Rideshare Fleet Routing With Passenger Counting and EV Charging
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Solution Overview
Problem
Current ridesharing systems face inefficiencies in managing vehicle routes and passenger capacity, leading to suboptimal utilization of vehicles and increased costs, as they lack real-time adjustments for passenger numbers and charging schedules based on predicted demand.
Innovation Solution
A ridesharing management system that includes real-time passenger counting using sensors and remedial actions, and a module for predicting future demand to optimize vehicle charging by directing electric vehicles to charging stations based on estimated completion times and demand, ensuring vehicles are available when needed.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If real-time passenger counting sensors are installed in ridesharing vehicles, then vehicle utilization is improved through accurate passenger capacity management, but device complexity increases
Solution Approach 1:
The patent replaces manual passenger counting methods with sensor-based automated detection systems. Sensors detect passenger presence and weight, automatically transmitting data to the management system to determine actual passenger capacity utilization, eliminating the need for manual tracking and reducing operational complexity despite adding technological components.
Solution Approach 2:
The management system automatically processes sensor data to determine whether vehicles have reached their actual passenger capacity threshold. The system self-adjusts routing decisions based on real-time sensor feedback without requiring manual intervention from dispatchers or drivers, improving utilization while keeping the operational interface simple.
2Reliability
If vehicles are directed to charging stations based on predicted demand, then service availability is improved, but loss of time occurs during charging operations
Solution Approach 1:
The management system predicts future ride demand in different geographic areas and directs vehicles to charging stations proactively before they are needed for service. By anticipating demand patterns, the system schedules charging operations in advance during periods of lower demand, ensuring vehicles are available when needed while minimizing service disruption.
Solution Approach 2:
The system dynamically adjusts charging schedules based on real-time and predicted demand data. Rather than using fixed charging times, the management system continuously monitors ride requests and modifies vehicle routing and charging timing to optimize service availability, reducing idle time during high-demand periods.
3Device complexity
If manual passenger capacity management is used, then device complexity is reduced, but productivity decreases due to suboptimal vehicle utilization
Solution Approach 1:
The management system implements continuous feedback loops where sensors monitor actual passenger capacity in real-time, and this data feeds back into routing and charging decisions. The system uses this feedback to dynamically adjust vehicle deployment, ensuring optimal utilization while maintaining relatively simple operational procedures through automated decision-making.
Data Source
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AI summary
The present disclosure relates to systems and methods for managing and routing ridesharing vehicles. In some implementations, the systems and methods may count the number of passengers entering a ridesharing vehicle, distribute vehicles in need of charge to charging stations based on predicted future demand, manage a fleet of petrol and electric ridesharing vehicles, route autonomous and non-autonomous vehicles, automatically adjust drop-off locations based on safety constraints, and preschedule a rideshare with an unknown pick-up location.