基于时间感知表格深度学习架构的双源无轨电车站点间能量预测及因果驱动因素量化
Inter-Stop Energy Prediction and Causal Driver Quantification for Dual-Source Trolleybuses via a Time-Aware Tabular Deep Learning Architecture
- Foshan University（佛山科学技术学院）
- South China Normal University（华南师范大学）
- Jilin University（吉林大学）
- The University of Hong Kong（香港大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
研究双源无轨电车站点间能量预测及因果驱动因素量化问题，提出时间感知表格深度学习框架，集成周期性时间编码并结合贝叶斯优化调参，通过三层因果解释管道分析，实验表现优异，确定了关键因素并提供规划阈值。
AI中文摘要：
双源无轨电车在架空线供电和车载电池运行之间交替，形成由路线属性、高频轨迹和每小时天气驱动的能源使用模式。现有模型难以表示这些异构输入，且很少解释消耗的因果驱动因素。本文提出了一种用于站点间能量管理的时间感知表格深度学习框架。将周期性时间编码集成到参数高效的批集成主干中，以联合学习静态和顺序特征，同时使用树结构密度估计的贝叶斯优化调整超参数。为超越预测，一个三层因果解释管道结合了用于边际效应的特征归因、用于因果方向发现的线性非高斯无环模型和用于净平均处理效应的元学习器。在富含气象记录的苏黎世无轨电车数据集上的实验实现了6.52%的平均绝对百分比误差和0.982的R值，优于十个统计、树集成和深度学习基线。消融结果表明周期性时间编码对精度提升贡献最大。因果分析确定再生制动比和平均速度是最强的节能因素，而滑行距离是过度消耗的主要驱动因素。这些发现为车辆技术、驾驶行为、容量分配和架空线网络规划提供了可操作的阈值。
英文摘要：
Dual-source trolleybuses alternate between overhead catenary supply and on-board battery operation, creating energy-use patterns driven by route attributes, high-frequency trajectories, and hourly weather. Existing models struggle to represent these heterogeneous inputs and rarely explain the causal drivers of consumption. This paper proposes a time-aware tabular deep learning framework for inter-stop energy management. Periodic time encoding is integrated into a parameter-efficient batch-ensemble backbone to jointly learn static and sequential features, while Bayesian optimization with tree-structured density estimation tunes hyperparameters. To move beyond prediction, a three-layer causal explanation pipeline combines feature attribution for marginal effects, a linear non-Gaussian acyclic model for causal direction discovery, and a meta-learner for net average treatment effects. Experiments on the Zurich trolleybus dataset enriched with meteorological records achieve a MAPE of 6.52% and R of 0.982, outperforming ten statistical, tree-ensemble, and deep learning baselines. Ablation results show that periodic time encoding contributes most to the accuracy gain. Causal analysis identifies regenerative braking ratio and average speed as the strongest energy-saving factors, while coasting distance is the main driver of excess consumption. The findings offer actionable thresholds for vehicle technology, driving behavior, capacity allocation, and catenary network planning.
