Driver response to variable message signs: a stated preference investigation

This paper uses a Stated Preference approach to undertake a detailed assessment of the effect on drivers’ route choice of information provided by variable message signs (VMS). Although drivers’ response to VMS information will vary according to the availability of alternative routes and the extent to which they are close substitutes, our findings show that route choice can be strongly influenced by the provision of information about traffic conditions ahead. This has important implications for the use of VMS systems as part of comprehensive traffic management and control systems. The principal findings are that the impact of VMS information depends on: the content of the message, such as the cause of delay and its extent; local circumstances, such as relative journey times in normal conditions; and drivers’ characteristics, such as their age, sex and previous network knowledge. The impact of qualitative indicators, visible queues and delays were examined. It was found that not only is delay time more highly valued than normal travel time, which is to be expected, but that drivers become more sensitive to delay time as delay times increased across the range presented.     

Driver response to variable message sign information in London

Variable message signs (VMS) have been installed in London to notify motorists of planned events and current network problems. To guide investment and operational decisions an understanding is required of the impacts of VMS information. This paper presents the results of a study of driver response to VMS information. The study employed questionnaires to investigate the effect of different messages on route choice. A statistical analysis of stated intention questionnaire data enabled logistic regression models to be developed relating the probability of route diversion to driver, journey and message characteristics. The resultant models indicate that the location of the incident and the message content are important factors influencing the probability of diversion. A survey of drivers' actual responses to a message activation showed that only one third of drivers saw the information presented to them and few of these drivers diverted, although many found the information useful. Only one-fifth of the number of drivers diverted compared to that expected from the results of the stated intention questionnaire. It is thought that the low response rate achieved for the stated intention survey will have exaggerated drivers' responsiveness to VMS messages. Interestingly, survey data for another UK city with a newly installed VMS system showed that the number of drivers diverting due to VMS information was very similar to that expected from the results of the stated intention questionnaire. It is suggested that the use of London's VMS signs to display warnings of disruptions expected on future dates may be reducing their effectiveness as a channel for more urgent warnings.     

Effects of different factors on drivers’ guidance compliance behaviors under road condition information shown on VMS

It is generally accepted that compliance behavior is affected by many factors. The purpose of this study is to investigate the effects of diverse factors on drivers’ guidance compliance behaviors under road condition information shown on graphic variable message sign (VMS), and based on this to find out a better information release mode. The involved data were obtained from questionnaire survey, and ordinal regression was used to analyze the casual relation between guidance compliance behavior and its influencing factors. Based on an overall analysis of conditions in driver’s route choice, an accurate method was proposed to calculate the compliance rate. The model testing information indicated that ordinal regression model with complementary log–log being the link function was appropriate to quantify the relation between the compliance rate and the factors. The estimation results showed that age, driving years, average annual mileage, monthly income, driving style, occupation, the degree of trust in VMS, the familiarity with road network and the route choice style were significant determinants of guidance compliance behavior. This paper also compared two different guidance modes which were ordinary guidance mode (M1) and predicted guidance mode (M2) through simulation. The average speed fluctuations and average travel time supported that M2 had better effect in improving traffic flow and balancing traffic load and resource. Some detailed suggestions of releasing guidance information were proposed with the explanation by flow-density curve and variation of traffic flows. These findings are the foundation to design and improve guidance systems by assessing guidance effect and modifying guidance algorithm.     

Multimodal,multiclass stochastic dynamic traffic assignment for evaluating information provision strategies

A multimodal, multiclass stochastic dynamic traffic assignment model was developed to evaluate pre‐trip and enroute travel information provision strategies. Three different information strategies were examined: user optimum [UO], system optimum [SO] and mixed optimum [MO]. These information provision strategies were analyzed based on the levels of traffic congestion and market penetration rate for the information equipment. Only two modes, bus and car, were used for evaluating and calculating the modal split ratio. Several scenarios were analyzed using day‐to‐day and within day dynamic models. From the results analyzed, it was found that when a traffic manager provides information for drivers using the UO strategy and drivers follow the provided information absolutely, the total travel time may increases over the case with no information. Such worsening occurs when drivers switch their routes and face traffic congestion on the alternative route. This phenomenon is the 'Braess Paradox'.     

