An impact analysis of traffic image information system on driver travel choice |
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Institution: | 1. KU Leuven, Center for Economic Studies, Naamsestraat 69, 3000 Leuven, Belgium;2. Department of Transport Science, KTH Royal Institute of Technology, Teknikringen 10, Stockholm, Sweden;1. State Key Laboratory of Integrated Service Networks, Xidian University, Xian 710071, China;2. Department of Computer Science and Digital Technologies, Northumbria University, Newcastle upon Tyne NE1 8ST, UK;1. The State Key Laboratory of Information Engineering in Surveying, Mapping, and Remote Sensing, Wuhan University, Wuhan 430079, China;2. School of Computer, Wuhan University, Wuhan 430072, China;1. University of Goettingen, Chair of Information Management, Platz der Goettinger Sieben 5, 37073 Goettingen, Germany;2. Technische Universität Dresden, Chair of Business Informatics, esp. Intelligent Systems and Services, Helmholtzstrasse 10, 01069 Dresden, Germany |
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Abstract: | 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. |
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Keywords: | Route choice Departure time choice Level of congestion Fuzzy logic Genetic algorithm |
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