Abstract
Accurate driving preferences classification is a crucial component for autonomous connected vehicles in making more safety and more efficient driving decisions. Most existing studies identify drivers’ driving preferences based on the historical data of the individual vehicle, and the selected variables are limited to the mechanical motion of the vehicle, which seldomly takes the influence of road traffic conditions and surrounding vehicles into account. This study proposes a driving preferences classification method by multivariate sequence clustering algorithm based on wide-range trajectory data. Based on the specific range of road sections, the selected variables for each trajectory are converted from the time domain to the space domain separately, to capture the dynamic changes of the features along the road area. Multivariate time series clustering combining a weighted Dynamic Time Warping (WDTW) and the k-medoids algorithm is used to classify driving preferences into different levels, and a popular internal evaluation metric is employed to determine the optimal cluster result. This study also investigates the heterogeneity of driving behaviors at different driving preference levels. The results show that the proposed method could better recognize drivers’ internal driving preferences.
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This study was supported by the project “Safety Design Technology of the Multi-Entrances and Exits for Urban Underground Expressway”.
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Wang, S., Jia, R., Zhang, L. (2023). Multivariate Sequence Clustering for Driving Preference Classification Based on Wide-Range Trajectory Data. In: Bie, Y., Gao, K., Howlett, R.J., Jain, L.C. (eds) Smart Transportation Systems 2023. KES-STS 2023. Smart Innovation, Systems and Technologies, vol 356. Springer, Singapore. https://doi.org/10.1007/978-981-99-3284-9_5
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DOI: https://doi.org/10.1007/978-981-99-3284-9_5
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