Effect of variable message signs on driver speed behavior on a section of expressway under adverse fog conditions—A driving simulator approach

Variable message signs (VMS) are used to provide dynamic information and one current application is to show different speed limits under different conditions. As speed is an important contributor to road accidents and also affects driver speed behavior, the present study focuses on how effective traffic advisory information is when helping drivers to divert from potentially dangerous conditions. Graphical representation of an Expressway section made it easy to isolate the effects of speed etc. by drivers with information provided through VMS under adverse fog conditions. Understanding and reacting to the VMS system by drivers is essential for its success. If drivers do not react by changing speed behavior then the VMS system will fail and further implementation may cease. In this paper an Analysis of Variance model, which is appropriate to the proposed experimental conditions, is used to study how subjects (drivers) will perceive provided information and also to find the effect of VMS on driver speed behavior on the simulated Expressway section.     

A model for assessing the effects of dynamic travel time information via variable message signs

In view of the serious traffic congestion during peak hours in most metropolitan areas around the world and recent improvement of information technology, there is a growing aspiration to alleviate road congestion by applications of electronic information and communication technology. Providing drivers with dynamic travel time information such as estimated journey times on major routes should help drivers to select better routes and guide them to utilise existing expressway network. This can be regarded as one possible strategy for effective traffic management. This paper aims to investigate the effects and benefits of providing dynamic travel time information to drivers via variable message signs at the expressway network. In order to assess the effects of the dynamic driver information system with making use of the variable message signs, a time-dependent traffic assignment model is proposed. A numerical example is used to illustrate the effects of the dynamic travel time information via variable message signs. This revised version was published online in June 2006 with corrections to the Cover Date.     

Validating the results of a route choice simulator

This paper describes the validation of a route choice simulator known as VLADIMIR (Variable Legend Assessment Device for Interactive Measurement of Individual Route choice). VLADIMIR is an interactive computer-based tool designed to study drivers’ route choice behaviour. It has been extensively used to obtain data on route choice in the presence of information sources such as Variable Message Signs or In-Car Navigation devices. The simulator uses a sequence of digitized photographs to portray a real network with junctions, links, landmarks and road signs. Subject drivers are invited to make journeys between specified origins and destinations under a range of travel scenarios, during which the simulator automatically records their route choices. This paper describes validation experiments carried out during the period Summer 1994 to Autumn 1995 and reports on the results obtained. Each experiment involved a comparison of routes selected in real life with those driven under simulated conditions in VLADIMIR. The analysis included investigation of the subjects’ own assessment of the realism of the VLADIMIR routes they had chosen, a comparison of models based on the real life routes with models based on VLADIMIR routes, and a statistical comparison of the two sets of routes. After an extensive series of data collection exercises and analyses, we have concluded that a well designed simulator is able to replicate real life route choices with a very high degree of detail and accuracy. Not only was VLADIMIR able to precisely replicate the route choices of drivers who were familiar with the network but it also appears capable of representing the kind of errors made and route choice strategies adopted by less familiar drivers. Furthermore, evidence is presented to suggest that it can accurately replicate route choice responses to roadside VMS information.     

An improved incremental assignment model for parking variable message sign location problem

Operators and planners of parking guidance and information system (PGIS) often encounter the difficulty when and how to provide parking information to drivers in peak hours. The aim of this study was to assign the parking demands onto the urban network, find the positions suitable to locate the parking variable message sign (parking VMS), and try to solve the problem when to provide parking information to drivers. Parking VMS, as the most common forms of the information display terminal of PGIS, becomes critical in designing PGIS. This paper started from analyzing the relationship between the location and the performances of parking VMS. If a parking VMS is placed right along the link approaching to the divarication intersection of the subsequent “shortest path” and the previous one, the travel time declines abruptly, and the guiding compliance ratio becomes superior. Then, the network‐based parking choice model for the parking VMS was proposed. Network modification and incremental assignment were used to find the divarication intersections caused by the changes of space availability. In addition, MATLAB software package (MathWorks, Inc., Natick, MA, USA) was adopted to calculate the entire process mentioned. The proposed model and the algorithm were applied to a numerical example, where the location of parking VMS was obtained. Copyright © 2015 John Wiley & Sons, Ltd.     

