11 in an Excel I took the number of parameters and the maximum likelihood of each model and computed the test. In each cell is test for the column model and the line model. For example, line ATA model and column WA model is the likelihood ratio test of those models. As it is written WA model, it means that this model was better according to the likelihood ratio test.

	Likelihood ratio Test	NGENE MODEL	ATA MODEL	SEV MODEL	WA MODEL	ECP MODEL
	NGENE MODEL	-	NGENE MODEL	SEV MODEL	WA MODEL	ECP MODEL
	ATA MODEL	NGENE MODEL	-	SEV MODEL	WA MODEL	ECP MODEL
	SEV MODEL	SEV MODEL	SEV MODEL	-	WA MODEL	ECP MODEL
	WA MODEL	WA MODEL	WA MODEL	WA MODEL	-	ECP MODEL
	ECP MODEL	ECP MODEL	ECP MODEL	ECP MODEL	ECP MODEL	-

Table 11. Likelihood ratio tests for the 6 models

Table 12 .

 12 Estimated parameter values of the model used for Ngene and corresponding robust p-value

		Estimate	Rob. p-value	Parameters	Estimate	Rob. p-value
	𝑨𝑺𝑪 𝑷𝑩	0.198	0.00142	𝛽 𝑅𝐼 𝑃𝐵	-0.00336	0.748
	𝑨𝑺𝑪 𝑩𝑺𝑺	-1.84	0	𝛽 𝑅𝐼 𝐵𝑆𝑆	-0.0314	0.125
	𝑨𝑺𝑪 𝑬𝑺𝑺𝑷𝑻	-3.15	0	𝛽 𝑅𝐼 𝑃𝐶	0.00298	0.784
	𝑨𝑺𝑪 𝑷𝑻	1.03	0	𝛽 𝑅𝐼 𝐸𝑆𝑆	-0.0852	0.317
	𝜷 𝑨𝑽𝑨𝑰𝑳 𝑩𝑺𝑺	0.0109	0.333	𝜷 𝑹𝑰 𝑷𝑻	-0.0148	0.0101
	𝜷 𝑪𝒐𝒔𝒕	-0.123	0.00372	𝜷 𝑹𝑶 𝑷𝑩	-0.0121	1.18E-13
	𝜷 𝑪𝑻 𝑷𝑪	-0.0118	0.198	𝛽 𝑅𝑂 𝐵𝑆𝑆	-0.00179	0.544
	𝜷 𝑵𝒃𝑻 𝑬𝑺𝑺𝑷𝑻	0.0182	0.925	𝛽 𝑅𝑂 𝐸𝑆𝑆𝑃𝑇	0.00727	0.139
	𝜷 𝑵𝒃𝑻 𝑷𝑻	-0.101	0.0908	𝛽 𝑅𝑂 𝑃𝑇	-0.00205	0.153
	𝜷 𝑶𝑻 𝑩𝑺𝑺	-0.00598	0.686	𝜷 𝑻𝑻 𝑷𝑩	-0.0155	0.0483
	𝜷 𝑶𝑻 𝑬𝑺𝑺𝑷𝑻	-0.0792	0.000651	𝛽 𝑇𝑇 𝐵𝑆𝑆	0.0196	0.262
	𝜷 𝑶𝑻 𝑷𝑻	-0.051	2.19E-12	𝜷 𝑻𝑻 𝑷𝑪	-0.0385	7.83E-06
	𝜷 𝑷𝑺𝑻 𝑷𝑩	-0.0119	0.164	𝛽 𝑇𝑇 𝐸𝑆𝑆	0.0797	0.163

