An agent-based approach to modelling driver route choice behaviour under the influence of real-time information

This paper presents an agent-based approach to modelling individual driver behaviour under the influence of real-time traffic information. The driver behaviour models developed in this study are based on a behavioural survey of drivers which was conducted on a congested commuting corridor in Brisbane, Australia. Commuters’ responses to travel information were analysed and a number of discrete choice models were developed to determine the factors influencing drivers’ behaviour and their propensity to change route and adjust travel patterns. Based on the results obtained from the behavioural survey, the agent behaviour parameters which define driver characteristics, knowledge and preferences were identified and their values determined. A case study implementing a simple agent-based route choice decision model within a microscopic traffic simulation tool is also presented. Driver-vehicle units (DVUs) were modelled as autonomous software components that can each be assigned a set of goals to achieve and a database of knowledge comprising certain beliefs, intentions and preferences concerning the driving task. Each DVU provided route choice decision-making capabilities, based on perception of its environment, that were similar to the described intentions of the driver it represented. The case study clearly demonstrated the feasibility of the approach and the potential to develop more complex driver behavioural dynamics based on the belief–desire–intention agent architecture.     

Exploration of route choice behavior with advanced traveler information using neural network concepts

A model of driver's route choice behavior under advanced traveler information system (ATIS) is developed based on data collected from learning experiments using interactive computer simulation. The experiment subjected drivers to 32 simulated days in which they were to choose between the freeway or a side road. A neural network model is used as a convenient modeling technique in this initial phase of the analysis. The results indicated that most subjects made route choices based mainly on their recent experiences. It was also demonstrated that route choice behaviors are related to the personal characteristics as well as the characteristics of the respective routes. Travel experiences have less effect on the choice of the side road compared to the freeway and the results indicate that the prediction accuracy of the model, the acceptance rate of advice, and the quality of advice are closely correlated. The model developed here was for advice consistently provided at a level of 75 percent accuracy. The paper concludes with a discussion of experimental limitations and suggestions for future research.     

On dynamics of traffic queues in a road network with route choice based on real time traffic information

Introducing real time traffic information into transportation network makes it necessary to consider development of queues and traffic flows as a dynamic process. This paper initiates a theoretical study of conditions under which this process is stable. A model is presented that describes within-one-day development of queues when drivers affected by real-time traffic information choose their paths en route. The model is reduced to a system of differential equations with delay. Equilibrium points of the system correspond to constant queue lengths. Stability of the system is investigated using characteristic values of the linearised minimal face flow. A traffic network example illustrating the method is provided.     

Using a weight-assessing model to identify route choice criteria and information effects

Shortest-path (minimum travel time) routing has been adopted over the past few decades. However, many studies have shown that a driver’s route and the shortest path differ widely in significant ways, and that most drivers use several criteria in selecting their routes. Since route choice criteria have been the subject of controversy, this study develops an individual behavioral-based mechanism for exploring the crucial criteria affecting drivers’ route-selection decisions. On the basis of the weight-assessing model and the habitual domain theory, this study presents the dynamic change of route choice criteria according to their dynamic weights. Furthermore, the effects of information on drivers’ route-formulating behaviors are investigated as well in order to provide some valuable suggestions for implementing Advanced Traveler Information Systems (ATIS) in the future. An empirical study in Taipei City was conducted to show the feasibility and applicability of our proposed method and the empirical results indicate excellent performance in practice.     

Incorporating an information acquisition process into a route choice model with multiple information sources

This study aims at analyzing drivers' behavior in acquiring and using traffic information in an environment with multiple information sources. Accordingly, information acquisition and reference models are developed in an effort to show the empirical relationship between drivers' reaction to multiple information sources, causal factors latent psychological ones, traffic conditions at the time of traveling and the accuracy of traffic information available. A route choice model is proposed that takes into account the information acquisition and reference process. Model validity is investigated using data collected on the Tokyo Metropolitan Expressway, which has four different types of information sources.     

An impact analysis of traffic image information system on driver travel choice

A driver is one of the main components in a transportation system that influences the effectiveness of any active demand management (ADM) strategies. As such, the understanding on driver behavior and their travel choice is crucial to ensure the successful implementation of ADM strategies in alleviating traffic congestion, especially in city centres. This study aims to investigate the impact of traffic information dissemination via traffic images on driver travel choice and decision. A relationship of driver travel choice with respect to their perceived congestion level is developed by an integrated framework of genetic algorithm–fuzzy logic, being a new attempt in driver behavior modeling. Results show that drivers consider changing their travel choice when the perceived congestion level is medium, in which changing departure time and diverting to alternative roads are two popular choices. If traffic congestion escalates further, drivers are likely to cancel their trip. Shifting to public transport system is the least likely choice for drivers in an auto-dependent city. These findings are important and useful to engineers as they are required to fully understand driver (user) sensitivity to traffic conditions so that relevant active travel demand management strategies could be implemented successfully. In addition, engineers could use the relationships established in this study to predict drivers’ response under various traffic conditions when carrying out modeling and impact studies.     

