Route Choice Behavior Research Articles

Understanding the route choice behaviour of metro-bikeshare users

• Route choice behaviour of metro-bikeshare users is studied using smartcard data. • The congestion status of metro-bikeshare integration network is measured. • Passengers’ socio-economic attributes and perceived congestion are considered. • Research results is help to improve personalized transit-oriented travel services. Understanding the determinants of the route choice behaviour on a multi-modal transit network of metro and shared bike is important to improve personalized multimodal travel services. This paper attempts to analyse the route choice behaviour of metro-bikeshare users considering passengers’ socio-economic attributes and perceived congestion which is approximated by load status. An abstract integrated metro-bikeshare network (IMBN) is built with virtual nodes by aggregating shared bike stations within the walkable distance and abstract routes by aggregating optional paths for each OD pair. Using the metro- and shared bike smart- card data from Nanjing, China, the route sets of metro-bikeshare users were extracted from the IMBN. A multinomial Logit model (MNL) was then applied to investigate the determinants of route choice behaviour for two types of users, namely “return-enter” and “exit-lease”, respectively. The results show that the models with the load status attributes have a better performance than the models without these attributes. We found the sensitivity of “exit-lease” users to the train crowding is significantly greater than that of the “return-enter” users. “Return-enter” users have a higher perception of out-of-vehicle travel time (OVTT) than that of in-vehicle travel time (IVT), while the “exit-lease” users have the opposite perception. Besides, the change rate of shared bike inventory, departure time and whether he or she is a regular user also have a significant impact on route choice behaviour. The findings can help policymakers and system operators to improve the services and the efficiency of the multimodal transportation system.

The Impact of Traffic Information Provision and Prevailing Policy on the Route Choice Behavior of Motorcycles Based on the Stated Preference Experiment: A Preliminary Study

It is anticipated that the prevalence of motorcycles in Asian countries will continue to increase, causing congestion and network imbalances concerning the nature of motorcycles. Literature demonstrates Variable Message Signs (VMSs) as an effective measure for addressing this issue. Understanding route choice behavior may thus aid in determining the appropriate traffic information to broadcast. This study aims to identify the impact of VMS messages related to traffic conditions and regulations on the route choice of motorcycle riders. In this instance, the core concept of ramp metering is adapted for non-highways to manage the proportion of motorcycles entering the traffic stream of the mainline. Two predetermined routes were offered through a stated preference survey to capture the responses to VMS. A binary logit model was initially introduced, further improved by including the individual characteristics and accommodating the unobserved factors across a series of observations (panel effects) by applying the mixed binary logit. It was revealed that traffic flow conditions significantly affect route preference; therefore, motorcycles tend to choose routes with lower volumes. However, waiting time at a ramp meter has no impact. The present research is a preliminary investigation for further implications in proposing traffic management strategies under mixed traffic situations.

Effects of personality traits and visual health on driver route-choice behaviour in response to VMS traffic information

ABSTRACT The advanced travel information system is a prominent intelligent transportation system for which variable message signs (VMSs) are the major components. In Iran, more attention has been paid to sign placement than to other components which are complementary to placement and combine to create relevant and effective messages for the driver. The current study used a stated preference survey to investigate the attitudes and reactions of drivers to VMS information that then were used to build models to reveal the reasons behind driver reactions to a VMS request to change route. The survey included variables affecting driver decisions, such as driver characteristics, attitude and perceptions of current VMS information and traffic in general. The influence of personality traits as subjective factors for driver behavior regarding route choice also was investigated. The results of the estimation of sequential hybrid binary logistic regression model showed that visual health had a significant positive effect on the driver decisions to divert to an alternate route when the VMS messages related to an accident or traffic congestion. Provision of expected delay information about the current route was found to be the most significant factor causing drivers to change route when a VMS indicated traffic congestion ahead. Personality traits such as Agreeableness, neuroticism, extraversion and openness significantly affected driver decisions about changing route when the VMS indicated traffic congestion or adverse weather conditions.

