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)

References

[1]	HAYWARD J C. Near-miss determination through use of a scale of danger[R]. Washington, D. C. : Highway Research Board, 1972.
[2]	VOGEL K. A comparison of headway and time to collision as safety indicators[J]. Accident Analysis & Prevention, 2003, 35(3): 427-433.
[3]	MILANES V, PEREZ J, GODOY J, et al. A fuzzy aid rear-end collision warning/avoidance system[J]. Expert Systems with Applications, 2012, 39(10): 9097-9107. doi: 10.1016/j.eswa.2012.02.054
[4]	NADIMI N, RAGLAND D R, AMIRI A M, et al. An evaluation of time-to-collision as a surrogate safety measure and a proposal of a new method for its application in safety analysis[J]. Transportation Letters-The International Journal of Transportation Research, 2020, 12(7): 491-500.
[5]	吴子祥, 黄合来, 陈吉光, 等. 视线遮挡条件下面向弱势道路使用者的避撞策略研究[J]. 交通信息与安全, 2021, 39(4): 9-15+34. https://www.cnki.com.cn/Article/CJFDTOTAL-JTJS202104003.htm WU Z X, HUANG H L, CHEN J G, et al. A study on collision avoidance strategy for vulnerable road users under visual obstruction[J]. Journal of Transport Information and Safety, 2021, 39(4): 9-15+34. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-JTJS202104003.htm
[6]	SAFFARZADEH M, NADIMI N, NASERALAVI S, et al. A general formulation for time-to-collision safety indicator[J]. Proceedings of the Institution of Civil Engineers-Transport, 2013, 166(5): 294-304. doi: 10.1680/tran.11.00031
[7]	HUANG C M, LIN S Y. An advanced vehicle collision warning algorithm over the DSRC communication environment: an advanced vehicle collision warning algorithm[C]. IEEE 27^th International Conference on Advanced Information Networking and Applications(IEEE AINA), Barcelona, Spain: IEEE Computer Society, 2013.
[8]	刘庆华, 邱修林, 谢礼猛, 等. 基于行驶车速的车辆防撞时间预警算法[J]. 农业工程学报, 2017, 33(12): 99-106. https://www.cnki.com.cn/Article/CJFDTOTAL-NYGU201712013.htm LIU Q H, QIU X L, XIE L M, et al. Early warning algorithm of vehicle anti-collision time based on driving speed[J]. Transactions of the Chinese Society of Agricultural Engineering, 2017, 33(12): 99-106. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-NYGU201712013.htm
[9]	ZHAO X M, JING S C, HUI F, et al. DSRC-based rear-end collision warning system-an error-component safety distance model and field test[J]. Transportation Research Part C: Emerging Technologies, 2019, 107(10): 92-104.
[10]	石建军, 汪旭, 付玉. 基于车联网的车辆纵向碰撞分级预警研究[J]. 重庆交通大学学报(自然科学版), 2020, 39(9): 1-7. https://www.cnki.com.cn/Article/CJFDTOTAL-CQJT202009001.htm SHI J J, WANG X, FU Y. Hierarchical early warning of vehicle longitudinal collision based on internet of vehicles[J]. Journal of Chongqing Jiaotong University(Natural Science), 2020, 39(9): 1-7. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-CQJT202009001.htm
[11]	迟仲达. 基于车路协同技术的车辆防碰撞预警系统开发[D]. 长春: 吉林大学, 2012. CHI Z D. Development of vehicle collision warning system based on intelligent vehicle infrastructure cooperative technology[D]. Changchun: Jilin University, 2012. (in Chinese)
[12]	SENGUPTA R, REZAEI S, SHLADOVER S E, et al. Cooperative collision warning systems: Concept definition and experimental implementation[J]. Journal of Intelligent Transportation Systems, 2007, 11(3): 143-155. doi: 10.1080/15472450701410452
[13]	赵晓翠, 杨峰, 邓宝. 高速公路互通立交分流区车头时距分布[J]. 公路交通科技, 2012, 29(2): 136-139. https://www.cnki.com.cn/Article/CJFDTOTAL-GLJK201202025.htm ZHAO X C, YANG F, DENG B. Headway distribution characteristics of diverging area on expressway interchanges[J]. Journal of Highway and Transportation Research and Development, 2012, 29(2): 136-139. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-GLJK201202025.htm
