Cyrille François
email: cyrille.francois@enpc.fr

Natacha Gondran

Jean-Pierre Nicolas

Spatial and territorial developments for Life Cycle Assessment applied to urban mobilitycase study on Lyon area in France

Keywords: Urban mobility, Life Cycle Assessment (LCA), Land-Use and Transport Interaction model, Transportation, Territorial dimension, Spatial assessment

Purpose The environmental assessment of urban mobility, defined as the movement of people in an urban area, exceeds the scope of transportation Life Cycle Assessment (LCA) with spatial, territorial and even social considerations. The objective of this study is to develop an original interdisciplinary method based on LCA coupled with a Land-Use and Transport Interaction (LUTI) model to better consider spatial and territorial dimensions in an environmental assessment of urban mobility. Methods Spatial and territorial issues emerge in all LCA stages for urban mobility and this study illustrates them with an application on the Lyon urban area. To consider most individual daily trips, the geographical boundary is related to the area of influence of the city and the functional boundary includes all transportation modes, with motorized, public and non-motorized transports. The Life Cycle Inventories (LCIs) combined EcoInvent 3.2 inventories with a specific LUTI model, named SIMBAD, local mobility surveys and an emissions and consumptions model (COPERT5). These refinements allow a spatial environmental impacts assessment of urban mobility, expressed per inhabitant per day, at different residential locations and open opportunities to develop precise assessment methods for local air pollutants with detailed description on both population and pollution concentrations.

Results and discussion

At territorial scale, this study highlights the major contribution to environmental impacts from private cars (around 90%) and the relevance to consider fuel, vehicle and infrastructure life cycles in a mobility assessment. Spatial interpretations show important variability in function of residential locations and urban form characteristics related to different mobility behaviours, distance travelled and transport technologies used. Through the proposed assessment method for local air pollution impacts on human health, hotspots are revealed in the urban area, especially in the urban centre or along main road axes. In order to test our methodology and open discussions on mobility solutions, two contrasted scenarios are explored on compact city and vehicle electrification both presenting impact transfers between global indicators or with the air pollution exposure indicator. Conclusions Urban mobility and its related environmental burden are not only related to technological choices but are also related to spatial characteristics and territorial context. The combination of urban and spatial tools and data, such as LUTI model, with the LCA methodology improves the local representativeness of environmental assessment of mobility and enlarges the ranges of analysis and perspective. Nonetheless, improvements remain to be made in relation to ongoing developments on Spatialized and Territorial LCA.

Introduction

Strategic directions for environmental problem and greenhouse gas emissions mitigation were initially addressed by international forums (Earth Summits since 1972, Conferences Of Parties) (Brown [START_REF] Weiss | The Evolution of International Environmental Law[END_REF]. The national and regional governments steadily enact laws (e.g. 2008 Climate Change Act in UK or Grenelle de l'Environnement in France in 2009) and strategic plans. However, a polycentric approach appears to be necessary to address global environmental issues [START_REF] Ostrom | A Polycentric Approach for Coping with Climate Change[END_REF]. Indeed, environmental concerns at local scale are omnipresent in debates and territorial projects. Agenda 21 plans, proposed during Rio de Janeiro Earth Summit 1992, emphasize territorial sustainable development and stakeholders' participation with the general principle "think globally, act locally" (UNCED 1992;[START_REF] Reed | Stakeholder participation for environmental management: A literature review[END_REF]). The term "Territory" generally refers to a geographical space based on an administrative boundary or on a natural or physical boundary. Nonetheless, it also embeds a social construction based on population and stakeholders space appropriation, making territories complex and dynamic systems [START_REF] Moine | Le territoire comme un système complexe : un concept opératoire pour l'aménagement et la géographie[END_REF]. Then, a territory appears as an appropriate area to raise concerns and implement projects, but also to establish local knowledge on environment with a necessity to integrate "think locally" and "think globally" in order to be able to implement relevant and coherent local actions. Depending on territory's typologies (urbanization, location, wealth, culture), the contribution of daily mobility and its associated transport system on environmental damages varies [START_REF] Newman | Sustainability and Cities: Overcoming Automobile Dependence[END_REF][START_REF] Kenworthy | Transport energy use and greenhouse gases in urban passenger transport systems: a study of 84 global cities[END_REF]. At every scale, from global to local, transport impacts on environment are significant. They represent, in 2018, 25% of CO2 emissions at global scale (IEA 2019), and 27% at European scale in 2017 (EEA 2018). In 2017, 29% of global final energy consumption and 65% of oil energy consumption (IEA 2019) was dedicated to transports. At local scale, transport is also blamed for environmental externalities. It contributes to air pollution and health damages in urban areas, around 23% of PM10 emissions in European cities with high variability along typologies and countries [START_REF] Belis | Critical review and meta-analysis of ambient particulate matter source apportionment using receptor models in Europe[END_REF][START_REF] Kumar | Ultrafine particles in cities[END_REF]. It represents the main source of noise pollution in Europe, with related health consequences [START_REF] Hellmuth | Methodological guidance for estimating the burden of disease from environmental noise[END_REF]EEA 2014). Many lands are occupied by roadways, especially in developed urban areas where up to 25% of built-up lands are roads [START_REF] Servant | L'Automobile dans la ville: l'envers d'un indéniable succès / The automobile in the city: the flip side of a genuine success story[END_REF][START_REF] Héran | La consommation d'espace-temps des divers modes de déplacement en milieu urbain, Application au cas de l'île de France[END_REF]. Territorial stakeholders are now under pressure to mitigate transports impacts in order to comply with regulations on climate, air quality or renewable energy development, to resolve health issues or to respond to citizen protests (e.g. climate March): they need to be able to deal with both global and local issues.

Mobility and Urban System

The concept of urban mobility represents the movement of people, or goods, in urban area and it is related to the socio-spatial organization of the urban society. It is central in a territory with the social and economic connections that it allows. The daily urban mobility of people can be defined as the whole movements linked to usual activities of people within the city and its space of influence. Then, daily urban mobility gathers both physical components of movements and social aspects associated to individuals and cities [START_REF] Kaufmann | Motility: mobility as capital[END_REF]). Mobility has a great impact on territories, it contributes to their economic development with the reduction of transport time and price and to their spatial organization with for instance the development of suburbs associated to car democratization [START_REF] Dupuy | Les territoires de l'automobile[END_REF]Hoffman and Felkner 2002;[START_REF] Enault | Vitesse, accessibilité et étalement urbain; analyse et application à l'aire urbaine dijonnaise[END_REF]. This connection is directly linked to the main role of mobility in urban systems, maintaining strong relations with others urban sub-systems (land-use, networks, housing and workplace development) [START_REF] Wegener | Land-Use Transport Interaction: State of the Art[END_REF]. Mostly considered as a social object, rather than technological one, urban mobility puts human in the centre of decision and analyses. Based on this social consideration, mobility solutions for sustainable development need to consider all ongoing dynamics influencing urban mobility in a territory: improvement or degradation of transport systems, urban planning, economic evolution, demographic evolution, behaviour changes, for example [START_REF] Banister | The sustainable mobility paradigm[END_REF]. In that respect, transport system appears as an essential key for urban mobility, allowing and facilitating spatial relations, but it remains a support system for social and economic interactions. Urban mobility is influenced by numerous and varied parameters, at different geographical scales. The main studied determinant is land-use, illustrated by the acknowledged relation between passenger car energy consumption and urban density [START_REF] Newman | Sustainability and Cities: Overcoming Automobile Dependence[END_REF]. Since then, many research highlight the influence of global urban form, e.g. density, diversity, accessibility, [START_REF] Cervero | Travel demand and the 3Ds: Density, Diversity, and Design[END_REF][START_REF] Schwanen | Policies for urban form and their impact on travel: the Netherlands experience[END_REF][START_REF] Naess | Urban form and travel behavior: experience from a Nordic context[END_REF][START_REF] Milakis | Stay local or go regional? Urban form effects on vehicle use at different spatial scales: A theoretical concept and its application to the San Francisco Bay Area[END_REF] and local urban form, e.g. design, walkability, car parks, (Ewing andCervero 2001, 2010;[START_REF] Banister | The sustainable mobility paradigm[END_REF]) on mobility. Cultural and socio-economic factors at territorial or individual scales also play a significant role on mobility through wealth, age, education, habits (Handy 1996;[START_REF] Ewing | Travel and the built environment: a synthesis[END_REF][START_REF] Stead | The relationships between urban form and travel patterns. An international review and evaluation[END_REF][START_REF] Kenworthy | Transport energy use and greenhouse gases in urban passenger transport systems: a study of 84 global cities[END_REF]. Simultaneously, urban mobility evolution will affect the territory, its occupants and its dynamics [START_REF] Stead | The relationships between urban form and travel patterns. An international review and evaluation[END_REF]Marshall and Banister 2007).

