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

arXiv:2610.11164 · cs.LG · Submitted 2026-10-08 · Read on arXiv

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Introduction to the show: ident: AI Radio. Generated commentary on the latest Artificial Intelligence papers.

Tom: I'm Tom, and with me are Jane, Lu, senior AI researcher at Tsinghua, Meng, lead engineer at a mysterious AI startup and Lalam, the in-house Large Language Model.

Jane: Today's paper: "RideBench: A Large-Scale Exogenous-Aware Benchmark for Ride-Hailing Time Series Forecasting".

Tom: The gist: RideBench introduces a comprehensive benchmark for exogenous-aware ride-hailing time series forecasting, revealing that while future-known exogenous variables benefit regular week-ahead forecasting,

Jane: First, who's behind it and why it matters.

Title and authors: Tom: So we're looking at the paper called RideBench: A Large-Scale Exogenous-Aware Benchmark for Ride-Hailing Time Series Forecasting. It sounds like they've built a really big test bed for how to predict ride-hailing demand using external information.

Jane: Yeah, that title tells you right away that this isn't just some small model evaluation; it’s about making sure models understand those outside factors, the exogenous variables.

Lu: It’s interesting because they synthesized a large dataset from DiDi marketplace data across two hundred spatial areas spanning four years at half-hourly granularity, which is a lot of real-world context to work with.

Meng: So what's the main point here? Are we just getting another dataset, or is this benchmark actually going to push things forward in how we build these forecasting systems?

Tom: It’s pushing it forward by showing exactly where current models are falling short, especially when those external signals—like weather or holidays—actually matter a lot.

The paper's summary: Jane: The paper summarizes RideBench as a comprehensive benchmark for exogenous-aware ride-hailing forecasting, covering both regular week-ahead forecasting and long-horizon eight-week ahead forecasting.

Tom: They highlight three big challenges first: complex exogenous effects, heterogeneous multi-area forecasting, and long-horizon operational planning.

Lu: Complex exogenous effects mean things like weather conditions changing travel demand or holidays reshaping commuting routines create localized demand surges through crowd movement or road control fourteen <ref:2610.11164#pg2,localized demand surges through crowd movement>.

Meng: And they also stress the problem of heterogeneous multi-area forecasting, where models have to learn shared temporal patterns but keep the specific demand patterns for each area separate.

Jane: Plus, long-horizon operational planning requires models to capture the broad trend of whether a series will rise or fall, not just getting every single data point exactly right.

The paper's improvements: Tom: Now they discuss how their benchmark addresses those challenges, and it points out that future-known exogenous variables provide clear benefits in regular week-ahead forecasting, especially under weather, holiday, and large-scale event impact scenarios.

Jane: But here’s the catch: current exogenous-aware models still struggle to capture disturbance-induced pattern changes when things get complicated, like with categorical holidays or sparse large-scale events.

Lu: That means they aren't fully robust yet under those specific kinds of external contexts, which is a key limitation they flag.

Meng: And for long-horizon forecasting, they found that existing models simply cannot do three things at once: keep the pointwise error low, get an accurate broad trend, and have reliable near-term forecasts simultaneously.

Tom: So the improvement here isn't just having more data; it’s building a testbed that forces researchers to solve these specific trade-offs in real-world scenarios.

Conclusion: Jane: To wrap up, the RideBench paper suggests that future ride-hailing forecasting needs models that can handle external variables, event types, spatial differences, and long-term planning all together.

Tom: It’s essentially calling for a new kind of model that doesn't just look at the past time series but actively incorporates these known future signals to make better decisions.

Lu: By releasing this large-scale dataset and evaluation protocol, RideBench serves as a testbed for studying those open challenges in practical ride-hailing forecasting one <ref:2610.11164#pg2>.

Meng: From an engineering standpoint, this means we need to design systems that are inherently more robust to these kinds of unpredictable external shocks.

Lalam: As a language model, I see this work pushing us toward creating AI systems that don't just predict the next number, but understand the context of why that number is happening across different regions and times.

Shengsheng Lin, Jing Hu, Zhengyang Hu, Jiazheng Sun, Zichun Cao, Siwei Sun, Zhichao Zou, Enyun Yu, Dongdong Li, Xinyi Hu

South China University of Technology, China

cs.LG

Submitted: 2026-10-08

Updated: 2026-10-08

Code: https://github.com/ACAT-SCUT/ridebench

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Importance score: 77/100

The gist: The gist: RideBench introduces a comprehensive benchmark for exogenous-aware ride-hailing time series forecasting, revealing that while future-known exogenous variables benefit regular week-ahead

Key concepts

RideHailing Dataset: This is a large time series dataset synthesized from DiDi's marketplace data across 200 spatial areas. It covers four years and half-hourly intervals, including future known information about weather, holidays, and events.
RideBench Benchmark: A comprehensive evaluation framework built on RideHailing for forecasting. It tests over 30 different modeling approaches across two tasks: regular week-ahead prediction and long-horizon 8-week predictions with many steps.
Exogenous Awareness: This refers to using future, known external information—like weather or holidays—to improve a forecast. The study shows this helps short-term accuracy but current methods struggle when these external factors cause complex, unexpected changes in demand patterns.
Long-Horizon Forecasting: This is the task of predicting ride-hailing demand far into the future, up to 8 weeks ahead. Successful models must balance predicting precise daily errors with accurately capturing the overall direction (trend) of demand over that extended period.

