01657404

Component

Employing different types of incentives in transportation systems to form advanced transportation congestion management solutions have recently drawn increasing attention. Instead of using presumed and fixed-amount incentives, this paper develops an integrated, personalized and real-time traveler information and incentive technology to incentivize more energy-efficient travel and mobility decisions. A behaviorally sound and computationally efficient agent-based modeling system is employed to simulate the entire transportation system in real-time. Then, a control optimization is developed based on behavior research, travel intent prediction, and incentive optimization. This system-level simulation and optimization is empowered by big-data analytics that provide a wide spectrum of transportation and traffic data in real-time. Through a demonstrative case study for a large-scale transportation system of the D.C. – Baltimore region, the capability of the proposed technology is highlighted with significant system-level energy savings, reasonable insights on individual travel behavior responses, as well as superior computational efficiency.

This paper was sponsored by TRB committee ADB10 Standing Committee on Traveler Behavior and Values.

18-01842

English

2018

Transportation Research Board 97th Annual Meeting

Digital/other

Figures

Ecodriving; Incentives; Mobility; Mode choice; Real time information; Route choice; Travel behavior; Traveler information and communication systems

Data and Information Technology; Energy; Transportation (General)

Transportation Research Board Annual Meeting 2018 Paper #18-01842

TRIS, TRB, ATRI

Jan 8 2018 10:27AM