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CompassLLM: A Multi-Agent Approach toward Geo-Spatial Reasoning for Popular Path Query

Md. Nazmul Islam Ananto, Shamit Fatin, Mohammed Eunus Ali, Md Rizwan Parvez

TL;DR

This work introduces CompassLLM, a multi-agent framework that casts geo-spatial popular path queries as a two-stage SEARCH-GENERATE problem solved with specialized LLM-powered agents. The Path Discovery, Popularity Ranking, Path Synthesis, and Path Selection agents coordinate to locate existing popular routes or synthesize valid ones that traverse observed edges, enabling real-time inference and reduced retraining needs. Experimental results on real-world and synthetic datasets show superior performance in path discovery ($F1$) and competitive performance in path generation ($Traversability$), with favorable token-usage and cost characteristics. The approach advances geo-spatial reasoning by embedding structured agent cooperation into LLM-based route reasoning, particularly benefiting scenarios with sparse historical data and dynamic data updates.

Abstract

The popular path query - identifying the most frequented routes between locations from historical trajectory data - has important applications in urban planning, navigation optimization, and travel recommendations. While traditional algorithms and machine learning approaches have achieved success in this domain, they typically require model training, parameter tuning, and retraining when accommodating data updates. As Large Language Models (LLMs) demonstrate increasing capabilities in spatial and graph-based reasoning, there is growing interest in exploring how these models can be applied to geo-spatial problems. We introduce CompassLLM, a novel multi-agent framework that intelligently leverages the reasoning capabilities of LLMs into the geo-spatial domain to solve the popular path query. CompassLLM employs its agents in a two-stage pipeline: the SEARCH stage that identifies popular paths, and a GENERATE stage that synthesizes novel paths in the absence of an existing one in the historical trajectory data. Experiments on real and synthetic datasets show that CompassLLM demonstrates superior accuracy in SEARCH and competitive performance in GENERATE while being cost-effective.

CompassLLM: A Multi-Agent Approach toward Geo-Spatial Reasoning for Popular Path Query

TL;DR

This work introduces CompassLLM, a multi-agent framework that casts geo-spatial popular path queries as a two-stage SEARCH-GENERATE problem solved with specialized LLM-powered agents. The Path Discovery, Popularity Ranking, Path Synthesis, and Path Selection agents coordinate to locate existing popular routes or synthesize valid ones that traverse observed edges, enabling real-time inference and reduced retraining needs. Experimental results on real-world and synthetic datasets show superior performance in path discovery () and competitive performance in path generation (), with favorable token-usage and cost characteristics. The approach advances geo-spatial reasoning by embedding structured agent cooperation into LLM-based route reasoning, particularly benefiting scenarios with sparse historical data and dynamic data updates.

Abstract

The popular path query - identifying the most frequented routes between locations from historical trajectory data - has important applications in urban planning, navigation optimization, and travel recommendations. While traditional algorithms and machine learning approaches have achieved success in this domain, they typically require model training, parameter tuning, and retraining when accommodating data updates. As Large Language Models (LLMs) demonstrate increasing capabilities in spatial and graph-based reasoning, there is growing interest in exploring how these models can be applied to geo-spatial problems. We introduce CompassLLM, a novel multi-agent framework that intelligently leverages the reasoning capabilities of LLMs into the geo-spatial domain to solve the popular path query. CompassLLM employs its agents in a two-stage pipeline: the SEARCH stage that identifies popular paths, and a GENERATE stage that synthesizes novel paths in the absence of an existing one in the historical trajectory data. Experiments on real and synthetic datasets show that CompassLLM demonstrates superior accuracy in SEARCH and competitive performance in GENERATE while being cost-effective.

Paper Structure

This paper contains 25 sections, 2 equations, 7 figures, 5 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. Related Works
  3. Problem Definition
  4. Methodology
  5. Path Discovery Agent
  6. Popularity Ranking Agent
  7. Path Synthesis Agent
  8. Path Selection Agent
  9. Experiments
  10. Dataset
  11. Real-world Data:
  12. Synthetic Data:
  13. Baselines & Implementation
  14. Evaluation Metrics
  15. Results
  16. ...and 10 more sections

Figures (7)

  • Figure 1: When SEARCHing for the most popular path from A to Z given the three trajectories (top), we can see that there is no single path spanning the entirety from A to Z. This incurs a GENERATE problem (bottom) where one must break down the trajectories into smaller segments and combine them to form a new path.
  • Figure 2: Overview of CompassLLM framework.
  • Figure 3: Comparison among approaches on synthetic data.
  • Figure 4: Synthetic Data Generation Process
  • Figure 5: Comparison of Real and Synthetic Data
  • ...and 2 more figures