Modelling bicycle route choice in German cities using open data, MNL and the bikeSim web-app

Publikation: Beitrag in Buch/Konferenzbericht/Sammelband/GutachtenBeitrag in KonferenzbandBeigetragenBegutachtung



Information on bicycle route choice is crucial for bicycle infrastructure planning. Emerging techniques of crowdsourcing (e.g. FietsTelweek or CITY CYCLING) help to bridge the data gap. The collected data often illustratates the spatial distribution of bicycle traffic within cities and, thus, can help to monitore traffic. However, it is not suitable to forecast changes in traffic flows resulting from infrastructural measures. Traffic demand models are proper tools for that purpose but the utilization requires modelling and data expertise. Furthermore, most models are not based on observed route choice behavior. The contribution presents results of the “bikeSim” project, which intends to tackle that challenge. First aim of the study was to analyze bicycle route choice based on a) revealed preference GPS route data and b) open transport supply data. Second, a new simulation platform called “bikeSim” was implemented to provide an accessible and easy-to-use tool for bicycle traffic simulation. The contribution presents the results of the study, which initially focused on the city of Dresden (Germany). It reveals that bicycle route choice in Dresden depends on different factors such as slope, type of bicycle infrastructure, surface and its quality as well as speed of motorized traffic. The multinomial logit model (MNL) realistically reproduces bicycle route choice decisions (accuracy of 88.6%). The implementation of the model into the bikeSim web-app enables planners to simulate changes in bicycle traffic resulting from changes in infrastructure (e.g. type of bike infrastructure) or on operational level (e.g. speed of motorized traffic). Thus, the route choice model and the web-app can significantly support decision-making in the planning process.


Titel2021 7th International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS)
Herausgeber (Verlag)IEEE
ISBN (Print)978-1-7281-8996-3
PublikationsstatusVeröffentlicht - 16 Juni 2021


Titel2021 7th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems
KurztitelMT-ITS 2021
Dauer16 - 17 Juni 2021
BekanntheitsgradInternationale Veranstaltung

Externe IDs

Scopus 85115827586
ORCID /0000-0002-5497-3698/work/142254588
Ieee 10.1109/MT-ITS49943.2021.9529273



  • Bicycle route choice, GPS data, Germany, Open data, Simulation