Modelling pedestrian crossing behaviour in urban roads: A latent variable approach

As pedestrians are the most exposed and vulnerable road users to traffic accidents, urban planners frequently propose alternatives to improve their safety. However, some solutions, such as pedestrian bridges and crosswalks at signalized intersections, usually imply longer walking distances compared to the direct crossing alternative which, in its turn, involves a higher risk.In this article, a hybrid framework is proposed to analyse the pedestrians’ choice on how to cross an urban road where three crossing options are available: crossing directly, crossing by using a pedestrian bridge or using a crosswalk at a signalized intersection. The decision process is modelled as a discrete choice model incorporating latent variables to consider perceptions and psychological factors, using stated preference data coming from a survey applied in Bogotá, Colombia.Results show that the latent variables security/safety and attractiveness of each crossing alternative are relevant to understand the pedestrian crossing behaviour. These latent variables are strongly determined by socioeconomic characteristics of the individual (age, gender, level of study) and conditioned by the circumstances of the trip (main mode of transport, walking or not with children). It was found that a longer walking distance to a pedestrian bridge or a signalized crosswalk increases the probability of direct crossing, having a more relevant effect in the case of the pedestrian bridge.     

Exploring the route choice decision-making process: A comparison of planned and observed routes obtained using person-based GPS

Trip decisions are complex and involve choosing the activity destination, mode and subsequently the route for travel. This paper presents detailed information on the planned and observed route choices for the home-to-work commute. Specifically, the study examines how people formulate their route plans and describe their attitudes and preferences for their selected routes. A geographic information system (GIS) records the planned route information with the route planning sequence. Observing route choice is a difficult procedure; however, through the use of the global positioning system (GPS), one can accurately record route choice. An automated activity–trip detection algorithm processes GPS data and displays results within an internet-based prompted recall diary. The diary is used to verify trip start and end times. This combination of GPS, GIS and diary responses provides great insight into the route choice decision-making process. Thirty-one individuals from Ontario, Canada participated in answering survey questions and the collection of person-based GPS data. Results indicate a preference to minimize travel time as stated by participants in deciding what route to travel. Participants also affirmed a desire to minimize the number of stop lights/signs, as well as, avoid congestion and maximize route directness. A comparison between planned and observed routes, reveals about one-fifth of participants deviated from their planned route. This study demonstrates the need for qualitative and quantitative survey methods for exploring planned and observed route choice.     

A critical assessment of pedestrian behaviour models

This paper concerns a review and critical assessment of the existing research on pedestrian behaviour in urban areas, focusing on two separate yet complementary aspects: route choice and crossing behaviour. First, an exhaustive review of the existing route choice models for pedestrians is presented. It is shown that the existing models are mainly more stochastic and more macroscopic than required and seldom incorporate the interactions between pedestrians and traffic. Second, the existing models on pedestrians crossing behaviour are presented and assessed. It is shown that, although their approach is usually detailed, deterministic and traffic-oriented, they are mainly devoted to a local level behaviour and focus on only one type of all the potential determinants. Most importantly, these two complementary and possibly interdependent aspects of pedestrian behaviour are always examined separately. The results of this review reveal a lack of an overall and detailed consideration of pedestrian behaviour along an entire trip in urban areas. Moreover, the need for an integrated approach based on flexibility, disaggregation and more determinism is identified. Accordingly, a set of modelling techniques are discussed as a general framework for further research in the field.     

Closing the gap between behavior and models in route choice: The role of spatiotemporal constraints and latent traits in choice set formation

A considerable gap exists between the behavioral paradigm of choice set formation in route choice and its representation in route choice modeling. While travelers form their viable choice set by retaining routes that satisfy spatiotemporal constraints, existing route generation techniques do not account for individual-related spatiotemporal constraints. This paper reduces the gap by proposing a route choice model incorporating spatiotemporal constraints and latent traits. The proposed approach combines stochastic route generation with a latent variable semi-compensatory model representing constraint-based choice set formation followed by compensatory choice. The model is applied to data focusing on habitual commuting route choice behavior in morning peak hours. Results show (i) the possibility of inferring spatiotemporal constraints from considered routes, (ii) the importance of incorporating spatiotemporal constraints and latent traits in route choice models, and (iii) the linkage between spatiotemporal constraints and time saving, spatial and mnemonic abilities.     

