Computer Science ›› 2020, Vol. 47 ›› Issue (6A): 66-69.doi: 10.11896/JsJkx.190600131

• Artificial Intelligence • Previous Articles     Next Articles

Signal Timing Scheme Recommendation Algorithm Based on Intersection Similarity

LUO Jia-lei and MENG Li-min   

  1. College of Information Engineering,ZheJiang University of Technology,Hangzhou 310023,China
  • Published:2020-07-07
  • About author:CHENG Zhe, born in 1994, postgra-duate.His main research interests include deep learning, computer vision and bioinformatics.LIANG Yu, born in 1968, postgraduate, professor, Ph.D supervisor.His main research interests include computer networks, software-defined networks and cloud computing.
  • Supported by:
    This work was supported by the National Natural Science Foundation of China (61871349) and Natural Science Foundation of ZheJiang Pvovince,China (LY18F010024,LQ19F010013).

Abstract: Signal timing control is an important part of urban traffic control system,and traditional signal timing work requires a lot of manpower and time cost,and the implementation effect depends on the experience level of the staff.It is difficult to meet the needs of real-time regulation.Therefore,a signal timing scheme recommendation algorithm based on intersection similarity is proposed.The intersection similarity calculation is performed based on various static and dynamic attributes of the intersection to improve the accuracy of intersection matching.According to the recommendation method of collaborative filtering,the scheme of similar intersections is recommended to the target intersection to improve the accuracy and effectiveness of the signal timing work.The experimental results show that the proposed algorithm can accurately recommend the signal timing scheme and has lower algorithm complexity.It is suitable for signal timing scheme recommendation in the context of massive data.

Key words: Collaborative filtering, Recommendation, Signal timing, Similarity, Traffic control

CLC Number: 

  • TP391
[1] WU L B,NIE L,LIU B Y,et al.An intelligent traffic signal control method under VANET environment .Journal of Computers,2016,39(6):1105-1119.
[2] GAO W C,LI G L,TANA.Overview of road network matching algorithms .Journal of Software,2018,29(2):225-250.
[3] BAO J L,WANG B,YANG X C,et al.Nearest neighbor query technology under the road network environment .Journal of Software,2018,29(3):642-626.
[4] SONG Z Z,LIN L.Signal timing optimization and simulation based on regional coordinated control .Computer Application,2018,38(S2):313-316,320.
[5] FOY M D,BENEKOHALR F,GOLDBERG D E.Signal timing determination using genetic algorithms.National Research Council,Washington D C,1992:108-115.
[6] PAPPIS C P,MAMDAM E H.AFuzzy Logic Controller for a Traffic Junction.IEEE Transactionson Systems.Man and Cygernetics,1977,1(10):707-717.
[7] ALVAREZ I,POZNYAK A,MALO A.Urban traffic control problem a game theory approach//International Federation of Automatic Proceedings.2009.
[8] SHAMSHIRBAND S.A distributed approach for coordination between traffic lights based on game theory.International Arab Journal of Information Technology,2012,2(2):148-153.
[9] LI L Y,CAO D Z.Optimal prediction of road traffic flow and optimal control of intersections .Control Theory and Application,1993,10(1):67-72.
[10] GU H Z,WANG W,CHEN S F.Research on prediction model of vehicle arrival at urban road intersection based on neural network .China Journal of Highway and Transport,1998(Z1):73-77.
[11] TANG Z K,ZHENG J S,WANG W Z.Phase-change control of single intersection based on fuzzy control neural network .Journal of ZheJiang University,2006(2):29-32.
[12]QU X M,YAO H Y,WANG Y G,et al.Research on Adaptive Control Strategy Based on Effective Green Light Time Utilization [J].Transportation Research,2015(1):54-58.
[13]SUN D H,YANG C C,LIAO X Y,et al.Timing parameter estimation of intersection signals based on GPS data of public transportation [J].Control and Decision,2018,33(4):724-730.
[14]XIA X H.Urban traffic signal timing decision-making under interactive coordination reinforcement learning [J].Computer Engineering and Applications,2018,54(11):265-270.
[15]RONG H G,HUO S X,HU C H,et al.Collaborative filtering recommendation algorithm based on user similarity [J].Journal of Communications,2014,35(2):16-24.
[16]CHEN H Y,LIU C H,SUN B.A summary of the similarity measure of time series data mining [J].Control and Decision,2017,32(1):1-11.
[17]KONG X X,SU B C,WANG H Z,et al.Research on recommendation model and algorithm based on label weight scoring [J].Journal of Computers,2017,40(6):1440-1452.
[18]PAN Y T,HE F Z,YU H P.A social recommendation algorithm based on the implicit similarity of trust relationships [J].Journal of Computers,2018,41(1):65-81.
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