Evaluating the benefits of a combined route guidance and road pricing system in a traffic network with recurrent congestion

When drivers do not have complete information on road travel time and thus choose their routes in a stochastic manner or based on their previous experience, separate implementations of either route guidance or road pricing cannot drive a stochastic network flow pattern towards a system optimum in a Wardropian sense. It is thus of interest to consider a combined route guidance and road pricing system. A road guidance system could reduce drivers' uncertainty of travel time through provision of traffic information. A driver who is equipped with a guidance system could be assumed to receive complete information, and hence be able to find the minimum travel time routes in a user-optimal manner, while marginal-cost road pricing could drive a user-optimal flow pattern toward a system optimum. Therefore, a joint implementation of route guidance and road pricing in a network with recurrent congestion could drive a stochastic network flow pattern towards a system optimum, and thus achieve a higher reduction in system travel time. In this paper the interaction between route guidance and road pricing is modeled and the potential benefit of their joint implementation is evaluated based on a mixed equilibrium traffic assignment model. The private and system benefits under marginal-cost pricing and varied levels of market penetration of the information systems are investigated with a small and a large example. It is concluded that the two technologies complement each other and that their joint implementation can reduce travel time more efficiently in a network with recurrent congestion.     

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.     

Modeling drivers' diversion from normal routes under ATIS using generalized estimating equations and binomial probit link function

Advanced Traveler Information Systems (ATIS) provide travelers with real time traffic information to optimize their travel choices. The objective of this paper is to model drivers' diversion from their normal routes in the provision of ATIS. Five different scenarios of traffic information are used. Generalized Estimating Equations (GEE) framework with repeated observations and binomial probit link function is introduced and implemented. GEE with four different correlation structures including the independent case are developed and compared with each other and with regular Maximum Likelihood Estimation (MLE). A travel simulator was used. Sixty-five subjects have traveled 10 simulated trial days each on a 40-link realistic network with real historical congestion levels. The results showed that providing traffic information increases the probability of drivers' diversion from their normal routes. Adding advice to the pre-trip and/or en-route information encourages drivers to divert. Providing en-route in addition to the pre-trip information with or without advice increases the diversion probability. High travel time on the normal route and less travel time on the diverted route increase the probability of diversion. High-educated drivers are less likely to divert. Expressway users are more likely to divert from their normal routes under ATIS. Drivers' familiarity with the device that provides the information and high number of traffic signals on the normal route increase the diversion probability.     

Route Choice Behaviour of Freeway Travellers Under Real-time Traffic Information Provision – Application of the Best Route and the Habitual Route Choice Mechanisms

Abstract

This paper investigates route choice behaviour on freeways between Taipei and Taichung in Taiwan under the provision of real-time traffic information. Two types of route choice selection rules (the best-route and habitual-route) are analysed using ordered probit models to identify the major influences on freeway travellers’ route choice behaviour. The level of service associated with each route is defined as a generalised cost saving (GCS) and specified non-linearly with a threshold inherent to travellers. The marginal (dis)utility thresholds in the ‘best’ and ‘habitual’ behaviour models are identified through a goodness-of-fit grid. The results confirm that the thresholds for changing the inertia behaviour of drivers should be larger than the ones for choosing the best routes. In addition, the drivers are more likely to choose either the best or the habitual routes once the GCS are greater than the identified threshold values.     

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.     

Route switching behavior on freeways with the provision of different types of real-time traffic information

This study investigates route switching behavior on freeways in reaction to the provision of different types of real-time traffic information. The experimental design of the stated preference survey is based on four types of real-time information provided to travelers who were randomly selected at rest areas. The four types of real-time information defined in this paper are qualitative, quantitative, qualitative guidance, and quantitative guidance. The bounded rationality framework, also known as indifference band approach, is applied to model the freeway route switching behavior. Two important variables, travel time and travel cost, are included in the indifference band. In this study, the best route switching rule, travelers’ current routes as compared to the best route, is investigated to further provide valuable insights into freeways travelers’ route switching behavior with the provision of different types of real-time traffic information.     

