mlsanityData to 14 Oct 2026

A Survey on Service Route and Time Prediction in Instant Delivery: Taxonomy, Progress, and Prospects

arXiv 2309.01194 ↗First posted 3 Sep 2023cs.AIcited by 5

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Abstract (arXiv)

Instant delivery services, such as food delivery and package delivery, have achieved explosive growth in recent years by providing customers with daily-life convenience. An emerging research area within these services is service Route&Time Prediction (RTP), which aims to estimate the future service route as well as the arrival time of a given worker. As one of the most crucial tasks in those service platforms, RTP stands central to enhancing user satisfaction and trimming operational expenditures on these platforms. Despite a plethora of algorithms developed to date, there is no systematic, comprehensive survey to guide researchers in this domain. To fill this gap, our work presents the first comprehensive survey that methodically categorizes recent advances in service route and time prediction. We start by defining the RTP challenge and then delve into the metrics that are often employed. Following that, we scrutinize the existing RTP methodologies, presenting a novel taxonomy of them. We categorize these methods based on three criteria: (i) type of task, subdivided into only-route prediction, only-time prediction, and joint route&time prediction; (ii) model architecture, which encompasses sequence-based and graph-based models; and (iii) learning paradigm, including Supervised Learning (SL) and Deep Reinforcement Learning (DRL). Conclusively, we highlight the limitations of current research and suggest prospective avenues. We believe that the taxonomy, progress, and prospects introduced in this paper can significantly promote the development of this field.

Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). Builds on 50 of its 126 references that are in the map. First posted on arXiv 3 Sep 2023.

Builds on

50 of 126 references.

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Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 3; 2025: 1; 2026: 1.

5 papers.

  1. 2024Towards Neural Scaling Laws for Time Series Foundation Modelscited by 33
  2. 2024Trajectory Data Management and Mining: A Survey from Deep Learning to the LLM Eracited by 23
  3. 2024Joint Estimation and Prediction of City-wide Delivery Demand: A Large Language Model Empowered Graph-based Learning Approachcited by 6
  4. 2025Conformal Predictive Distributions for Order Fulfillment Time Forecastingcited by 3
  5. 2026Bridge: Retrieval-Augmented Spatiotemporal Modeling for Urban Delivery Demandcited by 0