抄録
Railway level crossings in urban areas can impose substantial delays on road traffic and can produce poor safety outcomes when road users ignore warnings to avoid delays. Grade separation is a solution but comes at a substantial cost. This paper focuses on predicting the closure times of urban railway level crossings and is part of a broader research program examining the potential for Intelligent Transport Systems to reduce delays at urban railway level crossings. Using data generated from a simulation model, regression and neural network models are developed relating a range of explanatory variables to crossing closure time. The results highlight that if closure times are to be predicted more accurately, there is a need for improved real time data including train speed data and more accurate data on whether a particular train is to stop at a station adjacent to the crossing or run express through the level crossing.
