Exploring the Potential of Neural Networks for Bicycle Travel Time Estimation
Giulia Reggiani (Transport and Planning)
Azita Dabiri (TU Delft - Mechanical Engineering)
Winnie Daamen (Transport and Planning)
Serge P. Hoogendoorn (TU Delft - Civil Engineering & Geosciences)
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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.