Mobile Network Data for Transport Hub Passenger Categorization
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Solution Overview
Problem
Existing methods fail to accurately count and categorize individuals at transport hubs, such as airports or railway stations, based on their purpose, as they do not account for non-traveling individuals and lack differentiation among categories within the hub area during a specific observation time period.
Innovation Solution
A method and system utilizing a mobile telecommunication network to record and analyze event records of User Equipment, defining category patterns to identify sequences of events matching specific purposes, such as departing, arriving, commuting, or non-traveling, by correlating time and position information to categorize individuals automatically.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If transport companies keep passenger data confidential for privacy and competition reasons, then data security and privacy protection are improved, but the ability to exploit data for improving transport hub management deteriorates
Solution Approach 1:
The patent introduces mobile network operator data as an intermediary source that indirectly provides passenger flow information without requiring transport companies to share their confidential passenger lists. The mobile network operator acts as a mediator that collects data from users' mobile devices and provides aggregated location information to transport hub managers, thus preserving the confidentiality of transport company data while still enabling effective hub management through alternative data channels.
2Device complexity
If traditional counting methods are used at transport hubs, then infrastructure complexity is reduced, but measurement precision of passenger flow and categorization deteriorates
Solution Approach 1:
The patent replaces traditional mechanical counting infrastructure (such as manual counting, turnstiles, or physical sensors at transport hubs) with a digital information system that utilizes mobile network data. Instead of deploying complex physical counting devices at the transport hub, the system substitutes these with electronic data processing that analyzes mobile device location information from network operators, thereby reducing infrastructure complexity while improving measurement precision through automated digital tracking and categorization.
3Measurement precision
If aggregated mobile network data is analyzed to categorize individuals, then measurement precision of purpose-based classification is improved, but device complexity and data processing requirements worsen
Solution Approach 1:
The patent segments the classification task into distinct purpose-based categories (e.g., passengers, non-passengers, commuters, visitors) and processes mobile network data according to specific behavioral patterns associated with each category. By dividing the complex classification problem into separate categorical segments with defined criteria, the system improves measurement precision for each category while managing data processing complexity through structured segmentation rather than attempting unified analysis.
Solution Approach 2:
The patent performs preliminary actions by pre-defining category patterns and behavioral criteria for different individual purposes before analyzing the mobile network data. The system establishes classification rules and category definitions in advance, so that when data is received from the mobile network operator, it can be processed through predetermined classification logic rather than requiring complex real-time decision-making, thus reducing processing system complexity while maintaining high classification precision.
4Loss of information
If comprehensive data collection from mobile networks is implemented, then information completeness about individual purposes is improved, but loss of time for data acquisition and processing worsens
Solution Approach 1:
The patent leverages the continuous nature of mobile network data collection, where the mobile network operator continuously tracks and records user location information as part of normal network operations. This continuous data stream provides complete information about individual movements and purposes without requiring separate data collection campaigns or interruptions to normal activities, thus achieving information completeness while minimizing time loss since the data is already being collected continuously in the background.
Data Source
AI summary
A method of counting individuals reaching or leaving a transport hub includes defining at least two categories of individuals, each corresponding to a respective purpose to reach or leave the transport hub, and for each category, defining at least one respective category pattern associated with the category. Each category pattern is a sequence of events of interaction between a User Equipment and a communication station. The method also includes acquiring event records associated with User Equipment from the mobile telecommunication network, searching the event records associated with each User Equipment to identify sequences of events of interaction matching a category pattern, and upon finding a match, increasing a number of individuals belonging to the category associated with the category pattern.