Advertisement

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

  • Hai-bo Wang
  • Xiao-yuan Wang
  • 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)

Abstract

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.

Keywords

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

Notes

Acknowledgment

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).

References

  1. 1.
    Jeung H, Liu Q, Shen HT, Zhou X A hybrid prediction model for moving objects. In: Proceedings of the 24th international conference on data engineering. IEEE Computer Society, WashingtonGoogle Scholar
  2. 2.
    Mokhtar H, Su J (2004) Universal trajectory queries for moving object databases. In: IEEE international conference on mobile data management, pp 133–144Google Scholar
  3. 3.
    Nodelman U, Shelton CR, Koller D (2003) Learning continuous time Bayesian networks. In: Proceedings of the 19th international conference on uncertainty in artificial intelligence, pp 451–458Google Scholar
  4. 4.
    Qiao S, Shen D, Wang X, Han N, Zhu W (2015) A self-adaptive parameter selection trajectory prediction approach via hidden Markov models. IEEE Trans Intell Transp Syst 16(1):284–296CrossRefGoogle Scholar
  5. 5.
    Qiao S, Jin K, Han N, Tang C, Gesangduoji LA (2015) Trajectory prediction algorithm based on Gaussian mixture mode. J Softw 26(5):1048–1063MathSciNetGoogle Scholar
  6. 6.
    Qiao S, Tang C, Jin H, Long T, Dai S, Ku Y, Chau M (2010) PutMode: prediction of uncertain trajectories in moving objects databases. Appl Intell 33(3):370–386CrossRefGoogle Scholar
  7. 7.
    Ding Z, Li X, Yu B (2009) Indexing the historical, current, and future locations of network-constrained moving objects. J Softw 12:3193–3204CrossRefGoogle Scholar
  8. 8.
    Wang X, Zhang J, Ban X, Gao S (2013) Study on transformation mechanism of driver’s propensity under two-lane conditions. Appl Mech Mater 411–414:1911–1918CrossRefGoogle Scholar
  9. 9.
    Karami AH, Hasanzadeh M (2015) An adaptive genetic algorithm for robot motion planning in 2D complex environments. Comput Electr Eng 43(4):317–329CrossRefGoogle Scholar
  10. 10.
    Rose C, Smaili C (2005) A dynamic Bayesian network for handling uncertainty in a decision support system adapted to the monitoring of patients treated by hemodialysis. In: Proceedings of the 17th IEEE international conference on tools with artificial intelligenceGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Hai-bo Wang
    • 1
    • 3
  • Xiao-yuan Wang
    • 1
    • 2
  • 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

Personalised recommendations