Exploring the Potential of Neural Networks for Bicycle Travel Time Estimation

Giulia Reggiani*, Azita Dabiri, Winnie Daamen, Serge P. Hoogendoorn

*Corresponding author for this work

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

1 Citation (Scopus)
43 Downloads (Pure)


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.

Original languageEnglish
Title of host publicationTraffic and Granular Flow 2019
EditorsIker Zuriguel, Angel Garcimartín, Raúl Cruz Hidalgo
Number of pages7
ISBN (Print)9783030559724
Publication statusPublished - 2020
Event13th Conference on Traffic and Granular Flow, TGF 2019 - Pamplona, Spain
Duration: 2 Jul 20195 Jul 2019

Publication series

NameSpringer Proceedings in Physics
ISSN (Print)0930-8989
ISSN (Electronic)1867-4941


Conference13th Conference on Traffic and Granular Flow, TGF 2019

Bibliographical note

Accepted Author Manuscript


  • Bike travel time estimation
  • Neural networks
  • Signalized intersections


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