Table 13

 13 𝑈 𝐴𝑇𝐴 𝑃𝐶 = 𝐴𝑆𝐶 𝑃𝐶 + 𝛽 𝑇𝑇 𝑃𝐶 * 𝑇𝑇 𝑃𝐶 + 𝛽 𝐶𝑜𝑠𝑡 * 𝐶𝑂𝑆𝑇 𝑃𝐶 + 𝛽 𝐴𝑑𝑇 * (𝐶𝑇 𝑃𝐶 + 𝑃𝑆𝑇 𝑃𝐶 ) 𝑈 𝐴𝑇𝐴 𝑃𝑇 = 𝐴𝑆𝐶 𝑃𝑇 + 𝛽 𝑇𝑇 𝑃𝑇 * (𝑇𝑇 𝑃𝑇 + 𝑂𝑇 𝑃𝑇 ) + 𝛽 𝐶𝑜𝑠𝑡 * 𝐶𝑂𝑆𝑇 𝑃𝑇 + β NbT 𝑃𝑇 * 𝑁𝑏𝑇 𝑃𝑇 𝑈 𝐴𝑇𝐴 𝑃𝐵 = 𝐴𝑆𝐶 𝑃𝐵 + 𝛽 𝑇𝑇 𝐵 * 𝑇𝑇 𝑃𝐵 + 𝛽 𝐴𝑑𝑇 * 𝑃𝑆𝑇 𝑃𝐵 𝑈 𝐴𝑇𝐴 𝐵𝑆𝑆 = 𝐴𝑆𝐶 𝐵𝑆𝑆 + 𝛽 𝑇𝑇 𝐵 * 𝑇𝑇 𝐵𝑆𝑆 + 𝛽 𝐶𝑜𝑠𝑡 * 𝐶𝑂𝑆𝑇 𝐵𝑆𝑆 + 𝛽 𝐴𝑑𝑇 * (𝑂𝑇 𝐵𝑆𝑆 + 𝑃𝑆𝑇 𝐵𝑆𝑆 ) 𝑈 𝐴𝑇𝐴 𝐸𝑆𝑆𝑃𝑇 = 𝐴𝑆𝐶 𝐸𝑆𝑆𝑃𝑇 + 𝛽 𝑇𝑇 𝐸𝑆𝑆𝑃𝑇 * (𝑇𝑇 𝐸𝑆𝑆 + 𝑇𝑇 𝐸𝑆𝑆𝑃𝑇 ) + 𝛽 𝑂𝑇 𝐸𝑆𝑆𝑃𝑇 * 𝑂𝑇 𝐸𝑆𝑆𝑃𝑇 + 𝛽 𝐶𝑜𝑠𝑡 * (𝐶𝑂𝑆𝑇 𝐸𝑆𝑆 + 𝐶𝑂𝑆𝑇 𝑃𝑇 )

	Eq. 8 Utility functions of the ATA model
	Name	Value	Rob. p-value
	𝑨𝑺𝑪 𝑷𝑩	0.282	2.34E-07
	𝑨𝑺𝑪 𝑩𝑺𝑺	-1.73	0
	𝑨𝑺𝑪 𝑬𝑺𝑺𝑷𝑻	-2.89	0
	𝑨𝑺𝑪 𝑷𝑻	1.08	0
	𝜷 𝑨𝒅𝑻	-0.0185	3.46E-05
	𝜷 𝑪𝒐𝒔𝒕	-0.155	1.02E-05
	𝜷 𝑵𝒃𝑻 𝑷𝑻	-0.178	6.06E-05
	𝜷 𝑶𝑻 𝑬𝑺𝑺𝑷𝑻	-0.0958	3.58E-05
	𝜷 𝑻𝑻 𝑩	-0.0157	0.000104
	𝜷 𝑻𝑻 𝑷𝑪	-0.0306	8.86E-09
	𝜷 𝑻𝑻 𝑬𝑺𝑺𝑷𝑻	-0.019	0.0354
	𝜷 𝑻𝑻 𝑷𝑻	-0.0351	0

Table 13. Estimated parameters of the model containing the new explanatory variables and their T-Test

Table 14 .