自动驾驶汽车与行人交互中的沟通界面设计：基于行人过街决策模型的评估

自动驾驶汽车要进入人车混行的普通道路, 需确保与过街行人之间的交互安全和效率。为解决这一问题, 高等级自动驾驶汽车往往在车辆外部装置显示设备, 即外部人机界面(eHMIs)以和行人沟通信息。在具体设计上, 已有研究主要采用文字、图形、投影等视觉沟通形式, 传达车辆状态(是否在自动驾驶模式)、意图和对行人的过街建议等沟通信息, 并在真实路段实验、虚拟场景及实验室实验等情境中评估了界面的使用对行人过街意向、速度和准确性等指标的影响。然而, 以行人为中心的外部界面设计需系统地支持行人过街决策前各阶段的信息加工需求。因此, 我们结合行人过街决策过程和情境意识理论, 提出行人与自动驾驶汽车交互中的动态过街决策模型, 从行人认知加工视角评估各种界面的沟通效果。评估的结果启示, eHMIs应促进行人对车辆信息的感知、理解和预测。在感知阶段, 应采用多种类型界面、多呈现载体相结合, 增强信息的可识别性。在理解阶段, 需结合文字说明、合理选择沟通视角、信号标准化和培训提高可理解性。在预测阶段, 应结合车辆内隐运动信息, 帮助行人快速准确获取车辆未来行动意图。更重要的是, 未来研究应关注在多行人、多车辆混行情境下的信息沟通设计及其对行人的影响。理论方面, 未来研究也需要关注外部界面如何通过自下而上的通路影响情境意识和心智模型的形成。     

Investigations into pedestrian crossing choices on Cape Town freeways

South Africa is regularly identified in international literature as one of the countries with the highest number of traffic deaths per capita. Of these, around one third are pedestrians. Freeways constitute one of the highest risk locations for pedestrians – in most developed countries pedestrians are not seen in such locations, but for many South African pedestrians freeways are a regular part of their commute. Walking alongside and crossing of freeways are extremely common – and deaths associated with such activity also tragically so. Over the past four years in the Cape Town area alone there have been 413 pedestrian crashes causing 139 pedestrian deaths on freeways, as well as an unrecorded number of serious injuries.While pedestrian crossing behaviour has been extensively researched in many parts of the world, almost none has so far been carried out in the context of freeways. Little is known about pedestrian crossing decisions in locations where the stakes are so high.Following a study using traffic cameras associated with the Freeway Management System to measure the frequency and location of pedestrians crossing freeways, this article reports on surveys into crossing decisions of pedestrians on Cape Town’s freeways. Two successive surveys were conducted with pedestrians on or alongside the freeway. These included pedestrians who crossed using footbridges, and pedestrians who crossed at grade.In the analysis it was clear that while some of the traditional factors such as time saving and convenience were considered, far more important to pedestrians were issues of safety. Safety from fast-moving vehicles was one factor, while safety from criminals was a second and often conflicting factor which affected crossing choices. Crossing at grade was partly described in terms of utility maximisation (time and distance saving) but it was also, for many, one way of avoiding becoming a victim of crime. In terms of their perceptions of being involved in crashes, at-grade crossers reflected an astute awareness of the risks that they face. Many pedestrians articulated the belief that their choice of crossing was constrained by lack of alternatives. Until public transport and safe crossing locations are provided for these pedestrians it is clear that many will continue to cross dangerous roads, in full awareness of the risks they face.     

