Agent-based en-route diversion: Dynamic behavioral responses and network performance represented by Macroscopic Fundamental Diagrams. (March 2016)

• Record Type: Journal Article
• Authors: Xiong, Chenfeng; Chen, Xiqun; He, Xiang; Lin, Xi; Zhang, Lei
• Is Part Of: Transportation research. Volume 64(2016)
• Page Start: 148
• Page End: 163
• Publication Date: 2016-03
• Subjects: En-route diversion -- Agent-based simulation -- Dynamic traffic assignment -- Macroscopic Fundamental Diagram; Transportation -- Periodicals; Transportation -- Technological innovations -- Periodicals; 388.011
• DOI: 10.1016/j.trc.2015.04.008
• Languages: English
• ISSNs: 0968-090X

Abstract

Highlights: It develops a comprehensive agent-based framework for en-route diversion modeling. Agent behavior is modeled and calibrated through a consistent parametric approach. The model is highly transferable. The model is successfully implemented for a real-world case study in Maryland. A novel time-dependent performance measure is defined based on Macroscopic Fundamental Diagram. Abstract: 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.