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Abstract Route choice modelling is a critical aspect of analysing urban rail transit (URT) networks and provides a foundation for URT planning and operation. Unlike in a free-flow road network, the consideration set for route choice decisions in a URT network does not depend purely on the physical connectivity of the network and decision makers’characteristics. Instead, it is also contingent on the train schedules. This paper delves into the evolution of research on route choices in URT networks, encompassing both probabilistic route choice modelling derived from utility maximisation theory and logit curve with physical connectivity, and retrospective route choice modelling based on travel time chaining along with comprehensive transport data. The former is noted for its conciseness, simplicity, and interpretability in real-world applications, even though the methodologies may not be cutting-edge. The latter incorporates dynamic temporal information to understand activities of passengers in URT networks. Enhancements of each genres are also examined. However, these improvements might not fully address the inherent limitations of models relating to a dependency on the quality of parameters, experience of experts, and calculation efficiency. In addition, novel research adopting contemporary data mining techniques instead of classical models are introduced. The historical development of research on URT network route choices underscores the importance of amalgamating independent information networks such as surveillance networks and social networks to establish a comprehensive multi-dimensional network. Such an approach integrates passenger attributes across networks, offering a multi-dimensional understanding of passengers’ route choice behaviours. Our review work aims to present not only a systematic conceptual framework for route choices in URT networks but also a novel path for transport researchers and practitioners to decipher the travel behaviours of passengers.

Despite many years of research and rapid changes in the field of bicycle route choice modelling, a review of factors associated with cyclists' route choice decisions is missing. This paper reviews 33 studies that employed GPS data and statistical modelling to explain the route choice behaviour of cyclists on utilitarian trips. Factors associated with bicycle route choice are divided into network, contextual, and individual factors. Findings from this review demonstrate that a refined categorisation of model attributes is essential, as the preferences vary greatly between different route attributes. Moreover, the analysis indicates a strong influence of the local context on the attribute definitions and route choice preferences. Although some commonalities between studies w.r.t. the network factors (e.g. a preference towards bicycle infrastructure or avoidance of slopes) can be determined, many results are contradictory or intertwined with other factors. Specifically, results related to intersections, land use characteristics, and interaction with other modes of transport are not always conclusive and are strongly dependent on the local context. However, the explanation of some of the unclear effects can be facilitated by incorporating contextual or individual information in the models. Based on the analysis of the existing studies, this review encourages a joint consideration of studies employing quantitative and qualitative methods to explain cyclists' behaviour. Moreover, reporting model results in an accessible form (e.g. through relatable values or visualisation) is essential to build bridges between bicycle research and policymaking. Future research is encouraged to study cyclists' route choice decisions from the perspective of objective well-being, quantifying and modelling the influence of factors such as safety, health, energy expenditure, or noise exposure. [ABSTRACT FROM AUTHOR]

Travelers always perform some preference during the decision-making process. The preference will affect the decision results and can be improved by continuously learning. In order to understand the influence of individual preference on travel behavior choice , two individual preferences, including indifference preference and compulsive preference are considered in the paper. Two updating mechanisms of compulsive preference are proposed to obtain the choosing probability of all alternatives. Reinforcement learning models are established integrating the gain stimulating and loss stimulating considering expected utility. Nguyen Dupuis network is adopted for numerical simulation to study the updating process. Simulation results denote that the equilibrium state is much more efficient when preference learning mechanism is considered comparing with the traditional stochastic user equilibrium model, and can decrease the total travel time greatly, which can be applied for urban traffic management. Personalized traffic guidance is the effective solution to traffic congestion in the future [ABSTRACT FROM AUTHOR]

