|
|
|
| Cellular Automata Model for Analysis of Lane-changing Behavior on Damaged Pavement |
| CHEN Hong1, MA Xiao-tong2, ZHAO Dan-ting1 |
1. School of Highway Chang'an University, Xi'an Shannxi 710064, China;
2. Shandong Zhonghui Consulting Management Co., Ltd., Heze Shandong 274000, China |
|
|
|
|
Abstract Drivers often perform lane-changing behavior for driving benefits on a broken road. Accordingly, a lane-changing model for two-lane traffic under pavement damage conditions was established based on the cellular automata NaSch model by introducing slow start and new lane-changing rules. To simulate the characteristics of a driver, traffic flow, and lane change under different road conditions, a simulation process was established in four steps, namely, lane-changing demand, lane selection, clearance detection, and lane change execution. Road damage level is classified from the perspective of vehicle operation. Vehicle driving benefit is calculated through utility theory to establish a lane selection model. A lane-changing coefficient is also introduced to study the effect of pavement damage on vehicle operating characteristics. The driver is divided into four categories based on the differences among drivers' behavior, namely, adventurous, alert, cautious, and slow. The characteristics of the different types of drivers under pavement damage conditions were analyzed by setting simulation parameters. Results showed that the lane-changing coefficient would increase with the increase of pavement damage level. A high level of damage would result in a low benefit of the vehicle traveling on damaged roads, which further increases the probability of lane change that can properly simulate the effect of pavement damage on vehicle lane-changing behavior. The highest lane-changing rate was obtained by the adventurous driver in medium density, and the speed variance and lane-changing rate gradually increases with the increase of damage level. The high lane-changing rate also indicates that pavement distress aggravates lane-changing behavior and interferes with the normal flow of traffic operation, thereby providing a theoretical basis for strengthening traffic safety management of damaged road sections.
|
|
Received: 04 December 2017
|
|
Corresponding Authors:
CHEN Hong
E-mail: hongchen82@126.com
|
|
|
|
[1] SHAH Y U, JAIN S S, TIWARI D, et al. Development of Overall Pavement Condition Index for Urban Road Network[J]. Procedia-Social and Behavioral Sciences, 2013, 104(2):332-341.
[2] XU Zhi-gang. Study on the Automatic Identification Technology for Pavement Distress Image Based on Multi-features Fusion[D]. Xi'an:Chang'an University, 2012.(in Chinese)
[3] WANG Lin. The Characters Research of Lane-changing Behavior in Urban Section[D]. Beijing:Beijing Jiaotong University, 2014.(in Chinese)
[4] WANG Yong-ming, ZHOU Lei-shan, LÜ Yong-bo. Lane Changing Rules Based on Cellular Automaton Traffic Flow Model[J]. China Journal of Highway and Transport, 2008, 21(1):89-93.(in Chinese)
[5] CAO Xiao-ying, YOUNG M, SARVI M. Exploring Duration of Lane Change Execution[C]//Proceedings of the 36th Australasian Transport Research Forum, Brisbane:Australia, 2013.
[6] DING Shen-zhen, YANG Xiao-fang, WANG Bai-li. Aggressive Driving Behavior Lane-changing Model Research Based on Cellular Automata[J]. Electronic Science and Technology, 2017, 30(4):48-52. (in Chinese)
[7] GIPPS P G. A Model for the Structure of Lane-changing Decisions[J]. Transportation Research Part B, 1986, 20(5):403-414.
[8] MEHMOOD A. Integrated Simulation Model for Driver Behavior Using System Dynamic[D]. Waterloo:University of Waterloo, 2003.
[9] YANG Qi, KOUTSOPOULOS H N. A Microscopic Traffic Simulator for Evaluation of Dynamic Traffic Management Systems[J]. Transportation Research Part C Emerging Technologies, 1996, 4(3):113-129.
[10] HIDAS P. Modelling Lane Changing and Merging in Microscopic Traffic Simulation[J].Transportation Research Part C, 2002, 10(5):351-371.
