Marie-Axelle Granié¹
email: marie-axelle.granie@ifsttar.fr

Thierry Brenac¹

Marie-Claude Montel¹

Cécile Coquelet¹

Marine Millot²

Franck Monti²

Marjorie Pannetier¹

Advances in Transportation Studies

Keywords:

Purpose: Taking all users of the road system into accountnotably those who use non-motorized modes of transportationis a major challenge for urban engineers when designing roads and public spaces. And yet, although a body of knowledge exists concerning the effects of the environment on pedestrian accidentology, so far very little research has been carried out on the perception pedestrians have of the road environment (structure of buildings, vegetation, etc.) and its influence on decision-making when crossing. This exploratory research aims at studying this perception through a qualitative approach. Methods: We used the focus group method to study the perception of 20 two-way street environments with a certain level of diversity from the point of view of buildings (type, density, and heterogeneity), activities, position in relation to the city center, width of the sidewalks and type of traffic. The participants in the two focus groups were 11 regular or occasional pedestrians. Results: The verbal material obtained is analyzed in terms of perception of the environment, inferences about driver behavior (notably toward pedestrians), and influence on the pedestrian crossing behavior. Pedestrians prefer environments in which they have the "upper hand" or environments that are highly predictable. Conclusions: The results suggest a few tendencies or lines of approach concerning the design of spaces that make street crossing more comfortable for pedestrians. They also show that the focus group method is of great interest for studying the perception and interpretation of urban environments.

Introduction

One major challenge for urban engineers when designing roads and public spaces is to take all users into account, notably those who use non-motorized modes of transportation. Such approaches require thinking of urban spaces and their design from the point of view of these modes: what perception do users of non-motorized modes of transportation have of this? How do they understand it? Concerning pedestrians, it has been established that the frequency and seriousness of their accidents vary with density, the function of the zone (residential, business activities), the presence of shops, or the location in the city (city center, suburbs) [START_REF] Dissanayake | Modelling the effects of land use and temporal factors on child pedestrian casualties[END_REF][START_REF] Wedagama | The influence of urban land-use on non-motorised transport casualties[END_REF][START_REF] Graham | Spatial Variation in Road Pedestrian Casualties: The Role of Urban Scale, Density and Land-use[END_REF][START_REF] Clifton | Severity of injury resulting from pedestrian-vehicle crashes: What can we learn from examining the built environment[END_REF]. Of course, these differences come in part from the influences that the environment has on important parameters such as speed, increasing the risk and seriousness of accidents [START_REF] Aarts | Driving speed and the risk of road crashes: A review[END_REF][START_REF] Rosén | Literature review of pedestrian fatality risk as a function of car impact speed[END_REF][START_REF] Davis | Relating severity of pedestrian injury to impact speed in vehicle-pedestrian crashes, simple threshold model[END_REF][START_REF] Pitt | The severity of pedestrian injury in children: an analysis of the pedestrian injury causation study[END_REF]. It is, however, plausible that the differentiated effects of urban spaces on pedestrian accidents are also, at least in part, due to pedestrians' perception and interpretation of the environment and their influence on crossing decisions, but this question remains poorly understood.

Pedestrians' perceptions of the road environment have been widely studied by psychologists, with a view to improving risk evaluation [START_REF] Underwood | Reading the road: the influence of age and sex on child pedestrians' perceptions of road risk[END_REF][START_REF] Cho | The role of the built environment in explaining relationships between perceived and actual pedestrian and bicyclist safety[END_REF][START_REF] Mullan | Do you think that your local area is a good place for young people to grow up? The effects of traffic and car parking on young people's views[END_REF], and by urban planners, architects and geographers, with a view to making this environment more favorable to pedestrian [START_REF] Foltête | Urban layout, landscape features and pedestrian usage[END_REF][START_REF] Smith | Contribution of Perceptions in Analysis of Walking Behavior[END_REF][START_REF] Hine | Pedestrian travel experiences. Assessing the impact of traffic on behaviour and perceptions of safety using an in-depth interview technique[END_REF] in terms of the feeling of comfort, safety and security. Some of these studies, such as the one by Ewing et al. [START_REF] Ewing | Identifying and measuring urban design qualities related to walkability[END_REF] on "walkability" or "walking potential"i.e. the way individuals perceive the street as a walking spacelooked into the physical elements in the environment that determine pedestrians' reactions (feeling of safety or comfort, level of interest). For this, Ewing et al. identified the physical elements in the environment that determine the qualities of the urban design (recognizable, readable, open, on a human scale, transparent, unified, rich, coherent and in good upkeep) that have an influence on pedestrians' reactions. All these studies provide a better understanding of the determinants in pedestrian mobility, but not the way in which the built environment influences (or does not influence) pedestrian behavior in interactions with other types of users, notably when crossing a street. These studies do not enable us to determine whether perception and interpretation of urban environments by pedestrians, when they are faced with a crossing task, lead them to develop expectations on how their situation evolves (notably in terms of foreseeing driver behavior) so as to be able to take faster, better suited decisions.

Recent work suggests that the environment influences the crossing task through its topographical, infrastructural and regulatory aspects [START_REF] Bergeron | Effets des caractéristiques de l'environnement sur le comportement des piétons à des intersections urbaines, in Le piéton et son environnement. Quelles interactions ? Quelles adaptations ? Actes INRETS[END_REF]. Other physical elements in the environment, however, such as the nature and spatial distribution of buildings, pedestrian and traffic density, could influence the gathering of information on the crossing situation and its interpretation, notably concerning inferences on driver behavior [START_REF] Foot | Children's understanding of drivers' intentions[END_REF], as well as decision-making when crossing. Empirical evidence on these subjects is limited, however. We therefore need to gain a better grasp of the nature of pedestrians' perceptions of the environment and their mobilization when crossing in order to act more effectively in terms of prevention, but also of road engineering.

Experimentation using the focus group method

The objective of this exploratory study is to identify the differentiations made by pedestrians between different urban environments, notably in terms of inferences on driver behavior. For this, we used the focus group method, applied to the perception of various environments by pedestrians (who walk occasionally or daily). A focus group consists of a group of people brought together for a discussion, in our case on their perceptions of a given object. It is set up by the researcher to provide access to a set of information shared by a relatively homogenous group of individuals. Exchanges of contradictory points of view and reciprocal influences are used to bring to light the coherency of the opinions expressed, consensuses and points of disagreement, and require the participants to develop and refine personal positions and arguments. The method of focus group leads participants to develop their arguments to defend their position against other participants. The material gathered in this way enables us to precisely understand the different participants' points of view. This method is often used in different disciplines to gain a better grasp of how various objects are perceived [START_REF] Bruseberg | Focus groups to support the industrial/product designer: a review based on current literature and designers' feedback[END_REF][START_REF] Macdougall | Compétence, équité et représentations du lieu et de l'espace chez les enfants[END_REF][START_REF] Wibeck | Comprendre la complexité : les focus groups comme espace de pensée et d'argumentation sur les aliments génétiquement modifiés[END_REF].

Population

The 11 participants (6 women and 5 men) were recruited through acquaintances and none of them was expert in pedestrian issues. They were divided into two discussion groups. The first included five experienced drivers who are also occasional pedestrians (35-50 years old). The second included three experienced drivers and three pedestrians (who do not own a vehicle and walk daily) (23-30 years old). In this last group, the aim was to share opinions among people with different travel experiencesin terms of mode, and so in terms of vulnerability, speed, travel constraints and the more or less habitual point of view from which they perceive visual crossing sceneswhich could lead to divergences in their perception of the environment as pedestrians. Intra-group homogeneity in terms of age was used to facilitate interactions among participants, and not to compare the narratives obtained from the two groups.

Materials

The experimental material used as a support for the group discussions comprises 20 sets of four photographs, each of these sets presenting an urban environment (see an example in figure 1). The environments selected and represented in the pictures were from the different urban regions of the South East of France and were chosen to be unfamiliar to the participants. The sites are not situated in the living area of the participants and they are quite common places, not famous ones. Each set, presented in A4 format, shows left-hand, right-hand and central views of one site -providing a 180° view of a two-lane infrastructure environment (one lane in each direction). These photographs were taken from the pedestrian's point of view (height of the view and position on the sidewalk in a pre-crossing position). The environments have a certain degree of diversity from the viewpoint of the buildings (type, density, heterogeneity), neighborhood activities (housing, shops, services, industries, etc.), the site location within the city (central business district, center, suburbs, countryside) and how the road and its surroundings are organized (width of the sidewalks, whether there are pedestrian crossings, parked vehicles, nearby intersections, etc.). The sites were photographed under normal conditions of use (in terms of traffic, number of pedestrians, etc.). To control the situation presented and avoid overloaded situations that would overly focus the participants' attention (oncoming vehicles located right next to the viewer, for example) and may influence their perception more than environment, the photos were taken under normal and daily conditions of use, that is during working hours but outside rush hours .