Environmental Assessment of Transport System

Mobility is also based on a transport system that represents all ways of transport (cars, public transport, bike, walking) and their associated infrastructure. Reciprocally, transport system adapts itself to mobility demand. For this reason, technological improvements of transports do not necessarily lead to reduction of transport environmental impacts if mobility demand still grows (also known as "the rebound effect") [START_REF] Banister | The sustainable mobility paradigm[END_REF]. Nonetheless, most transport environmental assessments focus on transport systems or technologies rather than mobility. Different analytical tools have been used to assess environmental burdens of transport systems (e.g. onroad measures, laboratory measurement, fuel consumption based approaches, emissions and noises modelling) [START_REF] André | Real-world European driving cycles, for measuring pollutant emissions from high-and low-powered cars[END_REF]. Most assessments focus on use phase based on traffic flow estimation or counting with few impact categories assessed [START_REF] Féon | Evaluation environnementale des besoins de mobilité des grandes aires urbaines en France-Approche par Analyse de Cycle de Vie. Saint-Etienne[END_REF]. The application of Life Cycle Assessment (LCA) in the transport sector may expand analysis horizons on its life-cycle with multicriteria approach. LCA methodology is current in the transport sector for every component: car manufacturing (e.g. [START_REF] Bauer | The environmental performance of current and future passenger vehicles: Life cycle assessment based on a novel scenario analysis framework[END_REF][START_REF] Tagliaferri | Life cycle assessment of future electric and hybrid vehicles: A cradle-to-grave systems engineering approach[END_REF], fuel production (e.g. [START_REF] Hill | Life Cycle Analysis of Biofuels[END_REF][START_REF] Menten | A review of LCA greenhouse gas emissions results for advanced biofuels: The use of meta-regression analysis[END_REF], transport infrastructure (e.g. [START_REF] Santos | Environmental and economic assessment of pavement construction and management practices for enhancing pavement sustainability[END_REF]Karlsson et al. 2017) or public transports (e.g. Del [START_REF] Pero | Life Cycle Assessment of a heavy metro train[END_REF][START_REF] De Bortoli | Life Cycle Assessment to support decision-making in transportation planning : a case of French Bus Rapid Transit[END_REF]. Different scopes of analysis may be used to represent the transport system: cradle-to-grave scope covers the entire life cycle, cradle-to-gate concentrates on vehicle production, Well-to-Tank scope on fuel production, Tank-to-Wheel scope on fuel consumption and exhaust and Well-to-Wheel scope covers the fuel chain (Fig. 1) [START_REF] Féon | Evaluation environnementale des besoins de mobilité des grandes aires urbaines en France-Approche par Analyse de Cycle de Vie. Saint-Etienne[END_REF].

Fig. 1. LCA scopes descriptions for transport systems (Le Féon 2014) For internal combustion vehicles, the operation phase represents the main source of GHG emissions and energy consumption, nonetheless other LC phases appear important with a multicriteria analysis [START_REF] Chester | Environmental assessment of passenger transportation should include infrastructure and supply chains[END_REF][START_REF] François | Environmental assessment of urban mobility: Combining life cycle assessment with land-use and transport interaction modelling-Application to Lyon (France)[END_REF]. In recent years, knowledge on operation phase greatly develops with air quality regulation, car certification and tail pipes monitoring. Several consumption and emission models now exist to quantify car operation (e.g. COPERT [START_REF] Ntziachristos | COPERT Guidebook 2014 : Exhaust emissions from road transport[END_REF], HBEFA [START_REF] Eichlseder | Emission Factors from the Model PHEM for the HBEFA Version 3[END_REF], PHEM [START_REF] Luz | A new comparison between the life cycle greenhouse gas emissions of battery electric vehicles and internal combustion vehicles[END_REF]) including car fleet composition (age, fuel, size) and driving conditions. The part of vehicle production and end-of-life in LCA grows in recent years with the electric vehicle development and the increase of vehicle weight [START_REF] Del Duce | Electric passenger car transport and passenger car life cycle inventories in ecoinvent version 3[END_REF]ICCT 2017). Many LCA studies analyse the fuel production impacts in regards to new motorization and biofuel development. Environmental impacts are significantly different between energies and some important impact transfers may occur from the operation phase to fuel production, e.g. electric cars or biofuels. However, fuel production impacts have significant spatial variability depending on extraction site, production technologies, transport, electricity production mix and biofuel incorporation rates [START_REF] Luz | A new comparison between the life cycle greenhouse gas emissions of battery electric vehicles and internal combustion vehicles[END_REF]Eriksson and Ahlgren 2013;[START_REF] Menten | A review of LCA greenhouse gas emissions results for advanced biofuels: The use of meta-regression analysis[END_REF]. Transport infrastructures are less analysed in transport LCA, nonetheless their environmental impacts are substantial for specific transport system, especially for public transport (e.g. bus rapid transit [START_REF] De Bortoli | Life Cycle Assessment to support decision-making in transportation planning : a case of French Bus Rapid Transit[END_REF] or subways [START_REF] Pero | Life Cycle Assessment of a heavy metro train[END_REF]), and even in the global transport system [START_REF] Chester | Environmental assessment of passenger transportation should include infrastructure and supply chains[END_REF]. Karlsson et al. also illustrate the influence of infrastructure choice location on the construction impacts by analysing the typology and soil characteristics (Karlsson et al. 2017). LCA of mobility adds spatial and social variabilities to transport system analysis due to important fluctuations in population composition and distribution, mobility behaviours and transport modes accessibility. Comparative assessments carried on different urban areas highlight these variabilities based on surveys (e.g. [START_REF] Chester | Life-cycle environmental inventory of passenger transportation in the United States[END_REF][START_REF] Féon | Evaluation environnementale des besoins de mobilité des grandes aires urbaines en France-Approche par Analyse de Cycle de Vie. Saint-Etienne[END_REF] or on modelization (e.g. [START_REF] Nichols | Life-cycle energy implications of different residential settings: Recognizing buildings, travel, and public infrastructure[END_REF]. Le Féon deepens the analysis by relating emissions to trip purposes and reveals the importance of an appropriate functional unit selection. He compared results by person.kilometer, such as most transport LCA, with results by inhabitant and he concluded on the major role of daily distance with difference up to 30% of kilometres travelled per inhabitant and per day between several French cities (Le Féon 2014).

Spatialized and Territorial LCA

LCA has been initially developed as an aspatial assessment method with a global approach based on the assumption of spatial and temporal homogeneity of the environment [START_REF] Jolliet | Environmental life cycle assessment[END_REF]. Nonetheless this assumption can be a potential source of errors especially in regards to spatial distribution of sources, fate factors, exposure factors and effect factors [START_REF] Potting | Part II: spatial differentiation in life-cycle assessment via the sitedependent characterisation of environmental impact from emissions[END_REF][START_REF] Finnveden | Recent developments in Life Cycle Assessment[END_REF]. This observation led to the development of Spatialized LCA in the recent years. And more recently researchers propose new methods for the environmental assessment of a territory with LC thinking, Territorial LCA (Nitschelm et al. 2016;[START_REF] Loiseau | Territorial Life Cycle Assessment (LCA): What exactly is it about? A proposal towards using a common terminology and a research agenda[END_REF]. Spatial differentiation in LCA may reduce misconclusions from studies especially through the implementation of site-specific and site-dependant approaches [START_REF] Hellweg | Emerging approaches, challenges and opportunities in life cycle assessment[END_REF]. All stages of LCA methodology can include spatial and geographical aspects [START_REF] Aissani | Intégration des paramètres spatio-temporels et des risques d'accident à l'Analyse du Cycle de Vie : Application à la filière hydrogène énergie et à la filière essence. Phdthesis[END_REF] with respect of study objectives, uncertainty tolerance, geographical data availability and human resources [START_REF] Baitz | LCA's theory and practice: like ebony and ivory living in perfect harmony?[END_REF]Patouillard et al. 2016). At the goal and scope stage, the definition of the studied system and its boundary may imply spatial investigation, especially in case of a multifunctional system where spatial context influences the functions relevance [START_REF] Laurent | Optimisation fonctionnelle et spatiale de scénarios de méthanisation centralisée selon une approche systémique territoriale couplée à l'analyse du cycle de vie[END_REF]. During the LCI construction, spatial information can be used to improve representativity through contextualized data or regionalized databases [START_REF] Wernet | The ecoinvent database version 3 (part I): overview and methodology[END_REF]. With Geographical Information System (GIS), spatial information can be attached to processes and their associated elementary flows to quantify them and to connect them with corresponding receiving environment [START_REF] Liu | GIS-Based Regionalization of LCA[END_REF]Karlsson et al. 2017;[START_REF] García-Pérez | Environmental assessment at the urban level combining LCA-GIS methodologies: A case study of energy retrofits in the Barcelona metropolitan area[END_REF]. Spatial differentiation in LCIA aims to distinguish characterization factors (CFs) in regards of impact categories and the spatial variation of the receiving environment and its sensitivity [START_REF] Potting | Part II: spatial differentiation in life-cycle assessment via the sitedependent characterisation of environmental impact from emissions[END_REF]. Most spatialized LCIA detailed CFs at countries or watersheds levels for regional impacts: acidification [START_REF] Potting | Site-Dependent Life-Cycle Impact Assessment of Acidification[END_REF], eutrophication [START_REF] Huijbregts | Spatially Explicit Characterization of Acidifying and Eutrophying Air Pollution in Life-Cycle Assessment[END_REF], water scarcity [START_REF] Boulay | The WULCA consensus characterization model for water scarcity footprints: assessing impacts of water consumption based on available water remaining (AWARE)[END_REF]. The characterization method IMPACT World+ goes further by calculating CFs at different levels of geographical resolution (global, continental, countries or native regions) for all regionalized impacts [START_REF] Bulle | IMPACT World+: a globally regionalized life cycle impact assessment method[END_REF]. For interpretation, spatial information can be used for results mapping and addressing potential spatial heterogeneity of impacts and potential geographical transfers. Territorial LCA evaluates the environmental burden of a territory, and all related human activities. Very few Territorial LCA studies include all types of activities [START_REF] Loiseau | Adapting the LCA framework to environmental assessment in land planning[END_REF], most focus on specific sectors: agriculture, energy, water, waste, transport (Nitschelm et al. 2016;[START_REF] Mirabella | Current trends and limitations of life cycle assessment applied to the urban scale: critical analysis and review of selected literature[END_REF]. The territorial and urban context influences all stages of LCA, especially goal and scope definition with several issues. After the goal definition, the functional unit(s) of the selected system needs to be specified in regards to the multifunctionality associated to territories [START_REF] Pérez-Soba | Land use functions -a multifunctionality approach to assess the impact of land use changes on land use sustainability[END_REF]. Then, geographical boundaries should be set depending on the goals and the stakeholders involved. For urban territory, boundaries could be based on administrative concerns (administrative-based boundaries). This facilitates data collection and is more relevant in terms of decision-support related to local competences. However, these administrative boundaries may omit some important population and services located outside of the administrative area, but influencing it. Density-based boundaries are built on urban form continuity to better enclose services and population influenced by urban centre. Nonetheless, some other regional services might be excluded, such as economic, logistic or touristic services. The extension of geographical boundaries to all services (service-based boundaries) raises an allocation issue between territories sharing services (e.g. airport, industrial parks) (Albertí et al. 2019). Once Boundaries are fixed, activities are differentiated between foreground (on-site) activities that occur inside the territory's perimeter and the background (off-site) activities induced by foreground activities and all territorial consumption [START_REF] Azapagic | A life cycle methodology for mapping the flows of pollutants in the urban environment[END_REF][START_REF] Loiseau | Adapting the LCA framework to environmental assessment in land planning[END_REF]. This differentiation will be used in data collection process, on the one hand, foreground activities are directly influenced by territorial decision and related data should have good representativity with potential georeferencing [START_REF] O'keeffe | When considering no man is an island-assessing bioenergy systems in a regional and LCA context: a review[END_REF]. On the other hand, territory has no influence on background activities that could be represented by average values. Territorial and local contexts can be used for LCIA to qualify the receiving environment and to implement specific CFs. Territorial LCA appears appropriated to establish the environmental footprint of a territory considering both its activities and its relations with other territories (Nitschelm et al. 2016). Territorial LCA method might be partly based on Spatialized LCA methods. Nitschelm et al. propose a combined method for agricultural activities, the Spatialized and Territorial LCA, that emphasizes on the boundaries and functions definition, the localization and characterization of activities and environment typologies, the spatialization of emissions, the LCIA and the mapping of in-site and off-site impacts (Nitschelm et al. 2016). The objective of this article is therefore to contribute to methodological development on spatialized LCA and urban assessment through an application for urban mobility. This contribution focuses on the capabilities of transport management and urban planning tools to overcome potential difficulties related to spatialized and territorial LCA. The interdisciplinary method developed for the urban mobility gathers socio-spatial knowledge from land-use planning and technological and environmental information from engineering sciences to improve the spatial and territorial representativeness in a LCA.

