Research on Human Travel Correlation for Urban Transport Planning Based on Multisource Data

Area of Science:

• Transportation Science
• Urban Planning
• Data Science

Background:

• Human travel trajectory data is abundant but often analyzed in isolation.
• Single-source data analysis poses limitations for comprehensive urban transport planning.
• Integrating diverse datasets is crucial for accurate urban mobility insights.

Purpose of the Study:

• To develop a method for analyzing urban traffic patterns and population distributions using multisource data.
• To compare and correlate findings from license plate recognition (LPR) and cellular signaling (CS) data.
• To enhance urban transport planning and travel demand modeling.

Main Methods:

• Proposed novel methods for identifying resident stay points using LPR (vehicle speed thresholds) and CS (spatiotemporal clustering).
• Analyzed correlations between LPR and CS data using correlation coefficient (r) and p-values.
• Generated origin-destination (OD) matrices between traffic analysis zones (TAZs).

Main Results:

• Significant correlations were found between LPR and CS data for stay/move population distributions, even at an hourly level.
• High correlations were observed in OD matrices between TAZs, indicating similar patterns from both data sources.
• The study validated the consistency of population distribution and traffic patterns derived from multisource data.

Conclusions:

• Multisource data integration, specifically LPR and CS, provides a robust method for analyzing complex human mobility.
• The findings support improved travel demand modeling and urban transport planning.
• This approach offers valuable insights for urban management and decision-making.