- ----------------insert here figure 1-------------------------

Procedure

The objective of the experiment is to understand participants' reactions in terms of the crossing action when faced with the physical elements of the environments presented, not discussing it from the safety point of view as is usually the case, but rather from the viewpoint of comfort and difficulty. The objective is to understand which elements in the environment are used by pedestrians to define the difficulty of the crossing task. In fact, some studies have shown that the intention to cross is linked less to a risk evaluation than to an estimate of the difficulty of performing the task [START_REF] Holland | The effects of age, gender and driver status on pedestrians' intentions to cross the road in risky situations[END_REF][START_REF] Yagil | Beliefs, motives and situational factors related to pedestrians' self-reported behavior at signal-controlled crossings[END_REF][START_REF] Evans | Understanding pedestrians' road crossing decisions: an application of the theory of planned behaviour[END_REF].

The focus groups took place during two consecutive afternoons in the research laboratory. All 20 of the photographic sets were presented to each participant individually and in isolation, 15 minutes before the group discussion. Among the 20 environments, they were asked to choose the most pleasant, the most unpleasant, the easiest and the most difficult for crossing. The participants had 5 minutes to make these choices.

The 20 sets of photographs were available to the participants during the group discussion. The discussion is initiated by using the choices asked of the participants individually before the group discussion. The objective of the discussion is to differentiate between the environments in terms of the crossing activity, notably for pleasantness and difficulty. The entire group was asked sub-questions for each environment selected, in order to lead the participants to compare the different environments by identifying environments similar to the one chosen, determining the main elements leading to the choice of this environment in particular, and what differentiates it from other environments that the group considered to be comparable. The objective of the discussion was not to obtain a consensus within the group, but rather to use potential disagreements among the participants to bring each of them to develop their arguments. At the end of the discussions, each group was asked two general questions concerning the elements in the environment usually taken into account when crossing a street and the type of information the environment can provide for making the decision to cross.

Results

The group discussions (more than 2 hours and 50 minutes each) were fully transcribed and provide very rich material [START_REF] Montel | Categorisation of streets by drivers and associated expectancies: a cognitive analysis of driving activity for safer urban design[END_REF]338 words for the first focus group and 19,088 words for the second). This was studied to understand key points of the narratives in response to the four questions asked to the participants. The usual qualitative methods for content analysis were applied to the transcriptions. The text of the discussions was analyzed to pinpoint elements in the narrative to understand the choices of environments in response to the four selections requested, but also the physical elements mentioned, the labels assigned to the environments and the inferences that the participants made.

Pleasant environments for crossing

One environment where crossing was considered to be pleasant was chosen by most of the participants in the focus groups. It was a site located in a city center (figure 2). They would feel good here in general. It is an environment that appeared calm and pleasant for strolling (shade from large trees). This feeling was reinforced by the presence of cafés and shops, which make this street livelier and more "pedestrian". Some said that their feeling of safety was increased by the wide sidewalks, the narrow carriageway the same color as the sidewalks, the absence of road markings and no parking, which appeared to indicate to them that pedestrians have the rightof-way, that they have the "upper hand (G1)" and "here, the automobile driver is the one who has to pay attention to the pedestrian (G2)": "the fact that the sidewalk appears to be made of the same material, it really makes you think that it is a pedestrian zone or, at least that there are a lot of pedestrians crossing (G2)". Unorganized pedestrian crossing (no pedestrian crosswalks laid out) requires drivers to be more vigilant and to drive slowly: "I think vehicles will drive more slowly, because they expect regular pedestrian crossings and therefore, yes, I think they drive slower and so there is a feeling of safety (G2)".

-----------------insert here figure 2-------------------------Two residential environments were also selected as being pleasant for crossing (figure 3). Here, the calm, reassuring atmosphere (little apparent traffic) that is "pleasing to the eye (G1)" (low buildings, greenery) is what stands out. The sidewalks are clearly laid out, there is good visibility both for drivers and for the pedestrians themselves ("you have an unobstructed view (G1)") and the presence of pedestrian crosswalks make these environments the sites that appear the least dangerous. Moreover, despite the presence of small buildings, a "nature, a little more like a village (G1)" atmosphere is felt on these sites, further reinforcing this reassuring aspect: "when you are in a village you're calmer, there aren't as many crazies (G1)".

-----------------insert here figure 3-------------------------

Unpleasant environments for crossing

The environments considered the most unpleasant for crossing are located more in the peripheral sections of urban areas. The participants often qualify these environments with labels such as "commercial zone", "industrial park" (figure 4), "ghetto", "housing project" (group of multistory, social housing buildings), "suburb" (figure 5), "motorway" (figure 8), etc.

-----------------insert here figure 4-------------------------More frequently, the participants point out that the environment appears to be dedicated to cars, that there appear to be high speeds, the traffic is dense, or there are many heavy goods vehicles: "It's almost like a motorway… An expressway (G1)"; "They drive fast (G1)"; "It's unpleasant, because it looks like there's automobile traffic, but also trucks (G2)". The participants often observe that pedestrians do not appear to have any place in such environments. Road markings are mentioned when they are deteriorated (faded pedestrian crosswalk) or when they stand out: "I found it unpleasant because the solid white center line and the straightaway let cars drive fast, and that's scary (G2)". These comments apply more to environments located in commercial or industrial areas on the edges of urban areas, pointed out as being the most unpleasant by the majority of participants. Furthermore, some participants said that the landscape was unattractive, not very reassuring or dehumanized in residential areas or certain industrial or commercial spaces on the outskirts ("It's the 'housing project' side that I found ugly and not very reassuring (G1)", "It really fits the cliché of the suburbs (G1)"; "It's a [industrial] zone… I don't like it… I think it's inhuman (G1)").

-----------------insert here figure 5-------------------------For some participants, the unpleasantness of some of the environments presented is due to the fact that, in their opinions, these are areas where people undertake many maneuvers for parking and at intersections, and where there are sometimes obstacles to visibility. Lastly, during the discussions concerning the environments considered to be unpleasant, the participants often pointed out that pedestrians are less expected by drivers and that pedestrians have to control their crossing by themselves (without being able to rely on drivers): "It [see figure 4] is unpleasant, because people don't necessarily expect to see me there because, you know, it's an industrial park…(G2)"; "It's practically up to us pedestrians to pay attention even though it should be a concern for drivers too, so it adds more stress just when crossing(G2)".

Easy environments for crossing

Most of the participants feel that it is easy to cross at sites that they situate "in the city" but not in the "city center", often in residential neighborhoods (figure 6), or sometimes even at less urbanized sites that they situate on the city's periphery, at the "entry to the urban area", and even "somewhat countryside" (figure 7).

They mention the presence of houses or small residential buildings and little or no business activity. They notably emphasize the "unobstructed view", "straightaway with good visibility" and sometimes how few parked vehicles there are. Some pointed out the presence of crosswalks or pedestrian crossing facilities; others spoke of an absence of intersections or a deteriorated roadway.

-----------------insert here figure 6 &7-------------------------The environment, notably due to these elements, leads them to believe that automobile drivers drive slowly: "…it's a residential neighborhood […] I think that here, cars don't go faster than 30/40 km/h, even with the straightaway (G2)", "…the roadway is in bad shape in many places, so I'd say that you wouldn't drive very fast, you know (G1)". Sometimes speeds are also considered to be a bit faster but without hindering crossing, the important thing being visibility: there is good reciprocal visibility between drivers and pedestrians, who can see vehicles coming from a long way off, and they can be seen by the drivers. There appears to be little traffic: "I think a lot fewer cars go by, so for me, that's why I think it's easy (G2)".

Thus, for the participants, the situation appears simple and easy to manage for pedestrians. Few events could take them by surprise: "…you know where you are, it's clear, you have a good view, so we have to be careful, too, but I get the impression that it's easier, that there are fewer hidden dangers (G1)". The pedestrian can find plenty of traffic gaps to cross without having to excessively mobilize attentional resources: "…you have time to cross calmly, I don't feel like there's really any danger, I get the impression that everything is OK, I'm at ease, I look to the right and to the left and it's cool. You don't need to be particularly attentive, that's it (G1)", "both are fairly easy to cross, short and simple, there's not much traffic (G2)".

The participants also think that, in these locations, automobile drivers pay attention to pedestrians, notably because drivers are thought to be in their own residential neighborhood. The environments considered as easy are often felt by the participants to be closed spaces, spaces of domestic life, a sort of bubble where only the locals move around: "I think that it's people who live there […] in my opinion, the drivers pay attention to the pedestrians, they are in a neighborhood that they know well, so they pay attention (G2)".

The idea that drivers pay special attention to pedestrians in part led one participant, contrary to the others, to choose a very different environment located in the city center and which was quite often chosen as the most pleasant site (see figure 2).

In this environment, the less marked distinction between pedestrian spaces and vehicle spaces was emphasized, as well as the road treatment: "the fact that there are no defects on the ground, I don't have to worry about it, so it's easy to cross there […] you get the impression that you're on a kind of terrace, with a step, stairs (G1)".

Above and beyond pedestrian attention, this group of elements suggests to the participant that cars drive relatively slowly. Although there are many interactions between drivers and pedestrians, they seem easier: "you can talk while crossing, I don't have to look out much, so it's fairly easy […] I operate by ear in that kind of situation […] 90% of the time he'll stop to let you cross (G1)".