Make this detour and be unselfish! Influencing urban route choice by explaining traffic management

Traffic management policies aim to improve traffic flow by influencing the route choice of drivers, therefore preventing traffic jams in crowded cities. With respect to a system-optimum of the traffic network, drivers might have to make small-scale detours. The aim of this article is to encourage unselfish route choice behaviour in an urban context by informing drivers in advance about the objectives of traffic management. Two studies were conducted: (1) an online survey (N = 244) and (2) a driving simulation study (N = 48). The first study focussed on the general effect of recommendations for routes with longer travel times (system-optimal routes) when traffic management is explained. Other route choice attributes (travel time, red-light duration, time pressure) were analysed as well. Drivers were randomly confronted with 35 route choice scenarios consisting of a main route with certain red-light duration and an alternative route without. Results showed that the compliance with system-optimal routes is increased by around 10 percentage points when comparing the group with recommendation to the group without. This effect occurred independently of the variation of other route choice attributes. The second study aimed to determine if the compliance can be increased even more if drivers receive in-depth information about traffic management and experience ‘good’ as well as ‘bad’ recommendations in a driving simulator. Results showed no further effect of these manipulations on route choice. Only decision-making times and subjective evaluation were influenced by in-depth information compared to basic information. Altruism was partly correlated with decision-making. This article shows a novel approach to encouraging drivers to select routes with longer travel times for the benefit of the common good. Drivers’ knowledge gap regarding traffic management needs to be closed in order to enhance their understanding of traffic regulations. Finally, results of this research should be transferred to driver models within traffic simulations to estimate the effects on traffic networks.     

Enhancing the mental representations of space used by blind pedestrians, based on an image schemata model

The configuration of mental representation of space plays a major role in successful navigational activities. Therefore, navigational assistance for pedestrians who are blind should help them to better configure their mental representation of the environment. In this paper, we propose and exploit a computational model for the mental representation of urban areas as an aid to orientation and navigation for visually impaired pedestrians. Our model uses image schemata to capture the spatial semantics and configural elements of urban space necessary for this purpose. These image schemata are schematic structures that are continually requested by individuals in their perception, bodily movement and interaction with surrounding objects. Our proposed model also incorporates a hierarchical structure to provide different levels of detail tied to appropriate spatial perspectives at each scale. We presume that such computational model will help us to develop an appropriate structure of spatial data used to assist the target population. At the end of the paper, we illustrate the utility of our configural model by developing a typical scenario for the navigation of a blind pedestrian in an urban area.     

Fuzzy logic-based observation and evaluation of pedestrians’ behavioral patterns by age and gender

Pedestrian behavior is affected by a multitude of factors such as age, gender, and operating conditions. However, traditional statistical analysis based on observed movements or questionnaire survey is unable to model decision-making process of each pedestrian. This study develops an innovative approach based on fuzzy logic to model the underlying cognitions and behavioral patterns of pedestrians as inferred from field observation in order to evaluate age and gender effect of pedestrians in crossing a signalized crosswalk and when jaywalking. Fuzzy sets and rules are created to model the relationship between human cognitions and decisions of an individual pedestrian. Through calibrating the membership functions of different age and gender groups, behavioral patterns of pedestrians are evaluated and compared. Different from most previous studies, both older and younger pedestrians are found to be less risk-taking than adult pedestrians. Moreover, significant gender difference is found only for cognitions of most hazardous conditions. Consistent with previous studies, it is seen that men have better cognitive skills than women at detecting hazardous situations. The findings from this study are useful to better design safe pedestrian crossing facilities. The fuzzy logic-based approach also provides an innovative way to simulate pedestrian movements in microscopic simulation models.     

Revisiting the level-of-service framework for pedestrian comfortability: Velocity depicts more accurate perceived congestion than local density

The evaluation of pedestrian comfortability is important for the construction and management of walkable spaces. Pedestrian level-of-service (LOS), which is mostly categorized by density, has been widely applied and is believed to be capable of indicating the comfortability of the crowd. However, there is a lack of evidence that physically measured LOS reflects psychological comfortability, let alone the comparison between the performances of different candidate indicators, including the velocity and different local densities. Here, we show that walking velocity depicts pedestrian perceived congestion more accurately than density. In our experiments on pedestrians in a room-egress scenario with an inner obstacle, we obtained objective physical trajectory data from video analysis as well as subjective perception data from a questionnaire survey. The performance of the velocity and four types of local densities in reproducing pedestrian perceptions were numerically evaluated. We found that the velocity outperformed the local density. The lower performance of local densities was mainly caused by the pedestrians located at the back of the crowd, who walked at lower velocities and perceived higher congestion, despite their lower local densities that would correspond to less crowdedness from a physical viewpoint. Besides, the perceived congestion of pedestrians was shown to be affected by the initial pedestrian positions at the crowd, the obstacle layout, and the pedestrians’ age and gender. Furthermore, we suggest that the larger the gap between the desired and actual velocities, the larger the extent of the perceived congestion. We expect that our findings will contribute to a more accurate evaluation of pedestrian comfortability, which could help improve the walkable spaces of various infrastructures.     

