Route Choice Model Research Articles

Local detouredness: A new phenomenon for modelling route choice and traffic assignment

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.

Evaluating the influence of cyclists’ route choices incorporation into travel demand modelling: A case study in greater Helsinki

Cycling is a sustainable transport mode that endorses an active lifestyle. While cycling shows great potential, it is essential for urban planning to consider attributes influencing the choices that cyclists act upon. Cyclists’ route choices have been studied since the Eighties with knowledge being applied in cycling network planning. Yet, the role of cycling as a sustainable transportation mode has been largely absent from travel demand modelling. This paper researches cyclists’ route choice preferences and evaluates the opportunity of incorporating route choice modelling into travel demand modelling to improve the accuracy of cycling route choice. To this end, a route choice framework is developed in which a stated preference survey for data collection is conducted, a multinomial Logit model is applied to the data to identify the factors that significantly influence cyclists’ route choice behaviour. The generated route choice utility models are further integrated into an existing regional travel demand model to evaluate the performance of cyclists’ route choice modelling in the presence of additional factors. Then, the route choice model outputs are validated against two sets of external data. The results show that bike facilities, traffic volume, and trip length are the key factors influencing cyclists’ route choice preferences, and the generated route choice models can be an applicable improvement in incorporating the influences of cyclists’ preferences into travel demand modelling.

Behavior-Adaptive Sync-Flow Framework: Integrating frequency setting and passenger routing in oversaturated urban rail transit networks

Urban rail transit (URT) networks serve a large amount of travel demand in metropolitan areas. One of the fundamental challenges in URT demand management is the dynamic interaction between train frequencies and passenger routing behaviors, which becomes even more complicated in oversaturated scenarios. Introducing the behavior-adaptive sync-flow (BASF) framework, this study holistically addresses the tactical frequency settings (train flows) for URT networks and the responsive route choice behaviors of passengers (passenger flows). The proposed integrated model incorporates a schedule-based passenger assignment within space–time networks to model passengers’ routing behaviors based on time-dependent route travel costs. In addition to travel time, the often-overlooked factors such as in-train congestion, number of passengers being left behind, and number of transfers are considered as components influencing travel costs and, consequently, routing behaviors at an individual level. A gradient descent-based heuristic iterative solution approach is developed to address the integrated model by dividing it into three sub-problems. In this iterative process, passengers respond according to a logit-based route choice model based on time-dependent route travel costs, while frequencies are updated using a gradient descent-based strategy. Central to the proposed BASF framework are the mutual adaptations between URT line frequencies and passenger route choices. Numerical examples and a case study of the Beijing URT network highlight the effectiveness and scalability of the model framework in tackling complex, large-scale URT networks, particularly in oversaturated conditions, thus revealing the significance of supply–demand matching.

The k-th order mean-deviation model for route choice under uncertainty

This study introduces the k-th order mean-deviation model for optimizing route choice within large, stochastic, and time-variant networks. This model addresses the limitations of the traditional mean-standard deviation approach by better handling extreme outcomes in travel times. It features an objective function called the “travel time budget”, which combines the average path travel time with a safety margin. This margin is defined by a trade-off coefficient and a selected deviation measure (total or semi) of the travel time. The model is divided into three variations: 1) The mean-total deviation (MTD) model for symmetric travel time distributions, 2) The mean-upper-semi-deviation (MUSD) model for asymmetric distributions prioritizing upper semi-deviations, suitable for risk-averse travelers, and 3) The mean-lower-semi-deviation (MLSD) model for asymmetric distributions focusing on lower semi-deviations, preferred by risk-prone individuals. We explore these models’ alignment with the stochastic dominance (SD) rule and develop a solution methodology based on SD principles. Numerical experiments in two real-world transportation networks demonstrate the models’ effectiveness and show how the choice of deviation affects route selection decisions.

Using traffic assignment models to assist Bayesian inference for origin–destination matrices

Estimation of traffic volumes between each origin and destination of travel is a standard practice in transport engineering. Commonly the available data constitute traffic counts at various locations on the network, supplemented by a survey-based prior estimate of mean origin–destination traffic volumes. Statistical inference in this type of network tomography problem is known to be challenging. Moreover, the difficulties are increased in practice by the presence of a large number of nuisance parameters corresponding to route choice probabilities, for which we have no direct prior information. Working in a Bayesian framework, we determine these parameters using a stochastic user equilibrium route choice model. We develop an MCMC algorithm for model fitting. This requires repeated computation of stochastic user equilibrium flows, and so we develop a computationally cheap emulator. Our methods are tested on numerical examples based on a section of the road network in the English city of Leicester.