Difficult environments for crossing

Environments considered hard to cross are, like the environments considered to be unpleasant, located more on the periphery of urban areas. The participants often label them as "countryside", "motorway", "highway environment", "conventional road, totally", or "industrial park" and, a priori, they are characterized by abundant road markings and narrow or non-existent sidewalks (see figures 8 and9).

- ----------------insert here figures 8 &9 -------------------------Road markings are the element that comes up most frequently in explaining why crossing is difficult. As they give a feeling that the situation is complex, they make it hard for the pedestrian to understand: "As a pedestrian, you think, 'what's going on here?' I wouldn't get it. So I wouldn't really know what to expect (G2)". This feeling of complexity is notably related to the different possibilities for vehicle behaviors and trajectories suggested by the road markings and which are hard for the pedestrian to predict: "you'd better be really careful: they turn to the right, they turn to the left, there are two lanes (G1)". They feel that these markings make it harder for the pedestrian to make a decision: "it's more the fact that there are several pieces of information for possible orientations. So do you see everything you have to think about? It's not just the two directions: there are righthand turns, left-hand turns, so you take more time to decide (G1)". The participants also infer that drivers pay less attention to pedestrians given the maneuvers they have to perform: "If a pedestrian suddenly appears when the driver is already in the middle of his right-hand turn maneuver, he may be more concentrated on the road (G2)".

Beyond the directional arrow, the difficulty in crossing is also related to the centerline markings (or edge markings) and the condition of the roadway, which are interpreted as indicating that the environment gives the right-of-way to vehicles: "a clearly distinct carriageway, with lines, all that… you are entering into an environment that is not your own. You are entering into a road environment (G1)". This environment is perceived as being built to the detriment of pedestrians: "it looks like car territory. It's new, it's been redone and everything, so pedestrians, they didn't even give them a sidewalk. So maybe it's that effect that scares people (G2)".

The lane configuration is used to infer the speeds driven: "it's very straight, they must drive really fast (G1)". Speeds are also inferred from the markings designed for vehicles combined with the absence of markings for pedestrians (regarding the environment presented in figure 8): "no pedestrian crosswalk and a white line, you really get the impression of enormous speed (G2)".

The narrowness, or even absence, of sidewalks is used by the participants to estimate the potential pedestrian density. This pedestrian density is then used to infer how much attention is paid to them by drivers: "the sidewalks are pretty small. So, drivers don't really expect to see pedestrians. I think they probably drive faster. It's really deserted (G2)".

The width of the roadway to be crossed, along with speeds that are high and hard to assess, are perceived as making the pedestrian's task difficult due to the large amount of information to be taken into account, and extending the decision-making time: "The speed of the oncoming cars, on both sides, and the width of the road. You have to think about all that. You really have to think there, make a film in your head… (G2)".

One participant chose an environment located in a city center as the most difficult (while this same site is chosen by the majority in terms of pleasantness and ease of crossing), on the grounds of the situation's complexity, both for the pedestrian and for the driver, the sources of distraction for drivers and the possible misunderstandings between pedestrians and drivers.

Thus, the main elements used by the participants to judge the difficulty of crossing in the environments presented concern road markingsmeaningful and complexthe dimensions of the roadwaywideand sidewalksnarrow or absentleading them to infer high speeds and a low pedestrian density, these two elements producing low attention to pedestrians. The difficulty for pedestrians therefore comes from the amount and complexity of the information to be gathered and processed, in addition to a feeling of imminent danger due to vehicle speeds: decision-making is long and complex in a situation where there is no room for error.

More general results

Beyond the results presented above, some more general lessons can be learned from the analysis of the discussions in the focus groups.

Labels applied to the environments

Here we are interested in participant interventions in which they gave an overall qualification to the site photographed, applying a "label" such as housing project (group of multistory, social housing buildings), industrial park, city center, pedestrian zone, motorway, countryside, commercial, residential neighborhood, "résidence" (this French term generally stands for "group of multistory residential buildings"), etc. These qualifications can often be interpreted as references to categorical representations of the environment or public spaces present in the participants' memory. For example, one member of a focus group said "that reminds me of a fairly quiet neighborhood, a little "résidence" (G2)", and in the context it is clear that he was not referring to any particular neighborhood or particular "résidence" here, but rather to a more general representation. Among all the participant interventions (in both focus groups) containing some form of analysis or description of the photographed sites, approximately 31% (87 out of 277) used such labels. In these 87 interventions, a total of 101 labels or general qualifications appear (54 for focus group 1 and 47 for focus group 2).

Diverse wordings were used by the subjects, corresponding to different ways of evoking these labels and general qualifications. Some wordings appear to identify or assign the site to an environment category: "it's a residential neighborhood (G2)", "I chose that one, the city center (G1)", etc. Other wordings appear more to situate the site photographed in a certain category of environment: "it's probably in the city center (G2)", "here, this is […] in a commercial zone (G1)", etc.). Other expressions show a resemblance, an aspect, an impression ("it doesn't look like a national highway (G1)", "it makes you think of a pedestrian zone (G2)", "it's more like an industrial park (G2)") suggesting a proximity to a category. Rarely, there is a simple qualification, only concerning two wordings out of 101: "it's not commercial (G1)", "it's residential (G2)".

On the other hand, a notion of degree of belonging to the category, degree of resemblance, or at least a degree or intensity of the connection between the site examined and the category or label mentioned, appeared in approximately 40 of the 101 wordings used (approximately 40%). Here are a few examples: "it's almost an industrial park (G1)", "it's more like a residential road, say (G2)", "it's really the city center (G1)", "a bit […] more like a village (G1)", "it really fits the cliché of the suburb (G1)".

The labels that the participants most frequently used are those given in table 1, by descending order of frequency.

- ----------------insert here table 1 -------------------------These results obviously only make sense in reference to the 20 photographed sites presented to the participants.

Other labels only came up once or twice each in all the discussions in the two focus groups. They may, however, take on forms similar to some of the main labels and should therefore be taken into account to gain a better view of the results. Table 2 presents all the labels used, grouped together in a way that makes it easier to pool similar forms.

The first group of labels includes those using the idea of the city or different kinds of urban or non-urban environments with reference to the question of the urban landscape or a kind of gradient between the city center and the countryside. The second group includes labels concerning residential urban environments. The labels family zone, quiet neighborhood, applied to environments that the participants themselves qualify as residential, were added to this group. The notion of housing project, as well as the terms suburb and ghetto, also refer to residential areas. The third group comprises labels relative to commercial or industrial environments on the city's periphery. The fourth brings together labels formulated in terms of the kinds of roadways (motorway, national highway, road, etc.), or the environment's highway or automobile character (automobile environment, road environment). The fifth group includes labels referring to how pedestrians are accommodated in the environments in question.

- ----------------insert here table 2 -------------------------Table 2 shows that, overall, labels referring to the environment's urban character, the different degrees of proximity to the city center and the city-country gradient are those most used. Those relative to residential zones are also widely used.

With reference to research on cognitive categories and the notion of prototype [notably see [START_REF] Rosch | Principles of categorization[END_REF][START_REF] Rosch | Family ressemblances: studies in the internal structure of categories[END_REF][START_REF] Dubois | Sémantique et cognition[END_REF] and their applications to road spaces [START_REF] Mazet | Catégorisation et interprétation des scènes visuelles: le cas de l'environnement urbain et routier[END_REF][START_REF] Montel | Categorisation of streets by drivers and associated expectancies: a cognitive analysis of driving activity for safer urban design[END_REF], the use of such labels by the participants could be interpreted as reflecting the use of knowledge in the individual's memory, and more particularly of knowledge that is structured into mental categories formed in the context of a particular activity (here, travel and the use of public spaces). These categories meet a need for cognitive economy (by reducing the stimuli of the real world to behaviorally and cognitively usable proportions [START_REF] Rosch | Human categorization, in Studies in cross-cultural psychology[END_REF] and are inseparable from the context of their production and use, and notably from behavioral responses in the real world.

We cannot, however, exclude that the labels are actually just summaries, part of the narrative in communication between participants, and that they do not reflect the existence of categorical representations in their memory.

Further investigation is needed on the particular use of these labels during discussion within focus groups; it appears that they mainly serve as basis for inferences, with assignment to a category or label enabling the participant to make a statement on the amount of traffic, the number of pedestrians, speeds, whether drivers expect pedestrians, etc.: "It doesn't look like there are very many pedestrians, but it's a residential zone, so there may be a lot of pedestrians after all (G2)".

Inferences produced from the material presented

Out of the totality of participant interventions [START_REF] Dissanayake | Modelling the effects of land use and temporal factors on child pedestrian casualties[END_REF]269), 14% (180) include at least one inference, defined, broadly speaking, as any information added and deduced from the information already given by the visual elements present in the photographs proposed. In all, 244 inferences were encoded. Table 3 presents the elements in the scene that are the subjects of inferences and the classification by descending frequency of the results of these inferences.

The results show that the vast majority of inferences concern traffic or vehicle drivers (191 inferences) much more than pedestrians (51 inferences).

