Ridesharing Server Dynamic Drop-Off Sequencing
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Solution Overview
Problem
Current vehicle ridesharing systems face challenges in efficiently managing large fleets of vehicles to minimize delays and optimize routes, particularly in handling multiple user requests with varying priorities and traffic conditions, while also considering driver switching and dynamic tolling for infrastructure usage.
Innovation Solution
A system and method that utilize a ridesharing management server to receive and process user requests, assign vehicles based on proximity and future route assignments, calculate penalties for delays, and dynamically adjust routes and tolls, incorporating GPS data and real-time traffic conditions to optimize vehicle allocation and user drop-off order, and schedule driver switches during service.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If multiple users are served by a single rideshare vehicle, then vehicle utilization increases and ride costs are reduced, but delays occur when users have different priorities and destinations
Solution Approach 1:
The system dynamically adjusts the drop-off order of multiple users based on real-time factors including user priority levels, traffic conditions, and mandatory drop-off times. The optimization algorithm continuously recalculates the optimal sequence to minimize total delay while ensuring high-priority users (e.g., medical patients) are dropped off first, allowing the vehicle to serve multiple users efficiently without fixed routing
Solution Approach 2:
The system changes operational parameters such as drop-off sequence, pick-up timing, and route selection based on user priority classifications and real-time traffic data. By adjusting these parameters dynamically rather than following fixed routes, the system resolves the contradiction between serving multiple users and minimizing delays
2Reliability
If the system optimizes for high-priority users, then user satisfaction improves, but vehicle travel time increases due to multiple stops and route adjustments
Solution Approach 1:
The system performs preliminary optimization calculations before the vehicle departs, determining the optimal drop-off sequence based on user priorities, destinations, and estimated travel times. By pre-calculating the best route and order, the system ensures high-priority users are accommodated while minimizing overall travel time through efficient route planning
Solution Approach 2:
The system incorporates real-time feedback from traffic conditions and user priority updates to adjust the drop-off sequence dynamically. This feedback mechanism allows the system to respond to changing conditions while maintaining optimization for high-priority users without excessive travel time increases
3Reliability
If the system calculates penalties for delays, then service quality improves, but computational complexity increases
Solution Approach 1:
The system uses penalty calculations as a parameter in the optimization algorithm to guide route and sequence decisions. By incorporating penalty values for delays (especially for high-priority users) into the objective function, the system translates service quality requirements into computational parameters that drive the optimization without requiring complex real-time recalculations
Data Source
AI summary
A systems and methods for ridesharing are provided. The systems and method can include and involve ridesharing based on priority, switching between multiple modes of operation (e.g., fixed routes and on demand), and/or switching drivers mid-route. The at least one processor is also configured to determine a first estimated drop-off time of the first user based on the first pickup-up location, the first desired destination. The at least one processor is also configured to calculate a mandatory drop-off time for the first user based on the priority. The at least one processor is also configured to assign a rideshare vehicle that can transport the first user to the first desired destination to minimize a difference between the mandatory drop-off time and the estimated drop-off time.