Travel Time Calculation Using Delay Segmentation
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Solution Overview
Problem
Current systems lack accurate methods for determining travel times and speeds that account for travel delays, which are essential for efficient fleet management and operational optimization.
Innovation Solution
A method and system that identify speed, distance, and travel delay information to calculate estimated travel times and average speeds, utilizing telematics data and GPS-based systems to evaluate operational efficiencies and generate graphical presentations for fleet management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional speed and travel time calculation methods are used, then computational simplicity is maintained, but accuracy of travel time estimation deteriorates due to failure to account for travel delays
Solution Approach 1:
The travel time calculation is segmented into multiple components: base travel time (distance/speed) and delay time (from delay information). This segmentation allows the system to maintain computational simplicity for the base calculation while adding delay adjustments separately, thereby improving accuracy without proportionally increasing complexity.
Solution Approach 2:
Delay information is identified and stored in advance before final travel time calculation. By preliminarily gathering delay data (traffic conditions, weather, events) and associating it with route segments, the system prepares correction factors ahead of time, making the final estimation more accurate without adding complex real-time computation during travel.
2Productivity
If travel delay information is incorporated into calculations, then operational efficiency improves through better fleet management, but data processing complexity increases
Solution Approach 1:
The system uses a universal travel time estimation method that can be applied across multiple vehicles and routes. The same calculation framework (base time + delay adjustment) serves all fleet operations, allowing improved productivity through consistent application while avoiding the need for complex specialized processing for each individual case.
Solution Approach 2:
The system automatically identifies and processes delay information without requiring manual intervention. By self-service mechanisms that automatically gather delay data, associate it with relevant routes, and adjust travel time estimates, the system improves fleet management efficiency while keeping processing complexity manageable through automation rather than manual complexity.
3Measurement precision
If average speed is calculated excluding loss and acquisition periods, then speed measurement accuracy improves, but calculation complexity increases due to additional data filtering
Solution Approach 1:
The system extracts and separates the loss period and acquisition period from the overall travel data. By taking out these problematic segments where GPS signal is lost or being acquired, the system calculates average speed only over valid travel periods, thereby improving measurement accuracy. The extraction is done by identifying time gaps in location data and excluding those intervals from speed calculations.
Solution Approach 2:
Rather than attempting to calculate speed during all possible periods (which would include inaccurate loss/acquisition periods), the system applies partial action by calculating speed only over the valid travel portions. This partial calculation approach improves accuracy by excluding problematic data while keeping the calculation method relatively simple—essentially filtering out invalid time intervals from the average speed computation.
Data Source
AI summary
Computer program products, methods, systems, apparatus, and computing entities are provided for forecasting travel delays corresponding to streets, street segments, geographic areas, geofenced areas, and/or user-specified criteria. And from the forecasted travel delays, speed and travel times that take into account such travel delays can be determined.