Inferences mainly concern vehicle speeds (73 inferences) in the different environments ("I think that people drive a little faster than there, because they can; but not as fast as there, because there are pedestrian crosswalks, there are people (G1)"), vehicle density (39) and pedestrian density [START_REF] Rosch | Human categorization, in Studies in cross-cultural psychology[END_REF] ("looking at the pictures, it is true that you don't expect to see a lot of active traffic, you know. Even if at some times of the day it probably happens (G1)") and the attention paid to pedestrians by drivers (45) ("you can turn right there. I thought to myself that the car first pays attention not to collide with another car and then it pays attention to the pedestrians (G2)").

-----------------insert here table 3 -------------------------The type of wording used in the inferences was also analyzed (table 4). The results show that nearly half (114) of the inferences are formulated as an affirmation, a logical connection that directly links a visible element in the scene to a non-visible result ("there aren't many homes, so if there aren't a lot of homes, that means there aren't a lot of people walking to the bakery (G1)"; "there are trees, car drivers are afraid of trees! (G1)"). This type of wording is more prevalent among experienced drivers. A little more than one-third of the inferences are worded as simple hypotheses, a probable consequence of the visible situation ("it seemed to me that it was a little residential housing area, so normally there shouldn't be a lot of cars driving around (G2)"); this wording was more common among less experienced drivers and pedestrians. Only 14% are formulated as personal opinions and less than 8% as foresight on the situation in the near future. The inferences are either produced directly from the labeling of the environment ("in residential housing areas, there are indeed more children who can play, who may be outside (G2)"; "it is the typical case of urban streets with moderate speeds (G1)"), or deduced from the presence of cues taken from the environment ("as it is a long straightaway, I think cars may drive fast (G2)"; "there are cars parked in front of the bakery, there are parking spots alongside, a bus stop, I think there's a lot of movement (G2)").

-----------------insert here table 4 -------------------------

Effect of interactions on perceptions during the focus group discussions

The interactive dimension, specific to group discussions, is an important feature of this method. The use of focus groups is also of interest in that it provides participants with the possibility to discuss their points of view, to influence each other reciprocally and to develop their own points of view. These interactions thus may have led to the joint construction of an interpretation of the environment, as in this example of a discussion between 3 participants on the pleasantness of the city center environment: "N: The fact that there are terraces, wide sidewalks, that there aren't any road markings, that it's not very straight, that there are people, that the buildings are tall… all of that obstructs the perspective a bit… […] M: so that leads to a certain recognition of a pedestrian space for, well… for drivers, and so there is respect… yes, without being imposed, but respect from drivers for this space where he recognizes that there are… yes, they take into account… that is induced… N: Actually, in this kind of space we all share the space. So maybe what I was saying earlier about the pedestrian's having the upper hand…it's not that they have the upper hand, it's just that they have the upper hand here more than elsewhere. That means that you can't ignore the vehicles. M: And cross any way you want… N: Exactly… S: Pedestrians are in a more favorable environment, let's say… N: So their task is easier, so it's more pleasant."

The discussion also enables certain participants to understand the effect that the presence of certain elements in the environment has on their perceptions, as in this example of the effect of markings on the perception of pedestrians' place in the space: "C: Earlier, N. said that he seemed to focus on road markings, and I hadn't ever thought about it but, yes, when you see that the road has good markings for cars, you think, "this is car territory". I had never realized that… but that seems pretty true to me. So you can see that there [START_REF] Macdougall | Compétence, équité et représentations du lieu et de l'espace chez les enfants[END_REF], there aren't any road markings, it looks more… shared. So when the lines are nice and white and all, you think, "whoa, they must go zoom!" N: yeah, it's a track… C: I had never noticed, but… I pretty much agree. So there [START_REF] Ewing | Identifying and measuring urban design qualities related to walkability[END_REF], there are some markings, so you feel less at home, you know…"

The group dynamic and the interactions between the participants were not analyzed in depth, however, given our research objectives.

Discussion and conclusion

In terms of road engineering, the results suggest a few tendencies or lines of approach concerning the design of spaces that make street crossing more comfortable for pedestrians. For most of the participants, the preferred environments for crossing a street are those which clearly give pedestrians the upper hand over automobiles (wide sidewalks, no parking, no markings, a lively context with cafés and shops). We have reasons to think that such environments are also, in general, safe environments: for instance, it has been established for a long time that safety is improved by public space treatments which, beyond traffic calming, make the local and nonmotorized uses of the public space more salient to motorists [see for example 30].

Other participants appear to prefer environments that provide more organization and simplified use of the public space by pedestrians (presence of pedestrian crosswalks, well defined sidewalks and unobstructed visibility, in a rather calm context and with moderate traffic). Here again, these elements do not appear to be incompatible with a good level of safety, if we take the urban context into account. It is true that the scientific literature is sometimes critical of the safety provided by pedestrian crosswalks [START_REF] Elvik | The handbook of road safety measures[END_REF], but an in-depth examination of the research carried out on this point leads us to the more nuanced conclusion that pedestrian crosswalks reduce risk so long as they are installed in a clearly urban environment on roadways that are not too wide or fast [32, pp. 96-100].

In short, pedestrians prefer either environments that are complex but in which they have the "upper hand", or environments that are highly predictable and where it is easy to make decisions. This underlines that both of these organizations of the street can correspond to the pedestrians' needs. For a given place, the more appropriate choice depends on the surrounding area, the activities carried out and the other users of the place. This meet the principle of "complete street" that takes all the users into account but that is designed depending on surrounding [START_REF] Smith | Street design: Part 1 -Complete street[END_REF]. This also provides food for thought on how to implement certain contemporary road engineering concepts such as the "zones de rencontre" used in Switzerland, Belgium and France, whose principle is based on total mixed use of spaces for all user categories and on priority given to pedestrians in relation to vehicles, including motor vehicles (most European countries have similar concepts: "verkehrsberuhigter Bereich", "erf", "living street", for example). An assessment of these layouts from the pedestrian point of view would be useful to determine how pedestrians perceive them and identify their territory, which is supposed to be shared by all travel modes, in this situation.

From a methodological point of view, the focus group method provides a wealth of verbal material and proved to be of great interest for studying the perception and interpretation of environments by the participants in the role of pedestrians. The analysis of the transcriptions shows that the exchanges between the participants and the discussions on their differences of opinion led them to express their points of view more than during individual interviews. The method also provides a more detailed access to the elements in the environment that pedestrians take into account and the inferences that they can lead to.

The results obtained here should not be taken at face value, however, given the small number of users questioned. Furthermore, given its qualitative nature and low degree of structure, this method is still rather informal, and the results should be taken with caution, as part of an exploratory process. These results therefore need to be confirmed using the same methodology on a larger sample of pedestrians.

Moreover, the results of this exploratory study did not show whether the inferences produced by pedestrians from the environment really influence their crossing decisions. This issue was the subject of a second study [START_REF] Montel | Urban environments, pedestrian-friendliness and crossing decisions[END_REF] which showed that elements of built environment and public space design are used by pedestrians to make inferences (about the pedestrian and traffic density, attention to pedestrian and vehicles speed) and to decide to cross or not.

Nevertheless, the results obtained in these two studies were the product of pedestrian analyses of static environments. We cannot be sure that people would be aware of or even produce the same analyses, inferences and assessments in a real-life situation. The environment's influence on the pedestrians' inferences and analyses of the general crossing situation needs to be confirmed using other, more ecological methods such as the commented route method which could be used to study the influence of the approach environment and the environment at the time of crossing.

Overall, the qualitative analysis presented in this article shows that pedestrians take into account many elements and cues relative to the road infrastructure and other users, but also the public space and the urban environment in a broader sense, when they have to assess the degree of difficulty or pleasantness of crossing a street. The results obtained, which need to be consolidated with other analyses, provide a better understanding of pedestrian perceptions and suggest possible improvement in infrastructure design to provide better ergonomics and a more pleasant walking experience on urban road systems. [START_REF] Ewing | Identifying and measuring urban design qualities related to walkability[END_REF], city [START_REF] Davis | Relating severity of pedestrian injury to impact speed in vehicle-pedestrian crashes, simple threshold model[END_REF], exit of the city (2), entry to the urban area (1), urban area (1), urban environment (2), urban street (1), village (1), city center environment (1), countryside (3), country (3), country road [START_REF] Dissanayake | Modelling the effects of land use and temporal factors on child pedestrian casualties[END_REF]

Fig. 1 Fig. 9

 19 Fig. 1 Example of a set of photographs presenting left-hand, central and right-hand views

Table 2

 2 Groups of labels and corresponding numbers of occurrences in the focus group discussions

	Group of labels	Labels (and number of occurrences)	Total number
			of occurrences
	Urban character, nuances	City center
	between city and country

Table 3

 3 Inferences, classified in relation to their subject and their result, and corresponding numbers of occurrences

	39
	, nature (1)

Table 4

 4 Types of wording for inferences

	Type of wording

Acknowledgements

The present research was funded The French National Foundation for Road Safety and the French Directorate for Road Safety and Traffic.


