Overall Traffic Mode Prediction by VOMM Approach and AR Mining Algorithm With Large-Scale Data

Traffic state prediction has been a popular topic, since traffic congestion occurs in most cities and creates inconvenience to human daily life. In this paper, we propose a predicting method for a city’s overall traffic state, in order to help people avoid possible future congestion. Based on the variable-order Markov model theory and probability suffix tree, the proposed method makes use of the association rules to improve forecasting performance. Since the association rules are extracted from the historical traffic data and describe the traffic state relations among different regions, the proposed method can improve the predictive accuracy. The traffic system in Shanghai is considered as our experimental case because of its complicated and gigantic coupling transport network. The experimental results indicate more accuracy compared with other methods in long-term traffic status prediction.

[1]  A. R. Cook,et al.  ANALYSIS OF FREEWAY TRAFFIC TIME-SERIES DATA BY USING BOX-JENKINS TECHNIQUES , 1979 .

[2]  Billy M. Williams,et al.  Urban Freeway Traffic Flow Prediction: Application of Seasonal Autoregressive Integrated Moving Average and Exponential Smoothing Models , 1998 .

[3]  I Okutani,et al.  Dynamic prediction of traffic volume through Kalman Filtering , 1984 .

[4]  Michael J Demetsky,et al.  TRAFFIC FLOW FORECASTING: COMPARISON OF MODELING APPROACHES , 1997 .

[5]  Zhirui Ye,et al.  Unscented Kalman Filter Method for Speed Estimation Using Single Loop Detector Data , 2006 .

[6]  Daniel B. Fambro,et al.  Application of Subset Autoregressive Integrated Moving Average Model for Short-Term Freeway Traffic Volume Forecasting , 1999 .

[7]  H. M. Zhang,et al.  RECURSIVE PREDICTION OF TRAFFIC CONDITIONS WITH NEURAL NETWORK MODELS , 2000 .

[8]  Das Amrita,et al.  Mining Association Rules between Sets of Items in Large Databases , 2013 .

[9]  Ling Zhang,et al.  Short-term Traffic Flow Prediction Based on Incremental Support Vector Regression , 2007, Third International Conference on Natural Computation (ICNC 2007).

[10]  James M. Keller,et al.  A fuzzy K-nearest neighbor algorithm , 1985, IEEE Transactions on Systems, Man, and Cybernetics.

[11]  Hannu Toivonen,et al.  Sampling Large Databases for Association Rules , 1996, VLDB.

[12]  Lee D. Han,et al.  Online-SVR for short-term traffic flow prediction under typical and atypical traffic conditions , 2009, Expert Syst. Appl..

[13]  Ma Jun,et al.  Research of Traffic Flow Forecasting Based on Neural Network , 2008, 2008 Second International Symposium on Intelligent Information Technology Application.

[14]  Hongjun Lu,et al.  Beyond intratransaction association analysis: mining multidimensional intertransaction association rules , 2000, TOIS.

[15]  Dewei Li,et al.  Medium-term prediction of urban traffic states using probability tree , 2016, CCC 2016.

[16]  Shamkant B. Navathe,et al.  An Efficient Algorithm for Mining Association Rules in Large Databases , 1995, VLDB.

[17]  Dewei Li,et al.  Ensemble learning based urban traffic state prediction for coupling traffic network with large scale data , 2016, 2016 IEEE 19th International Conference on Intelligent Transportation Systems (ITSC).

[18]  Antony Stathopoulos,et al.  Fuzzy Modeling Approach for Combined Forecasting of Urban Traffic Flow , 2008, Comput. Aided Civ. Infrastructure Eng..

[19]  Heikki Mannila,et al.  Rule Discovery from Time Series , 1998, KDD.

[20]  Mark Dougherty,et al.  SHOULD WE USE NEURAL NETWORKS OR STATISTICAL MODELS FOR SHORT TERM MOTORWAY TRAFFIC FORECASTING , 1997 .

[21]  Mark Dougherty,et al.  SHORT TERM INTER-URBAN TRAFFIC FORECASTS USING NEURAL NETWORKS , 1997 .

[22]  Jian Pei,et al.  Mining frequent patterns without candidate generation , 2000, SIGMOD '00.

[23]  Philip S. Yu,et al.  An effective hash-based algorithm for mining association rules , 1995, SIGMOD '95.