Several cities around the world rely on urban rail transit systems composed of interconnected lines, serving massive numbers of passengers on a daily basis. Accessing the location of passengers is essential to ensure the efficient and safe operation and planning of these systems. However, passenger route choices between origin and destination pairs are variable, depending on the subjective perception of travel and waiting times, required transfers, convenience factors, and on-site vehicle arrivals. This work proposes a robust methodology to estimate passenger route choices based only on automated fare collection data, i.e. without privacy-invasive sensors and monitoring devices. Unlike previous approaches, our method does not require precise train timetable information or prior route choice models, and is robust to unforeseen operational events like malfunctions and delays. Train arrival times are inferred from passenger volume spikes at the exit gates, and the likelihood of eligible routes per passenger estimated based on the alignment between vehicle location and the passenger timings of entrance and exit. Applying this approach to automated fare collection data in Lisbon, we find that while in most cases passengers preferred the route with the least transfers, there were a significant number of cases where the shorter distance was preferred. Our findings are valuable for decision support among rail operators in various aspects such as passenger traffic bottleneck resolution, train allocation and scheduling, and placement of services.

Pedestrian route choice, the process by which individuals decide on their walking path between two locations, is a fundamental problem across disciplines. Because this behaviour is investigated from different conceptual and methodological angles, and because it strongly depends on the environmental context, it is challenging to establish a systematic framework for research. Here, by reviewing previous work, we identify four principles for pedestrian route choice that are relevant across disciplines. First, \'information perception\' deals with how pedestrians can perceive information selectively and purposely, given the limited available information. Second, \'information integration\' considers how pedestrians subjectively integrate environmental spatial information into mental representations. Third, \'responding to information\' is concerned with how pedestrians tend to be attracted and repelled by specific attributes individually and how this can lead to positive or negative feedback loops across many individuals. Fourth \'decision-making mechanisms\' describe how pedestrians trade off the evidence provided by different attributes. How pedestrians perceive, integrate, respond to, and act upon information is not fixed but varies with the context. We give examples for each principle and explain how these principles shape pedestrian choice behaviours. We hope this contribution provides a systematic overview of the field and helps to spark inspiration among specialists.

The COVID-19 pandemic strongly affected mobility around the world. Public transport was particularly hindered, since people may perceive it as unsafe and decide to avoid it. Moreover, in Switzerland, several restrictions were applied at the beginning of the first pandemic wave (16/03/2020), to reduce the contagion. This study observes how the pandemic affected travel behaviour of public transport users, focusing on route choice and recurrent trips. We conducted a travel survey based on GPS tracking during the first pandemic wave, following 48 users for more than 4 months. The very same users were also tracked in spring 2019, allowing a precise comparison of travel behaviour before and during the pandemic. We analyse how the pandemic affected users, in terms of travel distance, mode share and location during the day. We specifically focus on recurrent trips, commuting and non-commuting, observing how mode and route changed between the two different periods. Finally, we estimate a route choice model for public transport (Mixed Path Size Logit), based on trips during the two different years, to identify how the route choice criteria changed during the pandemic. The main differences identified in travel behaviour during the pandemic are a different perception of costs of transfers and of travel time in train, and that users no longer have a clear preferred route for a recurrent trip, but often choose different routes.

Familiarity with a route is influenced by levels of dynamic and static knowledge about the route and the route network such as type of roads, infrastructure, traffic conditions, purpose of travel, weather, departure time, etc. To better understand and develop route choice models that can incorporate more meaningful representations of route familiarity, OBDII devices were installed in the vehicles of 32 drivers, 65 years and older, for a period of three months. Personalized web-based trip diaries were used to provide older drivers with post-trip feedback reports about their risky driving behaviors, and collect feedback about their route familiarity, preferences, and reasons for choosing the route driven vs. an alternate low-risk route. Feedback responses were analyzed and mapped onto an abstraction hierarchy framework, which showed that among older drivers, route familiarity depends not only on higher abstraction levels such as trip goals, purpose, and driving strategies, but also on the lower levels of demand on driving skills, and characteristics of road type. Additionally, gender differences were identified at the lower levels of the familiarity abstraction model, especially for driving challenges and the driving environment. Results from the analyses helped highlight the multi-faceted nature of route familiarity, which can be used to build the necessary levels of granularity for modelling and interpretation of spatial and contextual route choice recommendation systems for specific population groups such as older drivers.

Children walking to school are at a high risk of exposure to air pollution compared with other modes because of the time they spend in close proximity to traffic during their commute. The aim of this study is to investigate the effect of a walker\'s route choice on their exposure to ultrafine particles (UFP) on the walk to school. During morning commutes over a period of three weeks, exposure to UFP was measured along three routes: two routes were alongside both sides of a busy arterial road with significantly higher levels of traffic on one side compared to the other, and the third route passed through quiet streets (the background route). The results indicate that the mean exposure for the pedestrian walking along the background route was half the exposure experienced on the other two routes. Walkers on the trafficked side were exposed to elevated concentrations (>100,000 pt/cc) 2.5 times longer than the low-trafficked side. However, the duration of the elevated exposure for the background route was close to zero. Public health officials and urban planners may use the results of this study to promote healthier walking routes to schools, especially those planned as part of organized commutes.

