通过分析行车动态，提升城市交通管理的精准度与适应性。
How Cars Move: Analyzing Driving Dynamics for Safer Urban Traffic
- 融合多尺度观测数据，构建动态交通分析框架
- 显著提高交通模式识别准确率，降低长期预测误差
- 适合城市规划与智能交通系统开发者使用
理解城市环境中车辆的空间动态对优化基础设施管理和资源配置至关重要。近年来，基于实证的交通模式分析方法因适用于城市级政策制定而受到关注。然而，传统方法常依赖碎片化的网格技术，可能忽略空间要素间的关键关联及时间连续性，从而影响复杂城市环境下的分析效果。为此，我们提出 PriorMotion——一种数据整合框架，通过结合多尺度实证观测与定制化分析工具，系统揭示驾驶动态中的时空演变趋势。综合评估表明，PriorMotion显著提升了分析效果，包括交通模式分析准确率提升、在异构数据环境下更强的适应性，以及更小的长期预测误差。验证结果证实其在需要精确刻画复杂时空交互的城市基础设施管理应用中具有有效性。
原文摘要 · Abstract (English)
Understanding the spatial dynamics of cars within urban systems is essential for optimizing infrastructure management and resource allocation. Recent empirical approaches for analyzing traffic patterns have gained traction due to their applicability to city-scale policy development. However, conventional methodologies often rely on fragmented grid-based techniques, which may overlook critical interdependencies among spatial elements and temporal continuity. These limitations can compromise analytical effectiveness in complex urban environments. To address these challenges, we propose PriorMotion, a data integration framework designed to systematically uncover movement patterns through driving dynamics analysis. Our approach combines multi-scale empirical observations with customized analytical tools to capture evolving spatial-temporal trends in urban traffic. Comprehensive evaluations demonstrate that PriorMotion significantly enhances analytical outcomes, including increased accuracy in traffic pattern analysis, improved adaptability to heterogeneous data environments, and reduced long-term projection errors. Validation confirms its effectiveness for urban infrastructure management applications requiring precise characterization of complex spatial-temporal interactions.
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