Estimating Vehicle Speed from Traffic Count and Occupancy Data

Martin L. Hazelton

《Journal of Data Science》 2卷3期 (2004/07) Pp. 231-244

https://doi.org/10.6339/JDS.2004.02(3).159

並列摘要

Automatic vehicle detectors are now common on road systems across the world. Many of these detectors are based on single inductive loops, from which data on traffic volumes (i.e. vehicle counts) and occupancy (i.e. proportion of time during which the loop is occupied) are available for 20 or 30 second observational periods. However, for the purposes of traffic management it is frequently useful to have data on (mean) vehicle speeds, but this is not directly available from single loop detectors. While detector occupancy is related in a simple fashion to vehicle speed and length, the latter variable is not measured on the vehicles that pass.In this paper a new method for speed estimation from traffic count and occupancy data is proposed. By assuming a simple random walk model for successive vehicle speeds an MCMC approach to speed estimation can be applied, in which missing vehicle lengths are sampled from an exogenous data set. Unlike earlier estimation methods, measurement error in occupancy data is explicitly modelled. The proposed methodology is applied to traffic flow data from Interstate 5 near Seattle, during a weekday morning. The efficacy of the estimation scheme is examined by comparing the estimates with independently collected vehicle speed data. The results are encouraging.

並列關鍵字

Bayesian inference ； inductance loop ； Metropolis-Hastings algorithm ； measurement error ； missing data