 14 𝑆𝐸𝑉 𝑃𝐶 = 𝐴𝑆𝐶 𝑃𝐶 + 𝛽 𝑇𝑇 𝑃𝐶 * 𝑇𝑇 𝑃𝐶 + 𝛽 𝐶𝑜𝑠𝑡 * 𝐶𝑂𝑆𝑇 𝑃𝐶 + 𝛽 𝐴𝑑𝑇 * (𝐶𝑇 𝑃𝐶 + 𝑃𝑆𝑇 𝑃𝐶 ) + 𝛽 𝐷𝐿 * (𝐷𝑅𝐼𝑉𝐸𝑅. 𝐿𝐼𝐶𝐸𝑁𝑆𝐸 == 1) + 𝛽 𝐸𝐷𝑈𝐶.𝐿𝐸𝑉 0 * (𝐸𝐷𝑈𝐶. 𝐿𝐸𝑉 == 0) + 𝛽 𝐸𝐷𝑈𝐶.𝐿𝐸𝑉 2 * (𝐸𝐷𝑈𝐶. 𝐿𝐸𝑉 == 2) + 𝛽 𝐸𝐷𝑈𝐶.𝐿𝐸𝑉 4 𝑃𝐶 * (𝐸𝐷𝑈𝐶. 𝐿𝐸𝑉 == 4) + 𝛽 𝐽𝑂𝐵 0 𝑂𝑅 1 * ((𝐽𝑂𝐵 == 0) + (𝐽𝑂𝐵 == 1)) + 𝛽 𝑇𝑌𝑃𝐸 2 𝑂𝑅 3 * ((𝑇𝑌𝑃𝐸 == 2) + (𝑇𝑌𝑃𝐸 == 3)) + 𝛽 𝑁𝐵.𝑀𝑉 * (𝑁𝐵. 𝐶𝐴𝑅 + 𝑁𝐵. 2𝑅𝑀) + 𝛽 𝑀.𝐶 * (𝑀𝑀 𝐶 + 𝑆𝑀 𝑐 ) + 𝛽 𝑆𝑈𝐵 𝑇𝐶 * 𝑆𝑈𝐵 𝑇𝐶 + 𝛽 𝑆𝑈𝐵 𝐵𝑆𝑆 𝑃𝐶 * 𝑆𝑈𝐵 𝐵𝑆𝑆 𝑈 𝑆𝐸𝑉 𝑃𝑇 = 𝐴𝑆𝐶 𝑃𝑇 + 𝛽 𝑇𝑇 𝑃𝑇 * (𝑇𝑇 𝑃𝑇 + 𝑂𝑇 𝑃𝑇 ) + 𝛽 𝐶𝑜𝑠𝑡 * 𝐶𝑂𝑆𝑇 𝑃𝑇 + β NbT 𝑃𝑇 * 𝑁𝑏𝑇 𝑃𝑇 + 𝛽 𝐸𝐷𝑈𝐶.𝐿𝐸𝑉 1 * (𝐸𝐷𝑈𝐶. 𝐿𝐸𝑉 == 1) + 𝛽 𝐸𝐷𝑈𝐶.𝐿𝐸𝑉 4 * (𝐸𝐷𝑈𝐶. 𝐿𝐸𝑉 == 4) + 𝛽 𝑅𝑇 0 𝑂𝑅 1 * ((𝑅𝑇 == 0) + (𝑅𝑇 == 1)) + 𝛽 𝑀𝑀 𝑇𝐶 * 𝑀𝑀 𝑇𝐶 + 𝛽 𝑀𝑀 𝑊 * 𝑀𝑀 𝑊 + 𝛽 𝑆𝑀 𝑇𝐶 𝑃𝑇 * 𝑆𝑀 𝑇𝐶 + 𝛽 𝑆𝑀 𝑊 * 𝑆𝑀 𝑊 + 𝛽 𝑆𝑈𝐵 𝐵𝑆𝑆 𝑃𝑇 * 𝑆𝑈𝐵 𝐵𝑆𝑆 𝑈 𝑆𝐸𝑉 𝑃𝐵 = 𝐴𝑆𝐶 𝑃𝐵 + 𝛽 𝑇𝑇 𝑃𝐵 * 𝑇𝑇 𝑃𝐵 + 𝛽 𝐴𝑑𝑇 * 𝑃𝑆𝑇 𝑃𝐵 + 𝛽 𝐸𝐷𝑈𝐶.𝐿𝐸𝑉 0 * (𝐸𝑈𝐶. 𝛽 𝑁𝐵.𝑉𝐸𝐿𝑂 * 𝑁𝐵. 𝑉𝐸𝐿𝑂 + 𝛽 𝑀𝑀 𝑃𝑀𝑀 * 𝑀𝑀 𝑃𝑀𝑀 + 𝛽 𝑆𝑀 𝑇𝐶 𝑃𝐵 * 𝑆𝑀 𝑇𝐶 + 𝛽 𝑆𝑈𝐵 𝑇𝐶 * 𝑆𝑈𝐵 𝑇𝐶 𝑈 𝑆𝐸𝑉 𝐵𝑆𝑆 = 𝐴𝑆𝐶 𝐵𝑆𝑆 + 𝛽 𝑇𝑇 𝐵𝑆𝑆 * 𝑇𝑇 𝐵𝑆𝑆 + 𝛽 𝐶𝑜𝑠𝑡 * 𝐶𝑂𝑆𝑇 𝐵𝑆𝑆 + 𝛽 𝐴𝑑𝑇 * (𝑂𝑇 𝐵𝑆𝑆 + 𝑃𝑆𝑇 𝐵𝑆𝑆 ) + 𝛽 𝑇𝑌𝑃𝐸 1 𝑂𝑅 2 * ((𝑇𝑌𝑃𝐸 == 1) + (𝑇𝑌𝑃𝐸 == 2)) + 𝛽 𝑀.𝑆𝑆 * (𝑀𝑀 𝑆𝑆 + 𝑆𝑀 𝑆𝑆 ) + 𝛽 𝑆𝑈𝐵 𝑇𝐶 * 𝑆𝑈𝐵 𝑇𝐶 + 𝛽 𝑆𝑈𝐵 𝐵𝑆𝑆 𝐵𝑆𝑆 * 𝑆𝑈𝐵 𝐵𝑆𝑆 𝑈 𝑆𝐸𝑉 𝐸𝑆𝑆𝑃𝑇 = 𝐴𝑆𝐶 𝐸𝑆𝑆𝑃𝑇 + 𝛽 𝑇𝑇 𝐸𝑆𝑆𝑃𝑇 * (𝑇𝑇 𝐸𝑆𝑆 + 𝑇𝑇 𝐸𝑆𝑆𝑃𝑇 ) + 𝛽 𝑂𝑇 𝐸𝑆𝑆𝑃𝑇 * 𝑂𝑇 𝐸𝑆𝑆𝑃𝑇 + 𝛽 𝐶𝑜𝑠𝑡 * (𝐶𝑂𝑆𝑇 𝐸𝑆𝑆 + 𝐶𝑂𝑆𝑇 𝑃𝑇 ) + 𝛽 𝑆𝑈𝐵 𝐸𝑆𝑆 * 𝑆𝑈𝐵 𝐸𝑆𝑆 Estimated parameters of the model containing socio-economic variables and their T-Test