As one of the most fundamental concepts in transportation science, Wardrop equilibrium (WE) has always had a relatively weak behavioral underpinning. To strengthen this foundation, one must reckon with bounded rationality in human decision-making processes, such as the lack of accurate information, limited computing power, and suboptimal choices. This retreat from behavioral perfectionism in the literature, however, was typically accompanied by a conceptual modification of WE. Here, we show that giving up perfect rationality need not force a departure from WE. On the contrary, WE can be reached with global stability in a routing game played by boundedly rational travelers. We achieve this result by developing a day-to-day (DTD) dynamical model that mimics how travelers gradually adjust their route valuations, hence choice probabilities, based on past experiences. Our model, called cumulative logit (CumLog), resembles the classical DTD models but makes a crucial change; whereas the classical models assume that routes are valued based on the cost averaged over historical data, our model values the routes based on the cost accumulated. To describe route choice behaviors, the CumLog model only uses two parameters, one accounting for the rate at which the future route cost is discounted in the valuation relative to the past ones and the other describing the sensitivity of route choice probabilities to valuation differences. We prove that the CumLog model always converges to WE, regardless of the initial point, as long as the behavioral parameters satisfy certain mild conditions. Our theory thus upholds WE's role as a benchmark in transportation systems analysis. It also explains why equally good routes at equilibrium may be selected with different probabilities, which solves the instability problem posed by Harsanyi. Funding: This research is funded by the National Science Foundation [Grants CMMI #2225087 and ECCS #2048075]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/trsc.2023.0132. [ABSTRACT FROM AUTHOR]

• A framework is presented to explore the influence of friendships in a social network on drivers' route choice. • This paper simulates the evolution of drivers' route choice in a hypothetical transportation network under different assumptions. • This research offers insight into the route choice process when drivers can access information about other drivers' travel experience in a social network. • The simulation results show that the information types mainly affect the change rate, and the memory length mainly affects the time for convergence. Route choice is a decision that drivers face daily under varying traffic conditions. Although many studies have focused on route choice, the actual behavior considering real-world networks and the mechanisms that govern it have eluded complete characterization. Part of the difficulty has been in accounting for the growing availability of information sources that may influence drivers' route choices, precluding collection of adequate observational data. In addition, with the continuing emergence of new forms and sources of information, such as social media with varying degrees of interactivity, engagement, and immediacy, existing frameworks and models for representing these choices and the influence of new information sources have been lacking. This study uses an agent-based modeling approach to investigate the effects of social influence on drivers' route choices. In the model, all drivers communicate with each other in the same social network, and this information is combined with their previous experiences. The first aim of this study is to investigate the impact of social network connectivity and information exchange on route choice. The second is to characterize situations in which all drivers in the social network are satisfied with their route choices. The simulation results show that an equilibrium is achieved when all drivers in the given social network are satisfied with their current route choices. [ABSTRACT FROM AUTHOR]

Safety is a major concern in bicycle traffic, and understanding safety-related factors that influence bicycle route choice is crucial for improving safety and promoting sustainable transportation. However, there is a notable lack of research on the impact of accident risk and perceived safety on cyclists' route choices. This study addresses this gap by investigating whether accident-prone areas or perceived insecure locations affect actual route choice decisions. The contribution explores this relationship by leveraging an extensive dataset comprising approximately 4000 trips from around 170 participants, alongside additional data on infrastructure, operations, exposure, accidents, and mobility diaries reporting critical incidents. The findings broadly confirm results from other studies regarding the influence of route characteristics (e.g., existence of cycling infrastructure, volumes of motorized traffic, or presence of signal-controlled intersections). Moreover, the study reveals that a high accident risk along a route does have a slight negative influence on route choice. Surprisingly, perceived safety does not significantly influence cyclists' route choice.

Abstract The express/local mode of municipal rail transit provides passengers with multiple alternatives to achieve more efficient and superior travel, in contrast to the conventional all-stop operation mode. However, the various route choices (including direct express trains, direct local trains, or transfers) covering different passenger groups pose a significant challenge to passenger flow assignment. To understand route choice behavior, it is crucial to measure the passenger heterogeneity (variability in individual and trip attributes) in order to propose targeted solutions for operation schemes and service planning. This paper proposes a hybrid model by integrating structural equation modeling and the mixed logit model under express/local mode to estimate the impact of passenger heterogeneity on route choice. An empirical study with revealed preference and stated preference surveys carried out in Shanghai revealed how individual and trip attributes quantitatively impact the sensitivity of factors in route choice. The results show that age and trip purpose are more significant factors. Compared to the control group, the probability of express trains is reduced by 10.22% for the elderly and by 11.36% for non-commuters. Our findings can provide support for more reasonable operation schemes and more targeted services.

