A Method for Quantitatively Analyzing Risks Associated with the Operation of Urban Buses Considering Chained Conflicts

LI Xiying; LIANG Jingru; HAO Tenglong

doi:10.3963/j.jssn.1674-4861.2022.03.003

Volume 40 Issue 3

Jun. 2022

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Article Navigation > Journal of Transport Information and Safety > 2022 > 40(3): 19-29

LI Xiying, LIANG Jingru, HAO Tenglong. A Method for Quantitatively Analyzing Risks Associated with the Operation of Urban Buses Considering Chained Conflicts[J]. Journal of Transport Information and Safety, 2022, 40(3): 19-29. doi: 10.3963/j.jssn.1674-4861.2022.03.003

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LI Xiying, LIANG Jingru, HAO Tenglong. A Method for Quantitatively Analyzing Risks Associated with the Operation of Urban Buses Considering Chained Conflicts[J]. Journal of Transport Information and Safety, 2022, 40(3): 19-29. doi: 10.3963/j.jssn.1674-4861.2022.03.003

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A Method for Quantitatively Analyzing Risks Associated with the Operation of Urban Buses Considering Chained Conflicts

doi: 10.3963/j.jssn.1674-4861.2022.03.003

LI Xiying^{1, 2, 3
,
,},
LIANG Jingru^{1, 2, 3},
HAO Tenglong^{1, 2, 3}

1.
School of Intelligent Systems Engineering, Sun Yat-Sen University, Guangzhou 510006, China
2.
Guangdong Provincial Key Laboratory of Intelligent Transportation System, Sun Yat-Sen University, Guangzhou 510006, China
3.
Key Laboratory of Video and Image Intelligent Analysis and Application Technology of Ministry of Public Security, Sun Yat-Sen University, Guangzhou 510006, China

Received Date: 2022-01-11
Available Online: 2022-07-25

Abstract

Abstract

A quantitative method for analyzing risks associated with the operation of buses in mixed traffic environment is studied by extracting data about traffic conflict and identifying a set of chain-conflicts. Regarding data collection, aerial video data are adopted based on which features of objects are extracted using YOLOv4 network. In this way, the trajectories with accurate attributes for buses and other related vehicles can be obtained. Regarding the identification of vehicle conflicts, the relative locations between pairs of vehicles which are likely to collide laterally in different lanes are set up as constraints. Based on the classic car-following model, the dynamic relationship of vehicle pairs in adjacent lanes is studied and added. With this, the classic time-to-collision(TTC)model is extended to a two-dimensional TTC model, which can identify lateral conflicts as well. Next, according to the Stimulus-Re-sponse Theory, the temporal and spatial scope caused by each conflicting vehicle who continuously disturb regional traffic is calibrated to study interrelationships between conflicted vehicles, and a time-series conflict tree model is established. With this, chained conflicts can be identified and the causal relationship between continuous risks can be traced using the conflict tree model. The risks of urban buses under different traffic settings are quantified from the following three aspects: ①the frequency of the conflicts is analyzed based on the two-dimensional TTC model; ②on this basis, the severity of conflicts is analyzed combined with cumulative frequency method; ③the probability and scope of conflicts are analyzed through ratio of chain conflicts and the length of conflict tree. Aerial video data of Guangzhou Bridge Road are collected for a case study. The results show that urban buses in frequently congested sections have high conflict risks, which reveal have high rate of severity and regional aggregation. The conflict frequency of buses in congested traffic flow exceeds 9 times per vehicle per minute. The average rate of serious conflicts of buses is 33.39%, which is much higher than the corresponding rate of regular passenger vehicles(i.e., 16.61%). The rate of regional chain-conflicts caused by buses is 30.75%, which is twice than that of cars(i.e., 14.67%)
- traffic safety,
- urban bus,
- traffic safety risk analysis,
- chain conflict recognition,
- traffic conflict technology

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References(24)

