Bicycle’s Trajectory Prediction in Pedestrian-Bicycle Mixed Sections Based on Dynamic Bayesian Networks

  • Hai-bo Wang
  • Xiao-yuan WangEmail author
  • Ya-qi Liu
  • Dong Kong
  • Li-ping Liu
  • Chen Chen
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 503)


Bicycle is the main factor that affects the traffic safety and the road capacity in pedestrian–bicycle sections of mixed traffic. It is important for implementing the bicycle safety warning and improving the active safety to predict bicycle trajectory in the mixed traffic environments under the condition of internet of things. The mutual influence of bicycle and its surrounding traffic participants in mixed pedestrian-bicycle sections was comprehensively analyzed and the phase of pedestrian-bicycle traffic was defined and reduced on the basis of phase field coupling theory. The experimental result shows that the model established in this paper has high accuracy and real-time performance, which provides the theoretical basis for the future research on the reduction of pedestrian-bicycle traffic conflicts and the construction of pedestrian-bicycle interactive security system.


Phase field coupling Pedestrian-bicycle mixed traffic Trajectory prediction Safety warning Traffic phase 



This study was supported by the State Key Laboratory of Automotive Safety and Energy under Project No. KF16232, Natural Science Foundation of Shandong Province (Grant No. ZR2014FM027, ZR2016EL19), Social Science Planning Project of Shandong Province (Grant No. 14CGLJ27), Project of Shandong Province Higher Educational Science and Technology Program (Grant No. J15LB07), and the National Natural Science Foundation of China (Grant No. 61074140, 61573009, 51508315, 51608313).


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Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Hai-bo Wang
    • 1
    • 3
  • Xiao-yuan Wang
    • 1
    • 2
    Email author
  • Ya-qi Liu
    • 1
  • Dong Kong
    • 1
  • Li-ping Liu
    • 1
  • Chen Chen
    • 1
  1. 1.Institute of Intelligent Transportation, School of Transportation and Vehicle EngineeringShandong University of TechnologyZiboChina
  2. 2.State Key Laboratory of Automotive Safety and EnergyTsinghua UniversityHaidian QuChina
  3. 3.College of Civil Aviation and FlightNanjing University of Aeronautics and AstronauticsNanjingChina

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