Print Email Facebook Twitter Bicycle Data-Driven Application Framework Title Bicycle Data-Driven Application Framework: A Dutch Case Study on Machine Learning-Based Bicycle Delay Estimation at Signalized Intersections Using Nationwide Sparse GPS Data Author Yuan, Y. (TU Delft Transport and Planning) Wang, Kaiyi (Universiteit van Amsterdam) Duives, D.C. (TU Delft Transport and Planning) Hoogendoorn, S.P. (TU Delft Transport and Planning) Hoogendoorn-Lanser, S. (TU Delft Corporate Innovations) Lindeman, Rick (Rijkswaterstaat) Department Transport and Planning Date 2023 Abstract Data-driven approaches are helpful for quantitative justification and performance evaluation. The Netherlands has made notable strides in establishing a national protocol for bicycle traffic counting and collecting GPS cycling data through initiatives such as the Talking Bikes program. This article addresses the need for a generic framework to harness cycling data and extract relevant insights. Specifically, it focuses on the application of estimating average bicycle delays at signalized intersections, as this is an essential variable in assessing the performance of the transportation system. This study evaluates machine learning (ML)-based approaches using GPS cycling data. The dataset provides comprehensive yet incomplete information regarding one million bicycle rides annually across The Netherlands. These ML models, including random forest, k-nearest neighbor, support vector regression, extreme gradient boosting, and neural networks, are developed to estimate bicycle delays. The study demonstrates the feasibility of estimating bicycle delays using sparse GPS cycling data combined with publicly accessible information, such as weather information and intersection complexity, leveraging the burden of understanding local traffic conditions. It emphasizes the potential of data-driven approaches to inform traffic management, bicycle policy, and infrastructure development. Subject data-driven bicycle applicationsGPS cycling datamachine learningbicycle delayssignalized intersections To reference this document use: http://resolver.tudelft.nl/uuid:9aa45ff6-8de0-455f-903b-2ced8af5857d DOI https://doi.org/10.3390/s23249664 ISSN 1424-8220 Source Sensors, 23 (24) Part of collection Institutional Repository Document type journal article Rights © 2023 Y. Yuan, Kaiyi Wang, D.C. Duives, S.P. Hoogendoorn, S. Hoogendoorn-Lanser, Rick Lindeman Files PDF sensors_23_09664_v2.pdf 3.1 MB Close viewer /islandora/object/uuid:9aa45ff6-8de0-455f-903b-2ced8af5857d/datastream/OBJ/view