@article{ref1,
title="Vehicle travel path recognition in urban dense road network environments by using mobile phone data",
journal="Transportmetrica A: transport science",
year="2021",
author="Guo, Yudong and Yang, Fei and Jin, Peter Jing and Liu, Haode and Ma, Sai and Yao, Zhenxing",
volume="ePub",
number="ePub",
pages="ePub-ePub",
abstract="Vehicle travel paths provide basic information for improving traffic forecasting models, tracking epidemics transmission, and road construction. Nevertheless, the challenge of recognition and verification still exists, especially in urban dense road networks. This paper proposes a vehicle path recognition model combined with mobile phone data. In path fitting module, the spatio-temporal density-based clustering algorithm and Gaussian filter were combined to smooth the position fluctuations of mobile phone data; then non-uniform rational B-splines were used to fit travel paths. In path recognition module, the modified probabilistic map matching algorithm was used to match fitting knots to road networks; then matching results were repaired considering the road network topology and the direction angles. The results were verified from trip lengths, urban environments, and road categories. The recognition accuracy was around 90%, 24.22% higher than that of existing methods. The error rate was around 6%, 30.28% lower than that of existing methods.<p /> <p>Language: en</p>",
language="en",
issn="2324-9935",
doi="10.1080/23249935.2021.1948931",
url="http://dx.doi.org/10.1080/23249935.2021.1948931"
}