|Table of Contents|

[1] Li Ye, Xing Lu, Wang Wei, Dong Changyin, et al. Evaluating impacts of different car-following typeson rear-end crashes at freeway weaving section [J]. Journal of Southeast University (English Edition), 2017, 33 (3): 335-340. [doi:10.3969/j.issn.1003-7985.2017.03.013]
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Evaluating impacts of different car-following typeson rear-end crashes at freeway weaving section()
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Journal of Southeast University (English Edition)[ISSN:1003-7985/CN:32-1325/N]

Volumn:
33
Issue:
2017 3
Page:
335-340
Research Field:
Traffic and Transportation Engineering
Publishing date:
2017-09-30

Info

Title:
Evaluating impacts of different car-following typeson rear-end crashes at freeway weaving section
Author(s):
Li Ye;  Xing Lu;  Wang Wei;  Dong Changyin
School of Transportation, Southeast University, Nanjing 210096, China
Keywords:
freeway;  safety;  logistic regression;  time to collision;  risk
PACS:
U491
DOI:
10.3969/j.issn.1003-7985.2017.03.013
Abstract:
The impacts of four different car-following types on rear-end crash risks at a freeway weaving section were evaluated using trajectory data, in which Type 1 represents car following car, Type 2 represents car following truck, Type 3 represents truck following car and Type 4 represents truck following truck. The time to collision(TTC)was introduced as the surrogate safety measure to determine the rear-end crash risks. Then, the trajectory data at a freeway weaving section was used for the case-controlled analysis. Three logistic regression models were developed with different TTC thresholds to quantify the impacts of different car-following types. The explanatory factors were also analyzed to investigate possible reasons for the results of logistic regressions. Results show that the rear-end crash risk of Type 3 is 3.167 times higher than that of Type 1 when the TTC threshold is 2 s. However, the odds ratios of Type 2 and Type 4 are both smaller than 1, which indicates a safer condition. The analysis of explanatory factors also shows that Type 3 has the largest speed differences and the smallest net gaps. This is consistent with vehicle operation features at a weaving section and is also the reason for the larger rear-end crash risks. The results of this study reflect the mechanism of rear-end crash risks of different car-following types at the freeway weaving section.

References:

[1] Li Z, Li Y, Liu P, et al. Development of a variable speed limit strategy to reduce secondary collision risks during inclement weathers [J]. Accident Analysis & Prevention, 2014, 72: 134-145. DOI:10.1016/j.aap.2014.06.018.
[2] Li Y, Wang H, Wang W, et al. Evaluation of the impacts of cooperative adaptive cruise control on reducing rear-end collision risks on freeways [J]. Accident Analysis & Prevention, 2017, 98: 87-95. DOI:10.1016/j.aap.2016.09.015.
[3] Skabardonis A, Mauch M. Evaluation of methodologies for analyzing freeway ramp weaving [J]. Transportation Research Record: Journal of the Transportation Research Board, 2015, 2483: 130-139.
[4] Li Y, Wang H, Wang W, et al. Reducing the risk of rear-end collisions with infrastructure-to-vehicle(I2V)integration of variable speed limit control and adaptive cruise control system [J]. Traffic Injury Prevention, 2016, 17(6): 597-603. DOI:10.1080/15389588.2015.1121384.
[5] Kusuma A, Liu R, Choudhury C, et al. Lane-changing characteristics at weaving section [C/OL]//Transportation Research Board 94th Annual Meeting. Washington, DC, USA, 2015. https://www.researchgate.net/publication/303617349_Lane-changing_characteristics_at_weaving_section.
[6] Wan X, Jin P J, Yang F, et al. Merging preparation behavior of drivers: How they choose and approach their merge positions at a congested weaving area[J]. Journal of Transportation Engineering, 2016, 142(9): 05016005. DOI:10.1061/(asce)te.1943-5436.0000864.
[7] He H, Menendez M. Distribution and impacts of lane changes at a freeway weaving section: An empirical study [C]//Transportation Research Board 95th Annual Meeting. Washington, DC, USA, 2016: 01595109.
[8] Abdel-Aty M, Abdelwahab H. Configuration analysis of two-vehicle rear-end crashes[J]. Transportation Research Record: Journal of the Transportation Research Board, 2003, 1840: 140-147. DOI:10.3141/1840-16.
[9] Pande A, Abdel-Aty M. Comprehensive analysis of the relationship between real-time traffic surveillancedata and rear-end crashes on freeways[J]. Transportation Research Record: Journal of the Transportation Research Board, 2006, 1953: 31-40. DOI:10.3141/1953-04.
[10] Wang X, Abdel-Aty M. Temporal and spatial analyses of rear-end crashes at signalized intersections [J]. Accident Analysis & Prevention, 2006, 38(6): 1137-1150. DOI:10.1016/j.aap.2006.04.022.
[11] Kim J K, Wang Y, Ulfarsson G F. Modeling the probability of freeway rear-end crash occurrence[J]. Journal of Transportation Engineering, 2007, 133(1): 11-19. DOI:10.1061/(asce)0733-947x(2007)133:1(11).
[12] Iliadi A, Farah H, Schepers P, et al. A crash prediction model for weaving sections in the Netherlands [C]//Transportation Research Board 95th Annual Meeting. Washington, DC, USA, 2016: 486-487.
[13] Weng J, Meng Q, Yan X. Analysis of work zone rear-end crash risk for different vehicle-following patterns[J]. Accid Anal Prev, 2014, 72: 449-457. DOI:10.1016/j.aap.2014.08.003.
[14] Oh C, Kim T. Estimation of rear-end crash potential using vehicle trajectory data [J]. Accident Analysis & Prevention, 2010, 42(6): 1888-1893. DOI:10.1016/j.aap.2010.05.009.
[15] Meng Q, Weng J. Evaluation of rear-end crash risk at work zone using work zone traffic data [J]. Accident Analysis & Prevention, 2011, 43(4): 1291-1300. DOI:10.1016/j.aap.2011.01.011.
[16] Aghabayk K, Sarvi M, Young W. Attribute selection for modelling driver’s car-following behaviour in heterogeneous congested traffic conditions [J]. Transportmetrica A: Transport Science, 2014, 10(5): 457-468. DOI:10.1080/23249935.2013.787558.
[17] Weng J, Xue S, Yang Y, et al. In-depth analysis of drivers’ merging behavior and rear-end crash risks in work zone merging areas [J]. Accident Analysis & Prevention, 2015, 77: 51-61. DOI:10.1016/j.aap.2015.02.002.
[18] Council F, Harkey D, Nabors D, et al. Examination of fault, unsafe driving acts, and total harm in car-truck collisions[J]. Transportation Research Record: Journal of the Transportation Research Board, 2003, 1830: 63-71. DOI:10.3141/1830-09.
[19] Yan X, Radwan E, Mannila K K. Analysis of truck-involved rear-end crashes using multinomial logistic regression [J]. Advances in Transportation Studies, 2009, 2009(17): 39-52.
[20] Romo A, Hernandez S, Cheu R L. Identifying precrash factors for cars and trucks on interstate highways: Mixed logit model approach[J]. Journal of Transportation Engineering, 2014, 140(3): 04013016. DOI:10.1061/(asce)te.1943-5436.0000621.
[21] Vogel K. A comparison of headway and time to collision as safety indicators [J]. Accident Analysis & Prevention, 2003, 35(3): 427-433. DOI:10.1016/s0001-4575(02)00022-2.
[22] Kiefer R J, LeBlanc D J, Flannagan C A, et al. Developing an inverse time-to-collision crash alert timing approach based on drivers’ last-second braking and steering judgments [J]. Accident Analysis & Prevention, 2005, 37(2): 295-303. DOI:10.1016/j.aap.2004.09.003.
[23] Meng Q, Qu X. Estimation of rear-end vehicle crash frequencies in urban road tunnels [J]. Accident Analysis & Prevention, 2012, 48: 254-263. DOI:10.1016/j.aap.2012.01.025.
[24] Hayward J C. Near-miss determination through use of a scale of danger [J/OL]. Highway Research Record, 1972. http://onlinepubs.trb.org/Onlinepubs/hrr/1972/384/384-004.pdf
[25] Federal Highway Administration. Next generation simulation(NGSIM)[EB/OL].(2002)[2016-09-15]. https://ops.fhwa.dot.gov/trafficanalysistools/ngsim.htm.

Memo

Memo:
Biographies: Li Ye(1992—), male, graduate; Wang Wei(corresponding author), male, doctor, professor, wangwei@seu.edu.cn.
Foundation item: The National Natural Science Foundation of China(No. 51638004, 51338003, 51478113).
Citation: Li Ye, Xing Lu, Wang Wei, et al. Evaluating impacts of different car-following types on rear-end crashes at freeway weaving section[J].Journal of Southeast University(English Edition), 2017, 33(3):335-340.DOI:10.3969/j.issn.1003-7985.2017.03.013.
Last Update: 2017-09-20