A Time-to-collision Hybrid Distribution Model Considering Congestion Under a Vehicle-to-vehicle Communication Environment

LAI Ziliang; WANG Jiangfeng; LI Ye; LIU Xinghua

doi:10.3963/j.jssn.1674-4861.2022.02.007

Volume 40 Issue 2

Apr. 2022

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Article Navigation > Journal of Transport Information and Safety > 2022 > 40(2): 53-62

LAI Ziliang, WANG Jiangfeng, LI Ye, LIU Xinghua. A Time-to-collision Hybrid Distribution Model Considering Congestion Under a Vehicle-to-vehicle Communication Environment[J]. Journal of Transport Information and Safety, 2022, 40(2): 53-62. doi: 10.3963/j.jssn.1674-4861.2022.02.007

Citation:

LAI Ziliang, WANG Jiangfeng, LI Ye, LIU Xinghua. A Time-to-collision Hybrid Distribution Model Considering Congestion Under a Vehicle-to-vehicle Communication Environment[J]. Journal of Transport Information and Safety, 2022, 40(2): 53-62. doi: 10.3963/j.jssn.1674-4861.2022.02.007

Citation:

LAI Ziliang, WANG Jiangfeng, LI Ye, LIU Xinghua. A Time-to-collision Hybrid Distribution Model Considering Congestion Under a Vehicle-to-vehicle Communication Environment[J]. Journal of Transport Information and Safety, 2022, 40(2): 53-62. doi: 10.3963/j.jssn.1674-4861.2022.02.007

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A Time-to-collision Hybrid Distribution Model Considering Congestion Under a Vehicle-to-vehicle Communication Environment

doi: 10.3963/j.jssn.1674-4861.2022.02.007

LAI Ziliang^{1, 2
,},
WANG Jiangfeng^{3
,
,},
LI Ye^{1, 2},
LIU Xinghua^{1, 2}

1.
The Key Laboratory of Road and Traffic Engineering of Ministry of Education, Tongji University, Shanghai 201804, China
2.
College of Transportation Engineering, Tongji University, Shanghai 201804, China
3.
Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport, Beijing Jiaotong University, Beijing 100044, China

Received Date: 2021-08-31
Available Online: 2022-05-18

Abstract

Abstract

Time-to-collision(TTC)is an effective variable to evaluate the risk of vehicle collision, however it is highly correlated with traffic states. In order to study the TTC distributionat different traffic states under a vehicle-to-vehicle(V2V)communication environment, a test environment based on the long-term evolution-vehicle (LTE-V) technology is developed, and a field experiment is carried out to collect driving behavior data on four typical urban roads. A dynamic conflict identification model considering acceleration and heading angle of tested vehicles is developed to estimate the TTC when the vehicle approached at any angle. Since there are several peaks with- in the distribution of the TTC data, traffic flows are divided into the following three states: congested, slow, and free-flow. A Gaussian mixture model(GMM) considering traffic congestion state is developed to describe the TTC distribution under different traffic states, and an expectation-maximization (EM) algorithm is used to estimate the parameters of the GMM. Three traditional distribution models of TTC including negative exponential, lognormal, and negative exponential / lognormal mixed are compared with the GMM. The goodness of fit of the model is evaluated by adjusted R², and the effectiveness of the model is verified by a K-S test. Then, the GMM is applied to the description of TTC distribution fitting under the condition of non V2V communication to further verify the applicability of the model. The results show that, the mean of Gaussian distribution for three traffic states of"congested, slow, and free-flow"gradually increases in the V2V communication environment, and the collision risk of each traffic scene decreases in turn. Moreover, the goodness of fit of the GMM is 0.950 5, which is 0.057 5 higher than the other mixed distribution models.
- intelligent transportation,
- time-to-collision,
- Gaussian mixture model,
- traffic state,
- vehicle-to-vehicle communication,
- expectation-maximization algorithm

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References(23)

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