Leveraging RAG-LLMs for Urban Mobility Simulation and Analysis
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Abstract (arXiv)
With the rise of smart mobility and shared e-mobility services, numerous advanced technologies have been applied to this field. Cloud-based traffic simulation solutions have flourished, offering increasingly realistic representations of the evolving mobility landscape. LLMs have emerged as pioneering tools, providing robust support for various applications, including intelligent decision-making, user interaction, and real-time traffic analysis. As user demand for e-mobility continues to grow, delivering comprehensive end-to-end solutions has become crucial. In this paper, we present a cloud-based, LLM-powered shared e-mobility platform, integrated with a mobile application for personalized route recommendations. The optimization module is evaluated based on travel time and cost across different traffic scenarios. Additionally, the LLM-powered RAG framework is evaluated at the schema level for different users, using various evaluation methods. Schema-level RAG with XiYanSQL achieves an average execution accuracy of 0.81 on system operator queries and 0.98 on user queries.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 9 of its 28 references that are in the map. First posted on arXiv 14 Jul 2025.
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References · 9
- 2018Spider: A Large-Scale Human-Labeled Dataset for Complex and Cross-Domain Semantic Parsing and Text-to-SQL Taskcited by 1,139
- 2024ChatSUMO: Large Language Model for Automating Traffic Scenario Generation in Simulation of Urban MObilitycited by 34
- 2025Understanding, Detecting, and Repairing Real-World In-Context-Learning-Based Text-to-SQL Errorscited by 14
- 2024GenAI-powered Multi-Agent Paradigm for Smart Urban Mobility: Opportunities and Challenges for Integrating Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) with Intelligent Transportation Systemscited by 9
- 2022Fleet Rebalancing for Expanding Shared e-Mobility Systems: A Multi-agent Deep Reinforcement Learning Approachcited by 8
- 2023A Review on AI Algorithms for Energy Management in E-Mobility Servicescited by 4
- 2024Optimal Design and Implementation of an Open-source Emulation Platform for User-Centric Shared E-mobility Servicescited by 2
- 2024Data-driven Energy Consumption Modelling for Electric Micromobility using an Open Datasetcited by 2
- 2024On Scalable Design for User-Centric Multi-Modal Shared E-Mobility Systems using MILP and Modified Dijkstra's Algorithmcited by 1
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- 2024Large Language Models as Urban Residents: An LLM Agent Framework for Personal Mobility Generation
- 2026MobilityBench: A Benchmark for Evaluating Route-Planning Agents in Real-World Mobility Scenarios
- 2026Towards Efficient and Evidence-grounded Mobility Prediction with LLM-Driven Agent
- 20255G Network Automation Using Local Large Language Models and Retrieval-Augmented Generation
- 2025Large Language Models and Their Applications in Roadway Safety and Mobility Enhancement: A Comprehensive Review