Fleet Rideshare Resource Depletion Reduction
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Solution Overview
Problem
Fleet management systems for taxi services face significant vehicle resource depletion due to the repetitive deployment and repositioning of vehicles, which can lead to inefficient use of resources.
Innovation Solution
A system and method that utilize a memory, controller, efficiency module, mobile computing device, and fleet vehicle to optimize rideshare system tasks by receiving and analyzing location data, generating mapping data, and producing dynamic status indicators to instruct vehicles to autonomously traverse and perform tasks in a way that minimizes resource depletion, including the use of a geosector divided geosurface map to select efficient routes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If vehicles are deployed for customer trips and automatically traverse to customer locations, then customer service is provided, but vehicle resources are depleted significantly
Solution Approach 1:
The system dynamically adjusts vehicle deployment strategies based on real-time conditions. The efficiency module continuously monitors vehicle locations, customer requests, and resource levels, then adapts routing and assignment decisions to optimize the balance between serving customers and preserving vehicle resources for future demand.
Solution Approach 2:
The system changes operational parameters such as vehicle assignment priorities, routing efficiency thresholds, and resource depletion limits. By adjusting these parameters dynamically, the system can shift between modes of operation to prevent resource depletion while maintaining adequate customer service levels.
2Productivity
If vehicles perform repetitive deployment and repositioning tasks, then rideshare services are maintained, but resource consumption increases
Solution Approach 1:
The efficiency module performs preliminary analysis of vehicle routes and assignments before deployment. By pre-calculating optimal paths and anticipating resource consumption patterns, the system can prevent excessive energy loss from inefficient routing and repositioning decisions.
Solution Approach 2:
The system implements continuous feedback loops where vehicle performance data, resource consumption metrics, and routing efficiency information are monitored and fed back to the efficiency module. This enables real-time adjustments to reduce energy loss while maintaining service continuity.
Data Source
AI summary
A system to reduce vehicle resource depletion risk which includes a memory, controller, efficiency module, mobile computing device, and fleet vehicle. The memory includes executable instructions. The controller executes the instructions. The controller communicates with an efficiency module. The efficiency module causes a fleet vehicle to optimally perform a rideshare task. The mobile computing device generates first location data and communicates the first location data to the controller. The fleet vehicle includes a vehicle system and a vehicle controls device and can communicate with the controller. The vehicle system generates second location data. The vehicle controls device commands the fleet vehicle to perform a rideshare task. The instructions enable the controller to: receive the first and second location data; perform the efficiency module to produce an output being partially based on the first and second location data and instructs the vehicle to perform a rideshare task; and communicate the output.