DOI: 10.14704/nq.2018.16.6.1657

First Vehicle Arrival Time Prediction at Signalized Intersection Based on Wavelet-Elman Neural Network

Guchang Ao, Huiling Zhang, Linyu Yang, Wuting Jiang

Abstract


There is a close relationship between the vehicle arrival time at signalized intersection and the vehicle delay at intersection entrance. When the red light of the signalized intersection is on, the first vehicle stopping at the stop line of the entrance is taken as the study object, and the time difference in the red light’s turning on and the first vehicle’s arriving at the stop line is defined as the first vehicle arrival time at signalized intersection. There is great randomness in the first vehicle arrival time series at signalized intersection. Firstly, the wavelet transform (WT) method is adopted to decompose the non-stationary original arrival time series into low-frequency signal and high-frequency signal. Then the dynamics and fast feedback of Elman neural network are used to predict different signals respectively. Finally, a final predicted result of the first vehicle arrival time is obtained when they are subject to linear superposition. The result shows that the error of the first vehicle arrival time prediction at signalized intersection based on wavelet-Elman neural network is small, and the predicted value is highly consistent with the actual value, which can provide reliable data source for delay parameter extraction and signal timing optimization at signalized intersection.

Keywords


Traffic Engineering, Signalized Intersection, First Vehicle Arrival Time, Wavelet Transform (WT), Elman Neural Network, Short-Time Prediction

Full Text:

PDF

References


Abdalla MI. Digital detection techniques via Elman neural network. Journal of Engineering and Applied Science 2002; 49(6): 1197-1208.

Akçelik R, Rouphail NM. Overflow queues and delays with random and platooned arrivals at signalized intersections. Journal of Advanced Transportation 1994; 28(3): 227-51.

Cai Q, Wang ZY, Zheng LY, Wu B, Wang YH. Shock wave approach for estimating queue length at signalized intersections by fusing data from point and mobile sensors. Transportation Research Record: Journal of the Transportation Research Board 2014; 2422: 79-87.

Cetin M, Comert G. Short-term traffic flow prediction with regime switching models. Transportation Research Record Journal of the Transportation Research Board 2006; 1965(1): 23-31.

Chen P, Zheng FF, Lu GQ, Wang YP. Comparison of variability of individual vehicle delay and average control delay at signalized intersections. Transportation Research Record Journal of the Transportation Research Board 2016; 2553: 128-37.

Darma Y, Karim MR, Mohamad J, Mohamad J, Abdullah S. Control delay variability at signalized intersection based on HCM method. In Proceedings of the Eastern Asia Society for Transportation Studies 2005; 5: 945-58.

Elman JL. Finding structure in time. Cognitive Science 1990; 14(2): 179-211.

Gao ZH, Qu ZW, Hu HY. Study on vehicle delay based on the vehicle arriving distribution at entrance lanes of intersection. Procedia Engineering 2016; 137: 599-608.

Gonçalves CP. Quantum neural machine learning: backpropagation and dynamics. NeuroQuantology 2016; 15(1): 22-41.

Hodge VJ, Krishnan R, Austin J, Polak J, Jackson T. Short-term prediction of traffic flow using a binary neural network. Neural Computing and Applications 2014; 25(7-8): 1639-55.

Hurdle VF. Signalized intersection delay models–a primer for the uninitiated. Transportation Research Record 1984; 971: 96-105.

Ji YJ, Chen XS, Wang W, Hu B. Short-term forecasting of parking space using particle swarm optimization-wavelet neural network model. Journal of Jilin University (Engineering and Technology Edition) 2016; 46(2): 399-405.

Li Y, Wei DD, Mu Z, Xiong ZH, Wang YH, Yin WS. Study of the time-collocation of signal lamp at intersection. Mathematical Modeling and Engineering Problems 2015, 2(2): 5-10.

Lu JJ, Wang ZQ. Internet traffic data flow forecast by RBF neural network based on phase space reconstruction. Transactions of Nanjing University of Aeronautics and Astronautics 2006; 23(4): 316-22.

Mallat S. A wavelet tour of signal processing. Academic Press 1999.

Miller AJ. Settings for fixed-cycle traffic signals. Journal of the Operational Research Society 1963, 14(4): 373-86.

Mousa RM. Analysis and modeling of measured delays at isolated signalized intersections. Journal of Transportation Engineering 2002; 128(4): 347-54.

Pan QR, Zhu YJ. Delay analysis of the vehicle at signalized intersection. Journal of Systems Science and Mathematical Sciences 2009; 29(6): 728-34.

Webster FV. Traffic signal settings. Road Research Technical Paper 1958; 39.

Weng XX, Tan GX, Yao SS, Huang Z. Traffic flow characteristics and short-term prediction model of urban intersection. Journal of Traffic and Transportation Engineering 2006; 6(1):103-07.

Yang QF, Zhang B, Gao P. Short-term traffic flow prediction method based on improved dynamic recurrent neural network. Journal of Jilin University (Engineering and Technology Edition) 2012; 42(4): 887-91.

Yao ZS, Xiong ZH, Shao CF. Relation analysis for road traffic flow short-term forecasting. Journal of Transportation Systems Engineering and Information Technology 2010; 10(2): 117-21.

Zhang HL, Li KP, Ao GC. Research on signalized intersection control delay extraction method under monitor environment. Journal of Beijing Jiaotong University 2010; 34(6): 40-45.

Zhang HL, Yang LY, Ao GC. Review of delay parameter acquisition at the signalized intersection. Journal of Chongqing Jiaotong University (Natural Science) 2017; 36(3): 90-97.


Supporting Agencies





| NeuroScience + QuantumPhysics> NeuroQuantology :: Copyright 2001-2017