# TraveLLM: Could you plan my new public transit route in face of a network disruption?

arXiv: https://arxiv.org/abs/2407.14926 · HTML: https://mlsanity.com/paper/2407.14926/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 44 references that are in the map.
- First posted on arXiv 20 Jul 2024.

## Builds on (most cited first, 8 of 8)

- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [A Survey of Large Language Models](https://mlsanity.com/paper/2303.18223/) (2023)
- [A Survey on Large Language Model based Autonomous Agents](https://mlsanity.com/paper/2308.11432/) (2023)
- [Large Language Models as Urban Residents: An LLM Agent Framework for Personal Mobility Generation](https://mlsanity.com/paper/2402.14744/) (2024)
- [LLMLight: Large Language Models as Traffic Signal Control Agents](https://mlsanity.com/paper/2312.16044/) (2023)
- [Driving Everywhere with Large Language Model Policy Adaptation](https://mlsanity.com/paper/2402.05932/) (2024)
- [LLM-A*: Large Language Model Enhanced Incremental Heuristic Search on Path Planning](https://mlsanity.com/paper/2407.02511/) (2024)
- [Efficient Sequential Decision Making with Large Language Models](https://mlsanity.com/paper/2406.12125/) (2024)

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

- [MapEval: A Map-Based Evaluation of Geo-Spatial Reasoning in Foundation Models](https://mlsanity.com/paper/2501.00316/) (2024)
- [MapTab: A Diagnostic Benchmark for Long-Horizon Multi-Criteria Multimodal Reasoning on Heterogeneous Topological Graphs](https://mlsanity.com/paper/2602.18600/) (2026)
- [Do Math Reasoning LLMs Help Predict the Impact of Public Transit Events?](https://mlsanity.com/paper/2511.00808/) (2025)
- [TransitLM: A Large-Scale Dataset and Benchmark for Map-Free Transit Route Generation](https://mlsanity.com/paper/2605.22355/) (2026)
- [Large Language Models in Transportation Systems Management and Operations: From Text Reasoning to Multi-modal Decision Support](https://mlsanity.com/paper/2606.00991/) (2026)

## Abstract (arXiv)

Existing navigation systems often fail during urban disruptions, struggling to incorporate real-time events and complex user constraints, such as avoiding specific areas. We address this gap with TraveLLM, a system using Large Language Models (LLMs) for disruption-aware public transit routing. We leverage LLMs' reasoning capabilities to directly process multimodal user queries combining natural language requests (origin, destination, preferences, disruption info) with map data (e.g., subway, bus, bike-share). To evaluate this approach, we design challenging test scenarios reflecting real-world disruptions like weather events, emergencies, and dynamic service availability. We benchmark the performance of state-of-the-art LLMs, including GPT-4, Claude 3, and Gemini, on generating accurate travel plans. Our experiments demonstrate that LLMs, notably GPT-4, can effectively generate viable and context-aware navigation plans under these demanding conditions. These findings suggest a promising direction for using LLMs to build more flexible and intelligent navigation systems capable of handling dynamic disruptions and diverse user needs.