Modeling traveler’s speed-route joint choice behavior with heterogeneous safety concern

In this study, a speed-route joint choice model considering traveler’s safety concerns is proposed to concurrently model traveler’s safety-oriented travel speed and route choice behavior. Specifically, the safe-speed choice behavior is modeled as a trade-off process between perceived traffic safety and efficiency using a disutility function. The safe-route choice behavior is described by the proposed Mean-excess Crash Risk Cost model, where the route safety is modeled as a random variable following a specific distribution, and traveler’s concerns about both reliability and unreliability aspects of safety variability are considered. The model is accommodative to account for the random nature and the traveler’s perception of traffic safety. Also, the travel time cost is considered, which is depicted as a parallel criterion of travel safety in the route choice model. Moreover, the heterogeneities of travelers’ safety concerns in both the choices of speed and route are considered in the proposed joint model. Then, the study formulated the equilibrium problem with the two behavior elements (speed and route) and two choice criteria (safety and time), based on the assumption that all travelers tend to maximize their disutility when choosing speed while minimizing their travel safety variability and travel time. To illustrate the model, Nguyen and Dupuis, Sioux falls, and Changsha arterial networks are conducted as numerical studies. The result demonstrates the model’s capability in depicting travelers’ trade-off between safety and time when selecting the optimal travel speed. Considering the impact of route safety unreliability makes the model sensible to describe travelers’ safety-concerned route choice behavior. The model is also flexible to account for travelers’ crash risk aversion heterogeneity.

Route flow estimation based on the fusion of probe vehicle trajectory and automated vehicle identification data

Accurate estimation of route flow is crucial to traffic control optimization. Existing route flow estimation models are typically built upon strong assumptions on traveler route choice behavior or turn to data-driven approaches which heavily rely on prior OD estimation and high sensor coverage rate. This study proposes a route flow estimation framework by fusing the probe vehicle trajectory and automated vehicle identification (AVI) data. The notion of probe vehicle route penetration rate (RPR) is utilized for the construction of a novel data-driven objective function. Sensor-OD flow and observable route flow penetration are formulated as model constraints. The solution uniqueness is guaranteed though an iterative procedure under the principle of entropy maximization. Numerical studies are conducted based on pNEUMA, a large-scale full sample trajectory dataset. Results show that the proposed model performs better than the traditional path flow estimation model, especially when the prior flow information is not available. Sensitivity analysis on AVI sensor coverage rate, probe vehicle route coverage rate and RPR distribution demonstrates the proposed model’s applicability in practice.

A highway vehicle routing dataset during the 2019 Kincade Fire evacuation

As the threat of wildfire increases, it is imperative to enhance the understanding of household evacuation behavior and movements. Mobile GPS data provide a unique opportunity for studying evacuation routing behavior with high ecological validity, but there are little publicly available data. We generated a highway vehicle routing dataset derived from GPS trajectories generated by mobile devices (e.g., smartphones) in Sonoma County, California during the 2019 Kincade Fire that started on October 23, 2019. This dataset contains 21,160 highway vehicle routing records within Sonoma County from October 16, 2019 to November 13, 2019. The quality of the dataset is validated by checking trajectories and average travel speeds. The potential use of this dataset lies in analyzing and modeling evacuee route choice behavior, estimating traffic conditions during the evacuation, and validating wildfire evacuation simulation models.

A general dynamic sequential learning framework for vehicle trajectory reconstruction using automatic vehicle location or identification data

Vehicle trajectory data derived from automatic vehicle location (AVL) and automatic vehicle identification (AVI) systems provide critical support for intelligent transportation systems. However, the field-obtained vehicle trajectories are usually incomplete due to sensor malfunction or communication issues. To recover the incomplete data, the existing reconstruction methods have to impose strong assumptions on driver route choice behaviors (network level) and/or traffic dynamics (link level). With the tremendous data available, leveraging data-driven approaches to address the vehicle trajectory reconstruction problem with minimal assumptions is promising. This paper proposes a general dynamic sequential learning framework to reconstruct vehicle trajectory points for both AVL and AVI data. First, an Isolation Forest based ensemble learning model is developed to extract trajectory sequences attributed to different trips in an entire trip chain of a vehicle. Second, the dynamic recurrent neural network (dynamic RNN) is tailored to learn the underlying patterns from the complete AVL and AVI trajectories, respectively. Third, a sequential prediction scheme is customized to reconstruct AVL and AVI trajectories based on the trained networks. To validate the proposed method, two experiments are conducted. One is a simulation experiment with AVL data gathered from a well-calibrated simulation model. The other is a field experiment with AVI data collected from a real-world automatic license plate recognition (ALPR) system. The results show that the proposed method achieves superior performance for both AVL and AVI data compared with the traditional methods. The impacts of different sampling rates and traffic conditions on the model performance are also discussed.