Drivers' route choice and learning mechanism under bounded information environment

In this paper, we propose the theory of Rational Beliefs as the model of expectations that extends the theory of rational expectations to the postulated environments. Under the rational beliefs paradigm, drivers do not have structural knowledge of traffic conditions and they choose their routes based only on personal experiences and decision‐making rules. We found that if drivers have different decision‐making rules and experiences, then they form different beliefs of traffic conditions (e.g. average travel time) even though they have the same public information and use the same routes. Under the rational beliefs model, drivers are not motivated to renew their beliefs because the beliefs are compatible with their experiences. Therefore, the heterogeneity of beliefs does not disappear even though they have long‐term learning. In order to investigate how drivers form their beliefs of traffic conditions under bounded information environments, numerical experiment is carried out.     

Optimizing route choice for lowest fuel consumption – Potential effects of a new driver support tool

Today, driver support tools intended to increase traffic safety, provide the driver with convenient information and guidance, or save time are becoming more common. However, few systems have the primary aim of reducing the environmental effects of driving. The aim of this project was to estimate the potential for reducing fuel consumption and thus the emission of CO₂ through a navigation system where optimization of route choice is based on the lowest total fuel consumption (instead of the traditional shortest time or distance), further the supplementary effect if such navigation support could take into account real-time information about traffic disturbance events from probe vehicles running in the street network. The analysis was based on a large database of real traffic driving patterns connected to the street network in the city of Lund, Sweden. Based on 15 437 cases, the fuel consumption factor for 22 street classes, at peak and off-peak hours, was estimated for three types of cars using two mechanistic emission models. Each segment in the street network was, on a digitized map, attributed an average fuel consumption for peak and off-peak hours based on its street class and traffic flow conditions. To evaluate the potential of a fuel-saving navigation system the routes of 109 real journeys longer than 5 min were extracted from the database. Using Esri’s external program ArcGIS, Arcview and the external module Network Analysis, the most fuel-economic route was extracted and compared with the original route, as well as routes extracted from criterions concerning shortest time and shortest distance. The potential for further benefit when the system employed real-time data concerning the traffic situation through 120 virtual probe vehicles running in the street network was also examined. It was found that for 46% of trips in Lund the drivers spontaneous choice of route was not the most fuel-efficient. These trips could save, on average, 8.2% fuel by using a fuel-optimized navigation system. This corresponds to a 4% fuel reduction for all journeys in Lund. Concerning the potential for real-time information from probe vehicles, it was found that the frequency of disturbed segments in Lund was very low, and thus so was the potential fuel-saving. However, a methodology is presented that structures the steps required in analyzing such a system. It is concluded that real-time traffic information has the potential for fuel-saving in more congested areas if a sufficiently large proportion of the disturbance events can be identified and reported in real-time.     

基于公交专用进口道的交叉口信号配时优化设计研究

本文研究设置公交专用进口道的城市交叉口信号配时,综合考虑社会车辆的运行,实现信号配时的公交优先与人均延误最小化。本文运用延误三角形及其相关理论建立延误模型,推导出人均延误函数,为公交优先单点定时控制提供了优化算法。同时本文利用VisualC仿真模型与改进算法对城市具体交叉口信号最佳周期及有效绿灯时间进行优化设计。     

Modeling the impact of pre-trip information on commuter departure time and route choice

The objective of this paper is to investigate the impact of pre-trip information on auto commuters’ choice behavior. The analysis is based on an extensive home-interview survey of commuters in the Taichung metropolitan area in Taiwan. A joint model for route and departure time decisions with and without pre-trip information is formulated. The model specifications are developed for both the systematic and random components. In particular, econometric issues associated with specifying the random error structure are addressed for parameter estimation purposes. Insights into the effects of attributes are obtained through the analysis of the model's performance and estimated parameter values. A probit model form is used for the joint model, allowing the introduction of state dependence and correlation in the model specification. The results underscore the important relationship between the different characteristics and the propensity of commuter choice behavior under two scenarios, with and without pre-trip information.     

