Travel Process Prediction Using Regression Analysis
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Solution Overview
Problem
Current travel process prediction systems are inadequate as they rely on user experiences and hearsay for estimating time spent at departure/arrival facilities, leading to low accuracy and anxiety due to uncertainty, especially at unfamiliar locations.
Innovation Solution
A travel process prediction system that statistically processes relationships between travel time and situational factors, using a passage time specifying unit, transportation specifying information, situation information, and a calculation unit to estimate passage times through regression analysis, providing more reliable predictions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional prediction methods based on user experiences and hearsay are used, then the system is simple to implement, but the prediction accuracy is low and users feel anxious
Solution Approach 1:
The system performs preliminary data collection and statistical analysis to establish prediction models before actual travel planning. Historical passage time data is collected and processed in advance to create regression equations that can be used for accurate predictions without requiring complex real-time calculations
Solution Approach 2:
The patent introduces an intermediary statistical analysis layer between raw historical data and final predictions. Regression equations and average value calculations act as intermediaries that transform complex historical data into simple, accurate prediction formulas that can be easily applied
2Reliability
If more time is allocated for staying at departure/arrival facilities to ensure safety, then user anxiety is reduced, but travel schedule efficiency decreases
Solution Approach 1:
The patent replaces the mechanical approach of adding excessive buffer time with an information-based statistical prediction system. Instead of mechanically increasing time allocations, the system uses statistical analysis of historical data to provide precise time estimates, replacing the need for conservative time padding with accurate predictive information
3Measurement precision
If statistical analysis of historical data is implemented, then prediction accuracy improves, but data processing complexity increases
Solution Approach 1:
The patent segments the data processing task into distinct components: data collection at passage points, historical data storage, regression analysis for specific passage points, and average value calculation for overall travel time. This segmentation allows each component to be processed independently using appropriate statistical methods, reducing overall complexity
Data Source
AI summary
An information acquiring apparatus acquires, when a travel object such as a traveler travels with transportation, passage time at which the travel object passes through each passage point at a departure/arrival facility, transportation specifying information indicating transportation, situation information indicating a situation and so forth. The travel process prediction apparatus stores the acquired information in an associated manner, and obtains a regression equation representing the relationship between items included in the transportation specifying information or situation information and passage time at a specific passage point, elapsed time while the travel object passes through two specific passage points or a result of comparison between the passage time and boarding completion time. The travel process prediction apparatus calculates a predicted value of future passage time, elapsed time or a result of comparison by substituting the content of the expected transportation specifying information or situation information for the regression equation.