Models for pedestrian gap acceptance behaviour analysis at unprotected mid-block crosswalks under mixed traffic conditions

Pedestrian safety is an important aspect while crossing the road and it can be explained by pedestrian gap acceptance behaviour. The statistical models such as multiple linear regression (MLR) is often used to model linear relationships between dependent variable (viz., pedestrian gap acceptance behaviour) and independent variables, due to their ability to quantitatively predict the effect of various factors on the dependent variable. However such linear models cannot consider the effect of several variables on the output variable, due to primary assumptions of normality, linear, homoscedasticity and multicollinearity. In this regard, the non-linear models based on the artificial neural network (ANN), which are free from assumptions of linear models, can be easily employed for obtaining the effect of several input variables on the pedestrian accepted gap size. However, researchers have rarely applied ANN modelling technique for predicting the pedestrian gap acceptance behaviour, as the pedestrian gap acceptance behaviour depends on several pedestrian, traffic and vehicular characteristics. The ANN based models would be quite useful in establishing relationship between these factors on the pedestrian gap acceptance behaviour at midblock crosswalks under mixed traffic conditions. In this direction, the present study adopts both MLR as well as ANN with different pedestrian, traffic and vehicular characteristics to assess the significant contributing factors for pedestrians’ gap acceptance behaviour at unprotected mid-block crosswalks under mixed traffic conditions. For this purpose, a video graphic survey was conducted at a six lane divided road at unprotected mid-block crossing in Mumbai, India. The data such as pedestrian (gender and age), vehicular, traffic and pedestrian behavioural characteristics were extracted to model pedestrian accepted gaps. The model results show that pedestrian rolling behaviour has a significant effect on pedestrian accepted gap size. The model results concluded that ANN has a better prediction with possibility to consider the effect of more number of variables on the pedestrian gap acceptance behaviour as compared to the MLR model under mixed traffic conditions. However, the quantification of significant contributing variables on pedestrian accepted gap size is easy by MLR model as compared to the ANN technique. So, both models have their own significant role in pedestrian gap acceptance analysis. The developed models may be useful to enhance the existing mid-block crosswalk facilities or planning new facilities by more accurate prediction of the pedestrian gap acceptance behaviour considering the influence of various factors under mixed traffic conditions.     

Introducing human factors in pedestrian crossing behaviour models

The objective of this research is the development of pedestrian crossing choice models on the basis of road, traffic and human factors. For that purpose, a field survey was carried out, in which a panel of 75 pedestrians were asked to take 8 short walking trips (each one corresponding to a different walking and crossing scenario) in the Athens city centre in Greece, allowing to record their crossing behaviour in different road and traffic conditions. The same individuals were asked to fill in a questionnaire on their travel motivations, their mobility characteristics, their risk perceptions and preferences with respect to walking and road crossing, their opinion on drivers, etc. The walking and crossing scenarios’ data were used to develop mixed sequential logit models of pedestrian behaviour on the basis of road and traffic characteristics. The modelling results showed that pedestrian crossing choices are significantly affected by road type, traffic flow and traffic control. The questionnaire data were used to estimate human factors (components) of pedestrian crossing behaviour by means of principal component analysis. The results showed that three components of pedestrian crossing behaviour emerge, namely a “risk-taking and optimisation” component reflecting the tendency to cross at mid-block in order to save time, etc., a “conservative” component, concerning individuals with increased perceived risk of mid-block crossing, who also appear to be frequent public transport users, and a “pedestrian for pleasure” component, bringing together frequent pedestrians, walking for health or pleasure, etc. The introduction of these components as explanatory variables into the choice models resulted in improvement of the modelling results, indicating that human factors have additional explanatory power over road and traffic factors of pedestrian behaviour. Therefore, the development of integrated choice and latent variables models appears to be an appropriate field for further research.     

