Adaptive Area Radius for Mobile Network O-D Matrix Computation
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Solution Overview
Problem
Existing methods for determining Origin-Destination (O-D) matrices for traffic analysis, particularly for public happenings, face limitations in accurately identifying movements of individuals attending events, leading to inefficient planning and resource management due to issues with area size selection and data collection methods.
Innovation Solution
An adaptive method using mobile telecommunication network data to dynamically determine the optimal area of interest based on operational information, calculating radius values, and combining user equipment data to identify attendees and compute O-D matrices, thereby improving the accuracy of traffic flow analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a fixed area size is used for defining the Area of Interest around public happenings, then the method is simple to implement, but the accuracy of identifying actual attendees is reduced due to inclusion of non-attendees in larger areas or exclusion of attendees in smaller areas
Solution Approach 1:
The patent applies dynamics by making the Area of Interest radius variable rather than fixed. The system dynamically adjusts the radius based on historical mobile station data and statistical analysis to optimize attendee identification accuracy for each specific public happening event.
Solution Approach 2:
The patent changes the parameter of area radius from a static value to a dynamically determined value. By calculating optimal radius values based on historical data and statistical thresholds, the system adapts the area size to match actual attendee behavior patterns for different events.
2Productivity
If traditional data collection methods (questionnaires, interviews, vehicle count stations) are used, then the data collection process is straightforward, but the time required to collect sufficient empirical data is long and costs are high
Solution Approach 1:
The patent uses mobile communication network data as a copy or proxy for direct observation of attendee movements. Instead of directly collecting data through questionnaires or physical counting, the system analyzes existing mobile station location data that naturally records movement patterns, significantly reducing data collection time and costs.
Solution Approach 2:
The system leverages mobile stations that already carry their own location information through normal network operations. The mobile communication network itself provides the data without requiring additional dedicated measurement infrastructure, making the data collection process more efficient.
3Measurement precision
If the Area of Interest radius is decreased to improve precision, then fewer non-attendees are included, but some actual attendees are excluded reducing completeness
Solution Approach 1:
The system uses feedback from historical data and statistical analysis to determine the optimal radius. By analyzing patterns of mobile station movements during previous events and calculating statistical thresholds, the system identifies the radius that best balances completeness and accuracy for each event type.
Solution Approach 2:
The patent performs preliminary analysis of historical data before defining the Area of Interest for new events. By pre-calculating optimal radius values based on past event patterns and statistical metrics, the system prepares accurate area definitions in advance, improving both attendee identification accuracy and data collection efficiency.
Data Source
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AI summary
A method of estimating flows of persons that gathered at an Area of Interest (107) for attending a public happening during a time interval on a day is proposed. Said Area of Interest (107) is defined by an Area of Interest center and an Area of Interest radius and is covered by a mobile telecommunication network (105) having a plurality of communication stations (105a) each of which is adapted to manage communications of user equipment in one or more served areas (105b) in which the mobile telecommunication network (105) is subdivided. The method comprises the steps of: a) defining a plurality of calculated radius values of the Area of Interest radius, and, for each calculated radius value: b) identifying (1210, 1212) a first number of user equipment associated with at least one event record of a corresponding event of interaction occurred between the user equipment and the mobile communication network (105) during the time interval on the day within the Area of Interest (107); c) identifying (1214, 1216) a second number of user equipment associated with at least one event record of a corresponding event of interaction occurred between the user equipment and the mobile communication network (105) during the time interval for each day of a predetermined number of previous days preceding the day within the Area of Interest (107); d) combining (1218, 1220) the first number of user equipment and the second numbers of user equipment for obtaining a statistical quantity; e) detecting (1222) the occurrence of the public happening if the statistical quantity reaches a certain threshold; f) computing (1236) an optimum radius value of the Area of Interest radius as the average of the calculated radius values within which the public happening is detected; g) identifying persons (1238 - 1268) that gathered for attending at the public happening within an Area of Interest having the Area of Interest radius equal to the optimum radius values during the time interval on the day within the Area of Interest (107) based on a first time fraction indicating a probability that the user equipment has been in the Area of Interest (107) during the time interval on the day and on a second time fraction indicating a probability that the user equipment has been in the Area of Interest (107) during the previous days for each user equipment identified at step b); h) computing (1270-1278) at least one matrix (700m',n) accounting for movements of persons identified at step g) within a Region of Interest (500) comprising the Area of Interest (107) to the Area of Interest (107) during at least one observation time period comprising the time interval, and i) computing (1280-1288) at least one matrix (700m',n) accounting for movements of persons identified at step g) within a Region of Interest (500) comprising the Area of Interest (107) to the Area of Interest (107) during at least one observation time period comprising the time interval.