Adaptive routing behavior with real-time information under multiple travel objectives

•A web-based experiment is used to study driver behavior with real-time information.•The experiment simulates various travel objectives using disutility functions.•Participant decision strategies were analyzed from over 40,000 data points.•A general model is proposed to describe driver behavior stra...

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Published inTransportation research interdisciplinary perspectives Vol. 10; p. 100395
Main Authors Venkatraman, Ravi, Boyles, Stephen D., James, Rachel, Unnikrishnan, Avinash, Patil, Priyadarshan N.
Format Journal Article
LanguageEnglish
Published Elsevier Ltd 01.06.2021
Elsevier
Subjects
Adaptive routing
Driver behavior
Online shortest path
Real-time information
Routing games
Real-time information
Adaptive routing
Routing games
Online shortest path
Driver behavior
Online AccessGet full text
ISSN2590-1982
2590-1982
DOI10.1016/j.trip.2021.100395

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Abstract •A web-based experiment is used to study driver behavior with real-time information.•The experiment simulates various travel objectives using disutility functions.•Participant decision strategies were analyzed from over 40,000 data points.•A general model is proposed to describe driver behavior strategies.•Trip objective, position within network, and experience influence driver decisions. Real-time information about traffic conditions is becoming widely available through various media and connected-vehicle technology. In such conditions, travelers have better knowledge about the system and adapt as the system evolves dynamically during their travel. Drivers may change routes during their travel in order to optimize their own objective of travel. Various travel objectives are captured in mathematical models via disutility functions. The focus of this research was to study the behavior of travelers with multiple trip objectives when they are provided real-time information, and assess their ability to determine “optimal” routing policies, compared to exact solutions based on the online shortest path problem. A web-based experiment was carried out to simulate a traffic network with limited information provision. The decision strategies of participants were analyzed and compared to a variety of decision policies established in the literature – optimal, greedy, and a priori – and a general model to describe the observed travelers’ decision strategies was calibrated from over 40,000 decision points extracted from the collected data. Apart from trip objective, other factors such as relative position in the network and experience gained were found to influence user decisions. This research is a step towards calibrating equilibrium models for adaptive behavior with multiple user classes.
AbstractList Real-time information about traffic conditions is becoming widely available through various media and connected-vehicle technology. In such conditions, travelers have better knowledge about the system and adapt as the system evolves dynamically during their travel. Drivers may change routes during their travel in order to optimize their own objective of travel. Various travel objectives are captured in mathematical models via disutility functions. The focus of this research was to study the behavior of travelers with multiple trip objectives when they are provided real-time information, and assess their ability to determine “optimal” routing policies, compared to exact solutions based on the online shortest path problem. A web-based experiment was carried out to simulate a traffic network with limited information provision. The decision strategies of participants were analyzed and compared to a variety of decision policies established in the literature – optimal, greedy, and a priori – and a general model to describe the observed travelers’ decision strategies was calibrated from over 40,000 decision points extracted from the collected data. Apart from trip objective, other factors such as relative position in the network and experience gained were found to influence user decisions. This research is a step towards calibrating equilibrium models for adaptive behavior with multiple user classes.
•A web-based experiment is used to study driver behavior with real-time information.•The experiment simulates various travel objectives using disutility functions.•Participant decision strategies were analyzed from over 40,000 data points.•A general model is proposed to describe driver behavior strategies.•Trip objective, position within network, and experience influence driver decisions. Real-time information about traffic conditions is becoming widely available through various media and connected-vehicle technology. In such conditions, travelers have better knowledge about the system and adapt as the system evolves dynamically during their travel. Drivers may change routes during their travel in order to optimize their own objective of travel. Various travel objectives are captured in mathematical models via disutility functions. The focus of this research was to study the behavior of travelers with multiple trip objectives when they are provided real-time information, and assess their ability to determine “optimal” routing policies, compared to exact solutions based on the online shortest path problem. A web-based experiment was carried out to simulate a traffic network with limited information provision. The decision strategies of participants were analyzed and compared to a variety of decision policies established in the literature – optimal, greedy, and a priori – and a general model to describe the observed travelers’ decision strategies was calibrated from over 40,000 decision points extracted from the collected data. Apart from trip objective, other factors such as relative position in the network and experience gained were found to influence user decisions. This research is a step towards calibrating equilibrium models for adaptive behavior with multiple user classes.
ArticleNumber 100395
Author Boyles, Stephen D.
Venkatraman, Ravi
Patil, Priyadarshan N.
James, Rachel
Unnikrishnan, Avinash
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Keywords Real-time information
Adaptive routing
Routing games
Online shortest path
Driver behavior
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Snippet •A web-based experiment is used to study driver behavior with real-time information.•The experiment simulates various travel objectives using disutility...
Real-time information about traffic conditions is becoming widely available through various media and connected-vehicle technology. In such conditions,...
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StartPage 100395
SubjectTerms Adaptive routing
Driver behavior
Online shortest path
Real-time information
Routing games
Title Adaptive routing behavior with real-time information under multiple travel objectives
URI https://dx.doi.org/10.1016/j.trip.2021.100395
https://doaj.org/article/fb42afb83c5a425b88be33996e4043e4
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