Up‐to‐the‐minute information as we drive—how it can help road users and traffic management

Road transport of today has a substantial need for a better information system. The European research programmes PROMETHEUS and DRIVE offer the prospect of new techniques and strategies. The role of information in road transport is analysed from the viewpoint of the individual drivers and from the viewpoint of the traffic manager. All necessary information must be brought together and a complex data‐bank system for road transport should be installed. As examples of advanced information systems the concepts of the ‘Wolfsburg wave’ for in‐car speed recommendation and of the LISB field experiment in route guidance in Berlin are explained.     

Route guidance systems for improving urban travel and location choices

Recent technological advances in navigation systems for private vehicles have the capability to provide drivers with highway route information on a dashboard-mounted video display screen. These technological advances, together with two-way radio communication of digital information, automatic measurement of traffic flows, and supercomputer technology, could be combined to provide useful information to drivers concerning expected travel times, best routes, and best departure times. This paper reviews the status of this technology and explores the information and prediction requirements for the computer models required to implement such a system. Research needed to evaluate the potential impact of such a system is also described.     

ITS applications for work zones

The use of Intelligent Transportation Systems (ITS) in construction work zones to disseminate traffic information has increased significantly in recent years, mainly with the use of Variable Message Signs (VMS). VMS are used based on the assumption that informed drivers will make better travel decisions, thereby reducing congestion. However, the extent of change in driver behavior is difficult to predict prior to ITS deployment. This difficulty leads to the larger problem of justifying investment in ITS. This article proposes an ITS deployment decision support tool using micro‐simulation. The approach determines the required diversion effectiveness of a work zone ITS deployment using VMS. The methodology was tested using the Glenmore Trail/Elbow Drive/5^th Street interchange project (GE5) in Calgary, Canada. The results indicate that the proposed approach will assist agencies in justifying ITS investment by exhibiting the potential resultant societal benefits.     

Routing aspects of electric vehicle drivers and their effects on network performance

This study investigates the routing aspects of battery electric vehicle (BEV) drivers and their effects on the overall traffic network performance. BEVs have unique characteristics such as range limitation, long battery recharging time, and recuperation of energy lost during the deceleration phase if equipped with regenerative braking system (RBS). In addition, the energy consumption rate per unit distance traveled is lower at moderate speed than at higher speed. This raises two interesting questions: (i) whether these characteristics of BEVs will lead to different route selection compared to conventional internal combustion engine vehicles (ICEVs), and (ii) whether such route selection implications of BEVs will affect the network performance. With the increasing market penetration of BEVs, these questions are becoming more important. This study formulates a multi-class dynamic user equilibrium (MCDUE) model to determine the equilibrium flows for mixed traffic consisting of BEVs and ICEVs. A simulation-based solution procedure is proposed for the MCDUE model. In the MCDUE model, BEVs select routes to minimize the generalized cost which includes route travel time, energy related costs and range anxiety cost, and ICEVs to minimize route travel time. Results from numerical experiments illustrate that BEV drivers select routes with lower speed to conserve and recuperate battery energy while ICEV drivers select shortest travel time routes. They also illustrate that the differences in route choice behavior of BEV and ICEV drivers can synergistically lead to reduction in total travel time and the network performance towards system optimum under certain conditions.     

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.     

Real time traffic information and its impacts on route switching behavior of expressway drivers

Non‐quantifiable factors (e.g. perceived, attitudinal and preferential factors) have not been investigated fully in past transportation studies, which has raised questions on the predictive capabilities of the models. In this study, Structure Integration Models, with one of their sub‐models, Measurement Equation, are combined with latent variables, which are integrated with another sub‐model, Structural Equation. The estimated latent variables are used as explanatory variables in decision models. As a result, the explanatory and predictive capabilities of the models are enhanced. The models can then be used to describe the various behaviors of travelers of different types of transportation systems in a more accurate way. In this study, the Structure Integration Model was applied to study the impacts of real‐time traffic information on the route‐switching behavior of road users on the Sun Yat‐Sen expressway, Taiwan. At present, the real‐time traffic information provided on this expressway includes radio traffic reports and changeable message signs. The results of this study can facilitate the provision of traffic information on highways.