In contemporary transportation planning route travel times are considered key indicators on the reliability of a road network. Mean and variance of travel times do not provide much insight in the travel time reliability since these metrics tend to obscure rare but relevant high t
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In contemporary transportation planning route travel times are considered key indicators on the reliability of a road network. Mean and variance of travel times do not provide much insight in the travel time reliability since these metrics tend to obscure rare but relevant high travel times under specific circumstances. We therefore introduce two metrics, based on just three characteristic percentiles (10th, 50th and 90th), which indicate both congestion as well as possible onset (dissolve) of congestion. High values of either metrics indicate high travel time unreliability. We subsequently propose a simple neural network model to predict these percentile values and hence unreliability based on time-of-day (TOD), and day-of-week (DOW).
The conclusion for traffic managers is twofold. The metrics can be used to identify not only the unreliability of travel times for a given DOW/TOD period, but also identify DOW/TOD periods in which it is likely that congestion sets in (or dissolves. Practically, this means identifying the uncertainty of start, end and hence length of morning and afternoon peak hours. Secondly, the neural network based model can serve as a long-term travel time prediction tool, but also as a travel time unreliability prediction tool, using the two metrics presented above on the model outcomes.@en