	𝐿𝐸𝑉 == 0) + 𝛽 𝐸𝐷𝑈𝐶.𝐿𝐸𝑉 4 * (𝐸𝐷𝑈𝐶. 𝐿𝐸𝑉 == 4)

*RT = RESIDENCY_TYPE

Number

of parameters: 40 Log likelihood: -3175.636

  ). So, if you only have them in main modes the interpretation still holds. Then if you have walk in your secondary modes, you still prefer the PT option (1.88-1.67 = 0.21 which is greater than 0) but less than if you only have it in main modes. And if you have PT in secondary modes as well then you have walk as secondary modes as well and you are still more likely to choose transit (1.88-0.701-1.67+1.1 = 0.609) less than if you only have it in main modes but more than if you only have walks in secondary modes.

  𝑊𝐴 𝑃𝐶 = 𝑈 𝑆𝐸𝑉 𝑃𝐶 𝑈 𝑊𝐴 𝑃𝑇 = 𝑈 𝑆𝐸𝑉 𝑃𝑇 𝑈 𝐴𝑇𝐴 𝑃𝐵 = 𝑈 𝑆𝐸𝑉 𝑃𝐵 + 𝛽 𝑅𝐼 𝐵 * 𝑅𝐼 + 𝛽 𝑅𝑂 * 𝑅𝑂 𝑈 𝑊𝐴 𝐵𝑆𝑆 = 𝑈 𝑆𝐸𝑉 𝐵𝑆𝑆 + 𝛽 𝑅𝐼 𝐵 * 𝑅𝐼 𝑈 𝑊𝐴𝐴 𝐸𝑆𝑆𝑃𝑇 = 𝑈 𝑆𝐸𝑉 𝐸𝑆𝑆𝑃𝑇 + 𝛽 𝑅𝐼 𝐸𝑆𝑆𝑃𝑇 * 𝑅𝐼

		Eq. 10 Utility functions for the WA model
	Name	Value Rob. p-value	Name	Value Rob. p-value
	𝑨𝑺𝑪 𝑷𝑩	0.8	0.000293	𝜷 𝑵𝒃𝑻 𝑷𝑻	-0.216	2.49E-05
	𝑨𝑺𝑪 𝑩𝑺𝑺	-8.39	0	𝜷 𝑶𝑻 𝑬𝑺𝑺𝑷𝑻	-0.085	0.000124
	𝑨𝑺𝑪 𝑬𝑺𝑺𝑷𝑻	-4.79	0	𝜷 𝑹𝑰 𝑩	-1.84	0
	𝑨𝑺𝑪 𝑷𝑻	0.675	0.00463	𝜷 𝑹𝑰 𝑬𝑺𝑺𝑷𝑻	0.884	0.0202
	𝜷 𝑨𝒅𝑻	-0.018	0.00033	𝜷 𝑾𝑹 𝑷𝑩	-1.69	0
	𝜷 𝑪𝒐𝒔𝒕	-0.0774	0.0512	𝜷 𝑹𝑻 𝟎 𝑶𝑹 𝟏 *	-0.917	3.77E-10
	𝜷 𝑫𝑳	0.514	0.00214	𝜷 𝑺𝑴 𝑻𝑪 𝑷𝑩	-0.525	0.000157
	𝜷 𝑬𝑫𝑼𝑪.𝑳𝑬𝑽 𝟎	-0.457	7.44E-06	𝜷 𝑺𝑴 𝑻𝑪 𝑷𝑻	1.21	0.00294
	𝜷 𝑬𝑫𝑼𝑪.𝑳𝑬𝑽 𝟏	0.718	0.0288	𝜷 𝑺𝑴 𝑾	-1.8	0.000104
	𝜷 𝑬𝑫𝑼𝑪.𝑳𝑬𝑽 𝟐	-0.669	0.000664	𝜷 𝑺𝑼𝑩 𝑩𝑺𝑺 𝑩𝑺𝑺	1.06	9.32E-07
	𝜷 𝑬𝑫𝑼𝑪.𝑳𝑬𝑽 𝟒	-1.75	2.35E-07	𝜷 𝑺𝑼𝑩 𝑩𝑺𝑺 𝑷𝑪	-0.584	0.000372
	𝜷 𝑬𝑫𝑼𝑪.𝑳𝑬𝑽 𝟒 𝑷𝑪	-3.55	0	𝜷 𝑺𝑼𝑩 𝑩𝑺𝑺 𝑷𝑻	-0.247	0.0311
	𝜷 𝑱𝑶𝑩 𝟎 𝑶𝑹 𝟏	-0.682	5.66E-07	𝜷 𝑺𝑼𝑩 𝑬𝑺𝑺 𝑬𝑺𝑺𝑷𝑻	2.76	0.00443
	𝜷 𝑮𝑬𝑵𝑫𝑬𝑹 𝟎	0.296	0.00299	𝜷 𝑺𝑼𝑩 𝑻𝑪	-0.857	0
	𝜷 𝑴𝑰𝑵𝑶𝑹	-0.737	1.07E-09	𝜷 𝑻𝑻 𝑷𝑩	-0.0708	0
	𝜷 𝑴𝑴 𝑷𝑴𝑴	1.49	0	𝜷 𝑻𝑻 𝑩𝑺𝑺	-0.0602	2.62E-11
	𝜷 𝑴𝑴 𝑻𝑪	-0.851	0.0337	𝜷 𝑻𝑻 𝑷𝑪	-0.045	4.35E-13
	𝜷 𝑴𝑴 𝑾	1.95	1.66E-05	𝜷 𝑻𝑻 𝑬𝑺𝑺𝑷𝑻	-0.0204	0.0775
	𝜷 𝑴 𝑪	1.24	0	𝜷 𝑻𝑻 𝑷𝑻	-0.0504	0
	𝜷 𝑴 𝑺𝑺	0.873	7.48E-07	𝜷 𝑻𝒀𝑷𝑬 𝟏 𝑶𝑹 𝟐	6.4	0
	𝜷 𝑵𝑩.𝑴𝑽	0.442	6.68E-10	𝜷 𝑻𝒀𝑷𝑬 𝟐 𝑶𝑹 𝟑	-0.486	0.000334
	𝜷 𝑵𝑩.𝑽𝑬𝑳𝑶	0.281	6.66E-16
	*RT = RESIDENCY_TYPE

