Intelligent control system of variable approach lane based on adaptive neuro-fuzzy inference system

LIU Yu-gang; WANG Zhuo-jun; LIU Yan-fang; YUAN Chuan-jie

Article Contents

Article Navigation > Journal of Traffic and Transportation Engineering > 2017 > 17(4): 149-158

LIU Yu-gang, WANG Zhuo-jun, LIU Yan-fang, YUAN Chuan-jie. Intelligent control system of variable approach lane based on adaptive neuro-fuzzy inference system[J]. Journal of Traffic and Transportation Engineering, 2017, 17(4): 149-158.

Citation:

LIU Yu-gang, WANG Zhuo-jun, LIU Yan-fang, YUAN Chuan-jie. Intelligent control system of variable approach lane based on adaptive neuro-fuzzy inference system[J]. Journal of Traffic and Transportation Engineering, 2017, 17(4): 149-158.

Citation:

PDF( 949 KB)

Intelligent control system of variable approach lane based on adaptive neuro-fuzzy inference system

1.
School of Transportation and Logistics, Southwest Jiaotong University, Chengdu 610031, Sichuan, China
2.
National United Engineering Laboratory of Integrated and Intelligent Transportation, Southwest Jiaotong University, Chengdu 610031, Sichuan, China

More Information

Author Bio:
LIU Yu-gang(1978-), male, associate professor, PhD, +86-28-87600165, liuyugang@home.swjtu.edu.cn
Received Date: 2017-03-22
Publish Date: 2017-08-25

Abstract

Abstract

In order to alleviate the traffic congestion caused by the uneven distribution of traffic flow, the variable approach lanes (VAL) of intersection entrance were taken as research object, and an intelligent control system based on adaptive neuro-fuzzy inference system (ANFIS) was established. The intelligent control system consisted of data acquisition subsystem, traffic status prediction subsystem and control subsystem, and the intelligent control of VAL was completed by the three subsystems. When the real-time traffic data detected by the data acquisition subsystem were transfered into the pre-trained traffic status prediction subsystem, the traffic statuses of left-turning and going-straight vehicles were obtained, and the attribute of VAL was determined according to the structured algorithm. Computation result shows that the test error of traffic status prediction subsystem is 0.075 097, which meets the accuracy requirement to predict the traffic status. The intelligent control system of VAL can significantly improve the trafficcongestion at the intersection. While the ratio of left-turning vehicles is 25%, the total delay of key entrance lane reduces by 6.1%, the average stopping number reduces by 9.5%, and the average queue length reduces by 6.1%. When the ratio of left-turning vehicles rises to 30%, the three indicators decrease by 8.1%, 12.4% and 8.0%, respectively. Obviously, the higher the proportion of left-turning vehicles is, the more significant the effect is.
- traffic control,
- intersection,
- variable approach lane,
- traffic status,
- control strategy,
- adaptive neuro-fuzzy inference system

FullText(HTML)

References(28)

