Abstract
A tool for travel time estimation of cyclists approaching a traffic light can monitor level of service of intersections in bike crowded cities. This work represents a first step in developing such a tool. Neural Network models are evaluated on how they perform in estimating individual travel time of cyclists approaching a signalized intersection. Based on simulated scenarios, in cities with low bicycle levels (deterministic scenario), Neural Networks are good travel time estimators whereas, in places with high bike volumes (where cyclists depart with a discharge rate) information on queued cyclists is crucial for travel time information.
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Reggiani, G., Dabiri, A., Daamen, W., Hoogendoorn, S.P. (2020). Exploring the Potential of Neural Networks for Bicycle Travel Time Estimation. In: Zuriguel, I., Garcimartin, A., Cruz, R. (eds) Traffic and Granular Flow 2019. Springer Proceedings in Physics, vol 252. Springer, Cham. https://doi.org/10.1007/978-3-030-55973-1_60
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DOI: https://doi.org/10.1007/978-3-030-55973-1_60
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