|Table of Contents|

[1] Sun Chao, Chen Xiaohong, Zhang H. Michael, et al. Evaluation of driving behaviorbased on massive vehicle trajectory data [J]. Journal of Southeast University (English Edition), 2019, 35 (4): 502-508. [doi:10.3969/j.issn.1003-7985.2019.04.013]

Evaluation of driving behaviorbased on massive vehicle trajectory data()

Journal of Southeast University (English Edition)[ISSN:1003-7985/CN:32-1325/N]

2019 4
Research Field:
Traffic and Transportation Engineering
Publishing date:


Evaluation of driving behaviorbased on massive vehicle trajectory data
Sun Chao1 2 Chen Xiaohong1 Zhang H. Michael1 3 Zhang Junfeng2
1 Key Laboratory of Road and Traffic Engineering of Ministry of Education, Tongji University, Shanghai 201804, China
2 Shenzhen Urban Transport Planning Center Co., Ltd., Shenzhen 518021, China
3 College of Civil and Environmental Engineering, University of California Davis, Davis 95616, USA
driving behavior global positioning system(GPS)navigating data automatic coding machine self-organizing mapping(SOM)
Based on the driver surveillance video data and controller area network(CAN)data, the methods of studying commercial vehicles’ driving behavior is relatively advanced. However, these methods have difficulty in covering private vehicles. Naturalistic driving studies have disadvantages of small sample size and high cost, one new driving behavior evaluation method using massive vehicle trajectory data is put forward. An automatic encoding machine is used to reduce the noise of raw data, and then driving dynamics and self-organizing mapping(SOM)classification are used to give thresholds or the judgement method of overspeed, rapid speed change, rapid turning and rapid lane changing. The proportion of different driving behaviors and typical dangerous driving behaviors is calculated, then the temporal and spatial distribution of drivers’ driving behavior and the driving behavior characteristics on typical roads are analyzed. Driving behaviors on accident-prone road sections and normal road sections are compared. Results show that in Shenzhen, frequent lane changing and overspeed are the most common unsafe driving behaviors; 16.1% drivers have relatively aggressive driving behavior; the proportion of dangerous driving behavior is higher outside the original economic special zone; dangerous driving behavior is highly correlated with traffic accident frequency.