Table 15 .

 15 Estimation of model parameters and their T-Test

Number of parameters: 43 Log likelihood: -2972.941

Table 16 .

 16 𝐸𝐶𝑃 𝑃𝐶 = 𝐴𝑆𝐶 𝑃𝐶 + 𝛽 𝑇𝑇 𝑃𝐶 * 𝑇𝑇 𝑃𝐶 + 𝛽 𝐶𝑜𝑠𝑡 * 𝐶𝑂𝑆𝑇 𝑃𝐶 + 𝛽 𝐴𝑑𝑇 * (𝐶𝑇 𝑃𝐶 + 𝑃𝑆𝑇 𝑃𝐶 ) + 𝛽 𝐸𝐷𝑈𝐶.𝐿𝐸𝑉 4 𝑃𝐶 * (𝐸𝐷𝑈𝐶. 𝐿𝐸𝑉 == 4) + 𝛽 𝑁𝐵.𝑀𝑉 * (𝑁𝐵. 𝐶𝐴𝑅 + 𝑁𝐵. 2𝑅𝑀) + 𝛽 𝑀.𝐶 * (𝑀𝑀 𝐶 + 𝑆𝑀 𝑐 ) + 𝛽 𝑆𝑈𝐵 𝑇𝐶 * 𝑆𝑈𝐵 𝑇𝐶 + 𝛽 𝑆𝑈𝐵 𝐵𝑆𝑆 𝑃𝐶 * 𝑆𝑈𝐵 𝐵𝑆𝑆 𝑈 𝐸𝐶𝑃 𝑃𝑇 = 𝐴𝑆𝐶 𝑃𝑇 + 𝛽 𝑇𝑇 𝑃𝑇 * (𝑇𝑇 𝑃𝑇 + 𝑂𝑇 𝑃𝑇 ) + 𝛽 𝐶𝑜𝑠𝑡 * 𝐶𝑂𝑆𝑇 𝑃𝑇 + β NbT 𝑃𝑇 * 𝑁𝑏𝑇 𝑃𝑇 + 𝛽 𝑀𝑀 𝑊 * 𝑀𝑀 𝑊 + 𝛽 𝑆𝑀 𝑊 * 𝑆𝑀 𝑊 + 𝛾 𝑃𝑇 * 𝜎 𝑃𝑇 + 𝛾 𝑃𝑇𝐵𝑆𝑆 * 𝜎 𝐵𝑆𝑆 𝑈 𝐸𝐶𝑃 𝑃𝐵 = 𝐴𝑆𝐶 𝑃𝐵 + 𝛽 𝑇𝑇 𝑃𝐵 * 𝑇𝑇 𝑃𝐵 + 𝛽 𝐴𝑑𝑇 * 𝑃𝑆𝑇 𝑃𝐵 + 𝛽 𝑀𝐼𝑁𝑂𝑅 * 𝑁𝐵. 𝑀𝐼𝑁𝑂𝑅 + 𝛽 𝑁𝐵.𝑉𝐸𝐿𝑂 * 𝑁𝐵. 𝑉𝐸𝐿𝑂 + 𝛽 𝑀𝑀 𝑃𝑀𝑀 * 𝑀𝑀 𝑃𝑀𝑀 + 𝛽 𝑆𝑀 𝑇𝐶 𝑃𝐵 * 𝑆𝑀 𝑇𝐶 + 𝛽 𝑆𝑈𝐵 𝑇𝐶 * 𝑆𝑈𝐵 𝑇𝐶 + 𝛽 𝑅𝐼 𝐵 * 𝑅𝐼 + 𝛽 𝑅𝑂 * 𝑅𝑂 + 𝛾 𝐵𝐼𝐾𝐸 * 𝜎 𝐵𝐼𝐾𝐸 + 𝛾 𝐵𝐼𝐾𝐸𝑃𝑇 * 𝜎 𝑃𝑇 𝑈 𝐸𝐶𝑃 𝐵𝑆𝑆 = 𝐴𝑆𝐶 𝐵𝑆𝑆 + 𝛽 𝑇𝑇 𝐵𝑆𝑆 * 𝑇𝑇 𝐵𝑆𝑆 + 𝛽 𝐶𝑜𝑠𝑡 * 𝐶𝑂𝑆𝑇 𝐵𝑆𝑆 + 𝛽 𝐴𝑑𝑇 * (𝑂𝑇 𝐵𝑆𝑆 + 𝑃𝑆𝑇 𝐵𝑆𝑆 ) + 𝛽 𝑇𝑌𝑃𝐸 1 𝑂𝑅 2 * ((𝑇𝑌𝑃𝐸 == 1) + (𝑇𝑌𝑃𝐸 == 2)) + 𝛽 𝑆𝑈𝐵 𝑇𝐶 * 𝑆𝑈𝐵 𝑇𝐶 + 𝛽 𝑅𝐼 𝐵 * 𝑅𝐼 + 𝛾 𝐵𝑆𝑆 * 𝜎 𝐵𝑆𝑆 + 𝛾 𝐵𝑆𝑆𝐵𝐼𝐾𝐸 * 𝜎 𝐵𝐼𝐾𝐸 𝑈 𝐸𝐶𝑃 𝐸𝑆𝑆𝑃𝑇 = 𝐴𝑆𝐶 𝐸𝑆𝑆𝑃𝑇 + 𝛽 𝑇𝑇 𝐸𝑆𝑆𝑃𝑇 * (𝑇𝑇 𝐸𝑆𝑆 + 𝑇𝑇 𝐸𝑆𝑆𝑃𝑇 ) + 𝛽 𝑂𝑇 𝐸𝑆𝑆𝑃𝑇 * 𝑂𝑇 𝐸𝑆𝑆𝑃𝑇 + 𝛽 𝐶𝑜𝑠𝑡 * (𝐶𝑂𝑆𝑇 𝐸𝑆𝑆 + 𝐶𝑂𝑆𝑇 𝑃𝑇 ) + 𝛽 𝑆𝑈𝐵 𝐸𝑆𝑆 * 𝑆𝑈𝐵 𝐸𝑆𝑆 Parameters estimate and their robust p-values for the EP model

