Mobile Body Distribution Forecasting via Inter-Facility Movement Correlation
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Solution Overview
Problem
Current methods struggle to accurately forecast the number of visitors to facilities, especially in tourist areas, due to variations in forecast accuracy and the difficulty in accounting for sudden events and inter-facility movements, which affects economic activation strategies.
Innovation Solution
A mobile body distribution situation forecast device that computes estimated facility staying time, inter-facility movement time, and correlation using past position information, current position data, and map information, allowing for accurate forecasting of visitor numbers by visualizing the forecasted situation on a map.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional forecast methods based on weekday/weekend patterns and past experiences are used, then the forecasting process is simple and easy to operate, but the forecast accuracy varies and cannot cope with sudden events or inter-facility movements
Solution Approach 1:
The system pre-computes inter-facility movement correlations and excursion behavior patterns from historical position information before actual forecasting is needed. This preliminary analysis of movement patterns between facilities allows the system to quickly respond to sudden events and inter-facility movements without requiring complex real-time calculations, thus improving forecast accuracy while maintaining operational simplicity
Solution Approach 2:
The system introduces an intermediary computational layer that processes position information and derives movement correlations between facilities. This intermediary layer translates raw position data into meaningful movement patterns that can be used for forecasting, bridging the gap between simple data collection and accurate prediction without requiring the end user to directly handle complex computations
2Measurement precision
If inter-facility movement correlation analysis is implemented, then the ability to forecast visitor numbers accounting for movements between facilities is improved, but the computational complexity and data processing requirements increase
Solution Approach 1:
The system extracts specific movement correlation metrics from comprehensive position information data, focusing only on the essential inter-facility movement patterns needed for forecasting. By extracting and isolating the relevant movement correlations between facilities rather than processing all possible data relationships, the system achieves accurate visitor number forecasts while reducing computational complexity
Solution Approach 2:
The system transforms raw position information into derived parameters such as movement correlation coefficients and excursion behavior metrics. By changing the parameters from raw coordinate data to meaningful movement patterns, the system enables accurate forecasting of visitor numbers while simplifying the computational requirements compared to processing raw position data directly
Data Source
AI summary
Forecasting a mobile body distribution situation after a prescribed time by forecasting an excursion action of a mobile body on the basis of an inter-facility movement correlation of the mobile body. A server system includes an inter-facility movement computation part for computing a facility staying time estimate, an inter-facility movement time estimate, and an inter-facility movement correlation on the basis of a trend in past position information from the mobile body, a plurality of pieces of facility information, and map information wherein a road map is included; a receiving part for receiving present position information of a plurality of the mobile bodies; and a mobile body distribution situation forecast part for forecasting the distribution situation of the mobile bodies after a prescribed time on the basis of the inter-facility movement correlation and the present position information of the plurality of mobile bodies.