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

Shengsheng Lin; Jing Hu; Zhengyang Hu; Jiazheng Sun; Zichun Cao; Siwei Sun; Zhichao Zou; Enyun Yu

cs.LGOct 8, 2026

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

Authors: Shengsheng Lin, Jing Hu, Zhengyang Hu, Jiazheng Sun, Zichun Cao, Siwei Sun, Zhichao Zou, Enyun Yu, +3 more

Organizations: South China University of Technology, China · Didi Chuxing, China · The University of Hong Kong, China

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.

Figures & tables

Figure 1. Three key challenges of real-world ride-hailing time series forecasting.

Benchmark	Future Exo. Con./Dis.	Scenario Eval.	Multi-Area Modeling	Max Eval. Horizon
Monash ( Godahewa et al., 2021 )	✗/✗	✗	✗	168
TSlib ( Wang et al., 2024a )	✗/✗	✗	✗	720
BasicTS+ ( Shao et al., 2024 )	✗/✗	✗	✗	720
TFB ( Qiu et al., 2024 )	✓/✗	✗	✗	720
ProbTS ( Zhang et al., 2024 )	✗/✗	✗	✗	720
GIFT-Eval ( Aksu et al., 2024 )	✗/✗	✗	✗	900

Table 1. Overview of existing benchmarks and RideBench.

Figure 2. Statistics and variable information of Ride-Hailing.

Figure 3. Illustration of the key characteristics of Ride-Hailing. (a) Multi-scale periodicity at daily, weekly, and yearly levels. (b) Exogenous effects under weather disturbance, holiday effect, and large-scale event impact. (c) Heterogeneous patterns across different areas, time periods and exogenous variables, where t-SNE ( Van der Maaten and Hinton, 2008 ) is used to visualize spatial and temporal heterogeneity.

Figure 4. Overall architecture of RideBench. The left illustrates the general benchmark workflow, and the right demonstrates the detailed implementation details.

	Scenarios	Overall			Weather Disturbance			Holiday Effect			Large-scale Event Impact			Rank
	Metrics	WMAPE	MAE	RMSE	WMAPE	MAE	RMSE	WMAPE	MAE	RMSE	WMAPE	MAE	RMSE	Average
	SeasonalMean	0.1285	4.916	14.37	0.1509	6.311	20.90	0.1701	7.052	20.85	0.1224	5.395	16.32	29
Sim.	SeasonalNaive	0.1490	5.693	17.75	0.1755	7.338	24.41	0.1981	8.203	24.57	0.1422	6.261	19.10	32
	PatchTST	0.1079	4.132	13.38	0.1391	5.821	20.84	0.1507	6.249	20.37	0.1130	4.983	16.56	10
	PETformer	0.1078	4.124	13.35	0.1396	5.839	20.86	0.1520	6.303	20.42	0.1130	4.982	16.48	11
	TimeMixer	0.1113	4.261	13.47	0.1411	5.902	20.90	0.1548	6.420	20.42	0.1159	5.113	16.73	19

Table 2. Regular week-ahead forecasting performance under the overall evaluation and the three exogenous scenarios. Darker shading indicates better performance. Average rank is obtained via arithmetic mean of ranks across all evaluation metrics.

	Setting	Overall				Trend			First Week
	Metrics	WMAPE	MAE	RMSE	Rank	Acc.( $\uparrow$ )	Corr.( $\uparrow$ )	Rank	WMAPE	MAE	RMSE	Rank
	SeasonalMean	0.1485	5.661	14.94	28	0.5875	0.2059	28	0.1391	5.349	14.86	31
Sim.	SeasonalNaive	0.1731	6.582	19.63	31	0.5518	0.1246	30	0.1613	6.182	19.36	32
	PETformer	0.1278	4.879	14.58	2	0.6046	0.2549	12	0.1162	4.463	14.33	2
	TimeMixer	0.1294	4.940	14.57	3	0.6061	0.2679	9	0.1182	4.542	14.43	3
	SegRNN	0.1273	4.862	14.66	4	0.6059	0.2470	13	0.1173	4.508	14.48	6

Table 3. Eight-week-ahead forecasting performance, reporting long-horizon point-wise errors, day-level trend quality (i.e., directional accuracy of day and correlation of day), and first-week accuracy. Rank is calculated as the arithmetic mean of metric ranks across all experimental settings.

Figure 5. Efficiency of representative forecasting models under regular and long-horizon settings.

Figure 6. Effect of incorporating exogenous variables under three scenario-specific evaluations.

Figure 7. Studies on key benchmark design factors. (a) Effect of increasing the number of training areas while keeping the 10 target areas fixed. (b) Effect of varying the input lookback length. (c) Impact of different training losses.

Panel of Figure 8 — Figure 8. Forecasting visualization under different evaluation scenarios.

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