Ridesharing Fleet Routing for Passenger Aggregation and Wait Time
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Solution Overview
Problem
Existing transportation systems struggle to efficiently manage the travel needs of large numbers of travelers by optimizing routes, minimizing waiting times, and aggregating passengers while considering real-time traffic conditions and historical data.
Innovation Solution
A computer-implemented method that analyzes travel requests from multiple travelers to calculate optimal routes for a fleet of vehicles, considering pickup and destination locations, traffic conditions, and passenger aggregation, and provides real-time updates and vehicle information to ensure efficient and affordable transportation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If a fleet of vehicles is used to transport multiple travelers, then the quantity of travelers that can be transported is increased, but the complexity of managing routes and coordinating vehicles increases
Solution Approach 1:
The system segments the transportation problem by assigning specific vehicles to specific routes with designated pickup and dropoff points. Each vehicle operates as an independent unit with a predefined route, allowing the system to manage multiple travelers through coordinated segmentation rather than monolithic control.
Solution Approach 2:
The computing system acts as an intermediary that receives travel requests, calculates optimal routes, assigns vehicles, and coordinates the entire transportation process. This intermediary function simplifies the complexity by centralizing the coordination logic and automatically managing the fleet without requiring manual intervention.
2Loss of time
If real-time traffic conditions are considered in route calculation, then travel time is minimized, but the computational requirements and system complexity increase
Solution Approach 1:
The system performs preliminary route calculation by analyzing historical traffic data and pre-computing optimal routes before actual travel occurs. This preliminary action allows the system to prepare route strategies in advance, reducing the computational burden during real-time execution while still achieving time optimization.
Solution Approach 2:
The system incorporates feedback mechanisms that continuously monitor actual traffic conditions and compare them against predicted conditions. This feedback loop allows the system to adjust routes dynamically based on real-time data while using historical patterns to reduce the computational complexity of real-time calculations.
3Productivity
If multiple destination locations are served by the same vehicle, then the aggregation of travelers is optimized, but the waiting time for travelers may increase
Solution Approach 1:
The system applies local quality by creating dedicated pickup and dropoff zones at specific locations rather than serving all travelers uniformly. Each zone is optimized for its specific needs, allowing travelers at different locations to be served efficiently without increasing waiting times, as each local area receives targeted attention.
Solution Approach 2:
The route calculation system dynamically adjusts routes and pickup/dropoff assignments based on real-time conditions and traveler requests. This dynamic approach allows the system to optimize aggregation by adapting to changing conditions, ensuring that travelers are picked up and dropped off efficiently without excessive waiting times.
4Reliability
If a centralized control system is used to manage the fleet, then the coordination of vehicles and routes is improved, but the system complexity and control requirements increase
Solution Approach 1:
The computing system performs self-service by automatically calculating routes, assigning vehicles, and coordinating the fleet without requiring external human intervention. This automation reduces the operational complexity of the centralized control while maintaining high reliability through systematic algorithmic decision-making.
Solution Approach 2:
The system replaces manual mechanical coordination with automated computational algorithms. Instead of requiring human operators to manually coordinate vehicles and routes, the system uses computer-based calculations to optimize routing and assignment, reducing control system complexity while improving coordination reliability.
Data Source
AI summary
A computer-implemented method that, in an embodiment, includes receiving a travel request from a traveler that includes a pickup and destination location and a number of travelers and analyzing the travel request and calculating routes for vehicles that are partially based on the pickup and destination location, the number of travelers, destination locations of travelers located in the vehicles, traffic conditions, minimizing a waiting time for the traveler, minimizing a travel time for the traveler, minimizing a travel time for the travelers located in the vehicles, and optimizing an aggregation of travelers on each of the plurality vehicles. In an embodiment, the plurality of vehicles includes 5 vehicles or more and based on the calculating, the method includes supplying a selected route to a selected vehicle and supplying to the traveler, identification information related to the selected vehicle thereby resulting in transportation of the traveler to the destination location. A systems and methods for ridesharing are provided. The systems and method can include splitting a plurality of GPS locations for a given vehicle into segments, determining a most probable location for each GPS location, and reconstructing the route, for a fleet of ridesharing vehicles.