Performance evaluation of path choice set generation algorithms for route choice modelling

Traffic assignment and its applications are sensitive to the selection of path choice sets between an OD pair. In literature, different path generation algorithms are proposed, namely, Link Labeling, Link Elimination, Link Penalty, Simulation, and Branch and Bound. The effectiveness of these algorithms hinges on algorithmic principles and input parameters. Consequently, choosing an appropriate algorithm for practical implementation becomes challenging. This paper aims to benchmark these algorithms with a case study using a real vehicle trajectory dataset from four OD pairs from Brisbane, Australia. The paper offers both qualitative and quantitative comparisons of the algorithms. The performance of the algorithms is evaluated based on their authenticity, redundancy, and applicability. The results highlight that the simulation method outperforms the other algorithms, specifically utilizing a truncated normal distribution. The paper offers valuable insights into the selection of path-generation techniques, aiding the enhancement of traffic assignment processes.

Perturbed Utility Stochastic Traffic Assignment

This paper develops a fast algorithm for computing the equilibrium assignment with the perturbed utility route choice (PURC) model. Without compromise, this allows the significant advantages of the PURC model to be used in large-scale applications. We formulate the PURC equilibrium assignment problem as a convex minimization problem and find a closed-form stochastic network loading expression that allows us to formulate the Lagrangian dual of the assignment problem as an unconstrained optimization problem. To solve this dual problem, we formulate a quasi-Newton accelerated gradient descent algorithm (qN-AGD*). Our numerical evidence shows that qN-AGD* clearly outperforms a conventional primal algorithm and a plain accelerated gradient descent algorithm. qN-AGD* is fast with a runtime that scales about linearly with the problem size, indicating that solving the perturbed utility assignment problem is feasible also with very large networks. Funding: This work has been financed by the European Union—NextGenerationEU.

Day-to-day behaviour for pedestrians in a circle antipode scenario: experiment and simulation

This study aims to investigate the behavioural evolution patterns of pedestrians who repeatedly traverse the same scenarios. To accomplish this objective, we implemented a repeated circle antipode experiment, ensuring consistent conditions for all participants. This experimental design allows for an effective examination of participant performance across multiple rounds. Our findings revealed that specific walking characteristics experienced significant changes with the progression of successive experiments, and participants demonstrated notable differences in their chosen routes. Recognizing the ability of the day-to-day dynamic model to describe the evolution of network flows and the similarities between traffic and pedestrian flows, we apply the modelling approach of the day-to-day dynamic model to the construction of pedestrian route choice modelling. Consequently, we developed a series of round-to-round pedestrian route choice models to characterize our experiment. These models factored in both historical walking experiences and the influence of neighbours. Our model proved to be reliable, achieving a route choice accuracy of approximately 80% in simulations of circle antipode experiments. The results of this study can provide valuable insights into pedestrian dynamics, aiding in understanding pedestrian behaviour during repetitive walking and facilitating the development of more accurate round-to-round route choice models.

Friend or foe? A spatial approach to overlay bicycle and scooter trajectories

Dockless e-scooter schemes have seen increasing popularity in 28 German cities. Increasing use on insufficiently dimensioned bicycle infrastructure can lead to conflicts between e-scooter riders and cyclists. A new approach was developed in order to detect potential zones of conflict by overlaying aggregated bicycle and e-scooter trajectories in the City of Dresden, Germany. Bicycle data is being obtained by the annual STADTRADELN campaign where cyclists record and transmit daily trips via GPS for a period of three weeks. Simultaneously, e-scooter API data has been collected over a course of 8 weeks from June to September 2021. Origin/Destination data has been generated and routed over a OSM network in order to obtain aggregate d e-scooter flows. We extrapolated the aggregated bicycle data to match them with the timeframe of the e-scooter data acquisition. Afterwards we spatially joined both: bicycle and e-scooter flows and calculated the link wise proportion of e-scooter trips in relation to bicycle trip volumes. Two important findings emerged: (1) Residential roads have a higher proportion of e-scooter trips. (2) E-scooters are exposed to high bicycle trip volumes on primary roads with bicycle infrastructure. We conclude that this approach can detect possible links of conflict, where overtaking cyclists or insufficient space can lead to dangerous situations. That approach is biased towards a missing route choice model for e-scooter riders or better route data of e-scooters, which needs further research.

Research on Pedestrian Wayfinding Behavior in Large Public Space Utilizing Non-immersive VR Platform

In this study, we conducted experiments to investigate pedestrian wayfinding behavior using a non-immersive virtual reality platform. We developed a route choice model that combines environmental factors and individual pedestrian attributes, employ-ing a hybrid Logit model. The dataset for pedestrian route choices is collected from partic-ipants’ walking trajectories within the Tianjin West Railway Station scenario. The results of our experiments demonstrate that the model effectively represents pedestrians’ route choices within the environment and exhibits strong predictive capabilities for wayfinding.

