RideBench：面向网约车时间序列预测的大规模感知外生变量基准测试集
RideBench: A Large-Scale Exogenous-Aware Benchmark for Ride-Hailing Time Series Forecasting
- South China University of Technology（华南理工大学）
- Didi Chuxing（滴滴出行）
- 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：
该研究发布了网约车数据集Ride-Hailing及基准测试集RideBench，评估30余种预测方法，发现现有模型与网约车需求不匹配，需能利用外生变量、支持长期预测的模型。
AI中文摘要：
我们发布了Ride-Hailing，这是一个基于滴滴市场数据合成的大规模网约车时间序列数据集，覆盖200个空间区域。Ride-Hailing包含连续四年的数据，粒度为半小时，涵盖三种代表性外生场景：天气干扰、节假日效应和大规模事件影响。基于Ride-Hailing，我们推出了RideBench，这是一个用于感知外生变量的网约车预测的综合基准测试集，涵盖常规的提前一周预测和长达8周的长期预测，最多支持2688个预测步长。RideBench评估了30余种代表性预测方法，包括仅内生变量模型、感知外生变量模型和时间序列基础模型。我们的结果表明，未来已知的外生变量在常规提前一周预测中具有明显优势，尤其是在天气、节假日和大规模事件（如重大体育赛事和音乐会）场景下。然而，当前感知外生变量模型仍难以在复杂外部环境下完全捕捉干扰引发的模式变化。对于长期预测，现有模型无法同时实现低逐点误差、准确的整体趋势和可靠的近期预测。这些发现揭示了现有预测模型与现实网约车需求之间存在明显不匹配，凸显了对能更好利用未来已知外生变量信息、跨异构区域扩展并支持长期规划的模型的需求。通过推出Ride-Hailing和RideBench，我们旨在鼓励学术界研究现实网约车预测中的这些实际挑战。
英文摘要：
We release Ride-Hailing, a large-scale ride-hailing time series dataset synthesized from DiDi's marketplace data across 200 spatial areas. Ride-Hailing spans four consecutive years at half-hourly granularity and covers three representative exogenous scenarios: Weather Disturbance, Holiday Effect, and Large-scale Event Impact. Built upon Ride-Hailing, we introduce RideBench, a comprehensive benchmark for exogenous-aware ride-hailing forecasting, covering both regular week-ahead forecasting and long-horizon 8-week-ahead forecasting with up to 2,688 prediction steps. RideBench evaluates over 30 representative forecasting methods, including endogenous-only models, exogenous-aware models, and time series foundation models. Our results show that future-known exogenous variables provide clear benefits in regular week-ahead forecasting, especially under weather, holiday, and large-scale event (e.g., major sporting events and concerts) scenarios. However, current exogenous-aware models still struggle to fully capture disturbance-induced pattern changes under complex external contexts. For long-horizon forecasting, existing models cannot simultaneously achieve low pointwise errors, accurate broad trends, and reliable near-term forecasts. These findings reveal a clear mismatch between existing forecasting models and real-world ride-hailing requirements, highlighting the need for models that can better exploit future-known exogenous information, scale across heterogeneous areas, and support long-horizon planning. By introducing Ride-Hailing and RideBench, we aim to encourage the community to study these practical challenges in real-world ride-hailing forecasting.
