Route Choice Behavior Research Articles

Experimental analysis of a hybrid route choice model to capture dynamic behavioral phenomena under advanced information systems

The driver route choice problem under real-time information provision is characterized by subjectively interpreted and/or linguistically expressed data in addition to quantitative inputs. In previous work, the authors propose a hybrid probabilistic-possibilistic model which treats qualitative and quantitative data simultaneously in a single framework. This paper presents experimental insights on the performance of the hybrid model based on extensive simulation analysis under potential real-world scenarios. It performs sensitivity analyses on the associated driver behavior model parameters, and investigates the model’s ability to capture dynamic qualitative phenomena under information provision. The results highlight the flexibility of the hybrid model in addressing a broad range of driver behavior characteristics and network conditions. They also suggest that the hybrid model can capture the evolution of the driver behavior characteristics over time and the influence of the randomness in network conditions on route choice behavior.

Investigating the effect of travel time variability on drivers' route choice decisions in Shanghai, China

This paper investigates the effect of travel time variability on drivers' route choice behavior in the context of Shanghai, China. A stated preference survey is conducted to collect drivers' hypothetical choice between two alternative routes with designated unequal travel time and travel time variability. A binary choice model is developed to quantify trade-offs between travel time and travel time variability across various types of drivers. In the model, travel time and travel time variability are, respectively, measured by expectation and standard deviation of random travel time. The model shows that travel time and travel time variability on a route exert similarly negative effects on drivers' route choice behavior. In particular, it is found that middle-age drivers are more sensitive to travel time variability and less likely to choose a route with travel time uncertainty than younger and elder drivers. In addition, it is shown that taxi drivers are more sensitive to travel time and more inclined to choose a route with less travel time. Drivers with rich driving experience are less likely to choose a route with travel time uncertainty.

Route Choice Behavior Model with Guidance Information

Using modeling and simulation methods, the driver's route choice behavior under guidance information is explored based on the combination of the decision field theory (DFT) and Bayesian theory. First, based on the Bayesian theory, a road condition dynamic updating model is presented in light of the guidance information and the driver's previous travel experiences. Then, the route choice behavior model under guidance information is formed by the fusion of the process-oriented vehicle dynamic route choice model and the road condition dynamic updating model. The developed model describes a driver's propensity to comply with received guidance information in terms of the interaction between perceived unreliability of the information, his previous travel experiences, preference for different road alternatives, decision-making speed/quality, and route selection criteria. The simulation results show that the combination of the DFT and Bayesian theory can effectively explain the driver's travel dynamics behavior.

Enhancing Realism in Modeling Merge Junctions in Analytical Models for System-Optimal Dynamic Traffic Assignment

The existing analytical system-optimal dynamic traffic assignment (SO-DTA) model formulated with the linear programming (LP) approach usually assumes system control over vehicles in the entire network. This property would give rise to unreasonable priorities at merge junctions that are sometimes physically impossible to realize for the given roadway configuration. In this paper, we demonstrate that models with and without considering the merge-priority ratio would exhibit very different traffic patterns and route-choice behavior. To realistically model traffic flow on a transportation network, one should properly distinguish the level of control by drivers, roadway geometry, and system providers. This paper also attempts to develop an LP module that explicitly considers the merge-priority ratio of a merge junction and can potentially be incorporated into the existing LP formulation of the SO-DTA problem based on the cell-transmission model. By more realistically modelling the behavior of vehicles at merge junctions, the obtained solution can be used as a benchmark to compare control strategies developed without explicitly considering the merge-priority ratio at merge junctions or strategies developed with heuristic approaches.

Identifying motion and interest patterns of shoppers for developing personalised wayfinding tools

The development of wayfinding and information tools for pedestrians faces several challenges. In contrast to common navigation tools used for vehicles, navigation services for pedestrians must fulfil more complex requirements in order to be accepted. For pedestrians, the shortest path does not always represent the optimal route for an individual's purposes, as studies have revealed that people often forgo to take the shortest path and prefer the ‘most beautiful’, ‘most convenient’ or ‘safest’ path. People exploring a new environment on foot would therefore especially benefit from systems providing information concerning route qualities, interesting facilities in the vicinity and other useful location-related suggestions. As part of the scientific project ‘UCPNavi’, we investigate group-specific spatio-temporal behaviour and related influence factors of shoppers in order to identify homogeneous behaviour types based on motion patterns and information requirements. We use an eclectic approach combining several complementary empirical methods of data collection and analysis to thoroughly comprehend pedestrian spatio-temporal behaviour. In this contribution, we present results of motion and interview data analysis based on data collected from more than 100 participants during two empirical phases on a shopping street in Vienna. We further introduce an initial pedestrian typology comprising five different homogeneous behaviour types based on qualitative-interpretative and quantitative-statistical data. Types are described according to characteristic attributes identified by route choice behaviour, walking patterns and interest foci. The relevant factors include velocities, stopping behaviour, categories of visited facilities and individual preferences. The resulting typology of lifestyle-based pedestrian mobility styles and the identified characteristic attributes can serve as a basis to create pedestrian interest profiles in ubiquitous environments and to customise navigational and environmental information for mobile applications in order to fulfil individual needs.

