|
|
|
| VHSSA Model for Predicting Short-term Traffic Flow of Urban Road |
| YUAN Jian1, FAN Bing-quan2 |
1. School of Optical Electrical& Computer Engineering, University of Shanghai for Science & Technology, Shanghai 200093, China;
2. School of Management, University of Shanghai for Science& Technology, Shanghai 200093, China |
|
|
|
|
Abstract A short-term traffic prediction model VHSSA (prediction model based on vertical and horizontal sequence similarity algorithm) is proposed for accurate traffic prediction and dynamic route planning. The model is based on the similarity of vertical and horizontal sequences and analyzes historical traffic time series data and cyclic similarity characteristics of traffic volume in urban roads. The model can overcome the deficiency of the traditional model VSSA (prediction model based on vertical sequence similarity algorithm), which focuses only on vertical cyclic sequence similarity. Complete data are transformed into basic sequences that reflect basic characteristics and fluctuant sequences as well as variation characteristics by utilizing the wavelet transformation function. This transformation can achieve both basic and complete sequence prediction. For complete sequence prediction, the paper corrected the fluctuant sequence based on confidence interval and overlapped it with the basic sequence. Verification experiments are conducted to compare the basic and complete sequences of VHSSA and VSSA. Results show that VHSSA prediction is better than VSSA prediction, and the error probability of VHSSA is lower than that of VSSA; the prediction error can meet the actual requirement.
|
|
Received: 19 July 2014
|
| Fund:Supported by the National Natural Science Foundation of China (No.51008196);the Shanghai Leading Academic Discipline Project (No.S30504) |
|
Corresponding Authors:
YUAN Jian, yuanjianwq@163.com
E-mail: yuanjianwq@163.com
|
|
|
|
[1] LU Feng, ZHENG Nian-bo, DUAN Ying-ying,et al. Travel Information Services:State of the Art and Discussion on Crucial Technologies[J]. Journal of Image and Graphics, 2009,14(7):1219-1229. (in Chinese)
[2] WILLIAMS B M, HOEL L A. A Modeling and Forecasting Vehicular Traffic Flow as a Seasonal ARIMA process:A Theoretical Basic and Empirical Results[J]. Journal of Transportation Engineering,2003,129(6):583-716.
[3] CHEN Chen-yi, WANG Yin, LI Li, et al. The Retrieval of Intra-day Trend and Its Influence on Traffic Prediction[J].Transportation Research Part C:Emerging Technologies,2012, 6(22):103-118.
[4] LI Xing-yi. Analysis Methods of Traffic Flow Based on Similarity[D]. Beijing:Beijing Jiaotong University, 2010. (in Chinese)
[5] HU Jia-xing, CHEN Yan, ZHANG Li-dong. Traffic Flow Prediction Algorithm Study via Chaos RBF Network[J]. Journal of University of Jinan:Science and Technology Edition, 2012, 26(2):152-155. (in Chinese)
[6] LIU Tan-qiu, SUN Xiang-hai, ZHONG Xiang. Short-term Traffic Flow Forecasting Based on a Three-regime SETAR Model[J]. Journal of Highway and Transportation Research and Development, 2010, 27(10):122-127. (in Chinese)
[7] YAO Zhi-sheng, XIONG Zhi-hua, SHAO Chun-fu. Relation Analysis for Road Traffic Flow Short-term Forecasting[J]. Journal of Transportation Systems Engineering and Information Technology, 2010, 10(2):117-121. (in Chinese)
[8] HONG W C. Traffic Flow Forecasting by Seasonal SVR with Chaotic Simulated Annealing Algorithm[J]. Neurocomputing, 2011(12/13):2096-2107.
[9] MIN W, WYNTER L. Real-time Road Traffic Prediction with Spatio-temporal Correlations[J]. Transportation Research Part C:Emerging Technologies, 2011, 19(4):606-616.
[10] ZHENG Zu-duo, AHN S, CHEN Dan-jue, et al. Applications of Wavelet Transform for Analysis of Freeway Traffic:Bottlenecks, Transient Traffic, and Traffic Oscillations[J].Transportation Research Part B:Methodological, 2011,45(2):372-384.
[11] SHEN Guo-jiang, WANG Xiao-hu, KONG Xiang-jie. Short-time Traffic Volume Intelligent Hybrid Forecasting Model and Its Application[J]. System Engineering-Theroy & Practice, 2011,31(3):561-568. (in Chinese)
[12] HAN Chao, SONG Su, WANG Cheng-hong. An Improved Multi-step Adaptive Forecasting Method for Short-term Traffic Flow[J]. Journal of Highway and Transportation Research and Development, 2005,22(1):115-118. (in Chinese) |
| [1] |
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. |
| [2] |
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. |
| [3] |
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. |
| [4] |
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. |
| [5] |
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. |
| [6] |
CAI Jing, LIU Ying, ZHANG Ming-hui. Adjustment Strategy of Freight Transport Structure in Beijing-Tianjin-Hebei Region[J]. Journal of Highway and Transportation Research and Development, 2019, 13(1): 88-93. |
|
|
|
|
2014, Vol. 8 
