Machine learning-based bicycle delay estimation at signalized intersections using sparse GPS data and traffic control signals

Abstract

Bicycle delay is an important variable to assess the performance of the cycling transportation system, especially as an indicator of intersection efficiency. This article estimates a machine learning (ML)-based model for estimating average bicycle delays at signalized intersections. This study evaluates various ML models with regressor features, including random forest, k-nearest neighbor, support vector regression, extreme gradient boosting, and neural networks. Sparse GPS cycling data (as reference data) from the Talking Bikes program in the Netherlands and the local control signal and flow detection information from the VLOG data provided by a Dutch city are adopted to train the ML models. The findings illustrate the viability of estimating bicycle delays by considering the interplay among weather conditions, temporal factors, junction topology, and local traffic conditions. The estimation model fit using the best-performing model - random forest - has doubled compared to the case without such additional traffic information, indicating its improved performance. Insights gained from the estimation model emphasize the potential of data-driven approaches to inform traffic management, bicycle policy, and infrastructure development.