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

arXiv: https://arxiv.org/abs/2403.00284 · HTML: https://mlsanity.com/paper/2403.00284/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 156 references that are in the map.
- First posted on arXiv 1 Mar 2024.

## Builds on (most cited first, 21 of 21)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling](https://mlsanity.com/paper/1412.3555/) (2014)
- [How transferable are features in deep neural networks?](https://mlsanity.com/paper/1411.1792/) (2014)
- [Adaptive Graph Convolutional Recurrent Network for Traffic Forecasting](https://mlsanity.com/paper/2007.02842/) (2020)
- [Deep Learning for Spatio-Temporal Data Mining: A Survey](https://mlsanity.com/paper/1906.04928/) (2019)
- [Learning Improvement Heuristics for Solving Routing Problems](https://mlsanity.com/paper/1912.05784/) (2019)
- [Visual Interpretability for Deep Learning: a Survey](https://mlsanity.com/paper/1802.00614/) (2018)
- [How Can Recommender Systems Benefit from Large Language Models: A Survey](https://mlsanity.com/paper/2306.05817/) (2023)
- [Image-text Retrieval: A Survey on Recent Research and Development](https://mlsanity.com/paper/2203.14713/) (2022)
- [Empowering A* Search Algorithms with Neural Networks for Personalized Route Recommendation](https://mlsanity.com/paper/1907.08489/) (2019)
- [Learning Points and Routes to Recommend Trajectories](https://mlsanity.com/paper/1608.07051/) (2016)
- [POIBERT: A Transformer-based Model for the Tour Recommendation Problem](https://mlsanity.com/paper/2212.13900/) (2022)
- [Finding k-Dissimilar Paths with Minimum Collective Length](https://mlsanity.com/paper/1809.06831/) (2018)
- [Backward Path Growth for Efficient Mobile Sequential Recommendation](https://mlsanity.com/paper/1304.2144/) (2013)
- [CrowdPlanner: A Crowd-Based Route Recommendation System](https://mlsanity.com/paper/1309.2687/) (2013)
- [Personalized and situation-aware multimodal route recommendations: the FAVOUR algorithm](https://mlsanity.com/paper/1602.09076/) (2016)
- [The Optimal Route and Stops for a Group of Users in a Road Network](https://mlsanity.com/paper/1706.07829/) (2017)
- [In-Route Task Selection in Crowdsourcing](https://mlsanity.com/paper/1809.05234/) (2018)
- [An Efficient Approximation Algorithm for Multi-criteria Indoor Route Planning Queries](https://mlsanity.com/paper/1809.07614/) (2018)
- [More Hierarchy in Route Planning Using Edge Hierarchies](https://mlsanity.com/paper/1907.03535/) (2019)
- [Predictive Collision Management for Time and Risk Dependent Path Planning](https://mlsanity.com/paper/2011.13305/) (2020)

## Built on by (most cited first, 5 of 5)

- [Trajectory Data Management and Mining: A Survey from Deep Learning to the LLM Era](https://mlsanity.com/paper/2403.14151/) (2024)
- [Continual Learning for Smart City: A Survey](https://mlsanity.com/paper/2404.00983/) (2024)
- [Modeling Temporal Dependencies within the Target for Long-Term Time Series Forecasting](https://mlsanity.com/paper/2406.04777/) (2024)
- [Reinforcement Learning-based Sequential Route Recommendation for System-Optimal Traffic Assignment](https://mlsanity.com/paper/2505.20889/) (2025)
- [Online Navigation Refinement: Achieving Lane-Level Guidance by Associating Standard-Definition and Online Perception Maps](https://mlsanity.com/paper/2507.07487/) (2025)

## 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.