Joshua Stipancic
email: joshua.stipancic@mail.mcgill.ca

PhD Corresponding Author

PhD Sohail Zangenehpour
email: sohail.zangenehpour@mail.mcgill.ca

Luis Miranda-Moreno
email: luis.miranda-moreno@mcgill.ca

Nicolas Saunier
email: nicolas.saunier@polymtl.ca

Marie-Axelle Granié
email: marie-axelle.granie@ifsttar.fr

INVESTIGATING THE EFFECTS OF GENDER ON BICYCLE-VEHICLE CONFLICTS AT URBAN INTERSECTIONS USING AN ORDERED LOGIT METHODOLOGY

Keywords: discrete choice, cycling, surrogate safety, gender, urban, order logit

In the literature, a crash-based modeling approach has long been used to evaluate the factors that contribute to cyclist injury risk at intersections. However, this approach has been criticized as crashes are required to occur before contributing factors can be identified and countermeasures can be implemented. Moreover, human factors related to dangerous behaviors are difficult to evaluate using crash-based methods. As an alternative, surrogate safety measures have been developed to address the issue of reliance on crash data. Despite recent developments, few methodologies and little empirical evidence exist on bicycle-vehicle interactions at intersections using video-based data and statistical analyses. This study investigates bicyclevehicle conflict severity and evaluates the impact of different factors, including gender, on cyclist risk at urban intersections with cycle tracks. A segmented ordered logit model is used to evaluate postencroachment time between cyclists and vehicles. Video data was collected at seven intersections in Montreal, Canada. Road user trajectories were automatically extracted, classified, and filtered using a computer vision software to yield 1,514 interactions for analysis. The discrete choice variable was created by dividing post-encroachment time into normal interactions, conflicts, and dangerous conflicts. Independent variables reflecting attributes of the cyclist, vehicle, and environment were extracted either automatically or manually. Results indicated that an ordered model is appropriate for analyzing traffic conflicts. Furthermore, exogenous segmentation was beneficial in comparing different segments of the population within a single model. Male cyclists, with all else being equal, were less likely than female cyclists to be involved in conflicts and dangerous conflicts at the studied intersections. Bicycle and vehicle speed, along with the time of the conflict relative to the red light phase, were other significant factors in conflict severity. These results will contribute to and further the understanding of gender differences in cycling within North America.

INTRODUCTION

Road safety is a substantial concern for transportation professionals due to the high economic and social cost of traffic crashes [START_REF] Abdel-Aty | Analysis of driver injury severity levels at multiple locations using[END_REF]. In 2012, traffic crashes resulted in 2077 fatalities and over 165,000 injuries in Canada, where cyclists and pedestrians account for approximately 18% of both fatalities and injuries annually (Transport Canada, 2014). While the safety of motorists has commanded much attention, the protection of vulnerable road users has become common only recently [START_REF] Kockelman | Driver injury severity: an application of ordered probit models[END_REF]. In North America, cyclists are twelve times more likely to be killed than motor vehicle drivers [START_REF] Moore | Mixed logit analysis of bicyclist injury severity resulting from motor vehicle crashes at intersection and non-intersection locations[END_REF][START_REF] Strauss | Multimodal injury risk analysis of road users at signalized and[END_REF], and 269 cyclist fatalities occurred in Canada between 2008[START_REF] Garrard | Women and Cycling[END_REF](Transport Canada, 2014).

Recent growth in bicycle activity and infrastructure improvement have increased awareness of bicycle safety issues in North America. In this context, the complex nature of cyclist-vehicle interactions must be understood by examining the factors that contribute to cyclist safety [START_REF] Klop | Factors Influencing Bicycle Crash Severity on Two-Lane, Undivided Roadways in North Carolina[END_REF][START_REF] Moore | Mixed logit analysis of bicyclist injury severity resulting from motor vehicle crashes at intersection and non-intersection locations[END_REF]. One method for evaluating these factors is traffic crash modelling. Crash models evaluate collision frequency using count regression models, or crash injury severity using different techniques such as ordered logit, discrete choice regression models, and tree-based or neural network techniques. Many studies have considered the different statistical methods available. A recent summary of literature on crash frequency and severity modelling is provided by Mannering and Bhatt (2014). Additional summaries were compiled by [START_REF] Miranda-Moreno | Statistical models and methods for identifying hazardous locations for safety improvements[END_REF], [START_REF] Lord | The statistical analysis of crash-frequency data: A review and assessment of methodological alternatives[END_REF], and [START_REF] Savolainen | The statistical analysis of highway crash-injury severities: A review and assessment of methodological alternatives[END_REF]. Traditional safety models calibrated with crash data are reactive, requiring crashes to occur before causes can be identified and countermeasures can be implemented. Additionally, crashbased methods require long observation periods. Given the low rate of crash occurrence, many years of accident data are required. Finally, the study of dangerous behaviors and other human factors can often not be investigated.

As an alternative or complementary approach, surrogate safety techniques analyze interactions and conflicts rather than crashes. Conflicts are events that are physically and predictably related to traffic crashes, and are placed immediately below collisions in Hyden's model [START_REF] Hyden | The Development of a Method for Traffic Safety Evaluation: The Swedish Traffic Conlficts Technique[END_REF], presented in FIGURE 1. As the pyramid model suggests, traffic interactions are inherently ordered through their proximity to a potential collision, or severity, as measured by various indicators. Modelling interactions rather than collisions provides several benefits. Interactions occur much more frequently than collisions and statistically sufficient data can be collected in a shorter time period. The use of interactions and conflicts is proactive, rather than reactive, and surrogate measures are insensitive to crash underreporting [START_REF] Kockelman | Driver injury severity: an application of ordered probit models[END_REF] and human factors can be incorporated in the analysis. [START_REF] Hyden | The Development of a Method for Traffic Safety Evaluation: The Swedish Traffic Conlficts Technique[END_REF] Despite the benefits offered by a proactive surrogate approach, dangerous bicycle behaviours in vehicle-bicycle interactions, and their associated factors, have rarely been studied using video analysis,

FIGURE 1 Safety pyramid based on Hyden's classic model

Collisions

Conflicts

Normal Interaction

Undisturbed Passage surrogate measures of safety, and regression models. A particular issue that remains to be investigated using microscopic conflict data is the relationship between biological gender and cyclist safety. Important behavioral differences have been identified for both pedestrians and motorists of different genders at intersections [START_REF] Holland | The effect of age, gender and driver status on pedestrians' intentions to cross the road in risky situations[END_REF][START_REF] Santamarina-Rubio | Gender differences in road traffic injury rate using time travelled as a measure of exposure[END_REF][START_REF] Tom | Gender differences in pedestrian rule compliance and visual search at signalized and unsignalized crossroads[END_REF]. However, very little is known about this in the cyclist context. Gender differences and their associated risks may help to explain the disparity in ridership currently experienced in urban populations as well as the implementation of the appropriate designs and facilities. In most countries, females cycle less than males, particularly in English speaking countries that are less bicycle-friendly, including Canada, the U.K., Australia, and the U.S. [START_REF] Garrard | Women and Cycling[END_REF]. Route conditions and vehicle interactions greatly influence individuals' likelihood to cycle [START_REF] Winters | Motivators and deterrents of bicycling: comparing influencers on decisions to ride[END_REF] and female cyclists, much more than males, prefer to use routes with maximum separation from motorized traffic [START_REF] Garrard | Promoting transportation cycling for women: The role of bicycle infrastructure[END_REF]. Studies in the city of Montreal show that routes with separated cycle tracks attract higher cyclist volumes than those without [START_REF] Strauss | Spatial modeling of bicycle activity at signalized intersections[END_REF]) and that streets with cycle tracks have a lower injury risk [START_REF] Lusk | Risk of injury for bicycling on cycle tracks versus in the street[END_REF].

While behavioural differences may exist between genders, recent research has only shown minor differences in actual crash risk for males and females [START_REF] Kaplan | Aggravating andmitigating factors associated with cyclist injury severity in Denmark[END_REF][START_REF] Martinez-Ruiz | Association of cyclists' age and sex with risk of involvement in a crash before and after adjustment for cycling exposure[END_REF]. The purpose of this study is to estimate a segmented ordered logit model for bicycle-vehicle interactions at urban intersections with cycle tracks. Intersections, being one of the most dangerous locations in the urban network, deserve specific attention [START_REF] Wang | Analysis of left-turn crash injury severity by conflicting pattern using partial[END_REF]. The objectives of this research are to investigate bicycle-vehicle conflicts and contributory factors using an ordered-logit modelling approach and to examine the influence of gender and other cyclist, vehicle, and environmental factors on conflict severity.

