Exploring the Potential of Neural Networks for Bicycle Travel Time Estimation

Conference paper (2020)

Authors

G. Reggiani Transport and Planning - CEG
A. Dabiri Team Bart De Schutter - Mechanical, Maritime and Materials Engineering
W. Daamen Transport and Planning - CEG
S.P. Hoogendoorn Transport and Planning
Research Group
Transport and Planning (CEG) (TU Delft)
Copyright: © 2020 G. Reggiani, A. Dabiri, W. Daamen, S.P. Hoogendoorn
DOI
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https://doi.org/10.1007/978-3-030-55973-1_60
Neural networks Signalized intersections Bike travel time estimation
More Info
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Published Date

2020

Language

English

Reuse Rights

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Faculty

Civil Engineering & Geosciences

Department

Transport and Planning

Research Group

Transport and Planning

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.