Simultaneous determination of the equilibrium market penetration and compliance rate of advanced traveler information systems

We consider a specific advanced traveler information systems (ATIS) whose objective is to reduce drivers’ travel time uncertainty with recurrent network congestion through provision of traffic information. Since the provided information is still partial or imperfect, drivers equipped with an ATIS cannot always find the shortest travel time route and thus may not always comply with the advice provided by ATIS. Thus, there are three classes of drivers on a specific day: drivers without ATIS, drivers with ATIS but without compliance with ATIS advice, drivers with ATIS and in compliance with ATIS advice. All three classes of drivers make route choice in a stochastic manner, but with different degree of uncertainty of travel time on the network. In this paper we investigate the interactions among the three classes of drivers in an ATIS environment using a multiple behavior stochastic user equilibrium model. By assuming that the market penetration of ATIS is an increasing function of the actual private gain (time saving minus the cost associated with system use) derived from ATIS service, and the ATIS compliance rate of equipped drivers is given as the probability of the actual travel time of complied drivers being less than that of non-complied drivers, we determine the equilibrium market penetration and compliance rate of ATIS and the resulting equilibrium network flow pattern using an iterative solution procedure.     

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.     

A conflict model and interactive simulator (FASTCARS) for predicting enroute driver behavior in response to real-time traffic condition information

This paper proposes a theoretical methodology and practical data collection approach for modeling enroute driver behavioral choice under Advanced Traveler Information Systems (ATIS). The theoretical framework is based on conflict assessment and resolution theories popularized in psychology and applied to models of individual consumer behavior. It is posed that enroute assessment and adjustment is a reactionary process influenced by increased conflict arousal and motivation to change. When conflict rises to a level at which conflict exceeds a personal threshold of tolerance, drivers are likely to alter enroute behavior to alleviate conflict through either route diversion of goal revision. Assessment and response to conflict arousal directly relate to the driver's abilities to perceive and predict network conditions in conjunction with familiarity of network configurations and accessible alternate routes.Data collection is accomplished through FASTCARS (Freeway andArterialStreetTrafficConflictArousal andResolutionSimulator), in interactive microcomputer-based driving simulator. Limited real-world implementation of ATIS has made it difficult to study or predict individual driver reaction to these technologies. It is contended here that in-laboratory experimentation with interactive route choice simulators can substitute for the lack of real-world applications and provide an alternate approach to data collection and driver behavior analysis. This paper will explain how FASTCARS is useful for collecting data and testing theories of driver behavior.     

Reactance or acceptance? Reactions towards the introduction of road pricing

We tested the opposite predictions of reactance and dissonance theory, two popular psychological theories, with regard to the responses of car drivers to the introduction of the road pricing. Reactance theory predicts that persons who are convinced that a toll will come are more opposed than less convinced persons. In contrast, dissonance theory expects that convinced persons are more in favour of road pricing than less convinced persons. Aim of the study was to test which theory is more appropriate to explain user reactions towards the toll introduction. We experimentally manipulated the perceived likelihood (low, middle, high and a control condition) of a toll introduction for private cars on German motorways (N = 140 car drivers). In accordance with the predictions of dissonance theory, results revealed clearly that convinced persons about a definite introduction of road pricing developed more positive attitudes towards road pricing than less convinced persons, i.e., the strength of conviction about the introduction of road pricing has a strong effect on the attitudinal evaluation of road pricing. The implications are discussed.     

Breaking car use habits: The effectiveness of a free one-month travelcard

Based on calls for innovative ways of reducing car traffic and research indicating that car driving is often the result of habitual decision-making and choice processes, this paper reports on a field experiment designed to test a tool aimed to entice drivers to skip the habitual choice of the car and consider using—or at least trying—public transport instead. About 1,000 car drivers participated in the experiment either as experimental subjects, receiving a free one-month travelcard, or as control subjects. As predicted, the intervention had a significant impact on drivers’ use of public transport and it also neutralized the impact of car driving habits on mode choice. However, in the longer run (i.e., four months after the experiment) experimental subjects did not use public transport more than control subjects. Hence, it seems that although many car drivers choose travel mode habitually, their final choice is consistent with their informed preferences, given the current price–quality relationships of the various options.     

