Adaptive Route Choice Models in Stochastic Time-Dependent Networks

Modeling passenger route choices is crucial for analyzing and predicting public transportation demand. One of the most popular methods is to use probabilistic route choice (PRC) models (also known as discrete choice models in general), which have broad applications in transportation, economics, politics, and other fields. However, its performance varies depending on the characteristics of the origin–destination (OD) trip data and should be examined carefully. This paper proposes a framework for validating the PRC model on its application to urban rail transit (URT) networks containing small‐scale OD trip data. The concept of small‐scale data is defined at first for each OD pair considering the desired confidence level and the variance of route choices. Then, a travel time range (TTR)‐based method is put forward to deduce passengers’ actual route choices as a benchmark for verifying PRC models. The difference and regularity analysis between the actual route choices and the model predictions are also performed with a twofold comparison. A case study on the Nanchang metro in China shows that the actual daily passenger volumes on routes of small‐scale OD pairs diverge remarkably from the estimations of the PRC model. The PRC model’s performance is further discussed when the small‐scale OD trip data accumulate to a larger scale over multiple days (e.g., several months). This study reveals the inherent limitation of PRC models in estimating the travel behaviors of passengers in a small‐scale population. Several practical implications are discussed to improve the route choice model and passenger flow analysis.

We use data from the Swiss national household travel survey to 1. analyze the socioeconomic determinants for intermodal travel in Switzerland and 2. estimate a first large-scale multimodal recursive logit route choice model for urban trip making. We show that intermodal travel is mostly associated with ownership of transit subscriptions, which allow free at the point-of-use public transportation. We also present a framework using open-source data to generate the multimodal network for the recursive logit model estimation. The fact that the model only needs a multimodal network to model the transport supply makes it independent of path sampling algorithms for the choice-set construction and it thus provides an alternative to classic mode and route choice models, since it can estimate mode and route choice parameters with directly observed routes, removing the sampling bias. By eliminating the need to sample alternative paths for estimation, it also simplifies the estimation process, making it a viable choice as an integral solution for joint route and mode choice modelling.

Modelling intermodal travel in Switzerland: A recursive logit approach

This study introduces a way to overcome the sensitivity of decision trees used for route choice behavior studies by using fuzzy logic while preserving the advantages of decision trees and the C4.5 algorithm, namely, comprehensibility and ease of application. Soft discretization of continuous values in fuzzy decision trees can provide a more robust classification. Also, the use of fuzzy logic makes it possible to accommodate qualitative attributes describing route characteristics. Apart from these features, fuzzy decision tree learning algorithms are also capable of assigning numeric values on decisions about the degree of certainty of each recommendation emanating from the fuzzy reasoning. This feature makes it possible to solve the multiple suggestion problem whereby the classic decision tree may suggest that more than one route is optimal. To investigate improvements resulting from the application of fuzzy decision tree learning algorithms, software for an adaptive route choice model using a fuzzy decision tree learning algorithm, fuzzy ID3, was developed and simulation experiments with the model were carried out. The comparison of results with the nonfuzzy adaptive route choice model indicates better predictive accuracy and more effective applicability for the fuzzy model in practice.

Frequent versus occasional drivers: A hybrid route choice model

The unprecedented growth of passenger throughput in large airport terminals highlights the importance of analyzing passengers’ movement to achieve airport terminal’s elaborate management. Based on the theory of original social force model, video data from a departure hall of a large airport terminal in China were analyzed to summarize passengers’ path planning characteristics. Then, a double‐level model was established to describe passengers’ path planning behaviors. At the decision level of the proposed model, the avoiding force model including common avoiding force and additional horizontal avoiding force was established on the basis of setting time and space limitations for taking avoiding action and was used to describe passengers’ path planning in close‐range space. At the tactical level of the proposed model, the route and node choice models were established to describe passengers’ path planning in long‐range space. In the route choice model, a distribution of intermediate destination areas was proposed, with detouring distance, pedestrian density, speed difference, and pedestrian distribution considered in choosing an intermediate destination area. In the node choice model, the walking distance, the quantity of people waiting, and luggage were considered in choosing a check‐in counter or security check channel. The main parameters of the proposed model were confirmed according to video data. Simulation results show that the proposed model can simulate departure passengers’ path planning behaviors at an acceptable accuracy level.