Quantitative modelling of cyclists’ route choice behaviour on utilitarian trips based on GPS data: associated factors and behavioural implications

ABSTRACT 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.

Mesoscopic model of cycling trip energy expenditure based on operating modes

Cyclist physical exertion is largely ignored in quantitative travel analysis, partly due to a lack of appropriate tools. Microscopic models of second-by-second energy expenditure based on equations of motion are data intensive and cannot be applied to hypothetical routes (such as needed for route choice modelling). Macroscopic models of aggregate energy expenditure based on a fixed assumed energy intensity are insensitive to traveller, trip, and contextual factors that are relevant for behavioural research and policy analysis (such as bicycle type or trip purpose). Building on concepts from motor vehicle emissions analysis, this paper proposes a mesoscopic approach to model cycling trip energy expenditure based on the distribution of travel time across discrete states of motion (“operating modes”) for different classes of traveller and trip (“model segments”). We aim to answer two key questions for model implementation: 1) which variables most effectively classify trips into model segments and 2) what operating mode definition most consistently characterizes cycling energy expenditure within model segments? We also evaluate the precision of the mesoscopic model relative to cycling energy estimates from microscopic and macroscopic models. Applied to a dataset of naturalistic cycling trips in Vancouver, Canada, the proposed mesoscopic model with six model segments based on 3 segmenting variables (rider gender, electric-assist bicycle, and high or low speed tier) explains up to 28 % of the variance in trip-level energy estimates from the microscopic model (within around 35 W of the microscopic estimates, on average). Further research to develop cycling trip energy models for general application is discussed.

A passenger flow spatial–temporal distribution model for a passenger transit hub considering node queuing

Passenger transit hub is a quintessential complex system, characterized by intricate interactions among humans, facilities, and the surrounding environment. External disturbances often precipitate crowd congestion and safety risks. Modeling the spatial–temporal distribution of passenger flow within the hub is an important element in operational management. Prevailing research predominantly focuses on static models or monitoring data to assess the spatial–temporal characteristics of passenger flow throughout the hub. Nevertheless, few studies have been found in modeling passenger flow distribution for passenger transit hub from the vantage point of traveler behavior in the intricate ‘human-facility-environment’ complex network. Thus, this paper proposes a computational model for the passenger flow spatial–temporal distribution based on traveler behavior. First, combining consideration of critical spatial facilities and passenger flow streamlines, a passenger flow network is established. Second, the instantaneous travel times of link and node are defined while considering queuing and congestion at crucial facilities in the hub. Then, a passenger flow spatial–temporal distribution model for the passenger transit hub is constructed, which consists of dynamic route choice model and dynamic passenger flow loading model. A solution algorithm is simultaneously designed. Finally, the effectiveness of the model and algorithm are verified by a numerical example. The results show that the proposed model can effectively capture real-time congestion and the dynamic distribution of passenger flow in the hub. Therefore, this study contributes to the safety management and layout optimization of the hub, holding significant importance for improving hub operational efficiency and service levels.

An Adaptive Route Guidance Model Considering the Effect of Traffic Signals Based on Deep Reinforcement Learning

Navigation or route guidance systems are designed to provide drivers with real-time travel information and the associated recommended routes for their trips. Classical route choice models typically rely on utility theory to represent drivers’ route choice behavior. Such choices, however, may not be optimal from both the individual and the system perspectives. This is simply due to the fact that drivers usually have imperfect knowledge about the time-varying traffic conditions. In this article, we explore and propose a new model-free deep reinforcement learning (DRL) approach to solving the adaptive route guidance problem based on microsimulation. The proposed approach consists of three interconnected algorithms, including a network edge labeling algorithm, a routing plan identification algorithm, and an adaptive route guidance algorithm. Simulation experiments on both a toy network and a real-world network of Suzhou, China, are performed to demonstrate the effectiveness of the proposed approach in terms of guiding a single vehicle as well as multiple vehicles through complex traffic environments. Comparative results confirm that the DRL approach outperforms the traditional shortest path method by further reducing the average travel time in the network.

Network-based representations and dynamic discrete choice models for multiple discrete choice analysis

In many choice modeling applications, consumer demand is frequently characterized as multiple discrete, which means that consumer choose multiple items simultaneously. The analysis and prediction of consumer behavior in multiple discrete choice situations pose several challenges. In this paper, to address this, we propose a random utility maximization (RUM) based model that considers each subset of choice alternatives as a composite alternative, where individuals choose a subset according to the RUM framework. While this approach offers a natural and intuitive modeling approach for multiple-choice analysis, the large number of subsets of choices in the formulation makes its estimation and application intractable. To overcome this challenge, we introduce directed acyclic graph (DAG) based representations of choices where each node of the DAG is associated with an elemental alternative and additional information such as the number of selected elemental alternatives. Our innovation is to show that the multi-choice model is equivalent to a recursive route choice model on the DAG, leading to the development of new efficient estimation algorithms based on dynamic programming. In addition, the DAG representations enable us to bring some advanced route choice models to capture the correlation between subset choice alternatives. Numerical experiments based on synthetic and real datasets show many advantages of our modeling approach and the proposed estimation algorithms.