Investigating temporal variations in pedestrian crossing behavior at semi-controlled crosswalks: A Bayesian multilevel modeling approach

“Semi-controlled” crosswalks are unsignalized, but have clear pavement markings and “yield to pedestrian” signs. At these locations, pedestrians and motorists frequently interact to determine who should proceed first. When interacting with drivers, pedestrian crossing decisions are complex events that involve a variety of human responses, as well as vehicle dynamics, traffic characteristics, and environmental conditions. In addition, these complexities can be subject to temporal effects. Without considering temporal variations in pedestrian-motorist interaction, statistical methods could lead to biased coefficient estimates and inaccurate conclusions.The study developed a Bayesian multilevel logistic regression (BMLR) model to capture heterogeneities in pedestrian interaction behavior during four different time periods. The proposed method incorporates time-specific effects that vary randomly between time-periods based on a weakly informative prior. The results indicate significant factors, some of which confirm previous research and some that are new ways to explain pedestrian behavior at the individual level. The identification of variables such as FlowOn and FlowWait sheds light on the interactions between pedestrians – providing more information than the single GroupSize measure.Some consequent safety implications are discussed from the perspectives of vehicle dynamics, vehicle flow rate and pedestrian volume. The more detailed metrics developed in this paper will provide a valuable starting point. for the design of crosswalk controls that will foster a higher degree of compliance and less delay.     

Two-step communication for the interaction between automated vehicles and pedestrians

An important challenge of automated vehicles (AV) will be the cooperative interaction with surrounding road users such as pedestrians. One solution to compensate for the missing driver-pedestrian interaction might be explicit communication via external human machine interfaces (eHMIs). Additionally, implicit communication such as a vehicle pitch motion might support AVs when interacting with pedestrians. While previous work explored various explicit and implicit communication cues, these concepts communicated the intention of the AV at one single time point. Currently, empirical findings on two-step communication lack so far. In this study, we empirically test the effect of a two-step AV communication that uses an implicit cue at a long distance and subsequently provides an implicit or explicit cues at a short distance. We compared its efficiency to single-step communication concepts providing implicit or explicit cues at the shorter distance only. To explore whether the right communication cue is used at the right distance, we analyzed pedestrians’ fixations while approaching an AV using an eye tracking device.We conducted a virtual reality study (N = 30) with AV communication concept that provided an active pitch motion or an eHMI and compared them with a two-step AV communication concept that provided an additional active pitch motion at a long distance when approaching the pedestrian. Furthermore, we recorded pedestrians’ fixation behavior while the AV approached.Consistently to previous work, single-step AV communication showed a beneficial effect on crossing behavior. Pedestrians initiated their crossing earlier while approaching an AV with an active pitch motion or an eHMI compared to the baseline condition. While active pitch motion reduced subjective safety feeling, eHMI increased it. However, the two-step communication concept did not further improve pedestrians’ crossing initiation times and their safety feeling. The pattern of fixation behavior differed as a function of AV distance. When the approaching AV was far away, pedestrians exclusively looked at the environment. During the approach, pedestrians gradually fixated the bumper and the hood and only then the windshield of the AV. Hence, it seems to be useful to present an AV intent communication at a certain distance from the pedestrian. These findings posit the importance of considering pedestrians’ fixation behavior when developing communication concepts between AVs and pedestrians.     

