Volume 43 Issue 1
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Article Navigation >  Journal of Transport Information and Safety  > 2025  >  43(1): 141-151
LONG Xueqin, MAO Jianxu, ZHAI Manrong, WANG Yuanze. A Free Lane-changing Decision Model Considering the Driving Style and the Interaction with Peripheral Vehicles[J]. Journal of Transport Information and Safety, 2025, 43(1): 141-151. doi: 10.3963/j.jssn.1674-4861.2025.01.013
Citation: LONG Xueqin, MAO Jianxu, ZHAI Manrong, WANG Yuanze. A Free Lane-changing Decision Model Considering the Driving Style and the Interaction with Peripheral Vehicles[J]. Journal of Transport Information and Safety, 2025, 43(1): 141-151. doi: 10.3963/j.jssn.1674-4861.2025.01.013
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A Free Lane-changing Decision Model Considering the Driving Style and the Interaction with Peripheral Vehicles

doi: 10.3963/j.jssn.1674-4861.2025.01.013

  • College of Transportation Engineering, Chang'an University, Xi'an 710064, China

  • Received Date: 2024-07-06
    Available Online: 2025-06-27
    Abstract
  • The interactions between lane-changing vehicle and peripheral vehicles will influence the lane-changing decision behavior. In response to this, a lane-changing decision model that integrates driving styles and interactions is developed based on the lane-changing utility. Utilizing the extreme gradient boosting (XGBoost) algorithm and the density-based spatial clustering of applications with noise (DBSCAN) clustering method, drivers' short-term driving styles are categorized into conservative, typical, and aggressive types. Based on traffic conflicts of trajectories, the spatiotemporal overlap points between vehicles are determined to classify interactions. A utility quantification model is constructed across three dimensions: speed enhancement, spatial safety, and temporal safety. The weights of three sub-utilities are calculated based on MIC, then a total utility model for lane-changing decision is established. Using historical data, the total utilities of lane-changing drivers are computed in comparison to that of lane-keeping drivers, resulting in the identification of lane-changing utility thresholds. Thereby rules for lane-changing decision are formulated. The result of model's accuracy indicates that the model considering interactions significantly outperforms the model that does not, underscoring the importance of interactions in lane-changing decision. Predictions of lane-changing behavior are conducted using the radial basis function (RBF) model and XGBoost model. For conservative, typical, and aggressive drivers, accuracies of the RBF method are 0.885, 0.820, and 0.813, while the XGBoost method achieves accuracies of 0.954, 0.902, and 0.900. AI models demonstrate high predictive accuracies for lane-changing decision across all driver types. However, the predictive accuracies for the typical and aggressive drivers are lower than that of the decision model proposed in this paper (0.921 and 0.923, respectively). Additionally, non-parametric statistical tests of lane-changing utilities further validate the rationality of the model.

     

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