• EV driver's long-distance charging and route choice preferences were shown. • Insights on customized long-distance trip and charging facility planning were given. • Personal attitudes such as planning ability affect drivers' charging times choice. • People with strong planning ability are more likely to choose charging once. • Simultaneous charging and resting is best for meeting needs and reducing travel time. The increasing demand for long-distance and intercity travel is driven by the progress in regional transportation integration and the establishment of charging infrastructure. This increase suggests that the use of electric vehicles (EVs) for long-distance trips is likely to become more prevalent in the future. Therefore, there is an urgent need for more studies on the travel behavior of EV drivers for long-distance trips. However, there are few studies looking at the travel behavior of EV drivers on long-distance trip and multiple charging during trips. To fill this gap, this study identified EV drivers' preferences for charging and route choice in long-distance or intercity travel on the basis of a stated preference survey, with the Shandong highway network as the travel scenario. A nested logit (NL)-based hybrid choice model was used to investigate the impacts of personal attitudes, namely planning ability, risk aversion and confidence in battery, on the drivers' charging times choice behavior. The NL structure consists of two levels, with the upper level focusing on the charging times decision process, and the lower level analyzing the choice behavior of routes with no charging, routes with charging once, and routes with charging twice. The results show that the initial state of charge, and attitudes significantly influence the decision on charging times. In addition, EV drivers who are risk-averse and lack confidence in the battery tend to charge, whereas drivers with strong planning ability prefer to charge once in long-distance travel. Charging and resting simultaneously is the best way to meet the needs of charging and resting, and reduce the time lost while driving. These results provide insights on drivers' travel planning and charging infrastructures planning for long-distance trips. [ABSTRACT FROM AUTHOR]

• 52 participants reached 4 predefined destinations via shared e-scooter or bicycle. • E-scooter riders prioritize road surface, safety, and rule compliance. • GPS riding data is comparable for both user groups. • E-scooter riders tend to take longer routes for routes with multiple turns. • Traffic planning should prioritize high-quality cycling infrastructure. Problem : The surge in popularity of electric kick scooters (e-scooters) poses new challenges for traffic planning, demanding a comprehensive understanding of route choice behavior to see how e-scooters are used, how they affect traffic flow, and where improvements can be made to the road infrastructure. Therefore, this study aimed to analyze route choices and preferences of e-scooter riders and cyclists in a quasi-experimental setup with both user groups having the same trip destinations. Method : Two groups of participants (n = 52) completed a ride with either a shared e-scooter or bicycle to reach four predefined destinations in Dresden, Germany. The riders were supposed to choose their routes and subsequently reported the difficulty of decision-making and the importance of several route choice factors related to the ride. Results : E-scooter riders rated road surface and safety as significantly more important for route choice than cyclists and tended to perceive the decision-making as more difficult. Riding data revealed broad comparability between the groups, with e-scooter riders tendentially having longer routes for complex decisions (unknown destinations, scenic routes, more turns required). Discussion : The study suggests that the route preferences of e-scooter riders may be influenced by a combination of road surface and safety considerations, highlighting the need for high-quality cycling infrastructure. Limitations exist regarding the naturally occurring differences in riding experience in e-scooter riding versus cycling. Practical implications indicate that planning for e-scooter riders can benefit from insights drawn from activities designed for cyclists. The provision of real-time road quality information is proposed, considering its potential impact on overall road safety. Summary : This study contributes to a better understanding of how e-scooter riders navigate through cities and delivers a valuable foundation for transport planners and engineers considering the rise in cycling and micro-mobility use. [ABSTRACT FROM AUTHOR]

This study aimed to identify the environmental factors of pedestrians’ preferred route and examine which factors are related to the choice of a healthy route. This study set the shortest and healthy routes in Ayanggyo neighborhood in Daegu Metropolitan City, South Korea. A healthy route is longer than the shortest route but has rich greenery and low traffic. A survey of 203 respondents was conducted from October 12 to 8 November 2022. This study used a logistic regression model with a walking route choice as the dependent variable. Results showed that 65% of participants chose a healthy route. People who considered low noise levels, presence of greenery and waterfront areas, low traffic speed, and presence of traffic safety facilities as important were likelier to choose healthy routes. The results suggest that pleasant and safe routes and built environments can make people walk longer. The findings of this study provide evidence to urban planners and policymakers that the built environmental conditions play an important role in encouraging residents’ walking behavior. This can contribute to urban policies supporting citizens’ active and healthy walking.