Methods

2.1.

Spatial and territorial considerations for urban mobility Regardless of the scale and size of territories, spatialization is central in mobility debates and it occurs at all four stages of a LCA: goal and scope, inventory, impacts assessment and interpretation [START_REF] Aissani | Intégration des paramètres spatio-temporels et des risques d'accident à l'Analyse du Cycle de Vie : Application à la filière hydrogène énergie et à la filière essence. Phdthesis[END_REF]. In goal and scope definition the main spatial debate is on the geographical boundary. The length of daily trips is increasing with the improvement of transport technologies' efficiency and speed [START_REF] Zahavi | Stability of Travel Components over Time[END_REF][START_REF] Schafer | The future mobility of the world population[END_REF] making difficult to set geographical limits especially at city and metropolis levels where metropolization process needs to be integrated [START_REF] Wiel | Mobilité, système d'interactions sociales et dynamiques territoriales[END_REF]. The relevance of spatialized and territorial LCI arises from two points. First the traffic conditions have a great impact on emissions and consumption of the transport system [START_REF] Smit | An examination of congestion in road traffic emission models and their application to urban road networks[END_REF]. Secondly, the implementation of local mobility policies influences the emissions and consumption factors and the kinds of transport technologies used, for example Low-Emission-Zone [START_REF] André | Experimental determination of the geographical variations in vehicle fleet composition and consequences for assessing low-emission zones[END_REF], car-pooling lanes [START_REF] Shaheen | Casual carpooling in the San Francisco Bay Area: Understanding user characteristics, behaviors, and motivations[END_REF] or electric vehicles incentives [START_REF] Querini | Combining Agent-Based Modeling and Life Cycle Assessment for the Evaluation of Mobility Policies[END_REF]. During LCIA, attention on spatialized characterization factors that take into account the densities of both pollutants and population is high especially in urban area. For daily urban mobility the impacts of air pollutants on human health is an important concern from regional to neighbourhoods' scales. The exposure factor, crossing air pollution level with population density, is highly variable in space and cannot be represented by generic value [START_REF] Krewitt | Country-specific damage factors for air pollutants[END_REF]. At the interpretation stage, the spatialization mainly focuses on maps representation to identify hotspots on emissions and impacts. In mobility assessment, the spatialization of results is an important step to interpret environmental and territorial equity with potential transfers between territories. Different approaches can be used to represent the emissions that are generated by, and on, a territory: the cadastral approach, which is often used in territorial GHG diagnostic, aims at mapping direct emissions, in spatial units (e.g. [START_REF] Cortinovis | Élaboration d'un cadastre d'émissions interrégional pour la plate-forme de modélisation de prévisions cartographiques ESMERALDA Elaboration of an interregional emissions inventory for the modelling platform of cartographic previsions ESMERALDA[END_REF], or the footprint approach (environmental, carbon or water), that can also be spatially addressed through the assessment and the mapping of environmental burdens that are generated by the consumptions of households that are located in the spatial units of a territory [START_REF] Minx | Carbon footprints of cities and other human settlements in the UK[END_REF][START_REF] Ivanova | Mapping the carbon footprint of EU regions[END_REF]. Spatialized environmental footprint of households has been studied in Swiss context including residential, mobility and food consumption burdens [START_REF] Saner | FoodPrints of households[END_REF]. Then the environmental assessment of urban mobility required an important level of spatialization at all stages of LCA, from boundary definition to impacts and contributors mapping, and territorial considerations. The current study covers some spatialized and territorial aspects of the LCA framework at each of LCA stages:

(1) goal and scope definition, (2) LCI, (3) LCIA and (4) interpretation (Table 1). The main particularity of the developed method is the coupling of a LCA with an urban model based on Land-Use and Transport Interaction, a LUTI model. In addition, the interpretation stage offers three original dimensions of analyses: (1) mobility behaviours and stakeholders analyses with trip purposes and households' discretization in regards to territory's boundary, (2) spatial analyses at spatial units of household's contributions and (3) local emissions and exposure analyses at fine scale to represent potential health damages as illustrated in Fig. 2. Then the proposed analysis framework covers main technological, spatial and social dimensions associated to daily urban mobility.

Goal

As few LCA studies exist on urban mobility, the goal of the study is to firstly develop and test an original methodology coupling on urban model and LCA. Beyond the research aspect, the method aims to inform and advise all stakeholders involved in transport and mobility policies from local (e.g. parking regulation) to regional (e.g. interurban train systems) through a reflective tool rather than a decision-support tool. In regards to this goal, spatial and territorial issues of an existent situation will be assessed with a prospective approach based on scenarios.

System

In a complete territorial LCA, all activities occurring within the territory should be represented [START_REF] Loiseau | Territorial Life Cycle Assessment (LCA): What exactly is it about? A proposal towards using a common terminology and a research agenda[END_REF], nonetheless most territorial studies focus on specific sectors. The present study focuses on the passenger transport and mobility ones. The mobility system chosen represents all trips generated by every residents of the territory for their daily activities, meaning activities during an average business day. This system definition removes all occasional holidays and weekend trips and the freight transport related to the territory.

System boundaries

By reducing the system to the territory's daily mobility of persons, the amount of data needed is reduced to these daily trips and to represent the related technologies, all long-distance and freight technologies are not included. As a consequence, the system under study includes personal transport, motorized and non-motorized, and urban public transports by bus and rail transport. The scope considered extends from the cradle to the grave including vehicles use, their manufacturing and end-of-life, all fuel production (traditional fuels, biofuels and electricity) and transport infrastructure life cycle. The suggestion to stop the scope at the territory gate has not been selected to get a complete picture of transports' environmental burden, in particular in view of trying to better represent the environmental impacts of electric mobility.

Functions

The functions definition is a crucial step in LCA, especially for territorial object such as daily mobility since territory is a multifunctional object itself. As daily mobility is related to all activities that may take place on a geographical area, its functions will embrace all categories of available activities, depending on territory demography, wealth, quality of life, culture. These activities categories refer in mobility studies at trip purposes: work, shopping, education, leisure, visits and others. At the territorial scale, daily mobility can be considered by stakeholders as both a driver for economic and social development or a support of territorial functions.

In our study, daily mobility was considered as a support function for trips of residents of a territory. The function under study was then "Meet the demand for travel by people in the urban system of an urban area during an average working day" (François 2019).

Functional unit

The associated functional unit chosen was "to meet the demand for travel per inhabitant per day" allowing discretizations by inhabitants' characteristics. In comparison with the passenger-kilometre unit, mostly used in transport assessment, the chosen unit embedded the daily distance travelled. This approach, restricted to the material aspects of the daily mobility of persons, was not intended to represent mobility as a driving force for economic and social development.

Territorial boundary

The geographical boundary definition for urban mobility is complex because it goes way beyond the administrative boundaries of the city and the influence area of the city is extending with ongoing urban expansion. To better represent the spatial influence of urban mobility, the French statistics bureau (INSEE) defined the urban area based as a work catchment area of an urban conurbation. In 2017, France has 81 urban areas with up to 100,000 inhabitants.

Mobility representation

To represent this urban system and its associated mobility, urban and transport model have been developed worldwide mainly at decision-support purposes (Marshall and Banister 2007). Land-Use Transport Interaction (LUTI) models offer a systemic approach of mobility by representing diverse urban relationships especially the Land-Use Transport interrelations [START_REF] Wegener | Land-Use Transport Interaction: State of the Art[END_REF]. From the spatial distribution of activities and population on the territory and its associated transport networks, a LUTI model estimates the flow distribution of trips and their potential consequences on land-use. An important variety of LUTI models exists, representing different urban systems and answering different issues. Beside the theoretical basis of models, the main differentiations are on the spatial coverage (from neighbourhood to regions), spatial resolution (from agent-based modelling to macro zones) and dynamic aspects (static or dynamic models) [START_REF] Hunt | Current operational urban land-use-transport modelling frameworks: A review[END_REF][START_REF] Wilson | The general urban model: Retrospect and prospect: The general urban model: Retrospect and prospect[END_REF][START_REF] Jones | Life cycle assessment in road infrastructure planning using spatial geological data[END_REF]. Despite the assets of LUTI modelling, dissensions remain between scientific rigour and operationality in regards to needed resources for development [START_REF] Saujot | To make LUTI models operational tools for 1 planning[END_REF].

2.4.

