Optimizing traffic prediction performance of neural networks under various topological, input, and traffic condition settings

This paper presents an approach to optimize the short-term traffic prediction performance on freeways using multiple artificial neural network topologies under different network and traffic condition settings. The approach encourages multimodel techniques that are capable of improving the prediction system performance over single-model approaches. Using a mix of neural network topologies, the short-term speed prediction performance was extensively evaluated under different input settings and various prediction horizons (from 5 to 20 min). To enable the networks to learn from historical information, a long-term memory component was introduced to the input patterns to allow the networks to build internal representation of recurrent conditions, in addition to the short-term memory that is encoded in the most recent information. Optimal settings were determined by maximizing the performance under different traffic conditions observed at the target location, as well as upstream and downstream locations. Comparative statistical analysis with naive and heuristic approaches showed that the optimized neural network approach resulted in better prediction performance. The study shows that the optimal settings were consistently more dependent on the long-term memory component as prediction horizon increases.

[1]  Steven I-Jy Chien,et al.  DYNAMIC TRAVEL TIME PREDICTION WITH REAL-TIME AND HISTORICAL DATA , 2003 .

[2]  Ramin Yasdi.  Prediction of Road Traffic using a Neural Network Approach , 1999, Neural Computing & Applications.

[3]  Billy M. Williams,et al.  Comparison of parametric and nonparametric models for traffic flow forecasting , 2002 .

[4]  Stephen G. Ritchie,et al.  Macroscopic Modeling of Freeway Traffic Using an Artificial Neural Network , 1997 .

[5]  Shaw-Pin Miaou,et al.  Real-Time Prediction of Traffic Flows Using Dynamic Generalized Linear Models , 1999 .

[6]  Laurence R. Rilett,et al.  Spectral Basis Neural Networks for Real-Time Travel Time Forecasting , 1999 .

[7]  Steven I-Jy Chien,et al.  Dynamic freeway travel time prediction using probe vehicle data: Link-based vs , 2001 .

[8]  Liping Fu,et al.  Estimation of time‐dependent, stochastic route travel times using artificial neural networks , 2000 .

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

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

[11]  David G. Stork,et al.  Pattern Classification , 1973 .

[12]  Hashem R Al-Masaeid,et al.  Short-Term Prediction of Traffic Volume in Urban Arterials , 1995 .

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

[14]  M. Ben-Akiva,et al.  Real-time Prediction Of Traffic Congestion , 1992, The 3rd International Conference on Vehicle Navigation and Information Systems,.

[15]  Isam Kaysi,et al.  INTEGRATED APPROACH TO VEHICLE ROUTING AND CONGESTION PREDICTION FOR REAL-TIME DRIVER GUIDANCE , 1993 .

[16]  Sherif Ishak,et al.  Performance evaluation of short-term time-series traffic prediction model , 2002 .

[17]  Denos C. Gazis,et al.  On-Line Estimation of Traffic Densities from Time-Series of Flow and Speed Data , 1971 .

[18]  Dongjoo Park,et al.  Direct Forecasting of Freeway Corridor Travel Times Using Spectral Basis Neural Networks , 2001 .

[19]  David G. Stork,et al.  Pattern Classification (2nd ed.) , 1999 .

[20]  Haitham Al-Deek,et al.  Travel-Time Prediction for Freeway Corridors , 1999 .

[21]  Baher Abdulhai,et al.  Short Term Freeway Traffic Flow Prediction Using Genetically-Optimized Time-Delay-Based Neural Networks , 1999 .

[22]  Ir J W C Van Lint,et al.  ROBUST FREEWAY TRAVELTIME PREDICTION WITH STATE-SPACE NEURAL NETWORKS , 2003 .

[23]  Jose C. Principe,et al.  Neural and adaptive systems , 2000 .

[24]  Billy M. Williams.  Multivariate Vehicular Traffic Flow Prediction: Evaluation of ARIMAX Modeling , 2001 .

[25]  Chronis Stamatiadis.  Travel time predictions under dynamic route guidance with a recursive adaptive algorithm , 1992 .

[26]  Moshe Ben-Akiva,et al.  Dynamic network models and driver information systems , 1991 .

[27]  S. P. Hoogendoorn,et al.  Freeway Travel Time Prediction with State-Space Neural Networks: Modeling State-Space Dynamics with Recurrent Neural Networks , 2002 .

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

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

[30]  Dongjoo Park,et al.  Forecasting Multiple-Period Freeway Link Travel Times Using Modular Neural Networks , 1998 .

[31]  Jian Lu.  PREDICTION OF TRAFFIC FLOW BY AN ADAPTIVE PREDICTION SYSTEM , 1990 .

[32]  Ivica Kostanic,et al.  Principles of Neurocomputing for Science and Engineering , 2000 .

[33]  Peter J. Bickel,et al.  Day-to-Day Travel-Time Trends and Travel-Time Prediction from Loop-Detector Data , 2000 .

[34]  D. Levinson.  Detecting the Breakdown of Traffic Trb 2003 Annual Meeting Cd-rom Paper Revised from Original Submittal , 2002 .