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.

► Bayesian analysis is performed for the estimation of average vehicle time headway.
► Light, congested, and disturbed traffic conditions are examined.
► A recursive approach is developed that can be applied to various traffic scenarios.
► Uncertainty of the online estimate is assessed via the Bayesian analysis.
► The estimation is based on the data collected from a single inductive loop detector.