TY  - JOUR
PY  - 2023//
TI  - Bicycle data-driven application framework: a Dutch case study on machine learning-based bicycle delay estimation at signalized intersections using nationwide sparse GPS data
JO  - Sensors (Basel)
A1  - Yuan, Yufei
A1  - Wang, Kaiyi
A1  - Duives, Dorine
A1  - Hoogendoorn, Serge
A1  - Hoogendoorn-Lanser, Sascha
A1  - Lindeman, Rick
SP  - e9664
EP  - e9664
VL  - 23
IS  - 24
N2  - 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.<p />  <p>Language: en</p>
LA  - en
SN  - 1424-8220
UR  - http://dx.doi.org/10.3390/s23249664
ID  - ref1
ER  -