DeeprETA: An ETA Post-processing System at Scale
Not on the map. Why?
Abstract (arXiv)
Estimated Time of Arrival (ETA) plays an important role in delivery and ride-hailing platforms. For example, Uber uses ETAs to calculate fares, estimate pickup times, match riders to drivers, plan deliveries, and more. Commonly used route planning algorithms predict an ETA conditioned on the best available route, but such ETA estimates can be unreliable when the actual route taken is not known in advance. In this paper, we describe an ETA post-processing system in which a deep residual ETA network (DeeprETA) refines naive ETAs produced by a route planning algorithm. Offline experiments and online tests demonstrate that post-processing by DeeprETA significantly improves upon the accuracy of naive ETAs as measured by mean and median absolute error. We further show that post-processing by DeeprETA attains lower error than competitive baseline regression models.
Key facts. Cited by 7 papers in the map (data through 14 Oct 2026). Builds on 16 of its 29 references that are in the map. First posted on arXiv 5 Jun 2022.
Builds on
16 of 29 references.
References · 16
- 2017Attention Is All You Needcited by 60,361
- 2015Deep Residual Learning for Image Recognitioncited by 52,096
- 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,305
- 2016XGBoost: A Scalable Tree Boosting Systemcited by 5,166
- 2017Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layercited by 3,509
- 2020Transformers are RNNs: Fast Autoregressive Transformers with Linear Attentioncited by 2,022
- 2020Rethinking Attention with Performerscited by 1,601
- 2020Linformer: Self-Attention with Linear Complexitycited by 1,474
- 2016Wide & Deep Learning for Recommender Systemscited by 747
- 2017DeepFM: A Factorization-Machine based Neural Network for CTR Predictioncited by 676
- 2017Deep Interest Network for Click-Through Rate Predictioncited by 544
- 2017Deep & Cross Network for Ad Click Predictionscited by 393
4 more
- 2021ETA Prediction with Graph Neural Networks in Google Mapscited by 130
- 2020Multi-Scale Representation Learning for Spatial Feature Distributions using Grid Cellscited by 90
- 2019Gradient Boosted Decision Tree Neural Networkcited by 3
- 2020FMA-ETA: Estimating Travel Time Entirely Based on FFN With Attentioncited by 1
Built on by
Citing papers per year: 2022: 3; 2023: 2; 2024: 1; 2025: 0; 2026: 1.
7 papers.
- 2022General Intelligence Requires Rethinking Explorationcited by 20
- 2022Desiderata for next generation of ML model servingcited by 5
- 2023G-Signatures: Global Graph Propagation With Randomized Signaturescited by 4
- 2022Interpretable (not just posthoc-explainable) medical claims modeling for discharge placement to prevent avoidable all-cause readmissions or deathcited by 1
- 2023Interpretable (not just posthoc-explainable) heterogeneous survivor bias-corrected treatment effects for assignment of postdischarge interventions to prevent readmissionscited by 1
- 2024DNAMite: Interpretable Calibrated Survival Analysis with Discretized Additive Modelscited by 1
- 2026PAtt: A Pattern Attention Network for ETA Prediction Using Historical Speed Profilescited by 0
- 2026PAtt: A Pattern Attention Network for ETA Prediction Using Historical Speed Profiles20 Jan 2026
- 2024DNAMite: Interpretable Calibrated Survival Analysis with Discretized Additive Models8 Nov 2024
- 2023Interpretable (not just posthoc-explainable) heterogeneous survivor bias-corrected treatment effects for assignment of postdischarge interventions to prevent readmissions19 Apr 2023
- 2023G-Signatures: Global Graph Propagation With Randomized Signatures17 Feb 2023
- 2022General Intelligence Requires Rethinking Exploration15 Nov 2022
- 2022Desiderata for next generation of ML model serving26 Oct 2022
- 2022Interpretable (not just posthoc-explainable) medical claims modeling for discharge placement to prevent avoidable all-cause readmissions or death28 Aug 2022