Cyclists route choice modeling from trip duration data in urban areas

TL;DR

This paper presents a novel method to infer cyclists' route choices in urban areas using only trip duration data, revealing diverse behaviors and route preferences without GPS or spatial data.

Contribution

It introduces a trip duration-based inference approach using log-normal mixture models to analyze cycling behaviors in urban bike-sharing systems.

Findings

Trip durations often match the fastest routes from OSM.

Mixture models uncover heterogeneous cycling behaviors.

The method provides insights into urban mobility without GPS data.

Abstract

The lack of GPS data limits the ability to reconstruct the actual routes taken by cyclists in urban areas. This article introduces an inference method based solely on trip durations and origin-destination pairs from bike-sharing system (BSS) users. Travel time distributions are modeled using log-normal mixture models, allowing us to identify the presence of distinct behaviors. The approach is applied to 3.8 million trips recorded in 2022 in the Toulouse metropolitan area, with observed durations compared against travel times estimated by OpenStreetMap (OSM). Results show that, for many station pairs, trip durations align closely with the fastest route suggested by OSM, reflecting a dominant and routine practice. In other cases, mixture models reveal more heterogeneous behaviors, including longer trips, detours, or intermediate stops. This approach highlights both the stability and diversity of cycling practices, providing a robust tool for usage analysis in data-limited contexts, and offering new insights into urban mobility dynamics without relying on spatially explicit data.