		Eq. 11 Utility functions for the ECP model
	Name	Value	Rob. p-value	Name	Value	Rob. p-value
	𝑨𝑺𝑪 𝑷𝑩	1.51	6.12E-09	𝜷 𝑵𝑩.𝑴𝑽	0.762	5.50E-05
	𝑨𝑺𝑪 𝑩𝑺𝑺	-12.4	0	𝜷 𝑵𝑩.𝑽𝑬𝑳𝑶	0.453	1.67E-07
	𝑨𝑺𝑪 𝑬𝑺𝑺𝑷𝑻	-3.69	0	𝜷 𝑵𝒃𝑻 𝑷𝑻	-0.321	1.45E-06
	𝑨𝑺𝑪 𝑷𝑻	1.2	7.65E-05	𝜷 𝑶𝑻 𝑬𝑺𝑺𝑷𝑻	-0.113	6.69E-06
	𝜷 𝑨𝒅𝑻	-0.0271	2.85E-05	𝜷 𝑹𝑰 𝑩	-2.42	0
	𝜷 𝑪𝒐𝒔𝒕	-0.121	0.0296	𝜷 𝑾𝑹 𝑷𝑩	-2.44	0
	𝜷 𝑬𝑫𝑼𝑪.𝑳𝑬𝑽 𝟒 𝑷𝑪	-2	0.00245	𝜷 𝑺𝑴 𝑻𝑪 𝑷𝑩	-1.41	7.17E-06
	𝜸 𝑷𝑩	-1.38	0	𝜷 𝑺𝑴 𝑾	-1.83	0.182
	𝜸 𝑷𝑩𝑷𝑻	-0.763	8.46E-14	𝜷 𝑺𝑼𝑩 𝑩𝑺𝑺 𝑷𝑪	-1.01	0.00327
	𝜸 𝑩𝑺𝑺	-1.47	0	𝜷 𝑺𝑼𝑩 𝑬𝑺𝑺 𝑬𝑺𝑺𝑷𝑻	4.52	0
	𝜸 𝑩𝑺𝑺𝑷𝑩	-0.893	0.000401	𝜷 𝑺𝑼𝑩 𝑻𝑪	-1.05	8.60E-05
	𝜸 𝑷𝑻	-1.46	0	𝜷 𝑻𝑻 𝑷𝑩	-0.0885	0