This study introduces the novel concept of local detouredness, i.e. detours on subsections of a route, as a new phenomenon for understanding and modelling route choice. Traditionally, Stochastic User Equilibrium (SUE) traffic assignment models have been concerned with judging the attractiveness of a route by its total route cost. However, through empirical analysis we show that considering solely the global properties of a route is insufficient. We find that it is important to consider local detouredness both when determining realistic and tractable route choice sets and when determining route choice probabilities. For example, analysis of observed route choice data shows that route usage tends to decay with local detouredness, and that there is an apparent limit on the amount of local detouredness seen as acceptable. No existing models can account for this systematically and consistently, which is the motivation for the new route choice model proposed in this paper: the Bounded Choice Model with Local Detour Threshold (BCM-LDT). The BCM-LDT model incorporates the effect of local detouredness on route choice probability, and has an in-built mechanism that assigns zero probabilities to routes violating a bound on total route costs and/or a threshold on local detouredness. Thereby, the model consistently predicts which routes are used and unused. Moreover, the probability expression is closed-form and continuous. SUE conditions for the BCM-LDT are given, and solution existence is proven. Exploiting the special structure of the problem, a novel solution algorithm is proposed where flow averaging is integrated with a modified branch-and-bound method that iteratively column-generates all routes satisfying local and global bounds. Numerical experiments are conducted on small-scale and large-scale networks, establishing that equilibrated solutions can be found and demonstrating the influence of the BCM-LDT parameters on choice set size and flow allocation.

Transportation systems are inherently uncertain because of random disruptions; nevertheless, real-time information can help travelers make better route choices under such disruptions. The first revealed-preference study of routing policy choice is presented. A “routing policy” is defined as a decision rule applied at each link that maps possible realized traffic conditions to decisions to be made on the link next. The policy represents a traveler's ability to incorporate real-time information not yet available at the time of decision. Two case studies are conducted in Stockholm, Sweden, and in Singapore. Data for the underlying stochastic time-dependent network are generated from taxi GPS traces through map-matching and nonparametric link travel time estimation. An efficient algorithm to find the optimal routing policy in large-scale networks is first presented, which is a building block of any routing policy choice set generation method. The routing policy choice sets are then generated by link elimination and simulation. The generated choice sets are first evaluated on the basis of whether they include the observed traces on a specific day, or coverage. The sets are then evaluated on the basis of “adaptiveness,” defined as the capability of a routing policy to be realized as different paths over different days. A combination of link elimination and simulation methods yields satisfactory coverage. The comparison with a path choice set benchmark also suggests that a routing policy choice set could potentially provide better coverage and capture the adaptive nature of route choice.

In the context of future-oriented urban road infrastructure adaptation, it is crucial to understand the factors that influence cyclists’ route choice behavior to promote cycling effectively. However, the increase in bicycle usage can also lead to crowded cycling infrastructure and increased objective safety risks, which has not been thoroughly investigated in previous studies. This study examine how crowding, safety risks, and route attributes influence cyclists’ route choices, differentiating between e-bike and regular bike users. Using stated preference data from 784 cyclists in Nanjing and a hybrid choice model (HCM), the research integrates latent attitudinal variables and socio-demographic factors. Findings show regular cyclists are more sensitive to crowding and accident risk, while e-bike users prioritize dedicated cycling infrastructure over travel time and road characteristics. The results underscore the importance of including attitudinal variables in route choice models and provide valuable guidance to safely manage rising cycling demand and improve navigation systems.

This study introduces a novel simulation-based approach for addressing the stochastic Dynamic Traffic Assignment (DTA) problem, specifically targeting large, congested networks under dynamic conditions which is a characteristic of urban mobility environment. The proposed methodology leverages an underlying random walk model for route selection, drawing inspiration from the concept of equivalent impedance in electrical networks. This alternative route choice model iteratively condenses non-overlapping subnetworks into virtual links, allowing for the dynamic estimation of equivalent time-dependent virtual travel costs. Consequently, the downstream link choice probabilities for all destinations are computed, and by employing a random walk model, the route choice decision-making process is shifted to nodes. This approach closely aligns with travelers' real-life behavior, supporting a finer temporal segmentation of evolving traffic conditions and improving the precision of performance assessments. Furthermore, the route choice model addresses the limitations of other Markovian route choice models in handling overlapping routes and scaling issues.

Estimating a Toronto pedestrian route choice model using smartphone GPS data

Handling uncertainty in route choice models: From probabilistic to possibilistic approaches

A decision-making rule for modeling travelers’ route choice behavior based on cumulative prospect theory

The dynamic route choice problem is to determine the best route choices at each time. Many route-based optimal control models for the route choice problem which were presented are not appropriate for large scale transportation networks. A variational inequality formulation for the route choice problems is presented and the algorithm of solution is provided. The model extends previous variational inequality model for the route choice problem to the case where risk is considered. The experimental result shows that the algorithm can find the best dynamic pricing schemes. The algorithm runs fast and can not be influenced by the different distribution of risks, it could solve many large scale dynamic route choice problems.

Determinants of route choice behavior: A comparison of shop versus work trips using the Potential Path Area - Gateway (PPAG) algorithm and Path-Size Logit