Public Transport Coverage Prediction for Departure Time Reliability
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Solution Overview
Problem
Navigation systems face challenges in accurately determining waiting times for public transport, which affects the precision of timing information for navigation routes.
Innovation Solution
A method, apparatus, and computer program product that quantify public transport coverage by predicting time to departure data, combining transit time data and waiting time data, and determining a time to departure probability prediction that satisfies a predefined temporal threshold.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If navigation systems use static information to determine timing information, then the system complexity is low, but the measurement precision of waiting time is insufficient
Solution Approach 1:
The system pre-calculates and stores timing information including waiting times for public transport routes before actual navigation queries are made. This preliminary computation of route characteristics and transport schedules allows the system to provide accurate waiting time predictions without complex real-time calculations, resolving the contradiction between precision and complexity.
Solution Approach 2:
The navigation system transitions from purely static information to a dynamic approach by incorporating real-time or near-real-time public transport schedule data. This dynamic element allows accurate waiting time calculation while maintaining manageable system complexity through modular architecture that separates static route data from dynamic schedule information.
2Reliability
If navigation systems aggregate time to departure data for multiple traveler journeys, then the public transport coverage prediction becomes more accurate, but the computational time and resources increase
Solution Approach 1:
The system performs preliminary aggregation of time to departure data for multiple traveler journeys in advance, storing these aggregated statistics for quick retrieval. By pre-computing probability predictions based on historical and scheduled data, the system achieves high reliability without incurring computational delays during actual user queries.
Solution Approach 2:
The system aggregates data for a representative sample of traveler journeys rather than exhaustively processing every possible journey. This partial action approach provides sufficiently accurate probability predictions while significantly reducing computational time and resources compared to complete aggregation.
Data Source
AI summary
A method, apparatus and computer program product are provided for quantifying public transport coverage. In this regard, for a traveler journey beginning at a starting location, transit time data is determined. The transit time data is indicative of a predicted amount of time to travel between the starting location and a departure location associated with a public transport boarding location for a public transport. Furthermore, waiting time data is determined. The waiting time is indicative of a predicted amount of time to wait at the departure location prior to departure via the public transport. Additionally, time to departure data is computed based on a combination of the transit time data and the waiting time data. For the traveler journey, a time to departure probability prediction is also determined.