Ride Sharing Navigation With Dynamic Route Adjustment
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Solution Overview
Problem
Existing ride-sharing systems face challenges in efficiently routing multiple passengers with different destination priorities and arrival time preferences, as they lack a mechanism to dynamically adjust routes based on real-time traffic and passenger preferences, leading to inefficiencies and potential dissatisfaction among passengers.
Innovation Solution
A computer-implemented method and apparatus that utilize a ride-sharing algorithm to generate and adjust routes based on rider destination hierarchies, allowing passengers to specify their priorities, with the system transmitting estimated times of arrival and alternative routes, and allowing passengers to contribute to re-routing decisions based on traffic data and route strategies such as LIFO, FIFO, cooperative, and prioritized routing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a fixed routing system is used for ride sharing, then the system operation is simple, but the route efficiency and passenger satisfaction deteriorate due to inability to adapt to real-time traffic and preferences
Solution Approach 1:
The patent implements dynamic routing by allowing the route to be adjusted in real-time based on traffic conditions and passenger preferences. The system transitions from a static pre-determined route to a dynamic adaptive route that can change during the ride, incorporating real-time data from traffic sensors and passenger feedback to optimize the path.
Solution Approach 2:
The system incorporates feedback mechanisms where passenger preferences and real-time traffic data are continuously fed back into the routing algorithm. This feedback loop allows the system to learn from passenger choices and traffic patterns, dynamically adjusting future route recommendations to balance efficiency and passenger satisfaction.
2Ease of operation
If multiple passenger destinations are optimized equally, then overall route efficiency improves, but individual passenger control and satisfaction deteriorate
Solution Approach 1:
The patent applies local quality by allowing different segments of the route to be optimized according to different passenger priorities. Each passenger can designate specific destinations or time points as high-priority, and the system adjusts the routing algorithm to give local optimization to those specific segments while maintaining overall route efficiency.
Solution Approach 2:
The system allows passengers to change routing parameters such as priority weights for different destinations, time constraints, and preference factors. By enabling dynamic parameter adjustment, the system balances individual passenger control with overall route efficiency, allowing passengers to modify their preferences in real-time based on changing conditions.
3Productivity
If real-time traffic data is incorporated into routing, then route efficiency improves, but system complexity and computational requirements worsen
Solution Approach 1:
The patent implements partial optimization by focusing computational resources on optimizing only the critical segments of the route where traffic conditions have the greatest impact. Rather than re-optimizing the entire route in real-time, the system identifies key decision points and adjusts only those portions, reducing computational complexity while maintaining route efficiency.
Solution Approach 2:
The system performs preliminary routing optimization before the ride begins, pre-calculating multiple potential routes and their characteristics. When real-time traffic data becomes available, the system only needs to select from these pre-computed options or make minor adjustments, rather than performing full optimization from scratch, thereby reducing real-time computational requirements.
Data Source
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AI summary
A first destination and a first route strategy are received from a first device. The first route strategy includes a first rider destination hierarchy for a ride sharing algorithm. A second destination and a second route strategy is received from a second device. The second route strategy includes a second rider destination hierarchy for a ride sharing algorithm. A route is generated to the first destination and the second destination based on the rider destination hierarchies specified by the route strategies.