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Mathematical Problems in Engineering
Volume 2013, Article ID 967358, 8 pages
Research Article

A Neural Network Model for Driver’s Lane-Changing Trajectory Prediction in Urban Traffic Flow

1Department of Transportation Engineering, Beijing Institute of Technology, Beijing 100081, China
2Institut für Verkehrssystemtechnik, Deutsche Zentrum für Luft-und Raumfahrt, Lilienthalplatz 7, 38108 Braunschweig, Germany

Received 13 September 2012; Accepted 10 November 2012

Academic Editor: Huimin Niu

Copyright © 2013 Chenxi Ding et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


The neural network may learn and incorporate the uncertainties to predict the driver’s lane-changing behavior more accurately. In this paper, we will discuss in detail the effectiveness of Back-Propagation (BP) neural network for prediction of lane-changing trajectory based on the past vehicle data and compare the results between BP neural network model and Elman Network model in terms of the training time and accuracy. Driving simulator data and NGSIM data were processed by a smooth method and then used to validate the availability of the model. The test results indicate that BP neural network might be an accurate prediction of driver’s lane-changing behavior in urban traffic flow. The objective of this paper is to show the usefulness of BP neural network in prediction of lane-changing process and confirm that the vehicle trajectory is influenced previously by the collected data.