Travel Time Calculation Using Segmented Delay Estimation
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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 assess driver and vehicle performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional speed and travel time calculation methods are used, then the calculation process is simple, but the accuracy of travel time estimation is insufficient because travel delays are not accounted for
Solution Approach 1:
The travel time calculation is segmented into multiple components: base travel time (distance/speed) and delay time (from traffic data). This segmentation allows the system to account for travel delays separately while maintaining a structured calculation approach that improves accuracy without overwhelming complexity.
Solution Approach 2:
The system performs preliminary actions by pre-collecting and storing traffic delay data, route information, and historical travel patterns before the actual travel time calculation. This preliminary preparation enables the system to quickly adjust base travel times with relevant delay information, improving estimation accuracy while keeping the real-time calculation process efficient.
2Measurement precision
If travel delay information is incorporated into the calculation, then the travel time estimation accuracy improves, but the data processing complexity increases
Solution Approach 1:
The system uses a multi-functional data processing platform that handles various types of data (telematics data, GPS coordinates, traffic delay information, route data) through a unified processing framework. This universal approach allows the system to process diverse data sources simultaneously, improving speed and travel time measurement accuracy without proportionally increasing processing complexity.
Solution Approach 2:
The system introduces intermediary components such as data normalization layers and integration modules that mediate between raw data sources and the calculation engine. These intermediaries standardize different data formats and quality levels, enabling accurate speed and travel time measurements while managing data processing complexity through structured intermediate representations.
3Productivity
If comprehensive telematics data and GPS information are used, then the operational efficiency assessment improves, but the system complexity and computational requirements increase
Solution Approach 1:
The system extracts only the most relevant features and parameters from comprehensive telematics data and GPS information for fleet management decisions. Instead of processing all available data, it selectively extracts key metrics such as travel times, speeds, route deviations, and delay patterns, improving operational efficiency assessments while reducing system complexity by focusing on essential information.
Solution Approach 2:
The system implements partial processing of telematics data by focusing on critical segments of the journey and key performance indicators rather than analyzing every data point in detail. This partial action approach provides sufficient information for fleet management efficiency improvements without requiring full processing of all comprehensive data, thereby managing system complexity effectively.
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.