[11] ZHENG Hong, RONG Jian, REN Fu-tian. A Lane Changing Model Based on Random Utility Theory[J]. Journal of Highway and Transportation Research and Development, 2004, 21(5):88-91. (in Chinese)
[12] CAO San. A Thesis Submitted in Partial Fulfillment of the Requirements for the Degree of Master of Engineering[D]. Wuhan:Huazhong University of Science & Technology, 2009. (in Chinese)
[13] LI Juan, Qu Da-yi, LIU Cong, et al. Study on Vehicle Lane-changing Behavior Based on Cellular Automaton[J]. Journal of Highway and Transportation Research and Development, 2016, 33(11):140-145. (in Chinese)
[14] HUA Xue-dong, WANG Wei, WANG Hao. A Tow-lane Cellular Automaton Traffic Flow Model with the Influence of Driving Psychology[J]. Acta Physica Sinica, 2011, 60(8):398-405. (in Chinese)
[15] SHI Jun-qing, CHENG Lin, CHU Zhao-ming, et al. Cellular Automata Model of Urban Road Network Traffic Flow[J]. Journal of Highway and Transportation Research and Development, 2015, 32(4):143-149. (in Chinese)
[16] SHI Dan-dan, ZHU Zheng-wang, LIU Hao-de. A Cellular Automaton Model of Traffic Flow Considering Vehicle-vehicle Communication[J]. Journal of Highway and Transportation Research and Development, 2009, 26(s1):142-146. (in Chinese)
[17] LABI S. Efficacies of Roadway Safety Improvements across Functional Subclasses of Rural Two-lane Highways[J]. Journal of Safety Research, 2011, 42(4):231-239.
[18] LEE J., NAM B, ABDEL-ATY M. Effects of Pavement Surface Conditions on Traffic Crash Severity[J]. Journal of Transportation Engineering, 2015, 141(10).
[19] MAO Cheng-yuan. Research on Vehicle Running Model and Characteristics of Traffic Flow Resulting from Pavement Distress[D]. Harbin:Harbin Institute of Technology, 2013. (in Chinese)
[20] JIA Gui-bin. Research on Road Capacity Resulting from Pavement Distress[D]. Beijing:Beijing Jiaotong University, 2015. (in Chinese)
[21] SUN D, ELEFTERIADOU L. Lane-changing Behavior on Urban Streets:A Focus Group-based Study[J]. Applied Ergonomics, 2011, 42(5):682-691.
[22] WANG Wei. Traffic Engineering[M]. Nanjing:Southeast University Press, 2000. (in Chinese) |
| [1] |
ZHOU Xing-yu, LI Hong-mei, ZHENG Wei-Hao, TANG Zhi-hui, YANG Li-jun. Short-term Traffic Flow Prediction Based on the IMM-BP-UKF Model[J]. Journal of Highway and Transportation Research and Development, 2019, 13(2): 56-64. |
| [2] |
LI Gao-sheng, PENG Ling, LI Xiang, WU Tong. Short-term Traffic Forecast of Urban Bus Stations Based on Long Short-term Memory[J]. Journal of Highway and Transportation Research and Development, 2019, 13(2): 65-72. |
| [3] |
HU Bao-yu, ZHAO Hu, SUN Xiang-long, WANG Di-xin, LIU Ning. Synchronous Transfer Model between Bus Lines and Rural Passenger Lines[J]. Journal of Highway and Transportation Research and Development, 2019, 13(2): 73-79. |
| [4] |
GUO Jian-ke, QIU Yu-kun, BAI Jia-yuan, WANG Li. Spatial Differentiation and Equalization of Medical Service Based on Accessibility of Urban Public Transport: A Case Study of Dalian[J]. Journal of Highway and Transportation Research and Development, 2019, 13(2): 80-89. |
| [5] |
ZHAO Ni-na, ZHAO Xiao-hua, LIN Zhan-zhou, GE Shu-fang. A Study on the Guide Signs Layout for Freeway Major Split Interchange[J]. Journal of Highway and Transportation Research and Development, 2019, 13(2): 90-102. |
| [6] |
JIANG Ming, CHEN Yan-Yan, FENG Yi-dong, ZHOU Rui. Key Design Indicators for Roadside Warning Piles[J]. Journal of Highway and Transportation Research and Development, 2019, 13(1): 79-87. |
|
|
|
|
2018, Vol. 12 