Analyzing the preference of route choice not only facilitates the understanding of individuals' decision‐making behavior, but also provides valuable information for improving traffic management strategies. As the layout of the road network, the variability of individual preferences and the spatial distribution of origins and destinations all play a role in route choice, it is a great challenge to reveal the interplay of such numerous complex factors. In this paper, we propose RouteVis, an interactive visual analytics system that enables traffic analysts to gain insight into what factors drive individuals to choose a specific route. To uncover the relationship between route choice and influencing factors, we design a quantitative analytical framework that supports analysts in conducting closed‐loop analysis of various factors, i.e., data preprocessing, route identification, and the quantification of influence and contribution. Furthermore, given the multidimensional and spatio‐temporal characteristics of the analysis results, we customize a set of coordinated views and visual designs to provide an intuitive presentation of the factors affecting people's travels, thus freeing analysts from tedious repetitive tasks and significantly enhancing work efficiency. Two typical usage scenarios and expert feedback on the system's functionality demonstrate that RouteVis can greatly enhance the capabilities of understanding the travel status. [ABSTRACT FROM AUTHOR]

Cycling is one of the main transport modes and cycling infrastructure is strongly embedded in transport infrastructure in the Netherlands. Nonetheless, the bicycle network still undergoes frequent improvements and expansions. One of the critical elements in deciding on improvements and expansions is to understand the route choice of cyclists, which helps identify bottlenecks in bicycle flows and substantiate the need for new bicycle infrastructure. Yet, the factors affecting the route choice of cyclists are still not fully understood. To address this, we develop a varying-contiguity spatially lagged exogenous (VCSLX) model and analyze the probability of a cyclist choosing a certain segment based not only on the characteristics of that segment but also considering the characteristics of its neighbors along a route. Characteristics that are included in this study are the presence of bicycle infrastructure, traffic control installations and artificial lighting, as well as pavement type, bicycle and motorized-vehicle volumes and different land-use zones. The model involves the analysis of the observed routes extracted from cycling trajectories from Fietstelweek data, as well as corresponding hypothetical shortest path routes identified from the origin-destinations of the observed trips and the cycling network. The results of the study can help to understand the factors convincing cyclists to deviate from the shortest possible routes. The study contributes to the current literature by focusing on the underexplored aspect of spatial dependencies between route segments in the route choice of cyclists.

To investigate pedestrian route choice mechanisms from a perspective distinct from that employed in discrete choice models (DCMs), this study utilizes machine learning models and employs SHapley Additive exPlanations (SHAP) for model interpretation. The data used in this paper come from several pedestrian flow experiments with two routes, which were recorded by UAV. Our findings indicate that logistic regression (similar to a binary logit model) exhibits good computational efficiency but falls short in predictive accuracy when compared to other machine learning models. Among the 12 machine learning models assessed, by calculating the new indicator named OP, we find that eXtreme Gradient Boosting (XGB) and Light Gradient Boosting (LGB) strike the best balance between accuracy and computational efficiency. Regarding feature contribution, our analysis reveals that bottlenecks exert the most significant influence on pedestrian route choice behavior, followed by the time it takes pedestrians to return from the end of the route to the origin (reflecting pedestrian characteristics and attitudes). While the pedestrian density of the shorter route contributes less compared to bottlenecks and return time, it exhibits a threshold effect, meaning that once the density of the shorter route surpasses a certain threshold, most pedestrians opt for the longer route. [ABSTRACT FROM AUTHOR]

We investigated the role of semantic similarities of place names in forming larger regions. The environment´s place names allowed two alternative semantic categorizations corresponding to two equally valid regionalizations. In a priming phase, participants were biased to one or the other categorization scheme. Region perception was assessed by having participants choose between two equidistant routes crossing different numbers of region boundaries. Participants selected routes with a lower number of region crossings according to the primed categorization in 61% of all cases. The results show that perceived semantic similarity of place names affects route choice and the formation of spatial regions. [ABSTRACT FROM AUTHOR]