Ensemble learning based urban traffic state prediction for coupling traffic network with large scale data

Current urban transportation system applications often deal with the scenarios of large scale multiple-layer road networks, with the requirement of qualified overall prediction accuracy. However, the common related studies mostly focus on the prediction of individual road segments or intersections. In this paper, we propose a generic method for large scale coupling urban traffic to forecast the future states of the overall network. In the preliminary stage, the features are extracted using unsupervised method. Subsequently, an ensemble learning via matching the historical data and the current information are implemented to predict the traffic state transition. The data set for experiment is composed of multiple layers of road network in Shanghai, which are collected by loop detectors every two minutes. We verify the effectiveness of the developed method by evaluating the experimental prediction performance using the actual data. The results also indicate that adding the interactive features of the coupling road network to the forecasting algorithm can generate superior prediction accuracy comparing with the independent prediction method.

[1]  Yanyan Xu,et al.  Short-term traffic volume prediction using classification and regression trees , 2013, 2013 IEEE Intelligent Vehicles Symposium (IV).

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

[3]  Dewei Li,et al.  A short-term traffic flow forecasting method and its applications , 2015 .

[4]  Carlos F. Daganzo,et al.  THE CELL TRANSMISSION MODEL, PART II: NETWORK TRAFFIC , 1995 .

[5]  Yugeng Xi,et al.  A Simplified Macroscopic Urban Traffic Network Model for Model-Based Predictive Control , 2009, CTS 2009.

[6]  D. T. Lee,et al.  Travel-time prediction with support vector regression , 2004, IEEE Transactions on Intelligent Transportation Systems.

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

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

[9]  Der-Horng Lee,et al.  Short-term freeway traffic flow prediction : Bayesian combined neural network approach , 2006 .

[10]  Panos G Michalopoulos,et al.  IMPROVED ESTIMATION OF TRAFFIC FLOW FOR REAL-TIME CONTROL (DISCUSSION AND CLOSURE) , 1981 .

[11]  R B Potts,et al.  THE OVERSATURATED INTERSECTION , 1963 .

[12]  Ran El-Yaniv,et al.  On Prediction Using Variable Order Markov Models , 2004, J. Artif. Intell. Res..

[13]  Wang Qiu-lan.  Dynamic Predictability Analysis for Traffic Data Serials Based on BP Neural Network , 2011 .

[14]  Muhammad Tayyab Asif,et al.  Unsupervised learning based performance analysis of n-support vector regression for speed prediction of a large road network , 2012, 2012 15th International IEEE Conference on Intelligent Transportation Systems.

[15]  Mascha C. van der Voort,et al.  Combining kohonen maps with arima time series models to forecast traffic flow , 1996 .

[16]  C. Daganzo.  THE CELL TRANSMISSION MODEL.. , 1994 .

[17]  Fei-Yue Wang,et al.  Traffic Flow Prediction With Big Data: A Deep Learning Approach , 2015, IEEE Transactions on Intelligent Transportation Systems.

[18]  Nancy L. Nihan,et al.  Use of the box and Jenkins time series technique in traffic forecasting , 1980 .