Individualized ETA Calculation Using Driver Profile Data
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Solution Overview
Problem
Existing ETA calculation methods fail to provide individualized estimates, as they do not consider a driver's specific habits, familiarity with the route, and other driver-specific characteristics, leading to inaccurate predictions.
Innovation Solution
A computer-implemented system that calculates an individualized ETA by incorporating historical driving data, real-time speed, average speeds of other drivers, level of familiarity, and various route-specific factors, using AI to predict arrival times and update them periodically.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional ETA calculation methods are used, then the calculation process is simple and fast, but the accuracy of ETA prediction is low because they do not consider individual driver characteristics
Solution Approach 1:
The system performs preliminary actions by collecting and storing historical driving data, route information, and driver characteristics before ETA calculation is needed. This pre-processing enables the system to quickly retrieve and analyze relevant data during actual ETA computation, improving accuracy without significantly increasing real-time calculation complexity
Solution Approach 2:
The ETA calculation system is segmented into multiple independent modules: data collection module, driver profile analysis module, route segmentation module, and ETA computation module. Each module handles specific aspects of the calculation, making the overall complex system manageable and allowing parallel processing to maintain efficiency
2Measurement precision
If individualized driver data is incorporated into ETA calculations, then the precision of arrival time estimation is improved, but the amount of data processing and system complexity increases
Solution Approach 1:
The system extracts only the most relevant features from large volumes of driver data, such as average speed, route familiarity, and driving patterns, rather than processing all raw data. This extraction approach maintains high ETA precision while significantly reducing the data volume that needs to be processed in real-time
Solution Approach 2:
The system transforms raw driver behavior data into standardized parameters and metrics that are easier to process and compare. By changing the parameter representation from raw data to processed metrics, the system reduces computational complexity while preserving the information needed for accurate ETA prediction
3Reliability
If real-time tracking and historical data analysis are implemented, then the reliability of ETA information is improved, but the computational resources and time required for calculation increase
Solution Approach 1:
Historical driving data and route characteristics are pre-analyzed and stored in optimized formats before real-time ETA calculation is needed. This preliminary preparation allows the system to quickly retrieve and combine pre-processed data with current location information, maintaining high reliability while minimizing real-time computation time
Solution Approach 2:
The system implements feedback mechanisms where actual arrival times are compared with predicted ETAs, and this information is used to continuously refine driver profiles and improve future predictions. This feedback loop enhances ETA reliability over time without requiring increased computational resources for each individual calculation
Data Source
AI summary
Disclosed is a system and method of improving a calculation and display of an individualized estimated time of arrival (ETA). Using at least one computing device, driver-related data, including real-time and historic data, associated with a driver may be collected and stored in memory. The computing device may also be used to perform analysis on the driving data and ETA-related data to determine the individualized ETA based at least in part on a set of individualized parameters. The computing device may also be used to display to the driver and/or user the individualized ETA and a corresponding level of familiarity of the driver with one or more segments of a route of the driver.