Origin-Destination Extraction from Large-Scale Route Search Records for Tourism Trend Analysis

2025-07-23

AI Summary

Accurate origin-destination (OD) data acquisition remains a major challenge in tourism trend forecasting. Method: This study proposes a novel approach to construct a three-dimensional OD map by leveraging highway route search logs—specifically, 380 million online search records—treating online search behavior as a leading indicator of physical mobility. The method integrates spatiotemporal sampling, efficient data compression, and interactive visualization to enable coupled analysis of virtual behavioral patterns and real-world traffic flows. Contribution/Results: We uncover statistically significant temporal correlations between search intensity and both seasonal tourism activities (e.g., spring flower viewing, autumn foliage watching, winter skiing) and hub-node traffic volumes. These findings support precise prediction of tourism peaks and facilitate dynamic traffic management strategies, thereby enhancing the responsiveness and efficiency of transportation systems during high-demand periods.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Search and Optimization: Heuristic Search
• Data Mining & Knowledge Management: Mining of Spatial, Temporal or Spatio-Temporal Data

Application Category

• Web Mining and Content Analysis: Web traffic and log analysis
• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Search and Retrieval-Augmented AI: Web query analysis, representation and understanding

Abstract

This paper presents a novel method for transforming large-scale historical expressway route search records into a three-dimensional (3D) Origin-Destination (OD) map, enabling data compression, efficient spatiotemporal sampling and statistical analysis. The study analyzed over 380 million expressway route search logs to investigate online search behavior related to tourist destinations. Several expressway interchanges (ICs) near popular attractions, such as those associated with spring flower viewing, autumn foliage and winter skiing, are examined and visualized. The results reveal strong correlations between search volume trends and the duration of peak tourism seasons. This approach leverages cyberspace behavioral data as a leading indicator of physical movement, providing a proactive tool for traffic management and tourism planning.

Problem

• Extracting Origin-Destination data from expressway search records
• Analyzing tourism trends via large-scale route search behavior
• Linking online search trends to physical travel patterns

Innovation

• Transform route search records into 3D OD map
• Analyze 380 million logs for tourism trends
• Use cyberspace data as movement indicator