  𝐷𝐿 , 𝛽 𝐸𝐷𝑈𝐶.𝐿𝐸𝑉 0 , 𝛽 𝐸𝐷𝑈𝐶.𝐿𝐸𝑉 1 , 𝛽 𝐸𝐷𝑈𝐶.𝐿𝐸𝑉 2 , 𝛽 𝐸𝐷𝑈𝐶.𝐿𝐸𝑉 4 , 𝛽 𝐽𝑂𝐵 0 𝑂𝑅 1 , 𝛽 𝐺𝐸𝑁𝐷𝐸𝑅 0 , 𝛽 𝑀𝑀 𝑇𝐶 , 𝛽 𝑀 𝑆𝑆 , 𝛽 𝑅𝑇 0 𝑂𝑅 1 ,𝛽 𝑆𝑀 𝑇𝐶 𝑃𝑇 , 𝛽 𝑆𝑈𝐵 𝐵𝑆𝑆 𝐵𝑆𝑆 , 𝛽 𝑆𝑈𝐵 𝐵𝑆𝑆 𝑃𝑇 and 𝛽 𝑇𝑌𝑃𝐸 2 𝑂𝑅 3

Table 21 .

 21 Cost elasticities for the EC model

			Car	PT	Bike	BSS	ESSPT
		Car	-0.050106	0.012498	0.011849	0.01059	0.015637
	COST_	PT Bike BSS	0.019575 NaN 0.001657	-0.025768 NaN 0.002648	0.031249 NaN 0.003497	0.041772 NaN -0.077163	0.030719 NaN 0.002758
		ESSPT	0.001036	0.002147	0.00124	0.001625	-0.148979

Table 22 .

 22 Time elasticities works as cost elasticities unless instead of deriving probabilities according to cost, we derive probabilities according to travel time. Travel time elasticities from model EC are available in Table22. The tables work the same way than the one for cost elasticities so line bike, column BSS correspond to the cross elasticity of𝑇𝑇 𝑃𝐵 on the probability of the BSS alternative. Time elasticities for the EC model

					Alternative
			Car	PT	Bike	BSS	ESSPT
		Car	-0.618403	0.153262	0.14767	0.133548	0.198473
		PT	0.544208	-0.510045	0.478858	0.731748	1.195665
	TT_	Bike	0.396577	0.388598	-1.250774	0.740725	0.359661
		BSS	0.03893	0.064918	0.080946	-1.844201	0.06081
		ESSPT	0.006677	0.010316	0.004846	0.007311	-0.719823

Table 23 .

 23 Reference modal split

perceived cost means that when ones computes it, they make an assumption on which factors the users will consider as part of the cost. For example, most individuals do not consider insurance fee when looking at the cost of a trip even if it is part of the cost of the car alternative.

For the ESSPT option, travel time and cost are divided by mode (i.e. one TT and one cost for ESS and another for TC, TTESSPT may differ from TTPT).

For these questions, the possible answers have been adapted to the ENTPE population.

For zone of residence, we have aggregated the EMD 2015 zones of residence by arrondissement for Lyon and by commune for the others.

Remerciements

Je souhaiterais commencer par remercier Silvia Francesca VAROTTO, ma tutrice de stage, pour m'avoir aidé, conseillé et guidé tout au long de cette aventure. Le sujet que nous avons construit m'a apporté nombre de connaissances et de compétences que j'aurai l'occasion d'employer au cours de la thèse à venir mais aussi dans mon cursus professionnel.

Ensuite j'aimerais remercier toutes les personnes du LICIT-ECO7 pour leur accueil chaleureux.

Puis je voudrais remercier toutes les personnes qui ont relu ce rapport et m'ont aidé à le parfaire.

Enfin, je finirai en remerciant Flore, Vincent, Matthias et Madeline pour les grands débats du midi.

Forecasting

Now that I have those models, I will look at the values of time that they give. Then, I will compute the modal split of the WA model and do some forecasting. The objective of forecasting is to see which trip attributes' variation the model is sensible to. This will help to understand how the new mobility can emerge and become more attracting for a Home -Work trip. Section 5.1 presents the values of time for each model. Section 5.2 will exhibit cost. Section 5.3 will be about the time elasticities. Section 5.4 will introduce the modal split of the WA model. Finally, section 5.5 will show several scenarios that aims at increasing the modal split of the bike sharing-system alternative and the e-scooter sharing-system combined with transit options.

Values of Time

The value-of-time is an indicator which tells how much one is willing to pay to save one hour of travel time. In our case, as we have simple utility functions, the value-of-time can be computed using Eq. 12.

𝑉𝑂𝑇 𝑗 = 𝛽 𝑇𝑇 𝑗

𝛽 𝐶𝑜𝑠𝑡

Eq. 12 Formula of the value-of-time

where j is an alternative, 𝛽 𝑇𝑇 𝑗 the parameter associated with travel time and 𝛽 𝐶𝑜𝑠𝑡 the parameter associated with cost.

