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

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  • Chenxi Ding
  • Wuhong Wang
  • Xiao Wang
  • Martin Baumann

Abstract

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.

Suggested Citation

  • Chenxi Ding & Wuhong Wang & Xiao Wang & Martin Baumann, 2013. "A Neural Network Model for Driver’s Lane-Changing Trajectory Prediction in Urban Traffic Flow," Mathematical Problems in Engineering, Hindawi, vol. 2013, pages 1-8, February.
    • Handle: RePEc:hin:jnlmpe:967358
    DOI: 10.1155/2013/967358
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    Cited by:

    1. Shang, Xue-Cheng & Li, Xin-Gang & Xie, Dong-Fan & Jia, Bin & Jiang, Rui & Liu, Feng, 2022. "A data-driven two-lane traffic flow model based on cellular automata," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 588(C).
    2. Zhao, Fangxia & Shang, HuaYan & Cui, JiHui, 2023. "Role of electric vehicle driving behavior on optimal setting of wireless charging lane," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 618(C).

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