Pedestrian temporal gap acceptance behavior at unsignalized intersections in Kanpur,India

Vehicle heterogeneity, lack of lane discipline, lack of infrastructure facilities, and weak enforcement of traffic rules are all characteristics of Indian traffic. These characteristics make Indian roads difficult for non-motorized road users, especially pedestrians. Most intersections in urban areas in India are unsignalized, which results in complex interactions between pedestrians and motorists. This study aims to understand pedestrian gap acceptance behavior at unsignalized intersections in India under heterogeneous traffic conditions. Pedestrian data were collected from six unsignalized intersections in the city of Kanpur. The critical gap was estimated using Wu’s (2006) macroscopic model, and then the effects of several variables on the critical gap were studied. A discrete choice model (binary logit model) was developed to understand the choice of accepted and rejected vehicular gaps depending on traffic, road, and peer pedestrian characteristics. The mean critical headway was 3.76 s using Wu’s model. The behavioral analysis revealed that pedestrians accept rolling gaps to cross unsignalized intersections, resulting in a reduction of the size of the critical gap. The adoption of rolling gap behavior can be attributed to the unyielding behavior of motorists, which forces pedestrians to accept short vehicular gaps in the traffic stream.     

Hybrid route choice model incorporating latent cognitive effects of real-time travel information using physiological data

The proliferation of information systems is enabling drivers to receive en route real-time travel information, often from multiple sources, for making informed routing decisions. A robust understanding of route choice behavior under information provision can be leveraged by traffic operators to design information and its delivery systems for managing network-wide traffic. However, most existing route choice models lack the ability to consider the latent cognitive effects of information on drivers and their implications on route choice decisions. This paper presents a hybrid route choice modeling framework that incorporates the latent cognitive effects of real-time information and the effects of several explanatory variables that can be measured directly (i.e., route characteristics, information characteristics, driver attributes, and situational factors). The latent cognitive effects are estimated by analyzing drivers’ physiological data (i.e., brain electrical activity patterns) measured using an electroencephalogram (EEG). Data was collected for 95 participants in driving simulator experiments designed to elicit realistic route choices using a network-level setup featuring routes with different characteristics (in terms of travel time and driving environment complexity) and dynamic ambient traffic. Averaged EEG band powers in multiple brain regions were used to extract two latent cognitive variables that capture driver’s cognitive effort during and immediately after the information provision, and cognitive inattention before implementing the route choice decision. A Multiple Indicators Multiple Causes model was used to test the effects of several explanatory factors on the latent cognitive variables, and their combined impacts on route choice decisions. The study results highlight the significant effects of driver attributes and information characteristics on latent cognitive effort and of route characteristics on latent cognitive inattention. They also indicate that drivers who are more attentive and exert more cognitive effort are more likely to switch from their current route by complying with the information provided. The study insights can aid traffic operators and information service providers to incorporate human factors and cognitive aspects while devising strategies for designing and disseminating real-time travel information to influence drivers’ route choices.     

A Leader Who Sees the World as I Do: Voters Prefer Candidates Whose Statements Reveal Matching Social-Psychological Attitudes

Politicians are increasingly able to communicate their values, attitudes, and concerns directly to voters. Yet little is known about which of these signals resonate with voters and why. We employ a discrete choice experiment to investigate whether and which social-psychological attitudes predict how adult British voters respond to corresponding attitudinal signals communicated by candidates in hypothetical social media posts. For all attitudes studied, covering social feelings (trust, collective nostalgia), social perceptions (nationalism, populist sentiment), and social commitments (national identification, authoritarianism, egalitarianism), we find that participants are much more likely to vote for candidates who signal proximity to their own attitudinal position and less likely for candidates who signal opposing views. The strongest effects were observed for national identification, authoritarianism, and egalitarianism, indicating the importance of commitment to a shared group and to particular principles for distributing power and resources within and between groups. We further demonstrate that social-psychological attitudes are not acting as mere proxies for participants' past votes or left–right ideology. Our results extend adaptive followership theory to incorporate preferences concerning intragroup coordination and intergroup hierarchy, while highlighting the social-psychological dynamics of political communication that may transcend the concerns of particular election cycles.     

