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A new cellular automaton model accounting for stochasticity in traffic flow induced by heterogeneity in driving behavior |
| Xiaoyong Ni(倪晓勇)1,2, Hong Huang(黄弘)1,2 |
1 Institute of Public Safety Research, Department of Engineering Physics, Tsinghua University, Beijing 100084, China;
2 Beijing Key Laboratory of City Integrated Emergency Response Science, Tsinghua University, Beijing 100084, Chinas |
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Abstract A new reliable cellular automaon (CA) model designed to account for stochasticity in traffic flow induced by heterogeneity in driving behavior is presented. The proposed model differs from most existing CA models in that this new model focuses on describing traffic phenomena by coding into its rules the key idea that a vehicle's moving state is directly determined by a driver stepping on the accelerator or on the brake (the vehicle's acceleration). Acceleration obeys a deformed continuous distribution function when considering the heterogeneity in driving behavior and the safe distance, rather than equaling a fixed acceleration value with a probability, as is the rule in many existing CA models. Simulation results show that the new proposed model is capable of reproducing empirical findings in real traffic system. Moreover, this new model makes it possible to implement in-depth analysis of correlations between a vehicle's state parameters.
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Received: 27 February 2019
Revised: 25 June 2019
Accepted manuscript online:
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PACS:
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89.40.-a
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(Transportation)
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45.70.Vn
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(Granular models of complex systems; traffic flow)
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07.05.Tp
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(Computer modeling and simulation)
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| Fund: Project supported by the National Key Research and Development Program of China (Grant No. 2018YFC0809900) and the National Natural Science Foundation of China (Grant Nos. 71774093 and 71473146). |
Corresponding Authors:
Hong Huang
E-mail: hhong@tsinghua.edu.cn
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Cite this article:
Xiaoyong Ni(倪晓勇), Hong Huang(黄弘) A new cellular automaton model accounting for stochasticity in traffic flow induced by heterogeneity in driving behavior 2019 Chin. Phys. B 28 098901
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