Life Cycle Inventory -LCI Data collection and inventory are differentiated between foreground and background activities in regards to their specification for the system. The data collection on vehicles use (distance, occupancy, speed) and infrastructure's use is estimated by a LUTI model developed on a specific urban area. In addition of networks and flows data, LUTI models may provide socio-economic information on inhabitants. The environmental inventory is then calculated through an emissions model and the EcoInvent database for respectively direct and indirect emissions and consumption, as presented in [START_REF] François | Environmental assessment of urban mobility: Combining life cycle assessment with land-use and transport interaction modelling-Application to Lyon (France)[END_REF].

2.4.1. Foreground activities: vehicles use LUTI models do not provide information on the technologies used and pollutants emitted on each link. Nonetheless, foreground activities need an appropriate level of detail with spatialization and local process specification. Territorial details on fleet composition allows specification on consumption and emissions factors. Depending on personal car data availability, distinct vehicles fleets are estimated for diverse population groups. These fleets descriptions are described more precisely, as well as other LCI data description, in [START_REF] François | Évaluation environnementale stratégique de la mobilité quotidienne des personnes d'une aire urbaine : couplage entre Modèle Transport-Urbanisme et Analyse de Cycle de Vie[END_REF]. They allow the conversion of vehicles flows into vehicle technology use in regards to engine motorization, vehicles size, Euro norms and potential depolluting technology. Similar fleet description is realized for the urban bus fleet based on the operator's data. The estimation of emissions and fuel consumption of vehicles on the network is made by the model COPERT 5 [START_REF] Ntziachristos | EMEP/EEA air pollutant emission inventory guidebook 2016 -Passenger cars, light commercial trucks, heavy-duty vehicles including buses and motor cycles[END_REF]. By integrating most vehicles' specifications (fuel, size, age, depolluting systems) and the average speed related to different types of driving conditions, COPERT 5 offers a high level of description for all exhaust (warm and cold engine) and abrasion (from brakes, tyres and road) emissions and all fuel consumption (incl. AC overconsumption). Then the coupling of LUTI outputs, car fleets statistics and COPERT 5 model offers one of the best environmental representation with spatial distribution on links and contextualization with local vehicles' technologies and local speed condition.

Background activities

Based on LUTI data, car fleets descriptions and consumption model, all, or most, quantities of background processes are accurately estimated, e.g. quantity of fuels, travel's total length for each vehicle type, length of each infrastructure type. Most background activities locations are unknown and are related to processes description provided by EcoInvent 3.2 database [START_REF] Wernet | The ecoinvent database version 3 (part I): overview and methodology[END_REF]. Nonetheless, few modifications were made to better represent all the range of vehicles and the different types of roads and infrastructure involved for the daily mobility in an urban area, especially for rail public transports.

2.5.

Impacts Assessment -LCIA Spatialized and territorial LCIA required respectively the implementation of spatialized characterization factors and the consideration of the local context while evaluating impacts. In this study, efforts have been focused on vehicles use spatialization rather than environmental characterization.

Aspatial assessment

The selected impact assessment methods, that were used, do not consider spatial differentiation nor territorial specification (Table 2). Nonetheless, the seven midpoint indicators that were chosen in this method were selected through the interviews of fifteen territorial stakeholders' interviews. It appears that the most relevant indicators to follow: climate change potential, particulate matter formation, photochemical ozone formation, terrestrial acidification, metal depletion, land use and primary energy demand. Other indicators appeared less relevant for transport systems in urban context for local stakeholders. For each impact category, the selection of the impact assessment method has been guided by the ILCD recommendations with respect of available data (Table 2).

GIS based assessment for air pollutants impacts on human health

The high concern of urban stakeholders for air quality and human health leads us to develop a spatialized LCIA method for local pollutants. The proposed method is based on GIS data processed at a small square level (250mx250m) to highlight specific potential hotspots for human health damage associated to air pollution generated by daily mobility. For each square, an exposure factor was approximated by the residential population density level, which was estimated based on built-up and population information obtained through the LUTI model excluding the presence of workers or other visitors. The intake factor was approximated by the daily quantity of pollutants emitted by all links in each square. This approach ignores air pollutants transport and dispersion as well as air pollutants that are generated by other sources than the transports of persons. Thus, it endeavours to present a first and rapid overview of the zones that are potentially exposed to air pollution. It cannot substitute a deeper assessment and analyse of urban air quality. In this article, only NOx emissions are considered to facilitate both calculation and interpretation. The human health damage level is estimated by crossing population density levels and emissions density levels, both in quintiles (Fig. 5). Human health damage indicator associated to the emissions of air pollutants by the daily mobility, varies on each square from zero, if no population or no emission are present, to six, in case of higher densities of the population and the emissions simultaneously. This high spatial resolution allows map representation and accurate analysis for different territories, e.g. the city centre.

2.6.

Interpretation Following the goal of this study, to develop a reflective tool for diverse territorial stakeholders, the associated analysis framework is going to be broad enough to cover main spatial and territorial issues related to environmental impacts of daily mobility. Three general points can be explored, the on-site and off-site impacts transfers, the Impact category method unit Abbr.

Climate change

ReCiPe/IPCC 2013 (GWP100) kg CO2-eq GWP Particulate matter

ILCD 2011 a g PM2.5-eq PMF Photochemical ozone

ReCiPe b g NMVOC-eq POF Terrestrial acidification

ReCiPe b g SO2-eq TAP Metal depletion

ReCiPe b g Fe-eq Metal Land use

ReCiPe b m².an LandUse

Primary energy

Cumulative Energy Demand c kWh CED heterogeneous distribution of emissions and contributors inside the territory and the prospective and sensitivity analysis.

In-site and off-site analysis

The geographical boundary definition distinguishes foreground and background activities. For a vast territory such as an urban area, in-site activities refer to vehicles operation emissions from vehicles use. Vehicle, fuel and infrastructure productions are considered to be located outside the territory, even if car-manufacturers, refineries or asphalt plants might be established on the territory. However, this spatial differentiation matches with a temporal differentiation because an important gap exists between vehicles operation emissions occurring on a daily basis and vehicle and infrastructure productions appearing years, even decades, earlier.

Contributors mapping

Due to the heterogeneous spatial distribution of population, activities, means of transport, transport technologies and behaviours, the environmental contribution of each inhabitant varies significantly inside the territory. To assess this spatial distribution, all possible connections between all spatial units were assessed by adding composing links. Then, environmental impacts were calculated for each spatial unit by retracing all trips simulated by LUTI models. This disaggregation allows two interpretations, one with an emitter point of view, considering emissions at residential areas, and another one with a generator's point of view considering emissions at areas of activity. In both cases, environmental indicators can be mapped and correlated with urban characteristics (density, functional mixing and accessibility).

Scenarios approach

To perform a sensitivity analysis in LCA, each parameter should be evaluated separately to assess his sensitivity. Nonetheless, in case of territorial LCA, parameters are too numerous to execute a complete sensitivity analysis. The scenarios approach is another way to test sensitivity of systems by exploring diverse prospective visions.

Based on the proposed method a wild range of sensitivity and prospective visions can be implemented and analysed: urban planning scenarios, economic and demographic evolutions, new mobility behaviours scenarios, transports networks scenarios, fleets evolutions or fuels productions (François 2019).

Case study

Our case study assesses the mobility of the second urban area of France, Lyon with up 2,300,000 inhabitants in 2017 composed of nearly 500 municipalities and crossing distinct administrative divisions. Lyon has the geographical specification to be a monocentric urban area with mostly radial trips and a high central density (up to 10 000 inhabitants per km² for Lyon City). The Lyon public transport system is well developed and diversified with bus, trolleybus, train, tramway and subway lines (Supplementary doc).

The LUTI model, SIMBAD, was developed by the Laboratoire Aménagement Économie Transport (LAET) on this urban area and it was initially designed to estimate sustainability of alternative policies in urban and transport planning [START_REF] Nicolas | Spatial differentiation in Life Cycle Assessment LCA applied to an agricultural territory: current practices and method development[END_REF]). Based on an iterative loop, SIMBAD considers initial locations of activities and residents in the territory in order to estimate the mobility through a four steps transport model: (1) trips generation, (2) spatial distribution, (3) modal share and (4) network affectation. Then, accessibility levels of each spatial unit are calculated and then activities and residents might relocate by using a land-use model. Baseline mobility characteristics are displayed in Table 3.

The model provides certain outputs for the LCI. Firstly, it describes the entire network for both road vehicles and rail public transport, with detail on each link (length, road category, average speed, public transport lines and frequencies). Secondly, the SIMBAD model calculates on each link the flows of passengers and vehicles in regards to nine different inhabitants' categories based on income and location (centre, inner-suburbs and outer-suburbs) for peak and off-peak hours. Based on local mobility survey in 2015, each nine inhabitants' categories personal car fleet was estimated to describe vehicles characteristics (Supplementary doc).

In this article, two contrasted scenarios are explored and analysed all things being equal: (1) the compact city scenario, where 10% of the outer-suburb housing and activities are relocated in the central area, the new mobility organization is presented Table 3; (2) the electric vehicles scenario, where a significant part of the personal car fleet is electrically powered, depending on inhabitants' income and location (considering the 2012 French national electrical mix) (Supplementary doc).

