This paper describes two techniques designed to estimate vehicle journey times on non-signalised roads, using 250 ms digital loop-occupancy data produced by single inductive loop detectors. A mechanistic and a neural network approach provided historical journey time estimates every 30 s, based on the data collected from the previous 30 s period. These 30s estimates would provide the traffic network operator with immediate post-event congestion information on roads where no close circuit television cameras were present. The mechanistic approach estimated Journey times every 30 s between pairs of detectors, using the knowledge of vehicle speed derived from the loops and the distances between them. The 30s average loop-occupancy time per vehicle, average time-gap between vehicles and percentage occupancy parameters derived from the inductive loops were presented to a neural network for training along with the associated vehicles' measured journey times. The neural network was shown to consistently out-perform the mechanistic approach (in terms of the mean absolute percentage deviation from the mean measured travel time), particularly when using pairs of detectors
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