A turning restriction design problem in urban road networks

Turning restriction is one of the commonest traffic management techniques and an effective low cost traffic improvement strategy in urban road networks. However, the literature has not paid much attention to the turning restriction design problem (TRDP), which aims to determine a set of intersections where turning restrictions should be implemented. In this paper, a bi-level programming model is proposed to formulate the TRDP. The upper level problem is to minimize the total travel cost from the viewpoint of traffic managers, and the lower level problem is to depict travelers’ route choice behavior based on stochastic user equilibrium (SUE) theory. We propose a branch and bound method (BBM), based on the sensitivity analysis algorithm (SAA), to find the optimal turning restriction strategy. A branch strategy and a bound strategy are applied to accelerate the solution process of the TRDP. The computational experiments give promising results, showing that the optimal turning restriction strategy can obviously reduce system congestion and are robust to the variations of both the dispersion parameter of the SUE problem and the level of demand.

Collection, spillback, and dissipation in pedestrian evacuation: A network-based method

We present a method of predicting pedestrian route choice behavior and physical congestion during the evacuation of indoor areas with internal obstacles. Under the proposed method, a network is first constructed by discretizing the space into regular hexagonal cells and giving these cells potentials before a modified cell transmission model is employed to predict the evolution of pedestrian flow in the network over time and space. Several properties of this cell transmission model are explored. The method can be used to predict the evolution of pedestrian flow over time and space in indoor areas with internal obstacles and to investigate the collection, spillback, and dissipation behavior of pedestrians passing through a bottleneck. The cell transmission model is further extended to imitate the movements of multiple flows of pedestrians with different destinations. An algorithm based on generalized cell potential is also developed to assign the pedestrian flow.

Empirical Method for Predicting Internal-External Truck Trips at a Major Port

This paper presents a case study to explore the truck-trip generation model for hauling containers at a major international seaport. An internal-external truck-trip forecast model is examined. It incorporates influential factors of regional freight activity attributes, economic growth attributes, and natural disaster attributes based on monthly data from 2000–2008. A best-fit truck-trip forecasting model is determined by comparing the prediction accuracy of a multiple regression model, time-series models, and a neural network model. The findings indicate that the back propagation neural network model generates better forecasting performance than the regression and time-series approaches. Additionally, this paper identifies the difference between truck trips and commodity-flow tonnages converted by truck payload factors, which would be significantly affected by truck-trip chains and truck drivers’ route choice behaviors. The analysis also reveals that a port truck-trip forecast model based on commodity flows would be very sensitive to the events of oil price fluctuations and new operation or infrastructure upgrade of competitive ports nearby, once the conversion difference goes up to 30%.

A prospect-based user equilibrium model with endogenous reference points and its application in congestion pricing

Given that the cumulative prospect theory provides a well-supported descriptive paradigm for decision making under risk or uncertainty, previous studies applied the theory to model travelers' route choice behaviors in stochastic networks and prospect-based user equilibrium models. The models rely on exogenous inputs of reference points and thus the resulting reference-dependence could be regarded as an ad hoc assumption. This paper proposes a conjecture on travelers' determination of reference points and encapsulates it into the prospect-based user equilibrium conditions. The conditions are formulated as an equivalent variational inequality and a heuristic solution algorithm is proposed to solve it. Both the model and the solution algorithm are demonstrated in two numerical examples. As one application, this paper further develops an optimal pricing model in which the proposed user equilibrium model is adopted to capture travelers' response to pricing signals under risk. The pricing model is formulated as a mathematical program with complementarity constraints, solved by a derivative-free algorithm. Another numerical example is presented to illustrate the pricing model and the solution algorithm.