Effectiveness of en route traffic information in developing countries using conventional discrete choice and neural‐network models

Providing commuters with traffic information or advising them of alternative routes during traffic incidents can alleviate congestion. For decades, advanced traveler information services (ATIS) have been devised and dedicated to this role. ATIS comprises a wide variety of technologies across the world, including radio traffic information (RTI) advisory service. RTI is common in both developed and developing countries. Although extensive literature and evaluation results of ATISs and RTI are available in developed countries, little attention has been devoted to that in developing countries. This work provides a modeling platform to study drivers' response to en route traffic information provided by Radio‐Payam broadcasting service in Tehran, the capital city of the developing country of Iran. The results are compared with counterpart cases in developed countries. Past studies and this study have employed conventional discrete models for drivers' response, such as ordered logit and ordered probit. This work evaluates the accuracy level of these conventional models in comparison with a developed neural‐network (NN) model, because it has been widely proven that NN models are highly precise. It has also been found that, apart from reliability, the conventional models are within an acceptable level of prediction accuracy compared with the NN models. The results show a high degree of similarities between the case of Tehran and its counterparts in the developing countries. The results also deliver some insights on how to improve or better implement the ATIS technologies. Copyright © 2012 John Wiley & Sons, Ltd.     

Traveler route choice: Travel time implications of improved information and adaptive decisions

The Wardrop user equilibrium model states that travelers choose the fastest available route and always choose the same route on repeated trips. However, travelers are not always capable of choosing the fastest route, and if travel time is uncertain, they may acquire information on the day of travel that helps to select a better route. Thus, travelers can reduce their travel time over the Wardrop “optimum” by selecting routes adaptively. The focus of this paper is to find the most promising approach for improving actual transit route choice through providing better traveler information. Actual and ideal travel time were estimated for each of six information scenarios, ranging from one where travelers use transit maps, to one where travelers use adaptive route choice, and to the hypothetical situation referred to as perfect information. Travelers using maps and travelers using maps and schedules took significantly longer than ideally possible on an experimental trip (24% longer with maps, 42% longer with maps and schedules). Ideal travel time under perfect information was 49% less than actual travel time with no information, and 6% less than that of the best non-adaptive decision rule. Time adaptive route choice resulted in no travel time reduction. The potential travel time improvement from giving travelers more information was not as great as that from making information more understandable. Adaptive route choice did not offer great potential on the studied trip. To be effective there must be several nearly equal route options, and trips must involve transfers, which excludes most travel on transit today.     

Calibration of a transit route choice model using revealed population data of smartcard in a multimodal transit network

Transportation - One of the major objectives of this study is to provide more realistic and accurate results related to transit passenger’s route choice behavior by using population data of...     

Modelling route choice behaviour in a tolled road network with a time surplus maximisation bi-objective user equilibrium model

In this paper, we propose a novel approach to model route choice behaviour in a tolled road network with a bi-objective approach, assuming that all users have two objectives: (1) minimise travel time; and (2) minimise toll cost. We assume further that users have different preferences in the sense that for any given path with a specific toll, there is a limit on the time that an individual would be willing to spend. Different users can have different preferences represented by this indifference curve between toll and time. Time surplus is defined as the maximum time minus the actual time. Given a set of paths, the one with the highest (or least negative) time surplus will be the preferred path for the individual. This will result in a bi-objective equilibrium solution satisfying the time surplus maximisation bi-objective user equilibrium (TSmaxBUE) condition. That is, for each O–D pair, all individuals are travelling on the path with the highest time surplus value among all the efficient paths between this O–D pair.We show that the TSmaxBUE condition is a proper generalisation of user equilibrium with generalised cost function, and that it is equivalent to bi-objective user equilibrium. We also present a multi-user class version of the TSmaxBUE condition and demonstrate our concepts with illustrative examples.     

An investigation of the reliability of real-time information for route choice decisions in a congested traffic system

This paper investigates the reliability of information on prevailing trip times on the links of a network as a basis for route choice decisions by individual drivers. It considers a type of information strategy in which no attempt is made by some central controller or coordinating entity to predict what the travel times on each link would be by the time it is reached by a driver that is presently at a given location. A specially modified model combining traffic simulation and path assignment capabilities is used to analyze the reliability of the real-time information supplied to the drivers. This is accomplished by comparing the supplied travel times (at the link and path levels) to the actual trip times experienced in the network after the information has been given. In addition, the quality of the decisions made by drivers on the basis of this information (under alternative path switching rules) is evaluated ex-post by comparing the actually experienced travel time (given the decision made) to the time that the driver would have experienced without the real-time information. Results of a series of simulation experiments under recurrent congestion conditions are discussed, illustrating the interactions between information reliability and user response.     

