# Route Planning in Transportation Networks

arXiv: https://arxiv.org/abs/1504.05140 · HTML: https://mlsanity.com/paper/1504.05140/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Apr 2015.

## Built on by (most cited first, 23 of 23)

- [Bayesian Algorithm Execution: Estimating Computable Properties of Black-box Functions Using Mutual Information](https://mlsanity.com/paper/2104.09460/) (2021)
- [Milestones in Autonomous Driving and Intelligent Vehicles Part II: Perception and Planning](https://mlsanity.com/paper/2306.01980/) (2023)
- [Fine-Grained Trajectory-based Travel Time Estimation for Multi-city Scenarios Based on Deep Meta-Learning](https://mlsanity.com/paper/2201.08017/) (2022)
- [LLMAP: LLM-Assisted Multi-Objective Route Planning with User Preferences](https://mlsanity.com/paper/2509.12273/) (2025)
- [ADD: An Automatic Desensitization Fisheye Dataset for Autonomous Driving](https://mlsanity.com/paper/2308.07590/) (2023)
- [Timetable Nodes for Public Transport Network](https://mlsanity.com/paper/2410.15715/) (2024)
- [OSRM-CCTV: Open-source CCTV-aware routing and navigation system for privacy, anonymity and safety (Preprint)](https://mlsanity.com/paper/2108.09369/) (2021)
- [Rethinking Spatio-Temporal Transformer for Traffic Prediction:Multi-level Multi-view Augmented Learning Framework](https://mlsanity.com/paper/2406.11921/) (2024)
- [Threshold Adaptation in Spiking Networks Enables Shortest Path Finding and Place Disambiguation](https://mlsanity.com/paper/2503.21795/) (2025)
- [Dynamic Evolution of Complex Networks: A Reinforcement Learning Approach Applying Evolutionary Games to Community Structure](https://mlsanity.com/paper/2506.17925/) (2025)
- [ERA*: Enhanced Relaxed A* algorithm for Solving the Shortest Path Problem in Regular Grid Maps](https://mlsanity.com/paper/2308.10988/) (2023)
- [Trip Planning for Autonomous Vehicles with Wireless Data Transfer Needs Using Reinforcement Learning](https://mlsanity.com/paper/2309.12534/) (2023)
- [Transformer-based Graph Neural Networks for Battery Range Prediction in AIoT Battery-Swap Services](https://mlsanity.com/paper/2407.16115/) (2024)
- [Rethinking Traffic Flow Forecasting: From Transition to Generatation](https://mlsanity.com/paper/2504.14248/) (2025)
- [TurnBack: A Geospatial Route Cognition Benchmark for Large Language Models through Reverse Route](https://mlsanity.com/paper/2509.18173/) (2025)
- [Multi-Objective Search: Algorithms, Applications, and Emerging Directions](https://mlsanity.com/paper/2510.25504/) (2025)
- [Multi-Objective Reinforcement Learning for Large-Scale Mixed Traffic Control](https://mlsanity.com/paper/2512.11247/) (2025)
- [AAC: Admissible-by-Architecture Differentiable Landmark Compression for ALT](https://mlsanity.com/paper/2604.20744/) (2026)
- [TransitLM: A Large-Scale Dataset and Benchmark for Map-Free Transit Route Generation](https://mlsanity.com/paper/2605.22355/) (2026)
- [From GPS Points to Travel Patterns: Flexible and Semantic Trajectory Generation with LLMs](https://mlsanity.com/paper/2605.30014/) (2026)
- [LegalFarePlan: A Label-Setting Framework for Fare-Transparent Urban Rail Route Planning under Non-Additive Fare Rules](https://mlsanity.com/paper/2607.09755/) (2026)
- [Learning-Augmented Algorithms: Guarantees, Construction Mechanisms, and System-Level Implications](https://mlsanity.com/paper/2609.04787/) (2026)
- [Customizable and Jointly Optimized Route Planning: A Deep Architecture Enabling Differentiable Shortest-Path Search](https://mlsanity.com/paper/2609.19996/) (2026)

## Abstract (arXiv)

We survey recent advances in algorithms for route planning in transportation networks. For road networks, we show that one can compute driving directions in milliseconds or less even at continental scale. A variety of techniques provide different trade-offs between preprocessing effort, space requirements, and query time. Some algorithms can answer queries in a fraction of a microsecond, while others can deal efficiently with real-time traffic. Journey planning on public transportation systems, although conceptually similar, is a significantly harder problem due to its inherent time-dependent and multicriteria nature. Although exact algorithms are fast enough for interactive queries on metropolitan transit systems, dealing with continent-sized instances requires simplifications or heavy preprocessing. The multimodal route planning problem, which seeks journeys combining schedule-based transportation (buses, trains) with unrestricted modes (walking, driving), is even harder, relying on approximate solutions even for metropolitan inputs.