LITERATURE REVIEW

Existing efforts in injury severity modelling primarily concern motor vehicle occupants in single or multiple vehicle crashes. Multiple modeling techniques have been proposed to investigate the relationship between injury severity levels and associated factors. Mannering and Bhatt (2014) provide a comprehensive literature review on the methods used in severity analysis in general, and [START_REF] Eluru | A mixed generalized ordered response model for examining pedestrian[END_REF] provides a summary of literature concerning analysis of cyclist injuries. Much empirical evidence has been reported in the literature identifying the key factors influencing injury severity, including characteristics of the roadway, environment, and road user, for passengers, drivers and pedestrians. [START_REF] Moore | Mixed logit analysis of bicyclist injury severity resulting from motor vehicle crashes at intersection and non-intersection locations[END_REF] utilized a multinomial logit and mixed logit to model cyclist injury severity at intersections and non-intersection locations using seven years of crash data from Ohio. The study stated "the injury mechanisms are substantially different […] at intersection and non-intersection locations". [START_REF] Klop | Factors Influencing Bicycle Crash Severity on Two-Lane, Undivided Roadways in North Carolina[END_REF] studied bicycle crash severity on rural roads in North Carolina using an ordered probit model and four years of crash data. Visibility and weather conditions were found to most significantly increase collision severity. [START_REF] Eluru | A mixed generalized ordered response model for examining pedestrian[END_REF] utilized data from the 2004 US national database to estimate a mixed generalized ordered response logit (MGORL) model of cyclist injury severity. The MGORL generalizes more standard ordered models, providing additional flexibility across observations. Cyclist age and vehicle speed were correlated with injury severity. In the existing literature, gender is often a contributory factor to injury severity.

Cyclist gender studies have focussed largely on variation in preference or behaviour. [START_REF] Bernhoft | Preferences and behaviour of pedestrians and cyclists by age and gender[END_REF] used a survey approach, finding that male cyclists in Denmark tended to act less cautiously, but also felt safer than their female counterparts. However, many of the differences between genders became statistically insignificant when controlling for other factors, such as travel patterns, which may themselves be gender specific. Similarly, a French study by [START_REF] Felonneau | Gender stereotypes and superior conformity of the self in a sample of cyclists[END_REF] noted that male cyclists tended to overestimate their ability more so than females whereas females tend to overestimate their carefulness more than males and proneness to take risk as cyclist seems to be gender specific even at an early age [START_REF] Granié | Différences de sexe et rôle de l'internalisation des règles sur la propension des enfants à prendre des risques à vélo[END_REF]. [START_REF] Johnson | Riding through red lights: The rate, characteristics and risk factors of non-compliant urban commuter cyclists[END_REF] studied red-light compliance and found that male cyclists were more frequently non-compliant than females. While gender differences are evident in terms of behaviour, behavioural differences do not necessarily create disparity in risk. [START_REF] Kaplan | Aggravating andmitigating factors associated with cyclist injury severity in Denmark[END_REF] evaluated injury severity using a generalized ordered logit model on Danish crash data collected over a 5-year period, and found no correlation between gender and injury risk. [START_REF] Martinez-Ruiz | Association of cyclists' age and sex with risk of involvement in a crash before and after adjustment for cycling exposure[END_REF] used 17 years of crash data from Spain to calculate the crash rate ratio by gender for different ages. Without adjusting for cycling exposure, males were more likely to be involved in a collision than females. When controlling for exposure, crash rates were approximately equal for males and females with age being a stronger determinant of crash risk. As female cyclists seem to favour sites with cycle tracks [START_REF] Garrard | Promoting transportation cycling for women: The role of bicycle infrastructure[END_REF]) a possible explanation for this lack of difference in the risk of accidents can be found in the behavior of male and female cyclists on road with cycle tracks.

While research on severity modelling includes consideration for cyclists and advanced formulations, few studies have attempted to apply modeling techniques to surrogate measures for bicycle safety [START_REF] Washington | Impact of Bicycle Boxes on Safety of Cyclists: a Case Study in Montreal[END_REF][START_REF] Zangenehpour | Are Intersections With Cycle Tracks Safer? A Control-Case Study Based On Automated Surrogate Safety Analysis Using Video Data[END_REF]. Analysis of differences between male and female cyclist risk by surrogate safety techniques has also been limited. Rather than using independent models for males and females, a segmented modelling approach is preferred in order to increase the number of observations available for estimation and to make differences between genders instantly comparable. The reliance on crash data has meant dependence on crash databases, which are subject to underreporting, errors, and omissions. This study attempts to address these shortcomings through the estimation of a segmented ordered logit model of interaction severity. METHODOLOGY The methodology consists of three steps; video data collection and processing using video tracking and classification methods; definition and computation of surrogate safety indicators, and; model formulation and estimation. The details of each step are provided in the following sections.

Video Data Collection and Processing

The methodology for data collection and processing uses a similar approach to the one implemented in several past studies [START_REF] Jackson | Flexible, Mobile Video Camera System and Open Source Video Analysis Software for Road Safety and Behavioral Analysis[END_REF][START_REF] Saunier | Large-Scale Automated Analysis of Vehicle Interactions and Collisions[END_REF][START_REF] Sayed | Automated safety diagnosis of vehicle-bicycle interactions using computer vision analysis[END_REF] . Video data was collected using an inexpensive and commercially available video camera which stores video and is powered internally. The camera is mounted to a using a telescoping fibreglass mast to ensure a clear view above the intersection and approaches. The camera system is introduced in detail by Zangenehpour, Miranda-Moreno, & Saunier (2015). Once collected, the video data was processed using an open-source road user tracking software, Traffic Intelligence [START_REF] Jackson | Flexible, Mobile Video Camera System and Open Source Video Analysis Software for Road Safety and Behavioral Analysis[END_REF][START_REF] Saunier | Large-Scale Automated Analysis of Vehicle Interactions and Collisions[END_REF][START_REF] Saunier | Traffic Intelligence[END_REF]. Road user trajectories (position and speed at each frame) were automatically extracted from the video footage. Road users were then classified as pedestrians, cyclists, or vehicles using a technique developed by [START_REF] Zangenehpour | Automated Classification in Traffic Video at Intersections with Heavy Pedestrian and Bicycle Traffic[END_REF], and the trajectories were filtered to isolate the desired interactions. Any erroneous interactions were removed manually from the data set.

Computation of Surrogate Safety Measures

Popular surrogate measures of safety include time-to-collision (TTC) and post-encroachment time (PET). TTC is "the time required for two vehicles to collide if they continue at their present speed and on the same path" (van der Horst, de Goede, de Hair-Buijssen, & Methorst, 2014) or more generally if their movements remain unchanged, which can include variations in speed and direction. PET is the difference in time between two road users occupying the same location in space, or the potential conflict point [START_REF] Peesapati | Evaluation of Postencroachment Time as Surrogate for Opposing Left-Turn Crashes[END_REF]. TTC is measured continuously and, depending on the choice or motion prediction method, will yield several measurements over time when there is a collision course (when some predicted trajectories would lead the road users to collide). PET is based on observed trajectories and can be computed only if trajectories intersect. Both measures can usually be computed for the same interaction and are complementary in the analysis of bicycle conflicts (van der Horst, de Goede, de Hair-Buijssen, & Methorst, 2014). Surrogate safety data has been successfully collected using video-based detection systems and extracted using computer vision techniques [START_REF] Jackson | Flexible, Mobile Video Camera System and Open Source Video Analysis Software for Road Safety and Behavioral Analysis[END_REF][START_REF] Sayed | Automated safety diagnosis of vehicle-bicycle interactions using computer vision analysis[END_REF]. PET was selected as the surrogate measure of safety in this research as all interactions involve intersecting trajectories. PET is better suited to interactions involving turning movements than common TTC with assumption of constant velocity and it is simpler and faster to compute than TTC with more realistic motion prediction methods.

Once trajectories were extracted using Traffic Intelligence, PET calculation for each interaction was automated using a Python script to count the number of frames (converted to seconds) between consecutive road users occupying the conflict point. The dependent choice variable was created by classifying PET as:

• Alternative 0: Normal interaction with PET greater than 5 seconds;

• Alternative 1: Conflict with PET between 3 and 5 seconds, and;

• Alternative 2: Dangerous conflict with PET less than 3 seconds. These thresholds have been used successfully in previous work, such [START_REF] Zangenehpour | Are Intersections With Cycle Tracks Safer? A Control-Case Study Based On Automated Surrogate Safety Analysis Using Video Data[END_REF], where readers are referred for more details on the determination of threshold values. This formulation yields results that are easy to interpret, as positive coefficients indicate an increase in the severity of conflicts (decrease in PET) as the variable associated with that coefficient increases [START_REF] Kockelman | Driver injury severity: an application of ordered probit models[END_REF].

Modelling Framework Definition and Model Estimation

Ordered response models are the most widely used statistical models in crash severity analysis. Unlike other multiple choice models that include utility variables for each alternative, ordered models have only one propensity, or latent variable [START_REF] O'donnell | Predicting the severity of motor vehicle accident injuries using models of ordered multiple choice[END_REF]. The ordered logit model assumes that the error term, representing the unobserved component of the latent variable, is logistic distributed with a mean of zero and a variance of  2 /3. The latent variable, y*, is defined by y* = βX + ε (1) where X is the vector of explanatory variables, β is the vector of unknown parameters, and ε is the logistic distributed random error term. This propensity is bound by unknown thresholds, i, which delineate alternatives. For the case presented herein, the probability of each choice alternative is

P(y=0) = CDF(1 -βX) (normal interaction) (2) P(y=1) = CDF(2 -βX) -CDF(1 -βX) (conflict) P(y=2) = 1 -CDF(2 -βX) (serious conflict)
where CDF is the cumulative distribution function of the logistic distribution defining ε.

