The proliferation of mobile phones has generated vast quantities of cellular signaling data (CSD), covering extensive spatial areas and populations. These data, containing spatiotemporal information, can be employed to identify and analyze intercity transport modes, providing valuable insights for understanding travel distribution and behavior. However, CSD are primarily intended for communication purposes and are not directly suitable for transportation research due to issues such as low spatial precision, sparse sampling granularity, and lacking traffic semantic features. This article proposes a Hybrid model for identifying individual intercity transport modes based on CSD. Several multidimensional mobility features are proposed that extract interpretable motion characteristics from CSD. A preliminary transport mode probability judgment is made based on the mobility features. Then, the complete transport mode is confirmed considering the temporal continuity correlation of the entire trace. Experiments confirm the Hybrid model's superior precision in identifying transport modes over baseline models, with an average F1 score of 0.92, maintaining high accuracy across various trajectory lengths. This model would support further studying individual intercity travel behavior patterns, aiding transportation planning and operational management decisions using CSD.

中文翻译：

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基于移动特征和蜂窝信令数据挖掘序列关系的城际交通模式识别混合方法

移动电话的普及产生了大量的蜂窝信号数据（CSD），覆盖了广泛的空间区域和人群。这些包含时空信息的数据可用于识别和分析城际交通模式，为了解出行分布和行为提供有价值的见解。然而，CSD主要用于通信目的，由于空间精度低、采样粒度稀疏、缺乏交通语义特征等问题，并不直接适合交通研究。本文提出了一种基于 CSD 的混合模型，用于识别各个城际交通模式。提出了几种多维移动特征，从 CSD 中提取可解释的运动特征。根据移动特征进行初步的交通方式概率判断。然后，考虑整个轨迹的时间连续性相关性来确定完整的传输模式。实验证实了混合模型在识别运输模式方面比基线模型具有更高的精度，平均 F1 得分为 0.92，在各种轨迹长度上保持高精度。该模型将支持进一步研究个人城际出行行为模式，利用 CSD 辅助交通规划和运营管理决策。