Day-to-day traffic dynamics considering social interaction: From individual route choice behavior to a network flow model

Social interaction is increasingly recognized as an important factor that influences travelers’ behaviors. It remains challenging to incorporate its effect into travel choice behaviors, although there has been some research into this area. Considering random interaction among travelers, we model travelers’ day-to-day route choice under the uncertain traffic condition. We further explore the evolution of network flow based on the individual-level route choice model, though that travelers are heterogeneous in decision-making under the random-interaction scheme. We analyze and prove the existence of equilibrium and the stability of equilibrium. We also analyzed and described the specific properties of the network flow evolution and travelers’ behaviors. Two interesting phenomena are found in this study. First, the number of travelers that an individual interacts with can affect his route choice strategy. However, the interaction count exerts no influence on the evolution of network flow at the aggregate-level. Second, when the network flow reaches equilibrium, the route choice strategy at the individual-level is not necessarily invariable. Finally, two networks are used as numerical examples to show model properties and to demonstrate the two study phenomena. This study improves the understanding of travelers’ route choice dynamics and informs how the network flow evolves under the influence of social interaction.     

The nonlinear equation system approach to solving dynamic user optimal simultaneous route and departure time choice problems

Dynamic user optimal simultaneous route and departure time choice (DUO-SRDTC) problems are usually formulated as variational inequality (VI) problems whose solution algorithms generally require continuous and monotone route travel cost functions to guarantee convergence. However, the monotonicity of the route travel cost functions cannot be ensured even if the route travel time functions are monotone. In contrast to traditional formulations, this paper formulates a DUO-SRDTC problem (that can have fixed or elastic demand) as a system of nonlinear equations. The system of nonlinear equations is a function of generalized origin-destination (OD) travel costs rather than route flows and includes a dynamic user optimal (DUO) route choice subproblem with perfectly elastic demand and a quadratic programming (QP) subproblem under certain assumptions. This study also proposes a solution method based on the backtracking inexact Broyden–Fletcher–Goldfarb–Shanno (BFGS) method, the extragradient algorithm, and the Frank-Wolfe algorithm. The BFGS method, the extragradient algorithm, and the Frank-Wolfe algorithm are used to solve the system of nonlinear equations, the DUO route choice subproblem, and the QP subproblem, respectively. The proposed formulation and solution method can avoid the requirement of monotonicity of the route travel cost functions to obtain a convergent solution and provide a new approach with which to solve DUO-SRDTC problems. Finally, numeric examples are used to demonstrate the performance of the proposed solution method.     

Investigating the learning effects of route guidance and traffic advisories on route choice behavior

This paper describes a study to investigate the effects of route guidance and traffic advisories on driver's route choice behavior. The study is a two-factor experiment with repeated measures on one factor where the between-subjects factor is the type of traveler information provided and the repeated, within-subjects factor is trips made between a specified origin and destination. Participants were recruited and randomly assigned to one of four groups: group 1 having only a basic map of the network; group 2 having access only to route guidance, group 3 having access to traffic advisory information, and group 4 having access to both route guidance and traffic advisory information. Each participant completed 15 trips between a specified origin-destination pair on a hypothetical network. The results of this study indicate that there may be significant short-term advantages to providing in-vehicle routing and navigation information to unfamiliar drivers. However, the results also indicate that the format and amount of information provided may not be significant as the benefits to having route guidance diminish when drivers become more familiar with the travel network.     

Stability analysis on a dynamical model of route choice in a connected vehicle environment

Research on connected vehicle environment has been growing rapidly to investigate the effects of real-time exchange of kinetic information between vehicles and road condition information from the infrastructure through radio communication technologies. A fully connected vehicle environment can substantially reduce the latency in response caused by human perception-reaction time with the prospect of improving both safety and comfort. This study presents a dynamical model of route choice under a connected vehicle environment. We analyze the stability of headways by perturbing various factors in the microscopic traffic flow model and traffic flow dynamics in the car-following model and dynamical model of route choice. The advantage of this approach is that it complements the macroscopic traffic assignment model of route choice with microscopic elements that represent the important features of connected vehicles. The gaps between cars can be decreased and stabilized even in the presence of perturbations caused by incidents. The reduction in gaps will be helpful to optimize the traffic flow dynamics more easily with safe and stable conditions. The results show that the dynamics under the connected vehicle environment have equilibria. The approach presented in this study will be helpful to identify the important properties of a connected vehicle environment and to evaluate its benefits.