In 2019, the reference value prescribed by the Ministry of Ecology Energy Territory for socio-economic calculations in urban areas for all modes is €201510.6/h for the home-work-study journey [START_REF] Meet | Valeurs de référence prescrites pour le calcul socio-économique[END_REF], which gives us a value of time of around €13.2/h. I will use this value as a reference to check the pertinence of each model.

In Table 17 are the value of time computed with the ATA model. In we see that people are willing to pay 23.77€ to avoid the rain on the way in which is a lot.

Unfortunately, we cannot do the same with rain on the way back because it is in the utility function of bike in which there is no cost. First what this tells is that having a good goodness of fit doesn't mean that the model makes

Revealing new modes' potential

In this section I present seven scenarios that aims at seeing which policy could be led to increase the modal shares of the new forms of mobility (BSS and ESSPT). Section 5.5.1 presents three scenarios regarding the bike sharingsystem. Section 5.5.2 shed light on two scenarios about e-scooter combined with transit. Finally, section 5.5.3 highlights two scenarios for both alternatives.

5.5.1.

Exploiting the bike sharing-system In this section are presented 3 scenarios that could increase the share of the BSS alternative.

Scenario 1

In the first one, I wanted to investigate what would happen if we had more parking spot for the bike sharingsystem. As the number of stops at arrival was not significant, I could not use that to do the simulation, so what I did instead is that I decreased out-of-vehicle time, which is basically the time from origin to first station, by 30% and the parking search time on the way back by 30%, considering that if you have more spots, you'll need less time to find one, and looked at how the modal split change. The new modal split is displayed in Fig 24. Just by looking at the shares we see that the first two scenarios are slightly increasing the share of bike sharingsystem, and that the e-bike increases it significantly.

In fact, in the first scenario, the share of BSS increases by 8.00%, 12.05% of the new users come from the car alternative, 53.81% comes from transit, 33.10% from bike, and 1.04% from ESSPT. In the second scenario, the increase in the modal share is of 6.40%. 11.95% of the transfer comes from car, 56.53% comes from public transportation users, 30.47% are from the private bike and 1.05% are from e-scooter combined with transit. For the last scenario, the share of bike sharing-system is heightened by 45.50%. 12.68% comes from the car, 53.91% from the PT option, 32.28% are coming from the bike alternative and 1.12% from ESSPT.

In order to increase the bike sharing-system share in the modal split it seems that the best option is the e-bike scenario. However, this increase is really high and is due, in fact, to really high values of time. If we reject that scenario, then the first was better, not only because it increases more the share of BSS but also because it the one that impact the least ESSPT. Moreover, this policy is interesting because doing one thing you reduce two variables.

Revealing the capacity of ESSPT

In this section I present two scenarios that aims at increasing the modal share of e-scooter combined with public transportation.

Scenario 1

The objective of this scenario is to see what would happen if we increased the availability of e-scooter. Again, there is no availability or density variables in the model so I will use the out-of-vehicle time instead and decreased by 30%. Results are shown in Fig 27

Scenario 2

This second scenario aims at seeing the impact of having a student free pass for the ESS leg will have on the share of ESSPT. Results are displayed in Fig 28.

parameters to implement each random error in each alternative, keeping only the significant parameters. This solution is not optimal in terms of interpretability. For better interpretability, I should have performed a Cholesky decomposition.

Implications for practice and future research

When designing an SP experiment for mode choice modeling, information on the return trip should be included. It should also be used when implementing the models in simulations. The results of the present study should be used to design a new survey using the estimates of the model as priors in order to have a design that is efficient according to the subject.

If the present study should be conducted again, the subject of the weather attributes would need to be explored in greater depth in order to acknowledge its effect, because it has one, while having consistent results. An attention should be given to the additional return-trip times to make sure that each alternative is competitive.

The number of levels should be increased for certain variables whose parameters were more complicated to estimate, notably cost. Also, the out-of-vehicle time attributes should be separated in different times (e.g. access, waiting and egress time) in order to understand which one are the most impactful.

The survey should be made so that there are no options that are too close in terms of travel time (such as private bike, and bike sharing-system), for example by using socio-economic variables to determine which alternatives is given to the respondent.

The study could be lead on a larger scale to be able to model mode choice of a larger environment (e.g. Lyon Metropole) making sure the sample is representative of the population and/or use INSEE data for adjustments.

Now that it has been shown that the information of the return trip has a significant impact on the choice of the inward trip the subject can be investigated in a wider range to maybe found other factors of the return trip that might have an effect on the inward choice.

For further use of the collected data, first a Cholesky decomposition should be implemented to better understand unobserved factors influencing the choices of each individual. Forecasting should be done with the ECP model. Also, the most recent RP data regarding the surveyed population (e.g. EMD 2015) should be use to refine the parameters estimated and thus reduce bias. Finally, the model should be integrated with a traffic simulation.