Estimating vehicle speed using single inductive loop detectors
Cherrett, T., Bell, H. and McDonald, M. (2001) Estimating vehicle speed using single inductive loop detectors. Proceedings of the Institution of Civil Engineers: Transport, 147, (1), 23-32. (doi:10.1680/tran.22.214.171.124491).
Full text not available from this repository.
This paper describes five techniques for estimating vehicle speeds using the digital output produced by a 2 m by 1.5 m single inductive loop detector sampled every 250 ms. The accuracy of each technique was then assessed on two further 2 m by 1.5 m loops fitted in single carriageways and two 2 m by 6.5 m loops spread over two carriageways. One-way ANOVA (Analysis of Variance) tests showed that there were highly significant differences between the estimation techniques in the mean deviations from the measured speeds over all four detectors during the peak period (08:00-08:50). Mean absolute percentage deviations (MAPD) from the measured speeds of between 18.8% and 47.5% were returned with an overall average of 28.4%. Settling on one technique, power regression of average loop-occupancy time per vehicle (ALOTPV) data reduced this to 22.7% based on three estimates. The poor results obtained from detector 3214KI (MAPD of 38.9%), situated immediately downstream from a signalised pedestrian crossing, indicate that estimation accuracy depends crucially on the match between the characteristics of the detector used for training and those used for testing. Despite the inaccuracies returned by some of the techniques, it was still possible to distinguish free-flow conditions and periods of queuing. The importance of estimation accuracy was not addressed.
|Keywords:||traffic engineering, transport management|
|Subjects:||T Technology > TE Highway engineering. Roads and pavements|
|Divisions:||University Structure - Pre August 2011 > School of Civil Engineering and the Environment
|Date Deposited:||28 Jun 2006|
|Last Modified:||27 Mar 2014 18:24|
|RDF:||RDF+N-Triples, RDF+N3, RDF+XML, Browse.|
Actions (login required)