Results

The environmental impacts of daily mobility of Lyon urban area inhabitants, expressed per inhabitant per day, and life cycle contributors are presented in Fig. 3. Public transports produced noticeable impacts with between 8% to 15% of total environmental impacts. Use phase (incl. exhaust and abrasion) does not appear as a predominant contributor on environmental impacts with a maximum contribution of 55% for GWP. In our case, only vehicle operation emissions are considered on-site, then off-site processes emerge as significant reflecting the dependency of this territorial mobility for abroad processes with impacts relocation. The daily mobility carbon budget for Lyon urban citizens is estimated at 3.22 kg CO2-eq being consistent with previous study made on others French cities (Le Féon 2014). The energy breakdown highlights the low share of renewable energy in the urban transport system and the relevant nuclear energy consumption in infrastructure processes and public transport fuel. environmental burdens due to the promotion of active modes and public transport and the reduction of travelled distance. Nonetheless, effect is not linear with side effects such as congestion and gentrification mechanisms. The electric vehicle scenario leads to some transfers of impacts from air pollution to metal depletion and it does not reduce total energy consumption. Impact transfer also occurs from on-site processes to off-site processes with electricity production and vehicle production and end-of-life gaining prominence. 2.18 (-7.2%) Our complete transport system effectiveness, expressed per passenger-kilometre, is estimated for GWP at 202 g CO2-eq per pkm, above other French mobility study estimation and below American metropolis transports assessment [START_REF] Chester | Comparison of life-cycle energy and emissions footprints of passenger transportation in metropolitan regions[END_REF][START_REF] Féon | Evaluation environnementale des besoins de mobilité des grandes aires urbaines en France-Approche par Analyse de Cycle de Vie. Saint-Etienne[END_REF]. Our studied object, assessed per inhabitant per day, bring multifunctionality that can be related to trip purposes. The Fig. 4 represents distributions of total home leavings, distance and GHG emissions for the six main purposes. Travelled distance and modal share vary with the trip purposes, making commuting trips the main contributors of total mobility environmental impacts. Then, at territory or household scales, the employment ratio influences significantly the environmental performance of the related daily mobility. For example, the GHG emissions per inhabitant per day are 3.5 times higher in a dual-career couple than an unemployed person, which lives alone.

Spatial distribution of emitters

The spatial distribution of GHG emissions levels associated with inhabitants in each residential area is presented in the Fig. 5. Similar distribution is obtained for other environmental indicators. People living in central areas produce individually less impacts, for their daily mobility, than other inhabitants of the Lyon urban area. Only 8% of the total surface presents a GHG emission level below the urban area mean, 3.2 kg CO2-eq. In terms of contributions, the city centre generates 17% of total daily mobility GHG emissions but it hosts 37% of the population. In comparison, the outer-suburb, located outside the administrative metropolis, generates 45% of GWP impacts but with 27% of the population living. In this peripheral area, the higher environmental burden is caused by the dominant use of individual cars, the modal share exceeds 80% in most residential areas. Fig. 5. GHG emissions per inhabitant per day in residential areas of Lyon urban area The spatial resolution of Fig. 5, based on IRIS spatial units, reveals important spatial heterogeneity on the territory. Nonetheless, results interpretation should be made carefully at small scale in regards of potential statistical and modelling bias, e.g. edge effect or too small population. Through an urban form analysis, environmental impact's levels appear correlated with urban form indicators, such as population density, job accessibility, distance to centre or public transport accessibility.

NOx exposure score

The NOx exhaust-gas exposure score associated to daily mobility on the Lyon urban area is presented in Fig. 6. The exposure score estimation is based on a risk matrix with population density representing the likelihood and pollutant emissions density representing the impact severity, both based on quintiles. Due to population and road location, a major part of the Lyon urban area does not have any issue of local air quality and human health damage related to daily transport. By contrast, the major part of the city centre faces a human health issue related to air quality. The mean exposure level in Lyon city centre, calculated with population ponderation, is 4.02, compared to 2.35 for the whole urban area. Inside the administrative metropolis, a spatial differentiation is appreciable between the East and the West parts. East part is more affected due to the presence of more highways and activities compared to the West part composed with low-rise housing areas. The mean exposure level of the urban area for the two simulated scenarios are displayed in Table 4. Through this methodology, the compact city scenario increases human health damage associated with daily transport operation emissions, even if total NOx emissions decrease. The densification of both population and mobility flows in the city centre is the main reason of this health issue degradation. On the other side, the electric vehicles scenario, by removing internal combustion vehicles, improves air quality and human health on the whole territory. Assessing Lyon urban mobility: the need of mobility data The environmental assessment of urban area mobility with the LCA approach is unusual (e.g. [START_REF] Chester | Comparison of life-cycle energy and emissions footprints of passenger transportation in metropolitan regions[END_REF][START_REF] Féon | Evaluation environnementale des besoins de mobilité des grandes aires urbaines en France-Approche par Analyse de Cycle de Vie. Saint-Etienne[END_REF][START_REF] Nichols | Life-cycle energy implications of different residential settings: Recognizing buildings, travel, and public infrastructure[END_REF] and often limited to its physical component (vehicles use). The present case study conducted on Lyon urban area presents a global GHG emissions per inhabitant per day of 3.2 kg CO -eq, which is include in the range estimated on four French other urban areas (2.4-3.4 kg CO -eq) with a 2 2 7 survey approach (Le Féon 2014). Compared to similar LCA studies, the estimated GHG efficiency of the Lyon 8 transport system (202 g CO2-eq per pkm) is above others French cities (from 169 to 184 g CO2-eq per pkm) but 9

below American metropolis (from 220 to 290 g CO2-eq per pkm) [START_REF] Chester | Comparison of life-cycle energy and emissions footprints of passenger transportation in metropolitan regions[END_REF][START_REF] Féon | Evaluation environnementale des besoins de mobilité des grandes aires urbaines en France-Approche par Analyse de Cycle de Vie. Saint-Etienne[END_REF]. This case 10 study also highlights the relevant contribution of vehicle production, fuel production and infrastructures phase in transport lifecycle.

The present case study also provides relevant information on environmental impacts of mobility for a territorial analysis, with off-site contribution (45% of GHG emissions), spatial distribution of contributors (below 1 kg CO2eq per inhabitant per day in town centre to above 10 kg in peripheral areas), trip purposes analysis (56% of GHG emissions are work related) or air pollutants exposure hotspots (mainly in city centre). Nonetheless, these analyses are the result of both important data collection, to build a LUTI model (transport networks, population and job distribution, mobility behaviours), to estimated vehicle fleets, and important data processing, to run iterative mobility model and to environmentally assess hundreds of thousands of transport links. Then the implementation of such spatial and territorial methods requires both data access and computing capability.

4.2.

Assessing local air pollution generated by daily mobility: the need of a territorial approach Nowadays, environmental impacts of transport systems are an important concern at global and local scale with significant contributions. However, mobility systems are pretty specific to local context in terms of behaviours and technologies. Then, this study makes a specific effort on foreground activities description through the application of an urban model and an emission model with local analyses of mobility behaviours and car fleets with surveys and databases. This description allows an accurate representation of on-site emissions and consumption, and a good appreciation of necessary background activities. The GIS information associated to foreground could be paired with spatialized characteristics factors to better assess local environmental impacts [START_REF] Mutel | Regionalized Life Cycle Assessment: Computational Methodology and Application to Inventory Databases[END_REF]. Nonetheless, most spatialized LCIA methods evaluate regional impacts such as acidification, eutrophication, water scarcity rather than air pollution. Human health arises as a major environmental concern in territorial assessment, especially in urban context, but few LCA characterization methods exist at local scale, especially coupled with GIS technologies. Humbert et al. proposed a characterization method for particulate matter intake based on emission release height and archetypes environment (indoor/urban/rural and remote location) [START_REF] Humbert | Intake Fraction for Particulate Matter: Recommendations for Life Cycle Impact Assessment[END_REF]. Along the emission-to-damage framework, location of both emissions and population is an important information to improve the pollutant intake calculation. Atmospheric dispersion modelling could be added to adjust pollutant concentration with meteorological conditions and local topographic contexts. In addition to that, temporal variations on both the emission and the population location affect significantly the intake factor and then the CF. For example, transport emissions occur more intensively during peak hours and people move during the day depending on their activity schedules. Moreover, people are exposed to high pollutant concentration when travelling in public transport or in personal vehicles [START_REF] Knibbs | A review of commuter exposure to ultrafine particles and its health effects[END_REF]. The representation of these phenomena is a challenge for territorial assessment of local air pollutant impacts on human health and social models, such as urban models, by representing human flows along the day might bring a part of the solution.

4.3.

Assessing globally and locally: the need of multiscaled tools The goal and scope definition is a crucial step in LCA and it varies significantly with the scale of analysis, for example global scale with Strategic Environmental Assessment (SEA) or local scale with Environmental Impacts Assessment. In case of urban mobility, different layers of territorial entities and stakeholders are implied from the region to the neighbourhood. Each layer has different environmental concerns, different levers of actions and projects, leading to multiple goals and scopes to analyse them. For daily mobility, small territories will lead to plenty of flow exchanges across the boundary with attribution choice among involved territories. By expanding territory boundaries to include almost all daily mobility flows of the inhabitants, attribution choices are avoided but sure expansion requires a lot of data. The territorial expansion also reduces the capability of such tool to answer specific issues arising at different levels, even by implementing disaggregated and spatialized analyses. On the contrary, specific mobility analysis conduct at specific territorial scale will lead to finer results but will miss potential multiscale synergies, such as walking policies at local scale emphasizes by central relocation promoted by regional policies. Then, the development of a multiscale mobility assessment tool requires important data and models to represent all interactions. Finally, the development of a multiscale mobility tool, such as proposed in this study, faces similar issues than Consequential LCA, system expansion, modelling consequences, data needed or comprehensiveness. Then, the mobility assessment goal and territorial boundary definition should be coherent with the strategic or the projectoriented objective, in the same way as choosing between Attributional or Consequential LCA [START_REF] Ekvall | Attributional and consequential LCA in the ILCD handbook[END_REF].

4.4.

Assessing various territorial mobility: the need of taking the multifunctionality of urban mobility into account Urban mobility is a complex system that can hardly be assessed, especially with the LCA approach. Indeed, the function definition is a tricky part because daily mobility performs distinctive functions. In this study, we proposed to analyse this multifunctionality by distinguishing between trip purposes, e.g. work, education, leisure, shopping. This approach facilitates the implementation of some diverse functions related to mobility but it nevertheless remains limited in regards to global territorial functions. Introduced as a support system for a territory in this study, the daily mobility should be related to the territory functions itself. These functions could be estimated with the land use functions framework defined by Perez-Soba et al. that identified them between economic, societal and environmental functions [START_REF] Pérez-Soba | Land use functions -a multifunctionality approach to assess the impact of land use changes on land use sustainability[END_REF]. For urban territory, UN-Habitat also developed the City Prosperity Index that covers the three main functions (economic, social and environmental) with six different dimensions: productivity, infrastructure, quality of life, equity and social inclusion, environmental sustainability, and governance and legislation [START_REF] Moreno | The City Prosperity Initiative: 2015 Global City Report[END_REF]. In 2016, the UN-Habitat database classified 295 cities around the world with this index. The development of a territorial functions evaluation method, with a scientific consensus, could allow comparative analysis of diverse territories and their associated mobility.