A new model for studying the SO-based pre-trip information release strategy and route choice behaviour

In reality, the predictive traffic information is rarely perfect. It is thus a rational assumption that travellers would not be completely in compliance with the guidance by advanced traveller information systems (ATIS). This article presents a Markovian decision programming (MDP) model for investigating the day-to-day receiving and adjusting process to the pre-trip information released by ATIS at route level. The goal is to seek an optimal information release strategy (IRS) for minimising the overall disutility under the assumption that travellers’ route choice is governed by a logit model on the base of adopted travel times. The properties of the model solution are analysed. It is found that there exists an optimal IRS that can drive the flow pattern to a system optimum (SO) if the behaviour adjustment parameter satisfies a condition as . The predictive information provided to drivers results in the oscillation of traffic flow among alternative routes when . The solution of the MDP model is obtained by solving a series of sequential minimisation programs. The model and algorithm are numerically verified on a test network.

Comparison of Walking Image among Different Age Groups in Taiwanese Cities

This study is an extension to a project that explores pedestrian behavior in Taiwan with an emphasis on impacts of sociocultural attributes. Data collection for the comparison among 4 cities was carried out by conducting an interview questionnaire survey. Impact of age of citizens in different cities towards pedestrian awareness and attitudes was examined. Three observations could be made from the analysis. Firstly, there is no significant difference among pedestrian behavior among Taiwanese cities. Secondly, there are significant differences of behavioral attributes among different age groups. Thirdly, analysis related to specific spatial characteristics of pedestrian facilities in city centers in Taiwan provides an insight into route choice behavior of pedestrians in different stages of the life cycle.

Incorporating Accessibility-based Equity into Stochastic Road Network Design Problem: Sensitive Analyses and Policy Implications

This paper aims at examining the sensitivity of an accessibility-based equity measure in the context of the stochastic road network design problem. An equity optimization model system based on the bi-level programming approach is proposed, where the upper level problem addresses equity optimization under road investment constraint and the lower level deals with a user equilibrium model reflecting route choice behavior with varied link capacity improvements. An accessibility-based equity measure is defined in terms of the Gini coefficient. To illustrate the applicability of the model, an empirical study using data collected in Dalian City, China is carried out. Results demonstrate that the accessibility-based equity measure can be applied to measure distributional differences in zonal accessibility. Comparative results also implicate that policy makers may need to trade-off the levels of zonal accessibility and the accessibility distribution.

Uncovering cabdrivers’ behavior patterns from their digital traces

Recognizing high-level human behavior and decisions from their digital traces are critical issues in pervasive computing systems. In this paper, we develop a novel methodology to reveal cabdrivers’ operation patterns by analyzing their continuous digital traces. For the first time, we systematically study large scale cabdrivers’ behavior in a real and complex city context through their daily digital traces. We identify a set of valuable features, which are simple and effective to classify cabdrivers, delineate cabdrivers’ operation patterns and compare the different cabdrivers’ behavior. The methodology and steps could spatially and temporally quantify, visualize, and examine different cabdrivers’ operation patterns. Drivers were categorized into top drivers and ordinary drivers by their daily income. We use the daily operations of 3000 cabdrivers in over 48 million of trips and 240 million kilometers to uncover: (1) spatial selection behavior, (2) context-aware spatio-temporal operation behavior, (3) route choice behavior, and (4) operation tactics. Though we focused on cabdriver operation patterns analysis from their digital traces, the methodology is a general empirical and analytical methodology for any GPS-like trace analysis. Our work demonstrates the great potential to utilize the massive pervasive data sets to understand human behavior and high-level intelligence.

A dynamic traffic assignment model for a continuum transportation system

A predictive continuum dynamic user-optimal (PDUO-C) model is formulated in this study to investigate the dynamic characteristics of traffic flow and the corresponding route-choice behavior of travelers within a region with a dense urban road network. The modeled region is arbitrary in shape with a single central business district (CBD) and travelers continuously distributed over the region. Within this region, the road network is represented as a continuum and travelers patronize a two-dimensional continuum transportation system to travel to the CBD. The PDUO-C model is solved by a promising solution algorithm that includes elements of the finite volume method (FVM), the finite element method (FEM), and the explicit total variation diminishing Runge–Kutta (TVD-RK) time-stepping method. A numerical example is given to demonstrate the utility of the proposed model and the effectiveness of the solution algorithm in solving this PDUO-C problem.

