A Study on Individual Spatiotemporal Activity Generation Method Using MCP-Enhanced Chain-of-Thought Large Language Models (2506.10853v1)

Abstract: Human spatiotemporal behavior simulation is critical for urban planning research, yet traditional rule-based and statistical approaches suffer from high computational costs, limited generalizability, and poor scalability. While LLMs show promise as "world simulators," they face challenges in spatiotemporal reasoning including limited spatial cognition, lack of physical constraint understanding, and group homogenization tendencies. This paper introduces a framework integrating chain-of-thought (CoT) reasoning with Model Context Protocol (MCP) to enhance LLMs' capability in simulating spatiotemporal behaviors that correspond with validation data patterns. The methodology combines human-like progressive reasoning through a five-stage cognitive framework with comprehensive data processing via six specialized MCP tool categories: temporal management, spatial navigation, environmental perception, personal memory, social collaboration, and experience evaluation. Experiments in Shanghai's Lujiazui district validate the framework's effectiveness across 1,000 generated samples. Results demonstrate high similarity with real mobile signaling data, achieving generation quality scores of 7.86 to 8.36 across different base models. Parallel processing experiments show efficiency improvements, with generation times decreasing from 1.30 to 0.17 minutes per sample when scaling from 2 to 12 processes. This work contributes to integrating CoT reasoning with MCP for urban behavior modeling, advancing LLMs applications in urban computing and providing a practical approach for synthetic mobility data generation. The framework offers a foundation for smart city planning, transportation forecasting, and participatory urban design applications.

A Study on Individual Spatiotemporal Activity Generation Using MCP-Enhanced Chain-of-Thought LLMs

The paper presents a novel approach to simulating human spatiotemporal activity by integrating Model Context Protocol (MCP) with Chain-of-Thought (CoT) reasoning within LLMs. This framework addresses the limitations of traditional rule-based and statistical behavior simulation methods, offering a scalable and efficient solution for generating high-quality spatiotemporal activity data crucial for urban planning.

Methodology and Implementation

The authors introduce a comprehensive framework that combines human-like reasoning, facilitated by CoT, with enhanced data processing capabilities via MCP. The CoT reasoning unfolds in a five-stage cognitive framework, supporting progressive decision-making processes and ensuring the generation of activity sequences that reproduce human-like behaviors. MCP enhances the interaction capabilities of LLMs through six specialized tool categories, enabling the processing of vast and disparate data inputs: temporal management, spatial navigation, environmental perception, personal memory, social collaboration, and experience evaluation.

The effectiveness of this framework is demonstrated through experiments conducted in Shanghai’s Lujiazui district, where the method successfully calibrated LLM-generated behaviors to correspond with real mobile signaling data patterns. Moreover, the framework is capable of efficiently handling large-scale data generation tasks, with parallel processing reducing per-sample generation time from 1.30 minutes to 0.17 minutes by scaling processes appropriately on dedicated hardware.

Results and Discussion

The empirical analysis yielded generation quality scores ranging from 7.86 to 8.36 across different base models used. These results indicate that the enhanced framework can produce realistic, high-quality individual spatiotemporal behaviors that align with existing data patterns. Consistency in performance across different model scales suggests that the MCP-CoT methodology is adaptable to various LLM architectures.

The component ablation paper further reveals that spatial navigation and CoT reasoning are the primary contributors to the model's effectiveness, while tools like experience evaluation showed marginal impact on the overall performance but had potential applicability in extended research scenarios. This points to the prioritization of certain tool categories for optimized performance in specific urban applications.

Implications and Future Research

Practically, this research contributes to the fields of urban computing and synthetic mobility data generation, providing valuable insights for smart city planning, transportation forecasting, and urban design. Theoretically, it enhances our understanding of integrating complex reasoning capabilities within LLMs, facilitating the simulation of intricate human behaviors.

Future research should focus on adapting this methodology across different geographical and cultural contexts, assessing its cross-spatial and cross-temporal validity. Furthermore, enhancing multimodal data inclusion and real-time environmental interaction capabilities could significantly advance the model's applicability and predictive accuracy.

In conclusion, this paper successfully demonstrates the capability of LLMs, when integrated with CoT reasoning and MCP protocols, in generating realistic spatiotemporal behaviors. It provides a robust foundation for addressing the complex and dynamic nature of urban human activity modeling, paving the way for more responsive and evidence-based urban development strategies.