Conclusion

The recent development of Territorial (T-) and Spatialized (S-) LCA methodologies opens the LCA approach to new territorial objects such as daily urban mobility. Based on T-LCA and S-LCA recommendations, this paper proposes an assessment method for the daily mobility in a specific urban area, Lyon in France. To implement a LCA on daily mobility, urban planning sciences and environmental assessment were crossed with diverse tools: a LUTI model, mobility surveys, and an emissions model. This interdisciplinary coupling leads to an analytical framework able to assess environment burdens associated to daily mobility in regards of three different dimensions: (1) transport modes and technology analysis, (2) spatial, territorial and urban form analysis, and (3) trip purposes, behaviours and household characteristic analysis. This article focuses on the second dimension and it presents diverse spatial analysis that can be potentially helpful for territorial strategies (urban planning, incentive or tax, air quality improvement). By representing foreground activities accurately, with flow distribution, local vehicle fleets and speed conditions, the method improves the local representativeness and highlights a great variability inside the territory. Furthermore, a wide range of mobility scenarios can be explored, e.g. urban planning, technologies, behaviours. In addition to proposing an original analytical framework for urban mobility, territorial and spatial challenges have been encountered. Firstly, local air pollutant impacts characterization in LCA methodologies appears insufficient in an urban context with high population density and high emissions concentration. The use of GIS information in the emission-to-damage chain seems promising to better represent intake factors and spatialized impacts. Secondly, functions related to urban mobility can be related to trip purposes but mobility functions are still strongly related to territorial functions. Then, the development and the standardization of a territorial functions evaluation method appears as a foundation for future Territorial LCA. Thirdly, urban mobility is a multiscale object and it could be assessed at local or at broader scale. The goal and boundary definition appears then as a crucial step in mobility assessment to choose proper scope and associated data and models. Finally, few LCA studies have been conducted on a complete urban transport system or on a mobility system, highlighting, among other things, the need to develop appropriate databases for transport technologies, especially for urban public transports. 0,0% 0,4% 1,1% 0,0% 0,2% 0,1% 0,0% 0,1% 0,1% 0,3% 0,0% 1,4% 0,2% 0,0% 1,4% 0,6% 0,0% 0,1% 0,7% 0,8% 0,0% 2,0% 0,5% 0,0% 1,2% 0,1% 0,0% 1,3%

Fig. 2 .

 2 Diagram for methodological framework applied for urban mobility LCA

Fig. 3 .

 3 Life cycle stages contributions to impact categories for daily mobility (PT: Public Transport, PV: Personal Vehicle) GWP: Climate change; PMF: Particulate matter; POF: Photochemical ozone; TAP: Terrestrial acidification; CED: Cumulative energy demand The main impacts of urban mobility scenarios are presented in

Fig. 4 .

 4 Home leavings, travelled distance and GHG emissions distribution between main trip purposes

Fig. 6 .

 6 Fig. 6. Population density (A) and NOx exhaust-gas emissions density (B) related to daily mobility on the Lyon urban area; Population NOx exposure score (C) calculated at a 250mx250m square resolution.

  Public transports sharing, occupancy and mean GHG emissions in Lyon urban area:

Table 1

 1 Territorial and spatial considerations for urban mobility

	LCA steps	Main territorial and spatial issues	Implementation for urban mobility
	Goal and scope
	Goal	• Depending on territorial urban	• Reflective tool for territorial
		planners and stakeholders	stakeholders from regional to
			local scale
	System	• Two approaches: related to a	• Transport system supporting the
		specific activity or all activities	daily urban mobility of people
		within the territory
	System boundaries	• From cradle-to-territorial gate, gate-to-grave, cradle-to-grave	• From cradle-to-grave in regards to the consumption issues of
		• Depending on production or	transports, incl. vehicles, fuels
		consumption issues	and transport infrastructures
	Functions	• Multifunctionality	• Functions of mobility related to
		• Spatial variability of functions	trip purposes (work, shopping,
			leisure, school …)
	Functional unit • Embodied territorial functions and context	• Per person per day, in regards of their daily activities schedules
		• Remains comparable

Table 2

 2 Selected impact categories, associated methods, units and abbreviations

a European Commission/Joint Research Centre, 2011 b Goedkoop et al., 2009 c Hischier et al., 2010

Table 3

 3 Modal splits and travelled distances of simulated mobility

	Per inhabitant		Compact city Electric vehicles
	per day	Baseline	scenario	scenario
	Nb of trips	2.72	2.78	2.72
	PV occupancy rate Passengers per car	1.26	1.26	1.26

Table 4 .

 4 The compact city scenario generates less environmental impacts than the original urban organization for all LCA indicators. Compact urban form reduces

	%PV	60%	56%	60%
	%PT	16%	18%	16%
	%soft modes	24%	26%	24%
	Distance travelled 16.2 km	14.7 km	16.2 km
	Personal vehicle	12.9 km	11.3 km	12.9 km
	Public transport	2.2 km	2.3 km	2.2 km
	Soft modes	1.1 km	1.1 km	1.1 km

Table 4

 4 Comparison of the main environmental burden in three mobility scenarios

	Scenarios	GWP	PMF	CED	Metal	NOx exposure
		kg CO2-eq	g PM2.5	kWh	g Fe-eq	score
	Baseline	3.22	1.83	17.1	337	2.35
	Compact city	2.94 (-8.7%)	1.68 (-8.2%)	15.9 (-7%)	307 (-8.9%)	2.57 (+9.4%)
	Electric vehicles	2.79 (-13.3%)	1.65 (-9.8%)	17.1 (0%)	459 (+36%)


Olivier Pelgrin
email: olivier@cs.aau.dk

Luis Galárraga
email: luis.galarraga@inria.fr

Katja Hose
email: khose@cs.aau.dk

TrieDF: Efficient In-memory Indexing for Metadata-augmented RDF

Metadata, such as provenance, versioning, temporal annotations, etc., is vital for the maintenance of RDF data. Despite its importance in the RDF ecosystem, support for metadata-augmented RDF remains limited. Some solutions focus on particular annotation types but no approach so far implements arbitrary levels of metadata in an application-agnostic way. We take a step to tackle this limitation and propose an in-memory tuple store architecture that can handle RDF data augmented with any type of metadata. Our approach, called TrieDF, builds upon the notion of tries to store the indexes and the dictionary of a metadataaugmented RDF dataset. Our experimental evaluation on three use cases shows that TrieDF outperforms state-of-the-art in-memory solutions for RDF in terms of main memory usage and retrieval time, while remaining application-agnostic.

Introduction

During the last 20 years, the Web has seen a proliferation of large collections of RDF data, i.e., triples subject, predicate, object describing real-world concepts ranging from common-sense to specialized domains. The triples are structured in what we call an RDF graph or knowledge graph (KG). KGs find applications in multiple AI-related tasks, such as question answering, information retrieval, smart assistants, etc.

Building and maintaining a large-scale KG is a titanic effort. It does not only require sophisticated RDF stores and collaborative tools, but also procedures and protocols to extract, cleanse, and integrate data from potentially heterogeneous sources. This is true regardless of whether the KG is manually or (semi-)automatically populated. A central aspect in KG construction is metadata management. RDF metadata includes, but is not limited to, provenance, validity intervals, spatial annotations, confidence statements, and versioning. As existing KGs grow and new initiatives come to existence, the need to manage statements about triples, i.e., RDF tuples, becomes more and more crucial.

There exist multiple solutions to represent metadata about RDF triples. Popular solutions are named graphs and reification. That said, these approaches are not free of limitations. A large number of fine-grained RDF graphs can be a challenge for quad stores [START_REF] Galárraga | Answering Provenance-Aware Queries on RDF Data Cubes Under Memory Budgets[END_REF]. Reification, on the other hand, quintuples the number of statements in a dataset; not to mention the fact that it also complexifies the queries. For these reasons, RDF engines support at most one level of metadata in an out-of-the-box fashion. This means that current stores can model statements about triples, but not statements about quads, i.e., 5-tuples. They can neither model a versioned collection of graphs nor RDF statements originating from different sources with multiple validity intervals. Support for higher levels of metadata -equivalent to n-ary relationships -remains limited to very specific scenarios such as archiving [START_REF] Pelgrin | Towards Fully-fledged Archiving for RDF Datasets[END_REF].

This work takes a step to tackle the aforementioned limitations and proposes TrieDF, an in-memory RDF tuple store. TrieDF stores tuples of arbitrary length in a trie, an inmemory prefix-based tree originally used for compact storage and efficient retrieval of strings. TrieDF models everything as a trie, namely all indexes and the dictionary. Our evaluation shows that such an architecture yields a significant speed-up in retrieval with little penalty in memory consumption w.r.t. existing triple/quad stores. We also illustrate the utility of TrieDF at handling 5-tuples in the context of provenance and version management.

Preliminaries

RDF Graphs. An RDF graph 𝐺 ⊆ (I ∪ B) × I × T is a set of triples s, p, o , with subject 𝑠, predicate 𝑝, and object 𝑜 that model binary assertions about entities, for example, :Denmark, :locatedIn, :Europe . The sets I, B, and T are countably infinite sets of IRIs, blank nodes, and RDF terms respectively, with T = I ∪ B ∪ L and L the set of literals. IRIs are web-scoped identifiers for entities such as http:// dbpedia.org/resource/Denmark (abbreviated :Denmark for default prefix http:// dbpedia.org/resource/); blank nodes are anonymous file-scoped identifiers; literals are non-referenceable data such as strings, numbers, and dates. Metadata-augmented RDF Graphs. Given an RDF graph 𝐺, a metadata-augmented graph Γ : 𝐺 → T 𝑛 is an injective function that annotates each triple of the graph with a 𝑘-tuple of RDF terms. We can also see Γ as a set of 𝑛-tuples 𝑞 = s, p, o, ... with 𝑛 = 𝑘 + 3. Metadata-augmented RDF graphs can model 𝑛-ary relationships in contrast to standard RDF graphs that can only model binary relationships.

