Modeling Individual Travel Time with Back Propagation Neural Network Approach for Advanced Traveler Information Systems
Publication: Journal of Transportation Engineering, Part A: Systems
Volume 146, Issue 6
Abstract
The heterogeneous driving behaviors from different travelers are not considered in current advanced traveler information systems (ATIS) such as Google Maps and 511 systems, which leads the systems to generate the same travel time for everyone who inputs the same origin and destination. This paper explores the modeling of individualized travel time based on the individual behavior of each driver as opposed to average traffic information, with the ultimate goal of enabling individualized traffic information provision for the ATIS and subsequently reducing travel-time prediction errors. A back propagation neural network model was built to quantitatively estimate the driving behavior differences (i.e., the delta) between individual drivers and the surrounding traffic, with both roadway geometrics and dynamic traffic conditions considered in the modeling process. A travel-time estimation algorithm is then proposed to derive link-level traffic information that considers individual behavioral difference. Finally, individualized route travel time is computed for each traveler based on the derived link-level traffic information and individual behavioral difference. The proposed model is implemented and tested on an open-source Next Generation Simulation (NGSIM) dataset, which demonstrated the feasibility and effectiveness of the proposed model. The proposed model has the potential of being directly applied to enhance existing ATIS travel-time prediction accuracies.
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Data Availability Statement
Some or all data, models, or code generated or used during the study are available from the corresponding author by request (including BPNN model training results and validation results).
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Published In
Journal of Transportation Engineering, Part A: Systems
Volume 146 • Issue 6 • June 2020
Copyright
©2020 American Society of Civil Engineers.
History
Received: Feb 3, 2019
Accepted: Nov 20, 2019
Published online: Mar 27, 2020
Published in print: Jun 1, 2020
Discussion open until: Aug 27, 2020
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