Attractive infrastructure for everyone? Different preferences for route characteristics among cyclists

• Large-scale stated-preference experiment to evaluate bike route choice behavior. • Higher route requirements for all analyzed subgroups compared to overall population. • Evidence of positive effect of lower car speeds for older cyclists and those with kids. • Vulnerable road users benefit stronger from bike-friendly infrastructure. Knowing about the specific route preferences of demographic groups is crucial when researching equity in cycling. In this study, we use a large-scale stated-preference experiment with 4389 respondents and 35,112 choice observations to evaluate route choice behavior. We set up discrete route choice models and focus on the specific needs of women, people cycling with children, and older adults. Results show that all groups of interest show higher, but not fundamentally different, route requirements. In this context, lower speed limits for motorized transport are very important for people cycling with children, and older adults. In addition, women and those traveling with children attach significantly higher value to separated bike infrastructure. These results show that vulnerable road users benefit stronger from a bike-friendly road infrastructure than the cyclist population as a whole.

Analysis of Perception Variance in Regret Choice Modeling Based on GPS Data Considering Building Environment Effects

The purpose of this paper is to deal with the perception variance problem in regret model by scaling perception variance and relaxing the distribution assumption of the random error term, which are corresponding to the attribute and alternative differences, respectively. Specifically, the power coefficient in regret function can capture individual perception variance of specific attributes and latent class structure is used to analyze individual heterogeneity of alternative differences by defining weight function. Accordingly, perceiving behaviors of individuals are analyzed in depth and generating absolute and relative behavior interpretation and asymmetric property. The proposed models are estimated and analyzed using GPS data of taxis in Guangzhou, and bicycle household surveys are collected in Tel Aviv metropolitan area. The results show both significant effects of attributes on drivers’ and cyclists’ route choice behavior and identification of different types (absolute or relative) of perceiving attribute and alternative regret differences of the two travelers by incorporating the ratio of length between chosen route and the shortest route.

Verify the Validity of Guidance Sign in Buildings: A New Method Based on Mixed Reality with Eye Tracking Device

Evacuation signs are one of the key factors which influence the building evacuation efficiency and evacuees’ route choice behaviors. It is important to test the effectiveness of evacuation signs, and an eye movement test was verified to be a good way. In this paper, both reality and MR (Mixed-Reality) evacuation experiments were carried out to collect the eye movement data of participants, such as gaze position, fixation duration, and number of gazes. In the reality experiment, participants took part in an emergency evacuation at a T-junction corridor with the glass-type eye-tracking device. In order to collect eye movement data in the MR experiment, the eye tracking device was integrated into the Microsoft HoloLens device (MR-ET for short). In either reality or MR experiments, each participant took part in five sub-experiments with five different types of evacuation signs. It was found that (1) the evacuation signs with a black background, green foreground and flashing lights have a better effect in guidance; (2) the flashing lights can better attract the attention of the participants, and enhanced the participants’ understanding of the signs. (3) An X-mark will hinder participants’ understanding of the signs and increase their reaction time. According to the results of the experiments, it is also found that the eye-tracking test in an MR environment is also a good method to test the effectiveness of the evacuation signs, and such a method can also be used in building safety design without damaging a wall or changing the reality environment of a building.