Exogenous segmentation is a method that provides heterogeneity in the coefficients across multiple segments, working particularly well when segments are few [START_REF] Bhat | An Endogenous Segmentation Mode Choice Model with an Application to Intercity Travel[END_REF]. For this study, segmentation allows for variation in the value of the parameters across cyclist gender. Segmentation is achieved through the generation of new independent segmented variables, which are the product of the existing independent variables and some discrete variable (gender). When the model is estimated, the parameter on the basic variables represents a contribution to both genders, while the parameters on the segmented variables represent the variation between genders. The propensity for the segmented model can be represented by y* = β1X + β2wX + β3w + ε (3) where X are the basic variables and β1 are their coefficients, wX and β2 are the segmented variables and their coefficients, respectively, and w and β3 are the segmentation variable (gender) and its coefficient, respectively.

The conflict severity model was developed in three steps, with each step incorporating additional gender heterogeneity. An ordered logit model (OL1) was estimated assuming homogeneity between genders. This model included all available variables without consideration for gender. With the addition of the gender, OL2 was created. The inclusion of this variable introduced simplistic heterogeneity, by allowing the latent propensity to change according to cyclist gender. The final segmented ordered logit model (SOL) included all available explanatory variables with segmentation according to gender. This allowed not only the propensity, but also the value of each parameter, to vary between genders. While similar results can be achieved through the estimation of separate models, segmentation increases the number of observations for estimation and makes differences between genders instantly comparable.

DATA DESCRIPTION Site Selection

Seven test sites were selected along Maisonneuve Boulevard, a one-way urban arterial in downtown Montreal, Canada. The sites, shown in FIGURE 3, featured two travel lanes and a fully separated bidirectional cycling facility, or cycle track, along Maisonneuve. The sites along Maisonneuve were selected specifically because of the present cycling infrastructure. The two-way separated cycle track is a relatively new design that has only been studied more recently. Maisonneuve is also one of the busiest cycling corridors on the island of Montreal, and the specific intersections were chosen because of their geometric uniformity. On all the observed intersections, the cycling lane is on the left of the two other travel lanes. Video data was collected using a single camera at each site with a clear view above the intersection. Approximately 4 hours of video was collected at each site in the afternoon peak period from June 16 to June 20, 2014, ensuring that traffic volume, weather, time of year, and time of day remained consistent.

Similar to Wang and Abdel-Aty ( 2008), a single traffic interaction was analyzed. Conflicts were limited to those between cyclists traveling straight through the intersection and left-turning vehicles. An example of a dangerous conflict is provided in FIGURE 2. Given the site geometry and design of the cycling infrastructure, these interactions represent the majority of safety concerns since cyclists wishing to go through the intersection should consider vehicles located on their right wishing to turn left. Any erroneous interactions were removed manually from the data set. Several interactions were also missed by the tracking algorithm, which could not be included as part of this study. Once filtered, 1514 useable observations remained for analysis.

FIGURE 2 Example of a dangerous bicycle-vehicle conflict

Data Exploration

Attributes of the cyclist, vehicle, and environment were compiled as independent variables. Cyclist and vehicle speed at the conflict point were automatically computed from the trajectories. Additional explanatory variables, including cyclist gender helmet use, whether the vehicle was a truck, van, or SUV, and whether the vehicle was the first in a platoon, were extracted manually from the video. As in previous studies [START_REF] Johnson | Riding through red lights: The rate, characteristics and risk factors of non-compliant urban commuter cyclists[END_REF], gender was classified based on physical appearance. The environmental attributes included variables representing if the interaction occurred immediately after a red light phase (both cyclist and vehicle were simultaneously waiting at a red light before the interaction occurred) and site-specific constants for each site, with Crescent used as the reference. Dummy variables were used to indicate whether the cyclist was the first object to reach the conflict point and whether pedestrians (or contra-flow cyclists) were present in the intersection at the time of the conflict, capturing the behavioural freedom of the road users. A summary is provided in TABLE 1. Site specific data has been omitted for brevity. (Vélo Québec, 2010). The distribution of conflicts was determined for males and females, and is presented below in FIGURE 4. The proportion of conflicts, dangerous conflicts, and normal interactions were determined for males and females and are provided in FIGURE 5. As the distributions seem to be drawn from the same underlying distribution given the result of the chi square test at 95 % confidence (test statistic of 1.23 compared to the critical value from the chi-square distribution of 5.99), these results point towards equivalency in terms of safety for male and female cyclists. The data exploration provided little evidence of differences between genders in the considered variables.

FIGURE 5 Proportion of Interaction Type According to Gender

RESULTS

Model Estimation

The models were estimated using all available parameters and systematically eliminating statistically insignificant variables. In the results that follow, variables retained were significant at 80% confidence or greater. Variables significant at 90% confidence are italicized, and variables significant at 95% confidence are bolded. Results for the ordered logit models are presented in TABLE 2. In Model OL1, which assumed gender homogeneity, both cyclist and vehicle speed were found to be significant, along with the red light variable and several site specific constants. A log-likelihood ratio test was used to compare OL1 to the constants-only model. The null hypothesis was that none of the variables help to explain conflict severity. LR for Model OL1 was 55.72, compared to the critical value from the chi-square distribution of 14.07 (7 degrees of freedom at 95% confidence). The null hypothesis was rejected, and OL1 was superior. Adding the gender variable in Model OL2 produced only minor changes in the model results. Although the male dummy variable is negative, which indicates that males have a lower propensity for conflicts than females with all else being equal, and although the gender variable was significant at 80% confidence, Model OL2 was inferior to OL1 by log-likelihood ratio test (LR of 1.88 compared to a chi-square value of 3.84). Based on this result, any evidence for variation in conflict severity between males and females is inconclusive. With regards to vehicle attributes, increasing vehicle speed increased conflict propensity, again for males only. This result is intuitive, as vehicles travelling at higher speeds have less time to take evasive action, and the interactions with road users are expected to have lower PETs. However, why conflict occurrence is independent of vehicle speeds for females is unclear. Additionally, males were more likely to engage in a conflict with the first vehicle in the platoon, although the strength of this result was relatively low. This may indicate that males tend to have a more aggressive cycling behaviour, or that the behavioural approach for managing different types of interactions is different for males and females, but a more detailed analysis would be beneficial. A data set with more observations of female cyclists may help to show the true effect of these variables and enable more significant parameters to be estimated.

The only significant environmental factors were the red light variable and several site-specific constants. Conflicts and dangerous conflicts were less likely to occur immediately after a red light phase. During the green phase, motorists may be less aware of present cyclists, and may make manoeuvres with less caution. After waiting at a red light, motorists are likely to be aware of cyclists waiting adjacent to them, and so yield to them with sufficiently safe spacing. Drivers are familiar with yielding to opposing traffic after a red light phase, and so act more cautiously. Additionally, speeds of both cyclist and vehicle are lower after a red light phase, which may also decrease the severity of conflicts. With regards to the sitespecific variables, Crescent, Peel, and St Denis showed no significant variation in conflict occurrence. Both males and females had a lower propensity for conflicts at Stanley, Metcalfe, and Union (although the strength of the effect at Union varied by gender). Only males had a lower propensity for conflicts at Mackay, while for females the site was no different than the reference site at Crescent. Sample probabilities of conflict severity were calculated for several sites, Crescent, Stanley, and Union to demonstrate the impact of the modelling results. The cases assumed cyclist and vehicle travelling at the respective mean speeds, with all other binary variables set to zero. Sample probabilities for normal interaction, conflict, and dangerous conflict were calculated for males and females and are provided in TABLE 5. These results show that males were safer at both Crescent and Stanley, as their probability of normal interaction was higher, and the probabilities of conflict and dangerous conflict were both lower. In fact, males were safer in many conditions, because any variables with positive coefficients were offset by the large negative coefficient on the male dummy variable. At Union, females were observed to be safer than males. However, this was the only one of the seven sites where this was true. The effect of the highly positive male variable can only be offset by high cyclist speeds, high vehicle speeds, or through conflicts with a platoon leader. In order to achieve parity between the genders, the cyclist or vehicle speeds must be increased by two standard deviations (or one standard deviation each). The results demonstrate that, although there are some specific situations where females were safer than males, in general, there are many more cases where males are as safe, if not safer, than female cyclists. Even if this type of cycling infrastructure, with a bidirectional cycle track located to the left of other travel lanes, is rather common in English speaking countries [START_REF] Johnson | Riding through red lights: The rate, characteristics and risk factors of non-compliant urban commuter cyclists[END_REF], these kinds of interactions with vehicles could seem complex to manage to less experienced cyclists, or those with little confidence on their own cycling abilities, such as females [START_REF] Handy | The contribution of comfort, convenience, and liking of bicycling to the bicycling gender gap: Evidence from Davis, California[END_REF]. The existence of an on-road path dedicated to cyclists may encourage less experienced cyclists to transfer those skills learned as drivers and thus to focus their attention, when the light is green for them, on traffic coming across and forgetting to take into account the vehicles traveling in the same direction as them. However, in this configuration of intersection, experience as a pedestrian is more appropriate to handle the situation from the cyclist's point of view. This result that female cyclists were more prone to conflicts with vehicles should be confirmed on future studies with other intersection configurations. It may help to explain why fewer females choose to cycle and therefore contribute to the disparity in ridership by gender. It is known that females prefer routes with less vehicular interactions [START_REF] Garrard | Promoting transportation cycling for women: The role of bicycle infrastructure[END_REF], and that they feel less safe than their male counterparts [START_REF] Bernhoft | Preferences and behaviour of pedestrians and cyclists by age and gender[END_REF]. These results suggest that the higher perception of risk for female cyclists is linked to a higher actual risk as well. CONCLUSIONS This paper proposes a methodology based on video analysis and an ordered-based modeling approach to investigate the relationship between cyclist gender and conflict occurrence at urban intersections with cycle tracks. The combination of video-based surrogate safety measures and ordered regression models show promise for future use in conflict analysis. Even basic model formulations were able to estimate parameters with significant correlation to conflict severity. Although the distribution of conflict severity showed no difference between genders, the segmented model identified a distinct difference in conflict severity for different genders, with males having a lower propensity of conflicts and dangerous conflicts than females, all else being equal. Therefore, the segmentation technique was successful in observing variation across segments of the population. Furthermore, the effect of other variables could be quantified. Interestingly for males, a higher cyclist speed resulted in an increased conflict severity, while conflict severity was independent of speed for females. This appears to support previous research that states that male cyclists tend to be less cautious and overestimate their own competency, while female cyclists tend to underestimate their cycling skills, which can have negative consequences on interaction management for both genders. However, as stated above, this does not necessarily mean that males are safer overall. [START_REF] Johnson | Riding through red lights: The rate, characteristics and risk factors of non-compliant urban commuter cyclists[END_REF] showed, males are more likely to run red lights. Although those that run red lights may avoid conflicts, this is a dangerous behaviour that may lead to more severe, though less frequent, conflicts and more severe, though less frequent, collisions. These cyclists are not present in this dataset because they are not interacting with vehicles turning left (the only conflict type considered) and are instead conflicting with vehicles travelling through the intersection in the transverse direction. The absence of these least risk-averse males from the data set may inflate the observed differences between genders. Additional study is required to determine the effect of red light compliance on cyclist safety.

