用刷卡与定位数据，精准推断高峰时段地铁乘客出行轨迹。
A data-driven approach to inferring travel trajectory during peak hours in urban rail transit systems
- 基于时空约束构建列车备选集，结合数据驱动方法推断行程细节。
- 无需外部数据，用KL散度与EM算法实现参数自适应估计，准确率超90%。
- 采用真实轨迹数据验证，适合城市交通规划与运营优化研究者。
精细化的城市轨道交通轨迹推断对运营组织具有重要意义。本文提出一种完全基于数据的方法，用于推断城市轨道交通系统中个体的出行轨迹。该方法利用自动售检票（AFC）和车辆定位（AVL）系统的数据，推断关键轨迹要素，如所选列车、进出站时间及换乘时间。方法包括基于时空约束建立列车备选集、数据驱动的自适应轨迹推断以及行程轨迹构建。为实现数据驱动的自适应推断，提出一种基于KL散度与期望最大化（EM）算法结合的参数估计方法（KLEM），摆脱了对外部或调查数据的依赖，提升了模型的鲁棒性与适用性。此外，为克服以往使用合成数据验证的局限，本文采用真实个体出行轨迹数据进行验证。结果表明，该方法在高峰时段城市轨道交通出行轨迹推断中可达到超过90%的准确率。
原文摘要 · Abstract (English)
Refined trajectory inference of urban rail transit is of great significance to the operation organization. In this paper, we develop a fully data-driven approach to inferring individual travel trajectories in urban rail transit systems. It utilizes data from the Automatic Fare Collection (AFC) and Automatic Vehicle Location (AVL) systems to infer key trajectory elements, such as selected train, access/egress time, and transfer time. The approach includes establishing train alternative sets based on spatio-temporal constraints, data-driven adaptive trajectory inference, and trave l trajectory construction. To realize data-driven adaptive trajectory inference, a data-driven parameter estimation method based on KL divergence combined with EM algorithm (KLEM) was proposed. This method eliminates the reliance on external or survey data for parameter fitting, enhancing the robustness and applicability of the model. Furthermore, to overcome the limitations of using synthetic data to validate the result, this paper employs real individual travel trajectory data for verification. The results show that the approach developed in this paper can achieve high-precision passenger trajectory inference, with an accuracy rate of over 90% in urban rail transit travel trajectory inference during peak hours.
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