How do street attributes affect willingness-to-walk? City-wide pedestrian route choice analysis using big data from Boston and San Francisco

This study adds to the nascent but growing literature on the use of big data for pedestrian route choice analysis. We explore behavioral preferences for various route attributes in Boston, MA using a large dataset of GPS trajectories (n = 11,165) sourced from a third-party smartphone app. Although the data are anonymized and limit our exploration of user heterogeneity, the sample size and area coverage are both much larger than seen in most previous studies. We estimate route choice preferences using a path size logit model, and operationalize the coefficients for policy-making through ‘willingness-to-walk’ measures. The value of these measures is demonstrated through an application of computing pedestrian accessibility to transit stations. Additionally, we compare our findings to a previous study in San Francisco, CA using similar data and methods, and previous literature to explore similarities and differences in pedestrian route choice behavior across major metropolitan areas more generally. While our findings can inform walkability policy and practice on several counts, we recommend future efforts to focus on supplementing this study by surveying hard-to-reach populations for more equitable policy-making.

Approximating Dynamic Equilibrium Analysis in Multi-Region Network Based on Macroscopic Fundamental Diagram

Modeling and control of road traffic in large-scale urban networks present considerable challenges. The traffic equilibrium phenomena, with the question of route choice behavior in case of heterogeneous urban networks, has not been thoroughly investigated in parsimonious and classical models due to the limitation, like large network size, spatiotemporal propagation of congestion, and the interaction between driver decisions, etc. In this paper, we propose a bi-level approximating dynamic equilibrium model (BLADEM) for the approximating dynamic equilibrium analysis in multi-region network based on macroscopic fundamental diagram (MFD). The proposed model combines the region-based model and the internal-region model. With the information from region MFD, the region-based model is used to implement the time-dependent regional route choice estimation. Traffic equilibrium condition (dynamic user equilibrium, DUE) is considered in an internal-region model with time-aggregated regional OD demand from the region level. Furthermore, the complexity of the proposed model is derived. Then, the comparative analysis of the algorithm complexity between the proposed model and the DUE model is given. The proposed model is evaluated based on the high-resolution vehicle trajectory data (or connected vehicles trajectory data) from the DiDi platform collected in Chengdu, China with more than 3,000,000 GPS points during a typical workday. The evaluation results show that the proposed model can obtain the approximating traffic dynamics compared with the DUE algorithm. Pleasantly, the improved calculation efficiency is between 21% and 42%. The results indicate the promising potential of using the proposed model to analyze approximating dynamic equilibrium in the multi-region heterogeneous network.

A topology-based bounded rationality day-to-day traffic assignment model

This paper analyzes the day-to-day adjustment process of users’ behaviors in a transport network which is affected by relevant alterations such as disruptions due to critical events which cause the impossibility to use one or more links. For representing the progressive adjustment of the flows on the network to reach a new equilibrium, a day-to-day discrete-time model is proposed, based on the idea that people are bounded rational in their choices, i.e. they often do not behave according to the optimal solution but they accept solutions they consider satisfying. Users, in their choice process, are influenced by the topological similarity between the route they are currently using and others. This means that they tend to prefer the solutions that are more similar to the one they are already using. In parallel, users exhibit a myopic behavior, i.e., they tend to overestimate the goodness of a route if, when using it, they suddenly experience a significant reduction in travel time compared to what they are used to. In the paper it is shown that such route choice behaviour implies that the steady state of the system corresponds to a Bounded Rational User Equilibrium, i.e., a state that does not diverge from the user equilibrium more than a certain value which increases when the relative importance given to the topological similarity grows. The model also assumes that these biases vanish, at least with respect to those routes that are most frequently used by users, after a sufficient amount of time. Under certain conditions, it is then shown that the steady state can eventually collapse into a User Equilibrium. The effectiveness of the proposed model is assessed via simulation results in which two test networks are analyzed in detail to show the evolution of the users’ behaviour in a transport network after a disruption.

