Cyclist’s Intention Identification on Pedestrian-Bicycle Mixed Sections Based on Phase-Field Coupling Theory

  • Haibo Wang College Civil Aviation and Flight, Nanjing University of Aeronautics and Astronautics
  • Haiqing Si College Civil Aviation and Flight, Nanjing University of Aeronautics and Astronautics
  • Xiaoyuan Wang College of Electromechanical Engineering, Qingdao University of Science and Technology
Keywords: phase-field coupling theory, pedestrian-bicycle mixed traffic section, intention identification, safety warning, traffic phase

Abstract

Bicycle is one of the main factors that affects the traffic safety and capacity on pedestrian-bicycle mixed traffic sections. It is important for implementing the warning of bicycle safety and improving the active safety to identify the cyclists’ intention in the mixed traffic environments under the condition of the “Internet of Things”. The phase-field coupling theory has been developed in this paper to comprehensively analyse the generation, spring up, increase, transfer, regression and reduction method of the traffic phase. The adaptive genetic algorithm based on the information entropy has been used to extract feature vectors of different types of cyclists for intention identification from the reduced pedestrian-bicycle traffic phase, and the theory of evidence has been provided here to build the identification model. The experimental verification shows that the extraction method of cyclists’ intention feature vector and identification model are scientific and reasonable. The theoretical basis can be applied to establishing the pedestrian-bicycle interactive security system.

Author Biographies

Haibo Wang, College Civil Aviation and Flight, Nanjing University of Aeronautics and Astronautics

Wang haibo is a doctoral candidate at the Nanjing University of Aeronautics and Astronautics, and major in transportation engineering. His research direction is the intelligent and control of Human-vehicle environment synergy.

Haiqing Si, College Civil Aviation and Flight, Nanjing University of Aeronautics and Astronautics

Si haiqing works as a professor with the College Civil Aviation and Flight, Nanjing University of Aeronautics and Astronautics. He is specialized in Aviation safety engineering, especially in cooperative intelligence of human-aircraft-environment. He has authored or coauthored more than 30 publications in international journals.

Xiaoyuan Wang, College of Electromechanical Engineering, Qingdao University of Science and Technology

 Wang xiaoyuan works as a professor with the College of Electromechanical Engineering, Qingdao University of Science and Technology. He also served as a visiting research fellow in State Key Laboratory of Automotive Safety and Energy, Tsinghua University. He is specialized in intelligent transportation systems, especially in cooperative intelligence of human-vehicle-environment. He has authored or coauthored more than 100 publications in international journals.

References

Hossain M. Capacity estimation of traffic circles under mixed traffic conditions using micro-simulation technique. Transportation Research Part A. 1999;33(1): 47-61.

Zhao D, Wang W, Li C, Li Z, Fu P, Hu X. Modeling of Passing Events in Mixed Bicycle Traffic with Cellular Automata. Transportation Research Record Journal of the Transportation Research Board. 2013;2387(2387): 26-34.

Wang H. New vector field microcosmic model for flow. China Journal of Highway and Transport. 2003;16(2): 99-102.

Jia B, Li X, Jiang R, Gao ZY. Multi-value cellular automata model for mixed bicycle flow. European Physics Journal B. 2007;56(3): 247-252.

Zhao X, Jia B, Gao Z, Jiang R. Traffic interactions between motorized vehicles and nonmotorized vehicles near a bus stop. Journal of Transportation Engineering-ASCE. 2009;135(11): 894-906.

Huang L, Wu J. A Study on the cyclist’s behavior at signalized intersections. IEEE Transactions on Intelligent Transportation System. 2004;5(4): 317-322.

Liang X, Mao B, Xu Q. Perceptual Process for Bicyclist Microcosmic Behavior. Procedia – Social and Behavioral Sciences. 2012;43(4): 540-549.

Chen DF. Modeling and Analyzing of Mixed Traffic Flow on the Urban Road Section Based on Social Force. MS thesis. Central South University, China; 2011.

Chen J, Xie Z. Cycle traffic conflict model on urban pedestrian-bicycle paths. Journal of Jilin University (Engineering and Technology Edition). 2009;39(2): 121-125.

Deng J. Mixed Traffic Flow Cellular Automaton Model Based on Shared-Use Sidewalk. Journal of Transportation Systems Engineering and Information Technology. 2011;11(2): 155-159.

Yu Z, Liu P, Long Y. Research Progress of Phase-field Method Based on Ginzburg-Landau Theory. Material & Heat Treatment. 2008;37(16): 94-98.

Wang X, Zhang J, Ban XJ, Tan D. Dynamic Feature Extraction Method of Driver’s Propensity under Complicated Vehicle Group. Advances in Mechanical Engineering. 2013; 1-10.

Wei L, Ying T. Modeling and simulation on bicycle traffic flow based on cellular automaton. Journal of Jilin University (Engineering and Technology Edition). 2011;41(1): 51-55.

Wu L, Wang X, Yang X, Wang X. Study on the recognition of traffic situation and its state transition mechanism. Communications Standardization. 2007;(2-3): 61-65.

Wang X, Zhang J, Liu Y, Wang Y, Wang F, Wang J. The drivers’ lane selection model based on mixed fuzzy many-person multi-objective non-cooperative game. Journal of Intelligent & Fuzzy Systems. 2017;32: 4235-424.

Wang K. Study on the coupling mechanism of vehicle cluster situation and driver’s propensity. MS thesis. Shandong University Technology, China; 2015.

Li F, Qian Y, Wang J, Liang J. Multigranulation information fusion: a Dempster-Shafer evidence theory-based clustering ensemble method. Information Sciences. 2016;1(10): 58-63.

Karami AH, Hasanzadeh M. An adaptive genetic algorithm for robot motion planning in 2D complex environments. Computers & Electrical Engineering. 2015;43(4): 317-329.

Published
2019-06-07
How to Cite
1.
Wang H, Si H, Wang X. Cyclist’s Intention Identification on Pedestrian-Bicycle Mixed Sections Based on Phase-Field Coupling Theory. PROMET [Internet]. 2019Jun.7 [cited 2019Jun.19];31(3):233-44. Available from: http://traffic.fpz.hr/index.php/PROMTT/article/view/2927
Section
Articles