References

[1]	NGODUY D. Effect of the car-following combinations on the instability of heterogeneous traffic flow[J]. Transportmetrica B: Transport Dynamics, 2015, 3(1): 44-58. doi: 10.1080/21680566.2014.960503
[2]	梁国华, 程国柱, 王春艳, 等. 高速公路大型车混入率与交通流稳定性关系[J]. 长安大学学报, 2014, 34(4): 1-8. https://www.cnki.com.cn/Article/CJFDTOTAL-XAGL201404020.htm LIANG G H, CHENG G Z, WANG C Y, et al. Relationship between mixing rate of freeway large vehicles and traffic flow stability[J]. Journal of Chang'an University, 2014, 34 (4): 1-8. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-XAGL201404020.htm
[3]	赵笑月, 汪攀, 朱顺应, 等. 基于视频识别的互通合流区交通冲突空间分布[J]. 公路交通科技, 2021, 38(5): 90-99. doi: 10.3969/j.issn.1002-0268.2021.05.011 ZHAO X Y, WANG P, ZHU S Y, et al. Spatial distribution of traffic conflicts in interchange merging area based on video recognition[J]. Journal of Highway and Transportation Research and Development, 2021, 38(5): 90-99. (in Chinese) doi: 10.3969/j.issn.1002-0268.2021.05.011
[4]	陈峻, 王涛, 李春燕, 等. 城市公交车与社会车辆混合流速度模型及交通运行状态分析[J]. 中国公路学报, 2012, 25 (1): 128-140. doi: 10.3969/j.issn.1001-7372.2012.01.019 CHEN J, WANG T, LI C Y, et al. Speed models of mixed traffic flow on bus-car and vehicle and analysis of traffic running state[J]. China Journal of Highway Transportation, 2012, 25 (1): 128-140. (in Chinese) doi: 10.3969/j.issn.1001-7372.2012.01.019
[5]	盖靖元. 基于车速离散度的路段交通安全评估方法研究[D]. 南京: 东南大学, 2016. GAI J Y. A study on the assessment of road traffic safety based on speed dispersion[D]. Nanjing: Southeast University, 2016. (in Chinese)
[6]	GOH K C K. Exploring bus lane impacts using traffic micro-simulation[C]. Australasian Transport Research Forum, Brisbane, Australia: ATRF, 2013.
[7]	GOH K C K, CURRIE G, SARVI M, et al. Experimental microsimulation modeling of road safety impacts of bus priority[J]. Transportation Research Record, 2014(1): 9-18.
[8]	QI W W, RUAN L J, ZHI Y, et al. Risk area identification model of bus bay stops based on distribution of conflicts[J]. Discrete Dynamics in Nature and Society, 2021(25): 1-11.
[9]	BANSAL P, AGRAWAL R, TIWARI G. Impacts of bus-stops on the speed of motorized vehicles under heterogeneous traffic conditions: A study of Delhi, India[J]. International Journal of Transportation Science and Technology, 2014, 3(2): 167-178. doi: 10.1260/2046-0430.3.2.167
[10]	DIAH J M, HAMIDUN N Q A. The methodology review of traffic safety monitoring by using video recording for express bus in Malaysia[C]. 14^th International Colloquium on Signal Processing and its Applications, Penang, Malaysia: IEEE, 2018.
[11]	朱顺应, 蒋若曦, 王红, 等. 机动车交通冲突技术研究综述[J]. 中国公路学报, 2020, 33(2): 15-33. https://www.cnki.com.cn/Article/CJFDTOTAL-ZGGL202002002.htm ZHU S Y, JIANG R X, WANG H, et al. Review of research on traffic conflict technique[J]. China Journal of Highway Transportation, 2020, 33(2): 15-33. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-ZGGL202002002.htm
[12]	谢济铭, 秦雅琴, 彭博, 等. 多车道交织区车辆跟驰行为风险判别与冲突预测[J]. 交通运输系统工程与信息, 2021, 21 (3): 131-139. https://www.cnki.com.cn/Article/CJFDTOTAL-YSXT202103016.htm XIE J M, QIN Y Q, PENG B, et al. Risk discrimination and conflict prediction of vehicle-following behavior in multi-lane weaving sections[J]. Journal of Transportation Systems Engineering and Information Technology, 2021, 21 (3): 131-139. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-YSXT202103016.htm