Assessing the Network-Wide Impacts of Dedicated Lanes for Connected Autonomous Vehicles

Dedicated lanes for autonomous vehicles (AV) are introduced as an effective strategy to improve mobility in mixed traffic of AVs and heterogeneous drivers. However, adding a new lane is costly, and dedicating an existing lane may increase traffic congestion in other lanes. Previous studies investigated the impacts of AV dedicated lanes on throughput at the segment level and/or assumed a fixed route choice. However, AV dedicated lanes change route choice behavior, which affects traffic distribution over the network. Therefore, this study explores the impacts of AV dedicated lanes on the traffic performance of large-scale networks by conducting a network-level cost–benefit analysis on the implementation of AV dedicated lanes under different demand and AV market penetration rate (MPR) scenarios. This study examines the impacts of implementing AV dedicated lanes to freeway links on traffic congestion at corridor and network levels. Various factors are explored: changes in aggregate flow–density relationships, throughput, and average travel times. To this end, DYNASMART-P software is updated to consider AV dedicated lanes and is used to simulate the mixed traffic. Traffic simulation analyses on the large-scale network of Chicago indicate that these impacts depend on the AV MPR, demand level, and AV dedicated lane implementation approach. Dedicated lanes for AVs are beneficial for a high demand level scenario at all AV MPRs. However, for the base demand scenario, (760,000 vehicles during a.m. peak), deploying such lanes is only justified for low AV MPRs. Furthermore, the impacts of AV dedicated lanes on traffic at the network level are different from those on single segments.

Preference for Street Environment Based on Route Choice Behavior While Walking.

This study aimed to better understand the relationship between the street environment and walking behavior by deciphering the pedestrians' street environment preference based on their route choice behavior while walking. The route data of 219 residents were collected using an unobtrusive tracking method and subjected to binary logistic regression models to analyze the pedestrian route choice behavior. The results revealed that except for the walking distance, the trip purpose and travel status are the potential factors influencing the route choice of pedestrians. Furthermore, it was revealed that on-street parking, garbage bins, and streetlights could influence the pedestrians to select longer distance routes. In addition, pedestrians were more likely to select the shortest distance route when they were engaged in leisure activities with an accompanist. The findings of this study would offer insights, from different perspectives, into the micro-scale street environment and the walking behavior of pedestrians.

Transfer Penalty Estimation of Bus Passengers Considering Travel Frequency in Chengdu, China

The paper aims to examine bus passengers’ route choice behavior in the context of transfer penalty estimation. Methodologically, it leverages passengers’ stop-level trip information inferred from automatic vehicle location (AVL) and automated fare collection (AFC) data and compares the stop-level origin–destination (O-D) pairs with direct paths and transfer paths. The route choice models were established with the inclusion of normalized travel frequency as a key variable. A case study was performed using the trip data of adult card holders from the bus systems in Chengdu, China. The results show that the normalized travel frequency is a variable that makes an informative contribution to the explanation of passenger route choices. Specifically, passengers with higher travel frequency prefer to choose the transfer path. Moreover, the transfer penalty function in the context of the monthly travel frequency was obtained and shows that the travel frequency can reduce a passenger’s transfer penalty. Finally, the stabilities of the normalized travel frequency function and transfer penalty function were assessed using the trip data from the weekdays in four weeks with the results showing that the normalized travel frequency function is similar in shape. In addition, the relationship between a passenger’s monthly travel frequency and their familiarity with the bus network was discussed using the second derivative of the normalized travel frequency function.

Study on the Route-Selection Behavior of Bus Passengers Based on an Evolutionary Game under the Condition of Information Guidance

Bus guidance information supplied by advanced traveler information systems provide a reference for route selection by bus passengers. This study analyzes the interaction between bus passengers’ route-choice behavior and the relationship in a game. The game revenue of the passengers waiting on the bus platform is quantified by parameters such as the number of passengers waiting on the platform, the number of passengers on the bus, and the passenger capacity of the bus. Based on the characteristics of passenger-bounded rationality, an evolutionary game model of bus choice behavior under information-induced conditions is established. Through the evolutionary game model, the distribution of the proportion of passengers receiving the guidance strategy is obtained when the evolution is stable. Finally, according to the distribution results, the passenger guidance strategy is discussed to achieve a win-win situation between passengers and bus operators.