Day-To-Day Route Choice Decision Simulation Based on Dynamic Feedback Information

In reality, the individual's day-to-day route choice behavior is a long-time evolution process, and travelers choose their traveling routes according to the combination of historical experience and real-time traffic information. Considering two classes of users, one equipped with advanced traveler information systems (ATIS) and the other without, this paper investigates the travel efficiency under two different information feedback strategies, namely, travel time feedback strategy and mean velocity feedback strategy. The simulation results given by the cellular automaton model show that the more users rely on historical experience, the quicker the system evolves to the equilibrium. To all users, the travel efficiency decreases with travel demand and firstly increases then decreases with the dependent degree to historical experience. However, as far as the ATIS market penetration rate is concerned, the users' travel efficiency under two feedback strategies exhibits different trends. The equipped travelers have shorter travel time, particularly under mean velocity feedback strategy. In addition, the difference of travel efficiencies between two classes of users decreases as days evolve.

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

To make practical use of research into travelers’ behavior in route choice modeling, a link is required to connect objective travel scenarios with the subjective decisions made by travelers. Cumulative prospect theory (CPT) offers an alternative framework of route choice behavior that goes beyond the conventional expected utility theory (EUT) models. This paper develops a general travel decision-making rule utilizing CPT. It investigates the mechanism of travelers’ behavior, examines the probability of applying CPT as a measure of commute utility, and establishes a general utility measurement system, the results of which are found to be more consistent with the experimental data than those of EUT-based route choice models. In addition, an approach to confirm the reference point value is suggested. The main techniques adopted in this study are demonstration analysis, a questionnaire survey, and statistical approaches.

A Multi Agent Based Simulation Framework for the Study of Transit Passenger’s Route Choice Behavior

In light of the characteristics of transit passenger’s route choice behavior, this paper introduces a multi-agent based simulation approach into the study of this behavior.At first, the paper analyzes factors affecting transit passenger’s route choice behavior and then studies the rules of behavior for transit passengers when making route choices. The paper further proposes a utility function for selected routes and examines ways to investigate and analyze corresponding data so as to provide a basis for the modeling of passenger agent’s route choice behavior. Following this, the paper builds up a simulation system for transit passenger’s route choice behavior based on the multi-agent simulation software Starlogo developed by Massachusetts Institute of Technology (MIT), and explains the process that will actually take place when using the simulation system. Finally, inadequacies of the study are analyzed and the focus of further research is indicated.

Short-term Choice of Travelers with Effect of Traffic Information

The travel information system strengths the relationship between travel and transportation system, which makes the activity-travel decision as a dynamic adjust process. After analyzing the travel demand change with the effect of information, the paper focuses on the short term choice of travelers. Based on the SP survey data of Jilin city, it discusses the interaction among depart time, travel mode, and route choice decision behavior. Considering the influence factors such as the of travelers attitudes, the study quantitatively analyzes the inner relationship of route choice behavior using Bivariate Probit model. The results show that the travel mode choice strongly influence the departure time or route choice, and the route choice is independent relatively. These fundamental works provide some knowledge to further process in the research of the traveler's behavior and provide some valuable advises to transportation programming and management.

Microscopic pedestrian simulation model combined with a tactical model for route choice behaviour

This study proposes a microscopic pedestrian simulation model for evaluating pedestrian flow. Recently, several pedestrian models have been proposed to evaluate pedestrian flow in crowded situations for the purpose of designing facilities. However, current pedestrian simulation models do not explain the negotiation process of collision avoidance between pedestrians, which can be important for representing pedestrian behaviour in congested situations. This study builds a microscopic model of pedestrian behaviour using a two-player game and assuming that pedestrians anticipate movements of other pedestrians so as to avoid colliding with them. A macroscopic tactical model is also proposed to determine a macroscopic path to a given destination. The results of the simulation model are compared with experimental data and observed data in a railway station. Several characteristics of pedestrian flows such as traffic volume and travel time in multidirectional flows, temporal–spatial collision avoidance behaviour and density distribution in the railway station are reproduced in the simulation.

Which road do I take? A learning-based model of route-choice behavior with real-time information

This paper presents a learning-based model of route-choice behavior when information is provided in real time. In a laboratory controlled experiment, participants made a long series of binary route-choice trials relying on real-time information and learning from their personal experience reinforced through feedback. A discrete choice model with a Mixed Logit specification, accounting for panel effects, was estimated based on the experiment’s data. It was found that information and experience have a combined effect on drivers’ route-choice behavior. Informed participants had faster learning rates and tended to base their decisions on memorization relating to previous outcomes whereas non-informed participants were slower in learning, required more exploration and tended to rely mostly on recent outcomes. Informed participants were more prone to risk-seeking and had greater sensitivity to travel time variability. In comparison, non-informed participants appeared to be more risk-averse and less sensitive to variability. These results have important policy implications on the design and implementation of ATIS initiatives. The advantage of incorporating insights from Prospect Theory and reinforced learning to improve the realism of travel behavior models is also discussed.