Recursive estimation of average vehicle time headway using single inductive loop detector data

Vehicle time headway is an important traffic parameter. It affects roadway safety, capacity, and level of service. Single inductive loop detectors are widely deployed in road networks, supplying a wealth of information on the current status of traffic flow. In this paper, we perform Bayesian analysis to online estimate average vehicle time headway using the data collected from a single inductive loop detector. We consider three different scenarios, i.e. light, congested, and disturbed traffic conditions, and have developed a set of unified recursive estimation equations that can be applied to all three scenarios. The computational overhead of updating the estimate is kept to a minimum. The developed recursive method provides an efficient way for the online monitoring of roadway safety and level of service. The method is illustrated using a simulation study and real traffic data.

[1]  Stephen G. Ritchie,et al.  STATISTICAL AND NEURAL CLASSIFIERS TO DETECT TRAFFIC OPERATIONAL PROBLEMS ON URBAN ARTERIALS , 1998 .

[2]  Baibing Li,et al.  On the recursive estimation of vehicular speed using data from a single inductance loop detector: A Bayesian approach , 2009 .

[3]  Donald J Dailey,et al.  A statistical algorithm for estimating speed from single loop volume and occupancy measurements , 1999 .

[4]  Martin L. Hazelton,et al.  Inference for origin–destination matrices: estimation, prediction and reconstruction , 2001 .

[5]  Gang-Len Chang,et al.  AN EMPIRICAL INVESTIGATION OF MACROSCOPIC LANE-CHANGING CHARACTERISTICS ON UNCONGESTED MULTILANE FREEWAYS , 1991 .

[6]  Bhargab Maitra,et al.  Evaluation of VMS-Based Traffic Information Using Multiclass Dynamic Traffic Assignment Model: Experience in Kolkata , 2010 .

[7]  D. L. Gerlough,et al.  Traffic flow theory : a monograph , 1975 .

[8]  Heng Wei,et al.  Examining Headway Distribution Models with Urban Freeway Loop Event Data , 2007 .

[9]  Ryuichi Kitamura,et al.  Can Travel Time Information Influence Network Flow? , 2007 .

[10]  Donald J Dailey,et al.  Travel-time estimation using cross-correlation techniques , 1993 .

[11]  Ruey Long Cheu,et al.  Automated detection of lane-blocking freeway incidents using artificial neural networks , 1995 .

[12]  Mike McDonald,et al.  Low speed car following behaviour from floating vehicle data , 2003, IEEE IV2003 Intelligent Vehicles Symposium. Proceedings (Cat. No.03TH8683).

[13]  Henry X. Liu,et al.  Estimation of the Time-dependency of Values of Travel Time and Its Reliability from Loop Detector Data , 2007 .

[14]  Martin L. Hazelton.  Estimating Vehicle Speed from Traffic Count and Occupancy Data , 2004 .

[15]  Mike McDonald,et al.  A Comparative Assessment of Driving Behaviours at Three Sites. , 2003 .

[16]  Taewan Kim,et al.  Interrelations of Reaction Time, Driver Sensitivity, and Time Headway in Congested Traffic , 2011 .

[17]  Mike McDonald,et al.  Determinants of following headway in congested traffic , 2009 .

[18]  John Rice,et al.  Accurate estimation of travel times from single-loop detectors 1 1 Funding for this research was pro , 1998 .

[19]  Charles W Noble.  Urban Planning and Development , 1950 .

[20]  S. James Press,et al.  Subjective and objective Bayesian statistics : principles, models, and applications , 2003 .

[21]  Daniel J. Dailey,et al.  A General Simulation Model for Use with Real Freeway Data to Perform Congestion Prediction Phase 3 , 2006 .

[22]  Baibing Li,et al.  Bayesian Inference for Origin-Destination Matrices of Transport Networks Using the EM Algorithm , 2005, Technometrics.

[23]  J. Charles Kerkering,et al.  Subjective and Objective Bayesian Statistics: Principles, Models, and Applications , 2003, Technometrics.

[24]  Zong Tian,et al.  Probabilistic Model for Signalized Intersection Capacity with a Short Right-Turn Lane , 2006 .

[25]  C. Lan.  A recursive traffic flow predictor based on dynamic generalized linear model framework , 2001, ITSC 2001. 2001 IEEE Intelligent Transportation Systems. Proceedings (Cat. No.01TH8585).

[26]  Jun-ichi Takayama,et al.  A TRAFFIC NETWORK EQUILIBRIUM MODEL FOR UNCERTAIN DEMANDS , 2003 .

[27]  D. L. Gerlough,et al.  Traffic flow theory : a monograph , 1975 .