Related Work

The need to store and manage metadata for RDF triples has given rise to a large literature body that we survey in two stages. First, we survey different techniques to encode metadata-augmented triples and store them in triple stores. In a second stage we discuss existing solutions to manage additional components in triples.

Encoding Metadata-augmented Triples

Reification is the process of encoding an n-ary statement through a set of binary relationships. For instance, consider the versioned triple :Aalborg, :cityIn, :Denmark, 3 -that states that the triple is present in revision 3 of the graph. Under the standard reification, the triple is assigned a surrogate IRI (or blank node) 𝑢 that is linked to all the components of the quad, resulting in 4 new triples: u, :subject, :Aalborg , u, :predicate, :cityIn , u, :object, :Denmark , and u, :version, 3 . Since reification incurs a significant overhead both for storage and querying, other approaches have proposed more compact encoding strategies. The authors of [START_REF] Nguyen | Don't like RDF reification?: making statements about statements using singleton property[END_REF] propose singleton properties as unique keys for statements in a context. In our example, such a context could be the graph revision where a triple occurs, e.g., :Aalborg, cityIn#3, :Denmark . A more flexible scheme, called companion properties [START_REF] Hernández | Querying Wikidata: Comparing SPARQL, Relational and Graph Databases[END_REF], proposes singleton properties per subject, for example, :Aalborg, cityIn#3.si, :Denmark , where si is a local identifier that can be used to model subject-level metadata.

Reification and single properties have been used to encode one level of metadata, e.g., versioning, probabilities, provenance, temporal validity, etc. However, porting these strategies to scenarios with arbitrary levels of statement-centered metadata, e.g., provenance plus versioning, requires a careful application-dependent combination of the different schemes. This need has motivated the development of RDF-star [START_REF] Hartig | Foundations of RDF★ and SPARQL★: An Alternative Approach to Statement-Level Metadata in RDF[END_REF], a data model that treats triples as first-class citizens and allows for nested statements such as :Aalborg, :cityIn, :Denmark , :version, 3 . Support for RDF-star is gaining traction in current RDF engines. Some commercial solutions such as RDFox, GraphDB, and Stardog can parse TriG, an RDF-star serialization text format. That said, none of those solutions can so far handle arbitrary levels of nestedness.

Beyond RDF Triples

RDF Named Graphs. Even though the RDF graph data model can be used to store metadata for RDF statements "natively", it was rather conceived as an analogy to documents and database tables. A named RDF graph is associated to an IRI 𝑔, and stores a presumably large collection of triples within a well-defined context, e.g., a particular data source. Nevertheless, named graphs have been used to store more finegrained metadata such as validity intervals, revision numbers, and changesets [START_REF] Galárraga | Answering Provenance-Aware Queries on RDF Data Cubes Under Memory Budgets[END_REF][START_REF] Pelgrin | Towards Fully-fledged Archiving for RDF Datasets[END_REF]. This can represent a challenge for classical RDF graph stores, such as Jena, Virtuoso, RDF4J, etc., that are not optimized for a large number of small RDF graphs. Since RDF named graphs cannot model metadata for quads, a few approaches have proposed highly specific solutions in the context of RDF/S inference [START_REF] Pediaditis | On Explicit Provenance Management in RDF/S Graphs[END_REF]. Property Graphs. In this data model, both nodes (entities) and edges (relationships) can be assigned attributes, such as labels, timestamps, probabilities, sources, etc. Despite this flexibility, property graphs cannot store arbitrary levels of metadata for triples out of the box. Like named graphs, they rather provide a generic solution to store metadata about triples. This agnosticism has propelled adaptations of the graph model and existing engines (e.g., Neo4J, GraphDB) to particular applications, such as version and history management [START_REF] Haeusler | ChronoGraph: A Versioned TinkerPop Graph Database[END_REF] and workflow provenance [START_REF] Galárraga | QBOAirbase: The European Air Quality Database as an RDF Cube[END_REF][START_REF] Hansen | Transparent Integration and Sharing of Life Cycle Sustainability Data with Provenance[END_REF][START_REF] Kashliev | Storage and Querying of Large Provenance Graphs Using NoSQL DSE[END_REF]. As for named graphs, solutions are application-dependent and not trivially portable to arbitrary settings. Relational Databases. Notable designs to store RDF in tables are the three-column table and the entity-relationship model (a relation per class, a column per predicate). Storing metadata about RDF in a relational setting does not require any extension to the original data model, and standard engines provide efficient support for the most common metadata types such as temporal metadata [START_REF] Kaufmann | Benchmarking Bitemporal Database Systems: Ready for the Future or Stuck in the Past?[END_REF], version control [START_REF] Huang | OrpheusDB: Bolt-on Versioning for Relational Databases[END_REF], and validity intervals. That said, the relational model is not optimized for the schema-free nature of RDF, which in the first place motivated the design of triple stores.

TrieDF

We now elaborate on TrieDF, our in-memory architecture to manage metadataaugmented RDF. TrieDF stores RDF tuples of arbitrary length in different indexes. The indexes do not store actual RDF terms but rather references to those terms in a space efficient dictionary (Figure 1b).

TrieDF borrows inspiration from tries -prefix-based trees for string storage. Nodes in a trie store single characters and each string is associated to a path in the tree. Strings with the same prefix, e.g., web, weave, weasel, share common nodes. TrieDF treats tuples as "strings" of items. Those items are either references to RDF terms (for indexes) or IRI chunks (for the dictionary). We elaborate on these use cases in the following sections.

Trie-based Indexes

Consider a version annotated RDF graph with quads s, p, o, v such that 𝑣 models the versions where the triple s, p, o is present. Figure 1b depicts an SPOV index for the quads 1, 2, 3, 1 , 1, 2, 3, 2 , and 1, 5, 6, 2 -the triples are encoded using a dictionary. We can infer that the triple 1, 5, 6 was added in the second revision of the graph. We now describe the implementation of TrieDF and the operations it supports. Implementation. Logically, each element of a tuple is associated to a node in the tree. Physically, nodes store only references to their children. Those references are organized in a red-black tree keyed by the value of the child node. If |𝑇 | is the number of nodes in a trie 𝑇, an index in TrieDF has a space complexity of 𝑂 (|𝑇 |𝑐 max ), where 𝑐 max is the highest out-degree of a node in 𝑇. This happens because red-black trees exhibit 𝑂 (𝑛) space complexity in the number of keys.

Tries are optimized for tuple lookup and prefix-based retrieval. These operations, as well as additions and deletions, are implemented as in standard tries. Therefore, looking up a tuple 𝑞 incurs a time complexity of 𝑂 (|𝑞|log(𝑐 max )).

Users of TrieDF can define indexes of arbitrary depth for all permutations of the elements of a tuple. Moreover, TrieDF can also operate as a standard triple store. In that case, the system stores triples in indexes SPO, POS, and OSP in line with standard engines. The nodes in the indexes store integers, precisely the memory addresses of the RDF terms in the dictionary [START_REF] Bazoobandi | A Compact In-Memory Dictionary for RDF Data[END_REF], which is also implemented as a trie as explained next.

Trie-based Dictionary Encoding

Dictionary encoding maps the terms of an RDF dataset to an integer space for the sake of efficient space consumption and query processing. Dictionary encoding is often implemented using two hash tables, i.e., one for encoding (string to integer) and one for decoding (integer to string).

Even though dictionary encoding reduces memory consumption drastically for RDF engines, it does not tackle the inherent redundancy of RDF terms. For instance, common prefixes in IRIs are still stored multiple times. Similarly to the work of Bazoobandi et al. [START_REF] Bazoobandi | A Compact In-Memory Dictionary for RDF Data[END_REF], the dictionary for IRIs is stored in an trie. In [START_REF] Bazoobandi | A Compact In-Memory Dictionary for RDF Data[END_REF], a node is usually associated to a single character, although multiple nodes can be compressed (fusioned) into a single node if they form a single branch subtree. A drawback of such an approach is that (a) A standard trie (b) Trie-based index and dictionary Fig. 1: Example tries updates may cause fusioned nodes to split. In that case the tree must be rearranged, which increases update time. To make updates simpler -at the expense of some redundancy -TrieDF compresses IRIs by automatically coalescing all the nodes that lie between occurrences of the "/" character [START_REF] Yuan | TripleBit: a Fast and Compact System for Large Scale RDF Data[END_REF]. In other words, each node is logically associated to a chunk of an IRI as depicted in Figure 1b. If a node marks the end of an IRI, the memory address of the node serves as the integer identifier used by the tuple indexes described in the previous section.

In practice dictionary tries are bidirectional. That is, nodes store references to their children and parent. That way, a dictionary can retrieve the identifier associated to an IRI (lookup) and vice versa (reverse lookup). (Figure 1b). Because the benefit of a prefix-based representation is significantly less pronounced for literals [START_REF] Bazoobandi | A Compact In-Memory Dictionary for RDF Data[END_REF][START_REF] Umbrich | Comparing data summaries for processing live queries over Linked Data[END_REF], they are currently stored in one-node tries.

Experiments

We evaluate TrieDF along three dimensions: loading time, main-memory consumption, and retrieval time (i.e., the time to return all the tuples that match a prefix). For this purpose, we test it in three use cases and compare it to other relevant in-memory solutions. Our scenarios cover a standard RDF graph, a versioned RDF graph (requiring quads), and a collection of 5-tuples.

TrieDF was written in C++. All experiments were run in a server with 256 GB of RAM, a 16-core CPU (AMD EPYC 7281), and an 8 TB HDD. The source code and experimental data is available at https://relweb.cs.aau.dk/triedf.

