mlsanityData to 7 Oct 2026

A Survey of Route Recommendations: Methods, Applications, and Opportunities

arXiv 2403.00284 ↗First posted 1 Mar 2024cs.AI cs.LGcited by 5

Not on the map. Why?

Abstract (arXiv)

Nowadays, with advanced information technologies deployed citywide, large data volumes and powerful computational resources are intelligentizing modern city development. As an important part of intelligent transportation, route recommendation and its applications are widely used, directly influencing citizens` travel habits. Developing smart and efficient travel routes based on big data (possibly multi-modal) has become a central challenge in route recommendation research. Our survey offers a comprehensive review of route recommendation work based on urban computing. It is organized by the following three parts: 1) Methodology-wise. We categorize a large volume of traditional machine learning and modern deep learning methods. Also, we discuss their historical relations and reveal the edge-cutting progress. 2) Application-wise. We present numerous novel applications related to route commendation within urban computing scenarios. 3) We discuss current problems and challenges and envision several promising research directions. We believe that this survey can help relevant researchers quickly familiarize themselves with the current state of route recommendation research and then direct them to future research trends.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 21 of its 156 references that are in the map. First posted on arXiv 1 Mar 2024.

Builds on

21 of 156 references.

References · 21

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  5. 2019Deep Learning for Spatio-Temporal Data Mining: A Surveycited by 171
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9 more
  1. 2018Finding k-Dissimilar Paths with Minimum Collective Lengthcited by 4
  2. 2013Backward Path Growth for Efficient Mobile Sequential Recommendationcited by 2
  3. 2013CrowdPlanner: A Crowd-Based Route Recommendation Systemcited by 2
  4. 2016Personalized and situation-aware multimodal route recommendations: the FAVOUR algorithmcited by 2
  5. 2017The Optimal Route and Stops for a Group of Users in a Road Networkcited by 1
  6. 2018In-Route Task Selection in Crowdsourcingcited by 1
  7. 2018An Efficient Approximation Algorithm for Multi-criteria Indoor Route Planning Queriescited by 1
  8. 2019More Hierarchy in Route Planning Using Edge Hierarchiescited by 1
  9. 2020Predictive Collision Management for Time and Risk Dependent Path Planningcited by 1

Built on by

Cited by year

Citing papers per year: 2024: 3; 2025: 2.

5 papers.

  1. 2024Trajectory Data Management and Mining: A Survey from Deep Learning to the LLM Eracited by 23
  2. 2024Continual Learning for Smart City: A Surveycited by 9
  3. 2024Modeling Temporal Dependencies within the Target for Long-Term Time Series Forecastingcited by 6
  4. 2025Reinforcement Learning-based Sequential Route Recommendation for System-Optimal Traffic Assignmentcited by 2
  5. 2025Online Navigation Refinement: Achieving Lane-Level Guidance by Associating Standard-Definition and Online Perception Mapscited by 1