Modeling user adoption of advanced traveler information systems: dynamic evolution and stationary equilibrium

This paper models the growth rate and the saturation market penetration level for advanced traveler information system (ATIS) products/services with heterogeneous drivers. The price of using and the benefit gained from ATIS services are considered two key factors in explaining the growth of adoption of ATIS products. The information benefit is measured as the travel time saving between equipped and unequipped drivers and evaluated by a mixed stochastic and deterministic network equilibrium model. A modified logistic type growth model is adopted to describe the cumulative adoption of ATIS products over years. The final stationary equilibrium level of ATIS market penetration is so determined that the value of the information provided will decline to the point at which no new users will find it advantageous to purchase that service. The endogenous growth and stationary equilibrium model of market penetration of ATIS services is useful for forecasting the growth process and the impacts on the system performance of ATIS.     

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.     

“Not just a taxi”? For-profit ridesharing,driver strategies,and VMT

The spread of GPS-based location services using smartphone applications has led to the rapid growth of new startups offering smartphone-enabled dispatch service for taxicabs, limousines, and ridesharing vehicles. This change in communicative technology has been accompanied by the creation of new categories of car service, particularly as drivers of limousines and private vehicles use the apps to provide on-demand service of a kind previously reserved for taxicabs. One of the most controversial new models of car service is for-profit ridesharing, which combines the for-profit model of taxi service with the overall traffic reduction goals of ridesharing. A preliminary attempt is here made at understanding how for-profit ridesharing compares to traditional taxicab and ridesharing models. Ethnographic interviews are drawn on to illustrate the range of motivations and strategies used by for-profit ridesharing drivers in San Francisco, California as they make use of the service. A range of driver strategies is identified, ranging from incidental, to part-time, to full-time driving. This makes possible a provisional account of the potential ecological impacts of the spread of this model of car service, based on the concept of taxicab efficiency, conceived as the ratio of shared versus unshared miles driven.     

Agent-based en-route diversion: Dynamic behavioral responses and network performance represented by Macroscopic Fundamental Diagrams

This paper focuses on modeling agents’ en-route diversion behavior under information provision. The behavior model is estimated based on naïve Bayes rules and re-calibrated using a Bayesian approach. Stated-preference driving simulator data is employed for model estimation. Bluetooth-based field data is employed for re-calibration. Then the behavior model is integrated with a simulation-based dynamic traffic assignment model. A traffic incident scenario along with variable message signs (VMS) is designed and analyzed under the context of a real-world large-scale transportation network to demonstrate the integrated model and the impact of drivers’ dynamic en-route diversion behavior on network performance. Macroscopic Fundamental Diagram (MFD) is employed as a measurement to represent traffic dynamics. This research has quantitatively evaluated the impact of information provision and en-route diversion in a VMS case study. It proposes and demonstrates an original, complete, behaviorally sound, and cost-effective modeling framework for potential analyses and evaluations related to Advanced Traffic Information System (ATIS) and real-time operational applications.     

Validation of in-car observations,a method for driver assessment

An in-car observation method with human observers in the car was studied to establish whether observers could be trained to observe safety variables and register driver’s behaviour in a correct and coherent way, and whether the drivers drove in their normal driving style, despite the presence of the observers. The study further discussed the observed variables from a safety perspective. First three observers were trained in the observation method and on-road observations were carried out. Their observations were then compared with a key representing a correct observation. After practising the observation method the observers showed a high correlation with the key. To establish whether the test drivers drove in a normal way during the in-car observations, comparisons of 238 spot-speed measurements were carried out. Driver’s speeds when driving their own private cars were compared with their speeds during the in-car observations. The analysis showed that the drivers drove in the same way when being observed as they did normally. Most of the variables studied in the in-car observations had a well documented relevance to traffic safety. Overall, in-car observation was shown to be a reliable and valid method to observe driver behaviour, and observed changes provide relevant data on traffic safety.     

A choice experiment on alternative fuel vehicle preferences of private car owners in the Netherlands

This paper presents results of an online stated choice experiment on preferences of Dutch private car owners for alternative fuel vehicles (AFVs) and their characteristics. Results show that negative preferences for alternative fuel vehicles are large, especially for the electric and fuel cell car, mostly as a result of their limited driving range and considerable refueling times. Preference for AFVs increases considerably with improvements on driving range, refueling time and fuel availability. Negative AFV preferences remain, however, also with substantial improvements in AFV characteristics; the remaining willingness to accept is on average € 10,000–€ 20,000 per AFV. Results from a mixed logit model show that consumer preferences for AFVs and AFV characteristics are heterogeneous to a large extent, in particular for the electric car, additional detour time and fuel time for the electric and fuel cell car. An interaction model reveals that annual mileage is by far the most important factor that determines heterogeneity in preferences for the electric and fuel cell car. When annual mileage increases, the preference for electric and fuel cell cars decreases substantially, whilst the willingness to pay for driving range increases substantially. Other variables such as using the car for holidays abroad and the daily commute also appear to be relevant for car choice.     