Vehicle speed was observed to increase conflict severity for males only, and males were more likely to have a severe conflict with the first vehicle in a platoon. This may indicate that males tend to have a more aggressive cycling behaviour, or that the behavioural approach for managing different types of interactions is different for males and females, but a more detailed analysis would be beneficial. Conflicts and dangerous conflicts were less likely to occur immediately after a red light phase. Drivers are familiar with yielding to opposing traffic after a red light phase, and so act more cautiously. Additionally, speeds of both cyclist and vehicle are lower after a red light phase, which may also decrease the severity of conflicts.

While the effects of many variables could be explained, the parameters of other variables require additional consideration. For example, the effect of the platoon leader variable was heterogeneous with respect to gender, although this variable was statistically significant at only 80% confidence. Explanation of these results requires more detailed analysis to describe behavioural differences in specific traffic interactions for males and females. Further limitations of this study were related to the technique for data extraction. Determining gender from video footage is a subjective exercise [START_REF] Johnson | Riding through red lights: The rate, characteristics and risk factors of non-compliant urban commuter cyclists[END_REF], although less subjective techniques are more invasive. Future work should consider increasing the scope of data collection to ensure more observations of female cyclists are available for analysis. More observations would allow a better estimation of the parameters, and may in fact reveal more significant variables related to conflict occurrence for females. However, as current methods require manual extraction of the data, this may be practically difficult. With this proof of concept established, future study should incorporate additional variables, including traffic and cyclist volumes, time of day, and cyclist age. Importantly, if the results obtained herein can be generalized, the relationship between gender and risk can be quantified and used to explain the disparity in ridership currently observed in urban populations within North America. ACKNOWLEDMENT Funding for this project was provided in part by the Natural Science and Engineering Research Council. The authors would like to thank Marina Jorge Amaral and Nawfal Kammah for their help with data preparation.

  FIGURE 3 Study sites along Maisonneuve at Crescent (a), Stanley (b), Peel (c), Mackay (d), Metcalfe (e), St Denis (f), and Union (g)

FIGURE 4

 4 FIGURE 4 Distribution of PET for male cyclists (a) and female cyclists (b)

TABLE 1 Variables and Statistics for All Captured Interactions

 1 of the raw variable data was conducted with descriptive statistics. Of the 1,514 observed interactions, only 496 involved female cyclists, reflecting the disparity in number of cyclists by gender. The data set contained 33 % female cyclists, which is close to the share of 37 % female cyclists reported by Vélo Québec in 2010

		Units	Description	Mean	Min.	Max.	Std. Dev.
	Cyclist
	Speed	km/h	Speed of the cyclist at the conflict point	13.16	1.71	43.51	5.39
	Male	Dummy Cyclist was male (1) or female (0)	0.67	0	1	0.47
	Helmet	Dummy Cyclist wearing a helmet (1) or not (0)	0.51	0	1	0.50
	Vehicle
	Speed	km/h	Speed of the vehicle at the conflict point	20.01	2.00	67.10	6.43
	Platoon Lead	Dummy Vehicle was first in a platoon	0.76	0	1	0.43
	Truck	Dummy Vehicle was a truck, van, or SUV	0.33	0	1	0.47
	Environment
	Red	Dummy Conflict occurred immediately after a red light	0.16	0	1	.365
	Bike First	Dummy The cyclist reached the conflict point first	0.59	0	1	.493
	Pedestrian	Dummy Peds were simultaneously crossing intersection	0.46	0	1	.499
	A preliminary exploration

TABLE 4 Coefficient Values for Male and Female Cyclists

 4

		Female	Male
	Explanatory variables	β1	β1 + β2
	Bike Speed	-	0.0272
	Helmet	-	-
	Vehicle Speed	-	0.0250
	Truck/Van	-	-
	Platoon Leader	-	0.2395
	Red	-0.7713	-0.7713
	Bike First	-	-
	Pedestrian	-	-
	Stanley	-0.3774	-0.3774
	Peel	-	-
	Mackay	-	-0.4946
	Metcalfe	-0.2384	-0.2384
	St Denis	-	-
	Union	-0.8953	-0.2296
	Male	-1.1703	-1.1703

TABLE 5 Sample probabilities of conflict severity for male and female cyclists

 5

		Crescent		Stanley		Union
		Female	Male	Female	Male	Female	Male
	P (Normal Interaction)	55%	63%	64%	71%	75%	68%
	P (Conflict)	25%	22%	22%	18%	16%	20%
	P (Dangerous Conflict)	20%	15%	14%	11%	9%	12%

 3. Non-significant β1 coefficients indicate that a parameter is not significantly correlated with conflict severity for female cyclists. Non-significant β2 coefficients indicate that parameter is homogeneous across genders. This formulation eliminates unnecessary variables and allows for immediate testing of differences between genders. Importantly, gender was found to be highly significant in this model. The SOL model was compared to OL1 using a loglikelihood ratio test. The null hypothesis was that safety is homogeneous across genders and segmentation is unnecessary. LR was calculated to be 11.08, compared to the critical value from the chi-square distribution of 5.99 (2 degrees of freedom at 95% confidence). The null hypothesis was falsified and segmentation is justified. From the results of the OL models, gender appears to have no effect on safety. However, it is not that there is no effect; it is that variation exists both in propensity and in the value of the parameters. Allowing for this variation is necessary to reveal gender's true effect, representing an ideal scenario for an exogenously segmented model.

Model Interpretation

Interpretation of the SOL model is facilitated by calculating the true value of each coefficient for both genders. For females, coefficients take the β1 values from the model estimation. For males, the β1 and β2 parameters are summed to obtain the true value, shown in TABLE 4. Variables related to cyclist attributes led to several interesting results. The primary observation is the negative, highly significant coefficient for the gender variable. In general, this coefficient had the largest magnitude. This result suggests that, all else being equal, when interacting with vehicles, males are less likely than females to have conflicts and dangerous conflicts. This result implies that males are less likely to be involved in more severe interactions, as defined by the PET intervals, and it can be concluded that male cyclists who interact with vehicles are safer than female cyclists who interact with vehicles. A second important observation is that the coefficient for bike speed is positive, but only for male cyclists. For females, cyclist speed has no effect on their potential for conflict at the studied sites. For male cyclists, travelling at a faster speed increases the propensity for conflicts and dangerous conflicts. Therefore, there is a compensating effect between gender and speed. Although being male reduces the chances of conflict, cycling faster increases the chances. Males travelling at high speeds (25 to 30 km/h) are no safer than females. This could indicate that males who travel at faster speeds are also less risk averse, and appears to support previous behavioural research that, even though male cyclists are generally safer, they are less cautious [START_REF] Bernhoft | Preferences and behaviour of pedestrians and cyclists by age and gender[END_REF] and over-estimate their own competency [START_REF] Felonneau | Gender stereotypes and superior conformity of the self in a sample of cyclists[END_REF].