[1] Tang Z Z, Zhang T J, He Y. Road safety assessment[M]. Beijing: China Communications Press, 2008.(in Chinese)
[2] Wu B, Yang Z Z, Xie J, et al. Vehicle speed distribution characteristics on work zone section of freeway[J]. Journal of Transportation Systems Engineering and Information Technology, 2016, 16(2): 219-224, 231. DOI:10.16097/j.cnki.1009-6744.2016.02.033. (in Chinese)
[3] Thiffault P, Bergeron J. Fatigue and individual differences in monotonous simulated driving[J]. Personality and Individual Differences, 2003, 34(1): 159-176. DOI:10.1016/s0191-8869(02)00119-8.
[4] Guo S, Teng J, Guo X, et al. Research on fatigue driving of commercial vehicle drivers based on multi-source real data [C]//China Intelligent Transportation Annual Meeting. Guangzhou, China, 2014:85-96.(in Chinese)
[5] Tao J. The deep analysis of traffic accidents drivers’ causes in operating vehicles[J]. Shanxi Science and Technology of Communications, 2018, 253(4):155-158.(in Chinese)
[6] Zhang L F, Chen C, Zhang J Y, et al. Modeling lane-changing behavior in freeway off-ramp areas using naturalistic driving data[J]. Journal of Tongji University(Natural Science), 2018, 46(3):318-325, 333.(in Chinese)
[7] Wang X S, Zhu M X, Xing Y L. Impacts of collision warning system on car-following behavior based on naturalistic driving data [J]. Journal of Tongji University(Natural Science), 2016, 44(7):1045-1051.(in Chinese)
[8] Wang X S, Li Y. Characteristics analysis of lane changing behavior based on the naturalistic driving data[J]. Journal of Transport Information and Safety, 2016, 34(1): 17-22. DOI:10.3963/j.issn 1674-4861.2016.01.001. (in Chinese)
[9] Wang X S, Yang M M. Cut-in behavior analyses based on naturalistic driving data[J]. Journal of Tongji University(Natural Science), 2018, 46(8): 1057-1063. DOI:10.11908/ j.issn.0253-374x.2018.08.008. (in Chinese)
[10] Olsen E C B, Lee S E, Wierwille W W, et al. Analysis of distribution, frequency, and duration of naturalistic lane changes[J].Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 2002, 46(22): 1789-1793. DOI:10.1177/154193120204602203.
[11] Bone S A, Mowen J C. Identifying the traits of aggressive and distracted drivers: A hierarchical trait model approach[J]. Journal of Consumer Behaviour: An International Research Review, 2006, 5(5): 454-464. DOI:10.1002/cb.193.
[12] Paaver M, Eensoo D, Kaasik K, et al. Preventing risky driving: A novel and efficient brief intervention focusing on acknowledgement of personal risk factors[J]. Accident Analysis & Prevention, 2013, 50: 430-437. DOI:10.1016/j.aap.2012.05.019.
[13] Ahmed M, Yu R, Abdel-Aty M. Safety applications of automatic vehicle identification and real-time weather data on freeways[C]//18th ITS World Congress. Orlando, FL, USA, 2011.
[14] Ahmed M, Abdel-Aty M. A data fusion framework for real-time risk assessment on freeways[J]. Transportation Research Part C: Emerging Technologies, 2013, 26: 203-213. DOI:10.1016/j.trc.2012.09.002.
[15] Li Y, Chen Y R. Driver vision based perception-response time prediction and assistance model on mountain highway curve[J]. International Journal of Environmental Research and Public Health, 2016, 14(1): 31. DOI:10.3390/ijerph14010031.
[16] Grengs J, Wang X G, Kostyniuk L. Using GPS data to understand driving behavior[J]. Journal of Urban Technology, 2008, 15(2): 33-53. DOI:10.1080/10630730802401942.
[17] Zhao Y N, Yamamoto T, Morikawa T. An analysis on older driver’s driving behavior by GPS tracking data: Road selection, left/right turn, and driving speed[J]. Journal of Traffic and Transportation Engineering(English Edition), 2018, 5(1): 56-65. DOI:10.1016/j.jtte.2017.05.013.
[18] Liao L, Wu J, Zou F, et al. Trajectory topic modelling to characterize driving behaviors with GPS-based trajectory data[J]. Journal of Internet Technology, 2018, 19(3): 815-824.
[19] Liu Y J, Zeng C, Wang S J, et al. An evaluation method of safety and energy-saving driving behavior based on satellite positioning data[J]. Journal of Highway and Transportation Research and Development, 2018, 35(1): 121-128, 158.(in Chinese)
[20] Zhu X Y, Hu X B, Chiu Y C. Design of driving behavior pattern measurements using smartphone global positioning system data[J]. International Journal of Transportation Science and Technology, 2013, 2(4): 269-288. DOI:10.1260/2046-0430.2.4.269.


Biographies: Sun Chao(1985—), male, Ph.D. candidate, senior engineer; Chen Xiaohong(corresponding author), female, doctor, professor, Tongjicxh@163.com.
Foundation items: The National Natural Science Foundation of China(No.71641005), the National Key Research and Development Program of China(No.2018YFB1601105).
Citation: Sun Chao, Chen Xiaohong, Zhang H. Michael, et al.Evaluation of driving behavior based on massive vehicle trajectory data[J].Journal of Southeast University(English Edition), 2019, 35(4):502-508.DOI:10.3969/j.issn.1003-7985.2019.04.013.
Last Update: 2019-12-20