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arXiv Machine Learning By Shengsheng Lin, Jing Hu, Zhengyang Hu, Jiazheng Sun, Zichun Cao, Siwei Sun, Zhichao Zou, Enyun Yu, Dongdong Li, Xinyi Hu, Weiwei Lin

RideBench: A Large-Scale Exogenous-Aware Benchmark for Ride-Hailing Time Series Forecasting

Read the original on arXiv Machine Learning →

RideBench is a new benchmark built on a large-scale ride‑hailing time‑series dataset called Ride‑Hailing, which spans four years of half‑hourly data across 200 spatial areas and includes three exogenous scenarios: weather disturbance, holiday effect, and large‑scale event impact. The benchmark evaluates over 30 forecasting methods for both week‑ahead and 8‑week‑ahead horizons, revealing that while future‑known exogenous variables improve week‑ahead accuracy, current models still struggle with disturbance‑induced pattern changes and long‑horizon forecasting. These results highlight a mismatch between existing models and real‑world ride‑hailing needs, underscoring the necessity for better exploitation of exogenous information, scalability, and long‑horizon planning capabilities.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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