A CASE STUDY ON CYCLIST ROUTE CHOICE MODEL

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.

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

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.

Route choice of bike share users: Leveraging GPS data to derive choice sets

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

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

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.

Transit route choice model is the key technology of public transit systems planning and management. Traditional route choice models are mostly based on expected utility theory which has an evident shortcoming that it cannot accurately portray travelers' subjective route choice behavior for their risk preferences are not taken into consideration. Cumulative prospect theory (CPT), a brand new theory, can be used to describe travelers' decision-making process under the condition of uncertainty of transit supply and risk preferences of multi-type travelers. The method to calibrate the reference point, a key parameter to CPT-based transit route choice model, determines the precision of the model to a great extent. In this paper, a new method is put forward to obtain the value of reference point which combines theoretical calculation and field investigation results. Comparing the proposed method with traditional method, it shows that the new method can promote the quality of CPT-based model by improving the accuracy in simulating travelers' route choice behaviors based on transit trip investigation from Nanjing City, China. The proposed method is of great significance to logical transit planning and management, and to some extent makes up the defect that obtaining the reference point is solely based on qualitative analysis.

Transfer volume forecasting is the basis of transfer station design, passenger flow organization and emergency scheme formulation. Along with rapid development of Metro networking operation in recent years, transfer volume forecasting for the Metro system in networking conditions becomes an important issue to be solved. In this paper, the transfer volume forecasting method was proposed on the basis of route choice analysis and passenger flow distribution. Taking into consideration the variables of level of service such as the ride time, number of transfers, transfer time and congestion degree and introducing in the index of angular cost value to describe the influence of network structures and route directions on route choices, the route choice model was established to analyze passengers' route choice preferences. Through equivalent ride time conversion of the above influential factors and analysis on the degree of passenger tolerance to travel time, the effective route set of all OD (Origin-Destination) pairs was constructed with the K-shortest paths algorithm. Then stochastic assignment was carried out with the MSA (method of successive averages) algorithm under constraint of the first and last train and the statistical method was put forward to calculate the transfer volumes of all transfer stations within a specified time interval. Finally, the transfer volumes of the Beijing Subway were estimated with the proposed transfer volume forecasting method. The results show that the angular cost value significantly influences the route choice behavior and the proposed method can provide good reliability and accuracy.

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.

Considering that metro network expansion brings us with more alternative routes, it is attractive to integrate the impacts of routes set and the interdependency among alternative routes on route choice probability into route choice modeling. Therefore, the formulation, estimation and application of a constrained multinomial probit (CMNP) route choice model in the metro network are carried out in this paper. The utility function is formulated as three components: the compensatory component is a function of influencing factors; the non-compensatory component measures the impacts of routes set on utility; following a multivariate normal distribution, the covariance of error component is structured into three parts, representing the correlation among routes, the transfer variance of route, and the unobserved variance respectively. Considering multidimensional integrals of the multivariate normal probability density function, the CMNP model is rewritten as Hierarchical Bayes formula and M-H sampling algorithm based Monte Carlo Markov Chain approach is constructed to estimate all parameters. Based on Guangzhou Metro data, reliable estimation results are gained. Furthermore, the proposed CMNP model also shows a good forecasting performance for the route choice probabilities calculation and a good application performance for transfer flow volume prediction.

Underground spaces connected with railway stations gather a lot of pedestrians. Clarifying pedestrians' movement in underground space is important for flow control and evacuation planning. Based on a pedestrian traffic survey data and estimated OD matrix, this study aims to clarify the pedestrians' route choice probability in underground space. Through the definition of “state” and “transition process”, pedestrians' route choice model is constructed according to the utility of routes. Next, Absorbing Markov Process is adopted to solve the trip assignment in the underground pedestrian network and build the functional relation between estimated flow counts and obtained data. Through minimizing the difference between estimated and observed flow counts, unknown variables are calibrated. Finally, the influential spatial motion attributes are examined through linear regression analysis.

With the growing use of crowdsourced location data from smartphones for transportation applications, the task of map-matching raw location sequence data to travel paths in the road network becomes more important. High-frequency sampling of smartphone locations using accurate but power-hungry positioning technologies is not practically feasible as it consumes an undue amount of the smartphone’s bandwidth and battery power. Hence, there exists a need to develop robust algorithms for map-matching inaccurate and sparse location data in an accurate and timely manner. This paper addresses the above-mentioned need by presenting a novel map-matching solution that combines the widely used approach based on a hidden Markov model (HMM) with the concept of drivers’ route choice. Our algorithm uses an HMM tailored for noisy and sparse data to generate partial map-matched paths in an online manner. We use a route choice model, estimated from real drive data, to reassess each HMM-generated partial path along with a set of feasible alternative paths. We evaluated the proposed algorithm with real world as well as synthetic location data under varying levels of measurement noise and temporal sparsity. The results show that the map-matching accuracy of our algorithm is significantly higher than that of the state of the art, especially at high levels of noise.

Drivers' route choice model is essential in transportation software such as navigation, fleet management, and simulation, where the random utility models (RUM) have dominated for years. The authors investigate here whether machine learning (ML) models could be applied into this field, and whether these approaches outperform the traditional models in goodness‐of‐fit and prediction. The application framework and data structure are proposed, where the challenging problems lie in: (i) to pool data from multiple origin–destination pairs; and (ii) to interpret results for behaviour analysis. All RUM and ML models are applied in a real network. Results suggest that the random forest, one of the ML models, has satisfying performances with acceptable computation time, making it suitable for large network and real‐time analysis. This study shows that the ML models can be adopted for behaviour analysis, such as to prioritise the importance of variables, compute the elasticity, and forecast for scenarios. Future directions on combining the RUM and ML models are discussed.