TrieDF for Triples

We first assess the performance of TrieDF when used as a standard in-memory triple store on DBpedia 2016-10 [1], YAGO 3.1 [START_REF] Suchanek | YAGO: A Large Ontology from Wikipedia and WordNet[END_REF], YAGO 4 [START_REF] Tanon | YAGO 4: A Reason-able Knowledge Base[END_REF], and Wikidata [START_REF] Erxleben | Introducing Wikidata to the Linked Data Web[END_REF]. For DBpedia we use the themes mapping-based objects and mapping-based literals. For YAGO 3.1 we consider the knowledge base's core, namely, the themes facts, meta facts, literal facts, date facts, and labels. For YAGO 4, we use the facts theme from the English only distribution. In regards to Wikidata we chose the simple-statements file of the 2016-08 RDF export . Details about the dataset sizes are available in the following table, in which we compare TrieDF with Jena and RDFLib , two fully-fledged inmemory platforms for RDF/SPARQL management. They are widely used in production environments and offer mature and well-tested implementations. Loading time of the triples evaluation in seconds.

DBpedia

Retrieval time. We measure the average runtime of the different systems on single triple pattern queries of the following shapes: (i) s, p, ?o , (ii) ?s, p, o , (iii) s, ?p, ?o , (iv) ?s, ?p, o , and (v) ?s, p, ?o (? denotes a variable). For each query shape, we generated 200 queries by drawing the bound components randomly from the datasets, and then adding variables to the unbounded components as in [START_REF] Pelgrin | Towards Fully-fledged Archiving for RDF Datasets[END_REF]. The queries are implemented as classical retrieval operations, e.g., :Denmark, :capital, ?o retrieves all tuples prefixed with :Denmark, :capital in the SPO trie index.

Figure 2 depicts the query runtime of the tested systems on the different datasets. The results show that TrieDF is at least one order of magnitude faster than the competitors for queries with one variable. When there are two variables, TrieDF still exhibits lower median runtimes, but its variance is large, specially for YAGO. This can be explained by the fact that those queries may sometimes have a large number of results. This phenomenon also affects Jena. While RDFLib is mostly insensitive to large result sets, it lags behind the other systems in terms of average retrieval time. Memory consumption. Figure 3a shows the peek memory consumption of the different systems during loading and query execution. We observe that TrieDF outperforms Jena in all datasets, and all the competitors in YAGO 3. For the other datasets, our approach uses at most 13% more memory than RDFLib. This happens for two reasons. First, RDFLib's indexes are of fixed depth, which allows for some space savings: leaves do not need to accommodate for an additional pointer to its potential children. Second, RDFLib does not implement explicit dictionary encoding but rather relies on Python's internal variable handling (Python variables are actually keys pointing to their actual value in a hash table) to store references to RDF terms in the indexes. Relying on Python incurs important memory savings for RDFLib, however, the system remains two orders of magnitude slower than TrieDF at retrieval.

TrieDF for Quads

In this section we evaluate TrieDF at managing RDF quads s, p, o, v , that represent triples annotated with a revision number 𝑣. Our evaluation is based on the BEAR [START_REF] Fernández | Evaluating query and storage strategies for RDF archives[END_REF] benchmark datasets, and an archive consisting of the DBpedia versions from the 3.5 to the 2016-10 release, where one release is equivalent to one revision [START_REF] Pelgrin | Towards Fully-fledged Archiving for RDF Datasets[END_REF]. For each release, we use the same DBpedia themes as in our experiments with triples. As for BEAR, we use both the BEAR-B and BEAR-C datasets. We omitted BEAR-A because our competitors could not parse the input files due to formatting issues.

In order to provide versioning capabilities to TrieDF, we store RDF quads in SPOV, POSV, and OSPV indexes. We also compare TrieDF with the Jena in-memory models and RDFLib. For both competitors, we use named graphs to store each revision. This storage strategy, called independent copies, optimizes for data retrieval [START_REF] Fernández | Evaluating query and storage strategies for RDF archives[END_REF][START_REF] Pelgrin | Towards Fully-fledged Archiving for RDF Datasets[END_REF] Retrieval time. We measure the average runtime of the different systems on 100 single triple pattern queries of different types on versioned RDF graphs, namely version materialization (VM), delta materialization (DM), and version queries (VQ). The queries were randomly generated according to the experimental setup in [START_REF] Pelgrin | Towards Fully-fledged Archiving for RDF Datasets[END_REF].

BEAR--B BEAR--C DBpedia Figure 4 shows the runtime of the evaluated systems for each type of query. We observe that TrieDF outperforms all the competitors by far, although the performance gap can vary drastically. In particular, TrieDF's indexes are optimized for VQ queries, for which they are 3 orders of magnitude faster than the competitors. Even though the independent copies approach used in Jena and RDFLib is optimal for VM queries (and to a lesser extent for DM queries), TrieDF is still one order of magnitude faster than the competitors. Memory consumption. Figure 3b shows the peek memory consumption of the different systems during loading and query execution. We first highlight that Jena uses much more memory than the other systems. The reason is that Jena implements a classical independent copies approach, where each revision is entirety stored in a different graph. This leads to a lot of duplicated data. In contrast, RDFLib stores graphs (called contexts) in separate hash tables that map triples to graphs and graphs to triples. This mitigates redundancy. In the same vibe, TrieDF stores version identifiers in the last component of each index, which reduces redundancy. While RDFLib showcases the lowest memory consumption, TrieDF strikes the best trade-off with at most 28% more peek memory usage than RDFLib in exchange for a speed-up of 3 orders of magnitude for retrieval.

TrieDF for 5-tuples

In this section we evaluate TrieDF on a 5-tuples setup where triples are annotated with provenance and version identifiers 𝑞, 𝑣, i.e., we store tuples s, p, o, q, v . We use a dump of 27M tuples of the NELL [START_REF] Mitchell | Never-Ending Learning[END_REF] dataset. The NELL extractors collect metadataaugmented knowledge iteratively from the Web. This metadata includes, among other fields, confidence scores for the extracted triples, extraction sources, extraction methods, and the iteration of promotion, i.e., the iteration at which a triple is considered true and "officially" added to the knowledge base. We use the two latter fields in this evaluation. Since RDF storage systems do not support 5-tuples, we compare TrieDF against relational database systems with support for in-memory tables. After a comparison between SQLite and MariaDB, we chose the former due to its good performance in our setting. 5-tuples in TrieDF are represented via SPOQV, POSQV, and OSPQV indexes. We test SQLite with and without those indexes. Loading time. We report loading times of 36.55s, 16.98s, and 47.27s for TrieDF, SQLite, and SQLite with indexes respectively. We observe that SQLite loads data significantly faster when no indexes are built, however when indexing is enabled, TrieDF is faster. Retrieval time. We tested both systems on 100 queries of the same types defined for the quads evaluation with randomly bounded 𝑞 and 𝑣. As suggested by Figure 4d, TrieDF achieves similar retrieval performance than an indexed 5-column SQLite in-memory table. The median runtime of TrieDF is better for VM and VQ queries. Memory consumption. Figure 3c shows the peak memory usage of both SQLite and TrieDF when loading and querying the NELL dataset. We observe that indexing multiplies memory consumption by a factor of 3 in SQLite. TrieDF still uses 26% more memory than indexed SQLite, however TrieDF cannot leverage its trie-based dictionary to its full capacity. This happens because NELL does not use prefixed IRIs. Despite this rather suboptimal setting, TrieDF still shows comparable performance to SQLite.

Conclusion

We have presented an in-memory architecture based on tries to index and access annotated RDF triples efficiently. Our solution provides the user with a flexible architecture to manage arbitrary RDF metadata in main memory. Our experimental evaluation has shown that such as an approach strikes an interesting trade-off between retrieval time and memory footprint: it can yield a speed-up of up to 3 orders of magnitude in retrieval time in return to little (and sometimes no penalty) in memory usage. We believe that TrieDF is a first step towards a holistic solution to manage knowledge beyond RDF triples. As future work we envision to explore different strategies to reduce TrieDF's memory footprint. We also envision to couple our architecture with suitable in-disk storage and provide SPARQL query support.

Fig. 2 :Fig. 3 :

 23 Fig. 2: Query runtime (microseconds) for triples queries (log scale)

Fig. 4 :

 4 Fig. 4: Query runtime for quads and 5-tuples (log scale)

  The following table shows the loading times of Jena, RDFLib, and TrieDF for the experimental datasets. Jena is consistently faster than the others systems, which can be explained by (i) a fast dictionary, (ii) a highly optimized RDF parser, and (iii) batching of insertions. Jena stores the dictionary in classical hash tables, which allows for constant time lookup and insertion of terms, in contrast to TrieDF that incurs a logarithmic lookup complexity. This, in contrast, optimizes for compactness (see paragraph on memory consumption). That said, TrieDF ranks second close to Jena, and outperforms RDFLib by a large margin.

			YAGO 3.1	YAGO 4	Wikidata
	Triples	38M	85M	22M	138M
	Size	4.9GB	12GB	3.0GB	17GB
	RDF terms	11M	59M	8.5M	54M
	Details about the experimental datasets for the triples evaluation.
	Loading time. DBpedia	YAGO 3.1	YAGO 4	Wikidata
	Jena	587.14	1281.65	289.42	1665.48
	RDFLib 2816.16	7102.30	1626.85	9587.51
	TrieDF	727.20	2105.37	358.20	2800.77

  at the expense of high memory consumption. Loading time. The table below shows the loading times of the evaluated systems. No results are provided for Jena in DBpedia, since the system runs out of memory. RDFLib lags behind Jena and TrieDF, with TrieDF being the fastest at loading the bulky revisions of BEAR-C, and Jena being the fastest at loading the more granular revisions of BEAR-B.

		DBpedia	BEAR-B	BEAR-C
	Jena	-	1094.83	418.74
	RDFLib	26433.76	4851.09	1663.40
	TrieDF	16074.17	3743.55	387.68
	Loading time of the quads evaluation in seconds.

http://tools.wmflabs.org/wikidata-exports/rdf/index.html https://jena.apache.org/ https://rdflib.readthedocs.org

http://rtw.ml.cmu.edu/rtw/resources

Acknowledgements. This research was partially funded by the Danish Council for Independent Research (DFF) under grant agreement no. DFF-8048-00051B and the Poul Due Jensen Foundation.