Real-Time Traffic Network State Prediction for Proactive Traffic Management

Real-time traffic management systems with integrated proactive decision support capabilities are expected to operate with (a) limited prediction accuracy (b) decision-making latency, and (c) partial coverage of the managed area. Such deficiencies are difficult to avoid in most real-world traffic network management applications, and there is a need to quantify the effect of these deficiencies on the performance of traffic network management systems. This paper studies the effectiveness of a proactive traffic management system. Various levels of prediction accuracy of the traffic network state, decision-making latency, and partial area coverage are considered. A traffic management system that emulates real-time operations is developed. The system adopts a closed-loop rolling horizon framework, which integrates network state estimation and prediction modules as well as decision support capabilities. A set of simulation experiments considers a hypothetical highway network. The results show that the effectiveness of the traffic management system can be affected by the deficiencies. However, the impact could be smaller if these deficiencies are kept under certain levels.

[1]  Romain Billot,et al.  Integrating the Impact of Rain into Traffic Management , 2010 .

[2]  Khaled Abdelghany,et al.  Rollback Approach for Demand Consistency Checking of Real-Time Traffic Network State Estimation Models , 2014 .

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

[4]  Jian Ma,et al.  AN ADAPTIVE KALMAN FILTER FOR SHORT-TERM TRAFFIC FLOW FORECASTING , 2011 .

[5]  N-E El Faouzi.  NONPARAMETRIC TRAFFIC FLOW PREDICTION USING KERNEL ESTIMATOR , 1996 .

[6]  Hans van Lint,et al.  Short-Term Traffic and Travel Time Prediction Models , 2012 .

[7]  Markos Papageorgiou,et al.  Real-time freeway traffic state estimation based on extended Kalman filter: a general approach , 2005 .

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

[9]  van Lint,et al.  Reliable Real-Time Framework for Short-Term Freeway Travel Time Prediction , 2006 .

[10]  Markos Papageorgiou,et al.  Real-time freeway traffic state estimation based on extended Kalman filter: Adaptive capabilities and real data testing , 2008 .

[11]  Ghassan Abu-Lebdeh,et al.  Neural Networks for Travel Time Prediction on Interrupted Flow Facilities , 2012 .

[12]  Billy M. Williams,et al.  Modeling and Forecasting Vehicular Traffic Flow as a Seasonal ARIMA Process: Theoretical Basis and Empirical Results , 2003, Journal of Transportation Engineering.

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

[14]  Markos Papageorgiou,et al.  Predictive Feedback Routing Control Strategy for Freeway Network Traffic , 2002, Proceedings of the 2002 American Control Conference (IEEE Cat. No.CH37301).

[15]  J. W. C. van Lint,et al.  Online Learning Solutions for Freeway Travel Time Prediction , 2008, IEEE Transactions on Intelligent Transportation Systems.

[16]  Haris N. Koutsopoulos,et al.  Network State Estimation and Prediction for Real-Time Traffic Management , 2001 .

[17]  Haitham Al-Deek,et al.  Predictions of Freeway Traffic Speeds and Volumes Using Vector Autoregressive Models , 2009, J. Intell. Transp. Syst..

[18]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[19]  Corinne Ledoux,et al.  An urban traffic flow model integrating neural networks , 1997 .

[20]  Casper J. Albers,et al.  Intervention and Causality: Forecasting Traffic Flows Using a Dynamic Bayesian Network , 2009 .

[21]  Romain Billot,et al.  Multilevel Assessment of the Impact of Rain on Drivers’ Behavior , 2009 .

[22]  Salim Hariri,et al.  Toward an Autonomic Architecture for Real-Time Traffic Network Management , 2012, J. Intell. Transp. Syst..

[23]  Hani S. Mahmassani,et al.  Dynamic Network Traffic Assignment and Simulation Methodology for Advanced System Management Applications , 2001 .

[24]  Henry X. Liu,et al.  Use of Local Linear Regression Model for Short-Term Traffic Forecasting , 2003 .

[25]  Zhirui Ye,et al.  Short‐Term Traffic Volume Forecasting Using Kalman Filter with Discrete Wavelet Decomposition , 2007, Comput. Aided Civ. Infrastructure Eng..

[26]  H. V. van Zuylen,et al.  Predicting Urban Arterial Travel Time with State-Space Neural Networks and Kalman Filters , 2006 .

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

[28]  Matthew G. Karlaftis,et al.  A multivariate state space approach for urban traffic flow modeling and prediction , 2003 .

[29]  Khaled Abdelghany,et al.  Real-Time Traffic Network State Estimation and Prediction withDecision Support Capabilities: Application to Integrated Corridor Management , 2016 .

[30]  Biswajit Basu,et al.  Multivariate Short-Term Traffic Flow Forecasting Using Time-Series Analysis , 2009, IEEE Transactions on Intelligent Transportation Systems.