Modeling the dynamic effect of information on drivers' choice behavior in the context of an Advanced Traveler Information System (original) (raw)
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Modelling Drivers’ Route Choice Behaviour through Possibility Theory Using Driving Simulator
This paper presents a modelling approach based on the Possibility Theory to reproduce drivers' choice behaviour under Advanced Traveller Information Systems (ATIS). The Possibility Theory is introduced to model uncertainty embedded in human perception of information through a fuzzy data fusion technique. Drivers' choice models are often developed and calibrated by using, among other, Stated Preferences (SP) surveys. An experiment is presented, aimed at setting up an SP-tool based on driving simulator developed at the Technical University of Bari. The obtained results are analysed in order to compare the outcomes of the proposed model with preferences stated in the experiment.
Application of Data Fusion for Route Choice Modelling by Route Choice Driving Simulator
Advances in Intelligent Systems and Computing, 2013
Modelling route choices is one of the most significant tasks in transportation models. Route choice models under Advanced Traveller Information Systems (ATIS) are often developed and calibrated by using, among other, Stated Preferences (SP) surveys. Different types of SP approaches can be adopted, alternatively based on Travel Simulators (TSs) or Driving Simulators (DSs). Here a pilot study is presented, aimed at setting up an SP-tool based on driving simulator developed at the Technical University of Bari. The obtained results are analysed in order to check the accordance with expectations in particular the results of application of data fusion technique are shown in order to explain how data collected by DSs, can be used to reduce the effect of choice of behaviour in unrealistic scenarios in TSs.
A rational approach to handling fuzzy perceptions in route choice
European Journal of Operational Research, 2006
The purpose of this paper is to develop a heuristic way for handling fuzzy perceptions in explaining route choice behavior from behavioral point of view. A hybrid model where route choice decision making is described in a hierarchy uses concepts from fuzzy logic and the analytical hierarchy process (AHP) is proposed for making possible a more proper description of route choice behavior in transportation systems. Teodorovic and KikuchiÕs [Transportation route choice model using fuzzy inference technique, Paper presented at the First International Symposium on: Uncertainty Modeling and Analysis: Fuzzy Reasoning, Probabilistic Models, and Risk Management, University of College Park, Maryland, 1990, p. 140] fuzzy Ôif-thenÕ rules are adopted to represent a typical driverÕs psychology for capturing essential preferences, pairwise, among alternatives that a driver may consider. The AHP is then incorporated in this model to capture the imaginary psychological process that represent underlying observable behavior to estimate driversÕ preference allotment among the alternatives. This new procedure is applied in a real world sample based on stated values of subjects. Findings show that this method provides intuitively and statistically promising results.
Dynamics of commuting decision behaviour under advanced traveller information systems
Transportation Research Part C-emerging Technologies, 1999
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).
EURO Journal on Transportation and Logistics, 2016
This paper is derived from one of our research mainstream, research theme: TRAFFIC & TRANSPORTATION PSYCHOLOGY ; TRAVEL PSYCHOLOGY Abstract: Travel time perception and learning play a central role in the modeling of day-to-day travel choice dynamics in traffic networks and have attracted the attention of many researchers, specifically for the analysis and operation of intelligent transportation systems and travel demand management scenarios. In this paper, a fuzzy learning model is proposed to capture the mechanism by which travelers update their travel time perceptions from one day to the next, taking into account their experienced travel times. In order to capture travelers’ mental representations of uncertain travel time involving imprecision and uncertainty, a combined artificial neural network and fuzzy logic (neuro-fuzzy) architecture called Adaptive-Network-based Fuzzy Inference System (ANFIS) is employed. This framework, which utilizes a set of fuzzy if-then rules, can serve as a basis for modeling the qualitative sides of travelers’ knowledge and reasoning processes. From the output of this study, the results of our laboratory-like experiment provide a good fit to the stated data of travelers’ behavior, and may reflect the fact that the neuro-fuzzy approach can be considered a promising method in learning and perception updating models. Finally, the proposed learning model is embedded in a microscopic event based simulation framework to evaluate its credibility within a day-to-day behavior of the traffic network. The results of the simulation, which converge to the equilibrium state of the test network, are finally presented, implying that the proposed perception-updating model operates properly.
Pseudo-dynamic travel model application to assess traveler information
Transportation, 2002
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.
Procedia Computer Science, 2020
Advance Traveler Information Systems (ATIS) inform drivers about traffic incidences and expected travel times/ delays en-route. An online computer study was conducted in Qatar to investigate drivers' willingness to divert to an alternative route given changes in expected travel conditions. Respondents' route choices were queried after exposure for 6 seconds to varying display strategies. The results from a binary logistic regression and a stated preference survey showed that delay times and displayed colors on a Graphical Route Information Panel (GRIP) effectively influence drivers to take the alternative route, while total travel times were preferred for Variable Message Signs (VMS).
Driver's response towards traffic information under travel time variability
2006
Advanced Traveler Information System (ATIS) has been applied in many countries because it has been proved as one of potential solutions to solve congestion problem and to improve the quality of driving. Providing drivers with additional traffic information, it is expected to influence their travel decision on route and destination selection, or even on cancellation of the trip. A number of studies have been conducted in order to improve the performance of ATIS in various ways, such as the setting of the system, the content of information, the accuracy of the information, etc. Yet the effectiveness of the system is heavily dependent on the driver's behavior and response to the given information. The behaviors are somewhat unique for specific individuals (cities). The primary objective of this study wais to analyze the behavior of drivers in different South East Asia major cities; Bangkok (Thailand), Kuala Lumpur (Malaysia), and Singapore (Singapore), pertaining changing route und...
The Impact of Travel Time Information on Travellers' Learning under Uncertainty
2003
In this work, route-choice simulations and laboratory experiments were conducted in order to evaluate the effect of feedback mechanism on decision-making under uncertainty, with and without provided information about travel times. We discuss the prediction of travellers' response to uncertainty in two route-choice situations. In the first situation travellers are faced with a route-choice problem in which travel times are uncertain but some information (which may be static or dynamic) about travel times of each (or some) route is provided. The second situation takes place in a more uncertain environment in which information about routes is not provided, and the travellers' only source of information is their own experience. Experimental results are in conflict with the paradigm about traveller information systems: providing travellers with information will not necessarily lead them to make better decisions. There are situations when propensity to choose a more efficient route might be decreased (instead of increased) when travel time information about the routes is provided. As a consequence of information, the propensity of travellers to maximize utility is not always increased. It was found out that providing travellers with static information about expected travel times increases the nonhomogeneity of travellers and reduces the maximization rate. These findings are described and explained. This better understanding of route-choice behaviour may improve traffic predictions based on route-choice modelling. The design of better cost-effective ATIS may benefit from such an insight.