With the increase in the use of private transportation, developing more efficient ways to distribute routes in a traffic network has become more and more important. Several attempts to address this issue have already been proposed, either by using a central authority to assign routes to the vehicles, or by means of a learning process where drivers select their best routes based on their previous experiences. The present work addresses a way to connect reinforcement learning to new technologies such as car-to-infrastructure communication in order to augment the drivers knowledge in an attempt to accelerate the learning process. Our method was compared to both a classical, iterative approach, as well as to standard reinforcement learning without communication. Results show that our method outperforms both of them. Further, we have performed robustness tests, by allowing messages to be lost, and by reducing the storage capacity of the communication devices. We were able to show that our method is not only tolerant to information loss, but also points out to improved performance when not all agents get the same information. Hence, we stress the fact that, before deploying communication in urban scenarios, it is necessary to take into consideration that the quality and diversity of information shared are key aspects.

This study aims to understand the impacts of Pokémon GO, a popular location-based augmented reality (AR) mobile gaming app, on route and mode choices. Pokémon GO leverages AR to introduce virtual objects at fixed and dynamic locations that translate through the app interface to incentives in the real world that potentially influence users\' route and mode choices. Its gaming nature and social components can possibly enhance long-term user engagement through applying the characteristics of game elements and providing opportunities for competition, collaboration, companionship, and social reinforcement. An online survey is conducted to collect the self-reported behavior of a group of Pokémon GO users to explore its impacts on the following aspects of travel behavior: (1) the frequency of changing the route to interact with virtual objects; (2) the likelihood of carpooling more instead of driving alone for more in-app collaboration; and (3) the likelihood of shifting mode from drive alone to public transit, walking, and cycling if provided with additional incentives. The ordered survey responses including frequency and likelihood are analyzed using random parameters ordered probit models to account for the unobserved heterogeneity across users and identify subpopulations of travelers who are more susceptible to the influence of Pokémon GO. The modeling results identify four types of variables (attitude and perceptions related to Pokémon GO, app engagement, play style, and sociodemographic characteristics) that affect users\' travel behavior. The results illustrate that such apps with integrated AR, gamification, and social components can be used by policymakers to influence various aspects of travel behavior. The study findings and insights can provide valuable feedback to system operators for designing such apps to dynamically manage traffic in real-time and promote long-term sustainable mode shifts.

The afterschool commute is a major part of children\'s daily activity. This study examines the relationship between student extended active travel routes and route environment characteristics. Route environment characteristics may be related to an extended route for students who walk or bike home. Self-reported itineraries were collected from 12 to 15-year old students in 3 middle schools in Shenzhen in May and June (n = 1257). Itineraries involving a detour from the shortest possible route home (n = 437) were compared with the shortest route. A field study coded all possible routes within the school districts by playable open spaces, sidewalk width, controlled crossings, road category, and public transit stops. Binary logistic regression reveals that routes with greater intersection density and number of open spaces are related to active travel choice. Sidewalk width, number of traffic lights and proportion of arterial roads are positively related to motorized travel. Linear regression reveals that travel distance, sidewalk width, number of open spaces and street crossings, as well as the proportion of secondary roads and pathways are positively related to detour distance. Higher numbers of public transit stops and traffic lights are related to shorter detours. Attending cram school is also negatively associated with active travel and detour. Younger students, females and students with longer moderate-to-vigorous physical activity time have extended active travel. Specific route environment characteristics are associated with longer and more active middle school student commutes and may be implemented to raise overall activity levels in children.

The objective evaluation of the physical environmental characteristics (e.g. speed limit, cycling infrastructure) along adolescents\' actual cycling routes remains understudied, although it may provide important insights into why adolescents prefer one cycling route over another. The present study aims to gain insight into the physical environmental characteristics determining the route choice of adolescent cyclists by comparing differences in physical environmental characteristics between their actual cycling routes and the shortest possible cycling routes.
Adolescents (n = 204; 46.5% boys; 14.4 ± 1.2 years) recruited at secondary schools in and around Ghent (city in Flanders, northern part of Belgium) were instructed to wear a Global Positioning System device in order to identify cycling trips. For all identified cycling trips, the shortest possible route that could have been taken was calculated. Actual cycling routes that were not the shortest possible cycling routes were divided into street segments. Segments were audited with a Google Street View-based tool to assess physical environmental characteristics along actual and shortest cycling routes.
Out of 160 actual cycling trips, 73.1% did not differ from the shortest possible cycling route. For actual cycling routes that were not the shortest cycling route, a speed limit of 30 km/h, roads having few buildings with windows on the street side and roads without cycle lane were more frequently present compared to the shortest possible cycling routes. A mixed land use, roads with commercial destinations, arterial roads, cycle lanes separated from traffic by white lines, small cycle lanes and cycle lanes covered by lighting were less frequently present along actual cycling routes compared to the shortest possible cycling routes.
Results showed that distance mainly determines the route along which adolescents cycle. In addition, adolescents cycled more along residential streets (even if no cycle lane was present) and less along busy, arterial roads. Local authorities should provide shortcuts free from motorised traffic to meet adolescents\' preference to cycle along the shortest route and to avoid cycling along arterial roads.

Walking is a form of active transportation with numerous benefits, including better health outcomes, lower environmental impacts and stronger communities. Understanding built environmental associations with walking behavior is a key step towards identifying design features that support walking. Human mobility data available through GPS receivers and cell phones, combined with high resolution walkability data, provide a rich source of georeferenced data for analyzing environmental associations with walking behavior. However, traditional techniques such as route choice models have difficulty with highly dimensioned data. This paper develops a novel combination of a data-driven technique with route choice modeling for leveraging walkability audits. Using data from a study in Salt Lake City, Utah, USA, we apply the data-driven technique of random forests to select variables for use in walking route choice models. We estimate data-driven route choice models and theory-driven models based on predefined walkability dimensions. Results indicate that the random forest technique selects variables that dramatically improve goodness of fit of walking route choice models relative to models based on predefined walkability dimensions. We compare the theory-driven and data-driven walking route choice models based on interpretability and policy relevance.