Drone Corridor Ranking via Traffic Metrics
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Solution Overview
Problem
Determining optimal routes for flying cars and drones within existing road networks is complex due to the need for identifying suitable corridors that minimize travel delays and enhance network efficiency, while existing methods often introduce human bias.
Innovation Solution
A method that uses probe data from vehicle location sensors to determine traffic metrics, including volume and delay, based on the topology of the road network, allowing for the ranking and identification of potential drone and flying car (DFC) corridors that augment the existing road network without human intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If human methods are used to select and rank potential DFC corridors, then the process can be simplified and more intuitive, but human bias is introduced that may compromise objectivity and optimal selection
Solution Approach 1:
The system performs self-service by automatically analyzing probe data from multiple vehicles to determine traffic metrics and rank corridors without requiring human intervention. The computer system independently processes location data, calculates trajectories, determines traffic volume and delay metrics, and generates objective rankings based on quantitative analysis rather than human judgment.
Solution Approach 2:
The patent replaces the mechanical (human) system of manual corridor selection with an automated computer-based system. The computer system substitutes human operators by using algorithms to process probe data, calculate traffic metrics, and generate corridor rankings, thereby eliminating human bias while maintaining systematic analysis.
2Measurement precision
If automated systems are used to rank DFC corridors based on traffic metrics, then objectivity is improved, but the complexity of data processing and analysis increases
Solution Approach 1:
The system segments the complex data processing task into distinct functional modules: (1) collecting probe data from vehicles, (2) determining trajectories from location data, (3) calculating traffic volume metrics, (4) calculating delay metrics, (5) computing comprehensive traffic metrics, and (6) generating corridor rankings. This segmentation makes the complex automated system more manageable and maintainable.
Solution Approach 2:
The patent introduces intermediate calculations and data structures as mediators between raw probe data and final corridor rankings. Traffic metrics serve as intermediary measures that aggregate complex traffic condition data into comparable values, facilitating the automated ranking process while managing computational complexity through structured data processing.
3Measurement precision
If comprehensive traffic metrics considering multiple factors are used, then the accuracy of corridor selection is improved, but the computational requirements and processing time increase
Solution Approach 1:
The system performs preliminary actions by pre-processing probe data to determine trajectories and calculating intermediate traffic metrics (volume and delay) before generating final corridor rankings. This preliminary computation allows the system to prepare and organize data efficiently, reducing the computational burden during the final ranking stage and overall processing time.
Solution Approach 2:
The patent transforms raw probe data into standardized traffic metrics parameters (volume, delay, and comprehensive traffic metrics) that can be directly compared and ranked. By changing the data representation into normalized parameters, the system improves accuracy while optimizing processing efficiency through standardized calculations rather than complex ad-hoc analysis.
Data Source
AI summary
One or more potential drone and/or flying car (DFC) corridors are identified based on the topology of a road network. Trajectories traveled by vehicles are determined from a plurality of instances of probe data received from a plurality of vehicle apparatuses onboard the vehicles. A volume of traffic for a path through the road network and corresponding to a potential DFC corridor is determined based on the trajectories. A delay metric for the path through the road network and corresponding to the potential DFC corridor is determined based on the trajectories. A traffic metric is then determined for the path based on a combination of the volume of traffic, the delay metric and a measure of the topology of the road network. The one or more potential DFC corridors are ranked by their corresponding traffic metrics.