01516125

Component

The research presented in this paper develops an agent-based modeling approach to predict experienced travel times using real-time and historical spatiotemporal traffic data. At the microscopic level, each agent represents an expert in the decision-making system, which predicts the travel time for each time interval according to experiences from a historical dataset. A set of agent interactions are developed to preserve agents that correspond to traffic patterns similar to the real-time measurements and replace invalid agents or agents with negligible weights with new agents. Consequently, the aggregation of each agent’s recommendation (predicted travel time with associated weight) provides a macroscopic level output – a predicted travel time distribution. Probe vehicle data from a 95-mile freeway stretch along I-64 and I-264 was used to test the proposed method. The results show that the agent-based modeling approach produces the least prediction error compared to other state-of-practice and state-of-art methods (instantaneous travel time, historical average and K-Nearest Neighbor), and maintains less than a 9% prediction error for trip departures up to 60 minutes into the future for a two-hour trip. Moreover, the confidence boundaries of the predicted travel times demonstrate that the proposed approach also provides high accuracy in predicting travel time confidence intervals. Finally, no offline training is required for the proposed approach making it easily transferrable to other locations and the fast computation time ensures the proposed method can be used in real-time applications.

This paper was sponsored by TRB committee ABJ30(3) Travel Time, Speed and Reliability.

01503729

14-3851

English

20p

2014

Transportation Research Board 93rd Annual Meeting

Digital/other

Figures (6) ; Maps; Photos (36) ; References (1) ; Tables

Data collection; Macroscopic traffic flow; Mathematical prediction; Microscopic traffic flow; Physical distribution; Probe vehicles; Real time information; Traffic simulation; Traffic speed; Travel time

Data and Information Technology; Highways; Operations and Traffic Management; I73: Traffic Control

Transportation Research Board Annual Meeting 2014 Paper #14-3851

TRIS, TRB, ATRI

Jan 27 2014 3:19PM