[13]	QI W W, WANG W, SHEN B, et al. A modified post encroachment time model of urban road merging area based on lane change characteristics[J]. IEEE Access, 2020 (8) 72835-72846.
[14]	龙科军, 张燕, 邹志云, 等. 基于车辆轨迹的信号交叉口机非冲突判别[J]. 交通运输系统工程与信息, 2021, 21(1): 69-74. https://www.cnki.com.cn/Article/CJFDTOTAL-YSXT202101012.htm LONG K J, ZHANG Y, ZOU Z Y, et al. Vehicle and non-motorized vehicle traffic conflict recognition at signalized intersection based on vehicle trajectory[J]. Journal of Transportation Systems Engineering and Information Technology, 2021, 21(1): 69-74. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-YSXT202101012.htm
[15]	XIONG X X, CHEN L, LIANG J. A new framework of vehicle collision prediction by combining SVM and HMM[J]. IEEE Transactions on Intelligent Transportation Systems, 2018, 19(3): 699-710. doi: 10.1109/TITS.2017.2699191
[16]	容颖, 温惠英, 赵胜. 高速公路单向双车道车辆群行车风险度量研究[J]. 重庆交通大学学报, 2019, 38(9): 95-100+121. https://www.cnki.com.cn/Article/CJFDTOTAL-CQJT201909016.htm RONG Y, WEN H Y, ZHAO S. Study on driving risk measurement for two-lane freeway vehicle group[J]. Journal of Chongqing Jiaotong University, 2019, 38(9): 95-100+121. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-CQJT201909016.htm
[17]	梁军, 张婉婉, 何美玲, 等. 车联网环境下道路交通事故链阻断效率评价方法研究[J]. 中国公路学报, 2018, 31(4): 211-224. https://www.cnki.com.cn/Article/CJFDTOTAL-ZGGL201804026.htm LIANG J, ZHANG W W, HE M L, et al. A blocking efficiency evaluation method for chains of road traffic incident in IOV environment[J]. China Journal of Highway Transportation, 2018, 31(4): 211-224. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-ZGGL201804026.htm
[18]	杨澜, 马佳荣, 赵祥模, 等. 基于车路协同的高速公路车辆碰撞预警模型[J]. 公路交通科技, 2017, 34(9): 123-129. https://www.cnki.com.cn/Article/CJFDTOTAL-GLJK201709018.htm YANG L, MA J R, ZHAO X M, et al. A vehicle collision warning model in expressway scenario based on vehicle-infrastructure cooperation[J]. Journal of Highway and Transportation Research and Development, 2017, 34(9): 123-129. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-GLJK201709018.htm
[19]	BOCHKOVSKIY A, WANG C Y, LIAO H. YOLOv4: optimal speed and accuracy of object detection[EB/OL]. (2020-4-23)[2021-11-22]. https://arxiv.org/abs/2004.10934.
[20]	SUN Y M, CAO B, ZHU P F, et al. Drone-based RGB-infrared cross-modality vehicle detection via uncertainty-aware learning[EB/OL]. (2021-10-14)[2021-11-22]. https://arxiv.org/abs/2003.02437.
[21]	EVERINGHAM M, GOOL V L, WILLIAMS C K I, et al. The pascal visual object classes(VOC)challenge[J]. International Journal of Computer Vision, 2010, 88(2): 303-338.
[22]	WOJKE N, BEWLEY A, PAULUS D. Simple online and realtime tracking with a deep association metric[EB/OL]. (2017-3-21)[2021-11-22]. https://arxiv.org/abs/1703.07402.
[23]	AUTEYJ, SAYEDT, ZAKIMH. Safety evaluation ofright-turn smart channels using automated traffic conflict analysis[J]. Accident Analysis & Prevention, 2012, 45(2): 120-130.
[24]	马艳丽, 祁首铭, 吴昊天, 等. 基于PET算法的匝道合流区交通冲突识别模型[J]. 交通运输系统工程与信息, 2018, 18 (2): 142-148. https://www.cnki.com.cn/Article/CJFDTOTAL-YSXT201802022.htm MA Y L, QI S M, WU H T, et al. Traffic conflict identification model based on post encroachment time algorithm in ramp merging area[J]. Journal of Transportation Systems Engineering and Information Technology, 2018, 18(2): 142-148. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-YSXT201802022.htm

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