References

[1]	ZHANG Hao-zhi, GAO Zi-you. Two-way road network design problem with variable lanes[J]. Journal of Systems Science and Systems Engineering, 2007, 16 (1): 50-61. doi: 10.1007/s11518-007-5034-x
[2]	LIU You-jun, HUANG Wei. The optimal design of signalized intersection based on variable approach-lane control[C]∥IEEE. 2009 2nd International Conference on Intelligent Computation Technology and Automation. New York: IEEE, 2009: 826-829.
[3]	LIANG Xiao, HU Chun-ping, MA Chao-yun, et al. Empirical study on variable lanes design of Chaoyang North Street in Beijing[C]∥IEEE. Proceedings of the 30th Chinese Control Conference. New York: IEEE, 2011: 5527-5531.
[4]	LI Xu, CHEN Jun, WANG Hao. Study on flow direction changing method of reversible lanes on urban arterial roadways in China[J]. Procedia-Social and Behavioral Sciences, 2013, 19: 807-816.
[5]	WOLSHON B, LAMBERT L. Reversible lane systems: synthesis of practice[J]. Journal of Transportation Engineering, 2006, 132 (12): 933-944. doi: 10.1061/(ASCE)0733-947X(2006)132:12(933)
[6]	WANG J W, WANG H F, ZHANG W J, et al. Evacuation planning based on the contraflow technique with consideration of evacuation priorities and traffic setup time[J]. IEEE Transactions on Intelligent Transportation Systems, 2013, 14 (1): 480-485. doi: 10.1109/TITS.2012.2204402
[7]	GU Shan-shan, CHEN Jun, ZHOU Yang, et al. Mechanism and implementation effect evaluation of variable lane[J]. Transactions of Beijing Institute of Technology, 2012, 32 (S1): 148-151. (in Chinese).
[8]	ALHAJYASEEN W K M, NAJJAR M, RATROUT N T, et al. The effectiveness of applying dynamic lane assignment at all approaches of signalized intersection[J]. Case Studies on Transport Policy, 2017, 2 (1): 65-72.
[9]	ZHAO Jing, LIU Yue, YANG Xiao-guang. Operation of signalized diamond interchanges with frontage roads using dynamic reversible lane control[J]. Transportation Research Part C: Emerging Technologies, 2015, 51: 196-209. doi: 10.1016/j.trc.2014.11.010
[10]	ASSI K J, RATROUT N T. Proposed quick method for applying dynamic lane assignment at signalized intersections[J]. IATSS Research, 2017, DOI: 10.1016/j.iatssr.2017.03.004.
[11]	MA Qing, WANG Min. A New control strategy of variable lane based on video detection[C]∥IEEE. 2014 5th International Conference on Intelligent Systems Design and Engineering Applications. New York: IEEE, 2014: 40-43.
[12]	LI Li-li, QU Zhao-wei, SONG Xian-min, et al. Research on variable lane signalized control method[C]∥IEEE. 2009International Conference on Measuring Technology and Mechatronics Automation. New York: IEEE, 2009: 575-578.
[13]	XU Hong-ling, YU Quan. Research on threshold of variable lane at signal control intersection[J]. Road Traffic and Safety, 2014, 14 (2): 43-48. (in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-DLJA201402010.htm
[14]	DING Jing. Research on variable approach lane at signalized intersection[D]. Dalian: Dalian University of Technology, 2015. (in Chinese).
[15]	ZHANG Ye, YUAN Zhen-zhou. Design of the left-turn variable lane at signalized intersection[J]. Journal of Transport Information and Safety, 2014, 32 (4): 26-30. (in Chinese). doi: 10.3963/j.issn.1674-4861.2014.04.005
[16]	FU Li-jun, GUO Hai-feng, DONG Hong-zhao. An adaptive control method of variable lane based on dynamic traffic flow[J]. Bulletin of Science and Technology, 2011, 27 (6): 899-903. (in Chinese). doi: 10.3969/j.issn.1001-7119.2011.06.019
[17]	ZHOU Peng, DING Chen. Research and implementation of the oriented decision algorithm of intelligent variable lane[J]. Journal of Wuhan University of Technology, 2012, 34 (8): 82-86. (in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-WHGY201208017.htm
[18]	ZHOU Hong-mei, DING Jing, QIN Xiao. Optimization of variable approach lane use at isolated signalized intersections[C]∥TRB. Transportation Research Record 2016Annual Meeting. Washington: TRB, 2016: 65-74.
[19]	XU Hong-ling. Research on signal intersection variable lane presignal relationship[D]. Beijing: Beijing University of Technology, 2014. (in Chinese).
[20]	ZHOU Yang, LIN Hao, GU Shan-shan, et al. Detector layouts and trigger conditions of upstream variable lanes at signalized intersections[J]. Transactions of Beijing Institute of Technology, 2013, 33 (12): 1298-1302. (in Chinese). doi: 10.3969/j.issn.1001-0645.2013.12.018
[21]	SUN Hai-rong. The research and application of the fuzzy neural network[D]. Baoding: North China Electric Power University, 2006. (in Chinese).
[22]	LIN Bi-hua. Research on neural fuzzy system and its application in coordinated control system of power plant[D]. Baoding: North China Electric Power University, 2005. (in Chinese).
[23]	HOU Ming-shan, LAN Yun. Traffic flow prediction based on adaptive neuro-fuzzy inference system[J]. Mechanical Science and Technology, 2006, 25 (10): 1178-1181. (in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-JXKX200610013.htm
[24]	SUN C. Rule-base structure identification in an adaptivenetwork-based fuzzy inference system[J]. IEEE Transactions on Fuzzy Systems, 1994, 2 (1): 64-73.
[25]	ZHOU Xian-wen. The study of intelligent traffic signal based on fuzzy neural network[D]. Chengdu: Xihua University, 2012. (in Chinese).
[26]	CHEN De-wang, WANG Fei-yue, CHEN Long. Freeway ramp control algorithm based on neuro fuzzy networks[J]. Journal of Traffic and Transportation Engineering, 2003, 3 (2): 100-105. (in Chinese). http://transport.chd.edu.cn/article/id/200302017
[27]	WANG Hui. Prediction of traffic flow state based on adaptive neuro fuzzy inference system[J]. Journal of Transport Information and Safety, 2007, 4 (25): 46-49. (in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-JTJS200704013.htm
[28]	LI Li-li, QU Zhao-wei, WANG Dian-hai. Study on guidance method for variable lane[J]. Computer and Communications, 2008, 26 (5): 53-56. (in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-JTJS200805016.htm