Assessing cyclists’ routing preferences by analyzing extensive user setting data from a bike-routing engine

Abstract

Introduction Many municipalities aim to support the uptake of cycling as an environmentally friendly and healthy mode of transport. It is therefore crucial to meet the demand of cyclists when adapting road infrastructure. Previous studies researching cyclists’ route choice behavior deliver valuable insights but are constrained by laboratory conditions, limitations in the number of observations, or the observation period or relay on specific use cases. Methods The present study analyzes a dataset of over 450,000 observations of cyclists’ routing settings for the navigation of individual trips in Berlin, Germany. It therefore analyzes query data recorded in the bike-routing engine BBBike and clusters the many different user settings with regard to preferred route characteristics. Results and Conclusion Results condense the large number of routing settings into characteristic preference clusters. Compared with earlier findings, the big data approach highlights the significance of short routes, side streets and the importance of high-quality surfaces for routing choices, while cycling on dedicated facilities seems a little less important. Consequentially, providing separated cycle facilities along main roads – often the main focal point of cycle plans – should be put into the context of an integrated strategy which fulfills distinct preferences to achieve greater success. It is therefore particularly important to provide a cycle network in calm residential streets as well as catering for short, direct cycle routes.

Digital Bicycling Planning: A Systematic Literature Review of Data-Driven Approaches

“…Cargo bikes are other recent popular topic in this sub-category for transportation of last mile goods deliveries in urban areas [29,30]. The most common analysis methods and approaches in this category that has been used in the reviewed studies are learning algorithms (e.g., [31]), cluster analysis (e.g., [32]), demand analysis (e.g., [33]), linear or non-linear regression analysis (e.g., [34]), and GIS analysis (e.g., [35]). The second cluster in this review is the built environment and decision-making divided into two smaller sub-categories of (1) built environment and travel behaviour and (2) decision-making and planning.…”

Informing about the invisible: communicating en route air pollution and noise exposure to cyclists and pedestrians using focus groups

“…landmarks) along the route (see Marquart et al [17]), next to pollution. These could be combined with factors such as directness or safety, shown to be decisive for cycling as well [69,72]. Moreover, cyclists and pedestrians know best which factors increase their personal wellbeing along routes.…”