arXiv：cs.LG· Shengsheng Lin, Jing Hu, Zhengyang Hu, Jiazheng Sun, Zichun Cao, Siwei Sun, Zhichao Zou, Enyun Yu, Dongdong Li, Xinyi Hu, Weiwei Lin·· 3 小时前
RideBench：面向网约车时间序列预测的大规模外生变量感知基准
RideBench: A Large-Scale Exogenous-Aware Benchmark for Ride-Hailing Time Series Forecasting
AI 导读
研究者发布基于 DiDi 市场数据合成的 Ride-Hailing 数据集，覆盖 200 个空间区域、连续四年半小时粒度，并包含天气干扰、节假日效应和大规模事件三类外生场景，同时推出 RideBench 基准。
正文
Authors:Shengsheng Lin, Jing Hu, Zhengyang Hu, Jiazheng Sun, Zichun Cao, Siwei Sun, Zhichao Zou, Enyun Yu, Dongdong Li, Xinyi Hu, Weiwei Lin
Abstract: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.
| Subjects: | Machine Learning (cs.LG) |
| Cite as: | arXiv:2610.11164 [cs.LG] |
| (or arXiv:2610.11164v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2610.11164 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Shengsheng Lin [view email]
[v1]
Thu, 8 Oct 2026 03:23:26 UTC (1,984 KB)
来源：arXiv：cs.LG · arxiv.org