Pseudo-dynamic travel model application to assess traveler information

This paper reports an effort to estimate potential benefits of Advanced Traveler Information Systems (ATIS) by combing regional travel demand and microscopic simulation models. The approach incorporates dynamic features not yet available in the commercial software market. The suggested technique employs data that are readily available to most urban planning organizations, and is straightforward in its application. The key reported measure of effectiveness is corridor and local system delay, and is sensitive to both the level of penetration of traveler information and the pre-trip and en-route choices drivers make based on this information. The technique is demonstrated on an urban freeway corridor in a medium sized mid-west city.     

How experience of use influences mass-market drivers’ willingness to consider a battery electric vehicle: A randomised controlled trial

Uptake of electric vehicles (EVs) by consumers could reduce CO₂ emissions from light duty road transport, but little is known about how mass-market consumer drivers will respond to them. Self-Congruity theory proposes that products are preferred whose symbolic meanings are congruent with personal identity. Further, Construal Level theory suggests that only those who are psychologically close to a new product category through direct experience with it can make concrete construals related to their lifestyles; most drivers lack this for EVs. For instance, potential performance benefits of EVs might offset range limitations for consumers who have such direct experience. The effect of direct experience was tested in a randomised controlled trial with 393 mass-market consumer drivers. An experimental group were given direct experience of a modern battery electric vehicle (BEV), and a control group an equivalent conventional car. Despite rating the performance of the BEV more highly than that of the conventional car, willingness to consider a BEV declined after experience, particularly if the range of the BEV considered was short. The participants willing to consider a short-range BEV were those high in self-congruity, for whom the BEV could act as a strong symbol of personal identity.     

Effect of advanced traveler information systems and road pricing in a network with non-recurrent congestion

The effect of the application of advanced transport information system (ATIS) and road pricing is studied in a transportation system under non-recurrent congestion. A stochastic network deterministic user equilibrium model (SNDUE) with elastic demand is formulated and used to evaluate the welfare and private impacts of different market penetrations of ATIS, together with road pricing for a simple network. Both marginal first-best road pricing and a second-best fixed road pricing are considered. The incentives of private users to use ATIS are analyzed and the characteristics of optimum tolls as a function of ATIS market penetration are shown. We conclude that ATIS is an efficient and necessary tool to reduce the effects of non-recurrent incidents in a transportation network, especially when non-recurrent congestion causes a significant deterioration of operational conditions of the network. If the impact of non-recurrent incidents on free flow costs is small or is reduced only to congestion effects, the use of road pricing would be more efficient. Social benefits obtained when jointly implementing ATIS and road pricing are practically the same whether first-best or second-best road pricing is used. Considering the private costs perceived by the network users, and the benefits experienced by equipped users, the maximum level of market penetration achieved could be limited because private benefits disappear after certain market penetration is obtained.     

Use and Effects of Advanced Traveller Information Services (ATIS): A Review of the Literature

Abstract

Rapid technological developments in the field of personal communication services probe visions of a next generation in Advanced Traveller Information Services (ATIS). These technological developments provoke a renewed interest in the use and effect of such next‐generation ATIS among academia as well as practitioners. To understand better the potential use and effects of such next‐generation ATIS, a thorough review is warranted of contemporary conceptual ideas and empirical findings on the use of travel information (services) and their effects on travellers’ choices. This paper presents such a review and integrates behavioural determinants such as the role of decision strategies with manifest determinants such as trip contexts and socio‐economic variables into a coherent framework of information acquisition and its effect on travellers’ perceptions.     

The car pooling problem: Heuristic algorithms based on savings functions

The present research deals with car pooling as a means of making better use of existing infrastructure and as a means of reducing traffic congestion with all its associated induced effects. Car pooling schemes involve several drivers getting together to share a private vehicle simultaneously, in order to reach their destinations points according to a semi‐common route rather than each driver using their own vehicle. The Car Pooling Problem belongs to the non‐polynomial computational complexity family of operations problems. In the current literature there are only a few studies on this optimization problem: the research group has designed several different new automatic and heuristic data processing routines to support efficient matching in car pool schemes. These are based on savings functions and belong to two distinct macro classes of algorithms to give two different modelings of this problem. They offer average savings of more than 50% in traveled distances demonstrating the effectiveness of a trivial matching scheme for real applications.     