Analysis and modeling of detours in pedestrian operational navigation

Traditional operational navigation models for pedestrian dynamics demonstrate limitations in reproducing the circle antipode experiment, an artificially designed multi-directional flow scenario. In the experiment, pedestrians take detours to avoid the congestion caused by others taking straight paths. Although the pedestrian detour action is commonly incorporated into the route choice model, it frequently goes unaddressed in the operational navigation model, resulting in a disparity between simulation outcomes and empirical observations. To reveal the mechanism underlying pedestrian detours in the circle antipode experiment, this study employed the K-means clustering method rather than using a threshold approach to categorize the experimental participants into groups taking direct or detour routes. Following this, a heuristic function is formulated to determine the desired direction of agents in a collision-free velocity model, reflecting the trade-off between shorter routes and faster speeds. The parameter of the proposed function is calibrated using the proportion of agents choosing detours, where the route types of agents are identified by a classifier based on the random forest. Compared to two traditional models that do not consider detours, the proposed model can more realistically reproduce the trajectory distributions, the travel time, the route length, and the time series of relevant variables in the circle antipode experiment. The study offers insights into employing machine learning methodologies for analyzing pedestrian flow, validating pedestrian dynamics models, and providing an accurate simulation tool for designing transportation facilities and crowd management at large events.

A CASE STUDY ON CYCLIST ROUTE CHOICE MODEL

This research focuses on improving cycle path design by analyzing the determinants of route choice for bicycle users. The study uses a large GPS dataset to develop a route choice model for the inner-city of Amsterdam. The main research question is to identify the factors that influence route choice decisions, which is broken down into sub-questions related to the contribution of the GPS dataset to route choice models, the relevant variables for bicycle users' route choices, and the effect of these variables on route choice. The study finds that route attributes, such as distance and cycle path quality, and contextual variables, such as trip type and time of day, have a significant effect on bicycle users' route choices. The results provide insights for designing more desirable bicycle infrastructure in urban areas.

Exploring the travel behavioral differences for the elderly mobility on public transit

ABSTRACT Developing an integrated transportation system has required strategies to improve mobility in public transportation networks. This study aims to construct a route choice model incorporating heterogeneous travel behaviors and suggest policy improvements considering travel behavioral characteristics for the elderly in transportation planning. A stated preference survey has been established to explore the travel behaviors on public transit. The behavioral differences of elderly travelers are compared with young adult travelers by the parameters of travel time attributes. Travel time multipliers are developed to compare the time impedance for the travels on public transit using the concept of willingness to pay. Travelers are reluctant to increase the transfer time for all types, resulting in multipliers ranging from 1.91 to 2.11. The results provide the implications of transport policy for the elderly considering a convenient transfer on the integrated transit system in an aging society.

An experiential learning-based transit route choice model using large-scale smart-card data

An experiential learning-based transit route choice model using large-scale smart-card data

Stability of Traffic Equilibria in a Day-to-Day Dynamic Model of Route Choice and Adaptive Signal Control

Adaptive traffic signal control has the potential to promote the efficient use of road intersections, thus contributing to the effectiveness of urban traffic management schemes. However, the reaction of drivers to repeatedly updated signal settings and the ensuing route choice dynamics may trigger the emergence of various kinds of network instability. In this study, the joint evolution of traffic flows and adaptive signal settings in a road network is investigated at the level of day-to-day dynamics with an explicit focus on the stability issue. We show how a Logit form signal control policy can be used, in interaction with route choice, to counter the emergence of instabilities possibly arising as a consequence of various behavioral factors and network conditions. After providing a general formulation of the model as a discrete time, deterministic nonlinear dynamical system, an explicit analysis of fixed-point stability is carried out for a simple network. Numerical results obtained from the implementation of the model on two example networks are presented in order to support the analytical findings of this study. We conclude that, in an integrated traffic management and information system, a properly calibrated adaptive signal control policy has the potential to offset the destabilizing effect of highly accurate driver information supplied by navigational aids. Our findings also suggest that the Logit-like control policy performs better than the Equisaturation signal setting method, in terms of average intersection delay at equilibrium, for all levels of driver information and travel demand tested in the experiment.