A Hybrid Tree Approach to Modeling Alternate Route Choice Behavior With Online Information

作者：Lee Chanyoung^*; Ran Bin; Yang Fan; Loh Wei Yin

来源：Journal of Intelligent Transportation Systems: Technology, Planning, and Operations , 2010, 14(4): 209-219.

DOI：10.1080/15472450.2010.516229

• Driver Compliance
• Variable-Message Signs
• Logistic Regression
• Artificial Neural Networks
• Hybrid Tree
• Data Mining
• Advanced Traveler Information Systems

One popular intelligent transportation systems (ITS) element that has been deployed in various areas of the United States is variable-message signs (VMSs). Such signs are part of advanced traveler information systems, and they can help drivers make better en route decisions regarding their trips. Unlike most other ITS elements (e.g., ramp metering, advanced signals), VMSs are not supported by mandatory regulations. Consequently, the expected benefit from implementing VMSs largely depends on the response of drivers. This study explored the factors affecting alternative route choices of car drivers with VMSs on the interstate highway system. This study adopted a method called LOTUS to analyze driver-compliance behavior with VMSs. Driver behavior under information provision through VMS data has been collected using the stated-preference approach. The study showed that travel-time saving is not the single dominant factor for driver route choice under information provision. This article explores variables that are associated with driver compliance.

• 出版日期2010