Is climate morality the answer? Preconditions affecting the motivation to decrease private car use

Persuasive messages are commonly used in campaigns promoting sustainable transport to motivate people to reduce private car use. This paper explores the preconditions affecting the motivation of people to reduce private car use when exposed to such messages. A sample of 1100 Swedish residents was analysed for the effect of variables related to accessibility, usual commute mode and attitudes. Significant variables were used to create a precondition index, which was cross-tabulated with demographic variables and stages drawn from the transtheoretical model. The results show that there are differences in the preconditions regarding motivation to reduce private car use between segments of the population. Results indicate that climate morality is the most critical factor affecting motivation, specifically the motivation of persistent drivers. Usual commute mode, car advocacy, health concern, attitudes towards cycling, car identity and travel time also influence motivation. Males, the middle-aged, people with low educational attainment, and rural residents have the least favourable preconditions concerning motivation to reduce private car use.     

Estimating the value of risk reductions for car drivers when pedestrians are involved: a case study in Spain

We estimated the benefits associated with reducing fatal and severe injuries from traffic accidents using a stated choice experiment where choice situations were generated through a statistically efficient design. Specifically, the risk variables were defined as the expected annual number of vehicle car-users that suffered their death or were severely injured in a traffic accident. In addition, and differing from previous research, the number of pedestrians that died or were severely injured in traffic accidents per year was also included as a risk attribute in the choice experiment, to attempt at measuring drivers’ willingness to pay to reduce the risk of hitting pedestrians in a crash. The empirical setting was a choice of route for a particular trip that a sample of car drivers periodically undertakes in Tenerife, Spain. Models were estimated accounting for random taste heterogeneity and pseudo-panel data correlation. The median of the distribution of simulated parameters was used to obtain a representative measure for the monetary valuation of risk reductions. We found that the ratio between the values of reducing the risk of suffering a serious injury and that of reducing a fatality was approximately 18 %. Further, and quite novel, we also found that the value of reducing a pedestrian fatality was 39 % of the value of reducing a car occupant fatality.     

Online calibration for microscopic traffic simulation and dynamic multi-step prediction of traffic speed

Simulating driving behavior in high accuracy allows short-term prediction of traffic parameters, such as speeds and travel times, which are basic components of Advanced Traveler Information Systems (ATIS). Models with static parameters are often unable to respond to varying traffic conditions and simulate effectively the corresponding driving behavior. It has therefore been widely accepted that the model parameters vary in multiple dimensions, including across individual drivers, but also spatially across the network and temporally. While typically on-line, predictive models are macroscopic or mesoscopic, due to computational and data considerations, nowadays microscopic models are becoming increasingly practical for dynamic applications. In this research, we develop a methodology for online calibration of microscopic traffic simulation models for dynamic multi-step prediction of traffic measures, and apply it to car-following models, one of the key models in microscopic traffic simulation models. The methodology is illustrated using real trajectory data available from an experiment conducted in Naples, using a well-established car-following model. The performance of the application with the dynamic model parameters consistently outperforms the corresponding static calibrated model in all cases, and leads to less than 10% error in speed prediction even for ten steps into the future, in all considered data-sets.     

Dynamics of commuting decision behaviour under advanced traveller information systems

This paper addresses departure time and route switching decisions made by commuters in response to Advanced Traveller Information Systems (ATIS). It is based on the data collected from an experiment using a dynamic interactive travel simulator for laboratory studies of user responses under real-time information. The experiment involves actual commuters who simultaneously interact with each other within a simulated traffic corridor that consists of alternative travel facilities with differing characteristics. These commuters can determine their departure time and route at the origin and their path en-route at various decision nodes along their trip. A multinomial probit model framework is used to capture the serial correlation arising from repeated decisions made by the same respondent. The resulting behavioural model estimates support the notion that commuters' route switching decisions are predicated on the expectation of an improvement in trip time that exceeds a certain threshold (indifference band), which varies systematically with the remaining trip time to the destination, subject to a minimum absolute improvement (about 1 min).