Influence of smartphone traffic application information on drivers’ route choice behaviours

The active traffic management system disseminates traffic information to drivers to guide their route choice, so as to alleviate traffic congestion. Most drivers are highly dependent on mobile navigation APP, and the display form of mobile navigation information is of great importance. In order to improve drivers’ compliance with guidance information, this research accommodates for information that disseminate time attribute (when to provide the traffic information) and display format attribute (voice navigation or not, flat map or three-dimensional [3D] map) and also explores the interaction effects between individual and trip characteristics. A revealed preference (RP) and stated preference (SP) survey consisting of 831 respondents is conducted in Nanjing, China. Then three kinds of panel logit model are established to fit the survey data. According to the model results, it is found that drivers with aggressive style are more sensitive to travel time, and drivers who are familiar with the road network are more sensitive to the 3D map with voice navigation format. The results of parameter estimation are also used for the trade-off analysis of various exogenous variables. The findings of this study provide useful insights for the development of mobile navigation APPs, personalized path recommendation and congestion pricing strategies.

Day-to-day dynamics in a simple traffic network with mixed direct and contrarian route choice behaviors

Contrarian behavior is an attitude leading individuals to act “contrary to the crowd” and can be observed in a variety of collective systems. The aim of this study is to investigate the effects of such behavioral trait on the day-to-day evolution of a traffic system in a simple two-route scenario. To this end, we formulate a nonlinear, discrete-time dynamical system and analyze how the fraction of contrarian-like choices affects fixed point uniqueness and stability, as well as mean travel cost evaluated both for the network as a whole and separately for subjects adopting either type of route choice behavior. We show that uniqueness and stability of the fixed point of the dynamical system are ensured inside certain ranges of the fraction of contrarian subjects within the traveling population, and that the width of such ranges depends on the sensitivity of actual costs to flows and on the level of accuracy of individuals’ cost perception. Moreover, instability is seen to take different forms (oscillatory behavior or convergence to alternate fixed points) depending on which limit of the stability region is crossed by the fraction of contrarians. Results of the analysis also indicate that introducing memory and learning in the process of expected cost formation tends to counter the destabilizing effect of strongly flow-dependent costs and highly cost-sensitive route choices. Finally, direct and contrarian travelers attain the same mean travel cost throughout the fixed point stability region, while a competitive advantage of the minority group emerges outside such region, albeit at the price of a deteriorated global network performance. Overall, the findings of the study support the conclusion that a well-balanced diversification of the traveling population in terms of direct and contrarian route choice attitudes has the potential to protect the system against instabilities induced by other behavioral and network features, and that this beneficial effect can be amplified by the occurrence of learning in the formation of perceived travel costs. It is suggested that contrarian behavior may contribute to mitigating the adverse effects of concentration of choices on the “best” routes and overreaction to the supplied information, induced by Advanced Traveler Information Systems, and thus should be explicitly considered in the design of traffic management strategies involving the deployment of such systems.

Analysis of the Conflict between Car Commuter's Route Choice Habitual Behavior and Traffic Information Search Behavior.

Motivated by the conflict between travelers’ habitual choice behavior and traffic information search behavior, in this paper, a behavioral experiment under different types of traffic information (i.e., per-trip traffic information and en-route traffic information) was designed to obtain data regarding car commuters’ daily route choices. Based on the observed data, participants’ route choices, habit strength, response time, and information search behaviors were analyzed. It is concluded that, in the beginning, the traffic information had a great influence on the habit participants’ route choices, let them think more, and made most of them switch from habit route to the best route (as recommended by traffic information); however, as time went on, the impact of traffic information declined, and several features of habits, such as automatically responding and repeated behavior, would reappear in some participants’ decision-making. Meanwhile, the different way of traffic information search behaviors (i.e., in active performance or in passive reception) could cause different information compliance ratios. These results would help to understand the interrelationship between car commuters’ daily route choice behaviors and traffic information search behaviors in short-term and in long-term, respectively, and provide an interesting starting point for the development of practical traffic information issuing strategies to enhance the impact of traffic information to alleviate traffic congestion during morning commuting.