Work

Surrogate safety measures from bikesharing GPS data for proactive bicycle safety analysis

Year: 2026

Type: article

Abstract: The aim of the paper was to evaluate the use of a surrogate safety measure (SSM) identified from public bicycle GPS data for proactive bicycle traffic safety analysis on macro scale. The analysis was conducted for over 900 crossings identified using OpenStreetMap and Carto databases, utilizing one month of GPS data from a bikesharing system and police-reported crashes from 2016 to 2019. Out of twenty-two potential SSMs, a measure with the strongest relationship to the observed number of crashes (assessed using the Akaike Information Criterion of GLMs) was selected and used as the basis for the bicycle crash prediction model. The model was validated using Cumulative Residual (CURE) plots for a control group and demonstrated strong predictive performance. The results offer a practical insights into sustainable urban transport planning, injury prevention, and the application of emerging data sources for environmental and public health benefits.

Source: Transportation Research Interdisciplinary Perspectives

DOI: 10.1016/j.trip.2025.101820

Authors: Sylwia Pazdan, Mariusz Kiec

Institution: Cracow University of Technology

Language: English

Topic: Traffic and Road Safety

Subfield: Safety, Risk, Reliability and Quality

Field: Engineering

Domain: Physical Sciences

SDGs: Sustainable cities and communities, Industry, innovation and infrastructure, Good health and well-being

Open Access status: gold

Estimated APC paid: $2,400