Roles of personal and environmental factors in the red light running propensity of pedestrian: Case study at the urban crosswalks

Pedestrian is vulnerable to mortality and severe injury in road crashes. Red light running violation of pedestrians is one of the leading causes to the crashes at signalized intersections, at which the crash involvement rates of pedestrians are high. Therefore, it is important to identify the factors that affect the propensity of red light running of pedestrian. In this study, effects of both personal factors (pedestrians’ demographics and behavior) and environmental factors (presence and behavior of other pedestrians, signal time, and traffic condition) on the individual decision of red light running violation are examined, using the video observation surveys at the signalized crossings that are prone to pedestrian-vehicle crashes and have moderate pedestrian and vehicular traffic volumes in the urban area. Crossing behaviors of 6320 pedestrians are captured. Results of a random parameter logit model indicate that pedestrian gender, age, number of lanes, presence of a companion, number of pedestrians around, presence of other violators in the same cycle, time to green, red time, traffic volume, and percentage of heavy vehicles all affect the propensity of red light running violation of pedestrians. Also, there are significant interaction effects by pedestrian’s gender and age, presence of other violators, with a companion, and traffic volume on the propensity. Findings are indicative to the development of effective engineering, enforcement and educational initiatives combating the red light running violation behavior of pedestrians. Therefore, pedestrian safety level at the signalized intersections can be enhanced.     

“Going through a little bit of growing pains”: A qualitative study of the factors that influence the route choice of regular bicyclists in a developing cycling city

As cities make concerted efforts to become more bicycle-friendly through policy changes and infrastructure, it is important that such efforts ultimately support people who currently bicycle and remove barriers that may prevent more people from bicycling. Travel surveys can reveal the nature and distribution of bicycling trips, but perceptions and behaviours of bicyclists are better understood through qualitative methods. In particular, developing cycling cities are unique settings to examine how the built environment supports bicycling as changes are made to increase bicycling levels. Through semi-structured interviews, this research explores the case of Hamilton, Ontario and the factors that influence route choice from the perspective of regular bicyclists. Major themes were identified using thematic analysis: (i) exclusion from road space; (ii) infrastructure; and (iii) streetscape. Bicyclists highly value infrastructure and seek routes that minimize interactions with cars, while avoiding many arterial roads that prioritize motorists. Routes that appear to be more human-oriented or that have nature are also attractive. Many regular bicyclists report that current bicycle infrastructure does not meet their preferences. Despite building nearly half of the planned infrastructure network, our findings suggest the built environment is not perceived to be oriented to bicycling. This study provides policy and practice recommendations for developing cycling cities in North America as they transition towards established cycling cities.     

A perception-based cognitive map of the pedestrian perceived quality of service on urban sidewalks

A better understanding of what walking entails is important in sustainable transportation planning. Recent research has shown that perceptions have a higher power to explain the quality of service (QoS) than objective measures have; the latter being typically used in the pedestrian level of service methodologies. Despite the existing research on perceptions and QoS in other transportation fields, very little information has been found in the literature that can explain a clear cognitive structure that defines the pedestrian QoS. In this research, we propose a cognitive map to make it possible to understand and explain pedestrian sidewalk QoS of a given infrastructure when walking, based on perceptions. We gathered the perceptions of 1056 pedestrians on-site, regarding the attributes (indicators) identified in the literature review in 30 locations in Bogotá, Colombia. Based on an Exploratory Factor Analysis, we developed a conceptual model through Structural Equation Modeling. We found that seven latent variables (LVs) (i.e., Sidewalk characteristics, Externalities, Surrounding, Discomfort, Bike hassles, Protection, and Amenities) interrelate to each other, forming a pedestrian cognitive map. QoS is directly explained by how pedestrians perceive the LVs sidewalk characteristics and surrounding and indirectly by the rest of LVs. The cognitive map shows three LVs related to the interaction between pedestrians and other transportation modes (i.e., externalities, discomfort, and bike hassles) that impact the perceived QoS negatively, and four LVs related to the interaction between pedestrians and different elements of the sidewalk (i.e., sidewalk characteristics, surrounding, amenities, and protection) that impact the perceived QoS positively. To understand pedestrian perceptions when walking and the path that these perceptions follow to develop a QoS perception provides an accurate tool by which to improve the QoS from the useŕs perspective, generating a new opportunity to provide better pedestrian facilities.