Terminology

Benchmark — 1× in this paper · explained in 2 episodes
Time Series Forecasting — 1× in this paper · explained in 4 episodes

Summary

The gist: RideBench introduces a comprehensive benchmark for exogenous-aware ride-hailing time series forecasting, revealing that while future-known exogenous variables benefit regular week-ahead forecasting, existing models struggle with disturbance-induced pattern changes and long-horizon planning requirements.

Dataset and Benchmark Introduction

RideBench is built upon RideHailing, a large-scale synthesized ridehailing time series dataset covering four consecutive years across 200 spatial areas with half-hourly granularity on Page 3. This dataset includes rich future-known exogenous signals covering three representative scenarios: Weather Disturbance, Holiday Effect, and Large-scale Event Impact. The benchmark is organized around two complementary forecasting tasks: 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.

Key Challenges in Ride-Hailing Forecasting

The paper identifies three key challenges for a practical benchmark for this domain: Complex exogenous effects, Heterogeneous Multi-area Forecasting, and Long-horizon Operational Planning. Complex exogenous effects involve factors like weather conditions altering travel demand, holidays reshaping commuting routines, and large-scale events triggering localized demand surges. Heterogeneous multi-area forecasting requires models to learn shared temporal and exogenous regularities while preserving area-specific demand patterns across different spatial contexts. Long-horizon operational planning demands that models capture macro-level temporal trends, including whether the target series will rise or fall, in addition to minimizing pointwise errors.

Evaluation Protocols and Findings

RideBench employs a comprehensive evaluation protocol consisting of overall forecasting accuracy (WMAPE, MAE, RMSE), scenario-specific robustness under Weather Disturbance, Holiday Effect, and Large-scale Event Impact. For the long-horizon 8-week-ahead setting, it adds day-aggregated trend metrics like Directional Accuracy of Day (Acc.) and Correlation of Day (Corr.) to assess broad trend modeling. Results show that future-known exogenous variables provide clear benefits in regular week-ahead forecasting, especially under weather, holiday, and large-scale event scenarios. However, current exogenous-aware models struggle to capture disturbance-induced pattern changes under complex external contexts. For longhorizon forecasting, existing models cannot simultaneously achieve low pointwise errors, accurate broad trends, and reliable near-term forecasts.

Model Performance and Limitations

The empirical study compares over 30 representative methods across five major methodological paradigms. In regular week-ahead forecasting, exogenous-aware models show the strongest performance across overall evaluation and scenario-specific evaluations, with TiDE [8] achieving consistently strong performance under external disturbances. Conversely, in long-horizon forecasting, several endogenous-only models achieve stronger overall accuracy. The paper reveals two key limitations for real-world deployment: current exogenousaware models still struggle to capture disturbance-induced pattern changes, especially under categorical holidays and sparse large-scale events. Furthermore, longhorizon forecasting requires a better balance among pointwise accuracy, broad trend quality, and near-term reliability.

Design Factors and Trade-offs

The study also investigates various design factors such as Exogenous Variable Utilization, Area Scaling Study, Lookback Study, and Loss Function Study. The area scaling study indicates that adding more training areas helps models learn transferable structures and improve forecasts on fixed target areas. The lookback study suggests that a 28-day lookback window is adopted as the default setting for sufficient historical context while keeping computational cost moderate. The loss function study shows that MAE achieves the best overall performance because it is more robust to heavy-tailed forecasting errors introduced by exogenous disturbances.

Conclusion

RideBench suggests that ride-hailing forecasting requires models that can jointly handle future-known exogenous variables, categorical event semantics, spatial heterogeneity, and long-horizon planning requirements. The work aims to encourage the community to study these practical challenges in real-world ride-hailing forecasting.

ACKNOWLEDGMENTS

This work was supported by the CCF-DiDi GAIA Collaborative Research Funds (No. 202534) and the Shandong Provincial Natural Science Foundation Project (ZR2024LZH012).

REFERENCES

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Improvements for AI systems

Bold Header: Exogenous-Aware Model Enhancement for Disturbance Capture

The improved system will capture disturbance-induced pattern changes by enhancing existing exogenous-aware models, as current models still struggle to capture disturbance-induced pattern changes, especially under categorical holidays, sparse large-scale events, and area-dependent exogenous responses. This allows the system to better utilize future-known signals beyond simple continuous covariates.

Bold Header: Long-Horizon Trend Modeling Integration

The system will achieve superior long-horizon forecasting by simultaneously achieving low pointwise errors, accurate broad trends, and reliable near-term forecasts, addressing the finding that existing models cannot simultaneously achieve low pointwise errors, accurate broad trends, and reliable near-term forecasts in the 8-week setting.

Bold Header: Multi-Objective Long-Horizon Optimization

The system will be optimized using a loss function that balances multiple objectives, as suggested by the finding that no existing model dominates all aspects of 8-week-ahead forecasting, aiming to achieve a better balance among longhorizon pointwise accuracy, trend quality, and first-week accuracy.