[14]	王畅, 付锐, 张琼, 等. 换道预警系统中参数TTC特性研究[J]. 中国公路学报, 2015, 28(8): 91-100+108. https://www.cnki.com.cn/Article/CJFDTOTAL-ZGGL201508013.htm WANG C, FU R, ZHANG Q, et al. Research on parameter TTC characteristics of lane change warning system[J]. China Journal of Highway and Transport, 2015, 28(8): 91-100+108. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-ZGGL201508013.htm
[15]	王颖, 方志纯, 简注清, 等. 车辆荷载特性影响下的碰撞时间分布规律[J]. 交通运输系统工程与信息, 2020, 20(5): 240-246. https://www.cnki.com.cn/Article/CJFDTOTAL-YSXT202005035.htm WANG Y, FANG Z C, JIAN Z Q, et al. Effect of vehicle load characteristics on distributions of time-to-collision[J]. Journal of Transportation Systems Engineering and Information Technology, 2020, 20(5): 240-246. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-YSXT202005035.htm
[16]	王江锋, 熊慧媛, 徐亮, 等. 考虑风险等级的车车通信MTTC混合分布模型[J]. 哈尔滨工业大学学报, 2021, 53(9): 53-61. https://www.cnki.com.cn/Article/CJFDTOTAL-HEBX202109007.htm WANG J F, XIONG H Y, XU L, et al. Mixed distribution model of modified time-to-collision considering risk levels in vehicle-to-vehicle communication[J]. Journal of Harbin Institute of Technology, 2021, 53(9): 53-61. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-HEBX202109007.htm
[17]	GAN X F, WENG J X, LUO J B. Finite mixture distribution method to model vehicle headways at port collector-distributor roads[J]. Journal of Transportation Engineering, Part A: Systems, 2021, 147(12): 04021084.
[18]	王福建, 戴美伟, 孙凌涛, 等. 基于多类跟驰行为的车头时距混合分布模型[J]. 浙江大学学报(工学版), 2015, 49(7): 1288-1294. https://www.cnki.com.cn/Article/CJFDTOTAL-ZDZC201507013.htm WANG F J, DAI M W, SUN L T, et al. Mixed distribution model of vehicle headway based on multiclass car following[J]. Journal of Zhejiang University(Engineering Science), 2015, 49(7): 1288-1294. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-ZDZC201507013.htm
[19]	崔会芬, 楚彭子, 杨京帅, 等. 城市平面交叉口交通冲突影响因素分析[J]. 武汉理工大学学报(交通科学与工程版), 2017, 41(5): 762-765. https://www.cnki.com.cn/Article/CJFDTOTAL-JTKJ201705010.htm CUI H F, CHU P Z, YANG J S, et al. Analysis on influencing factors of traffic conflict at urban grade intersection[J]. Journal of Wuhan University of Technology(Transportation Science & Engineering), 2017, 41(5): 762-765. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-JTKJ201705010.htm
[20]	唐敏, 王东强, 曾鑫钰. 汽车碰撞预警主动安全预测方法[J]. 计算机科学, 2020, 47(4): 318-322. https://www.cnki.com.cn/Article/CJFDTOTAL-JSJA202004049.htm TANG M, WANG D Q, ZENG X Y. Active safety prediction method for automobile collision warning[J]. Computer Science, 2020, 47(4): 318-322. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-JSJA202004049.htm
[21]	GOKGOZ T, HACAR M, MEMDUHOGLU A, et al. An experimental study on map projection transformation using GIS tools[J]. Sigma Journal of Engineering and Natural Sciences-Sigma Muhendislik ve Fen Bilimleri Dergisi, 2017, 35(1): 101-117.
[22]	马瑄. 基于改进DBSCAN算法和K-S检验的收视数据集异常检测方法[D]. 北京: 北京邮电大学, 2021. MA X. Anomaly detection method based on improved DBSCAN algorithm and K-S test[D]. Beijing: Beijing University of Posts and Telecommunications, 2021. (in Chinese)
[23]	蒋硕然, 陈亚瑞, 秦智飞, 等. 基于KS检验的高斯混合模型分裂与合并算法[J]. 中国科学技术大学学报, 2018, 48(6): 477-485. https://www.cnki.com.cn/Article/CJFDTOTAL-ZKJD201806006.htm JIANG S R, CHEN Y R, QIN Z F, et al. Split and merge algorithm for Gaussian mixture model based on KS test[J]. Journal of University of Science and Technology of China, 2018, 48(6): 477-485. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-ZKJD201806006.htm

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