Multi-Vehicle Ridesharing Route Aggregation for Reduced Waiting
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Solution Overview
Problem
Existing transportation management systems fail to efficiently optimize routes and aggregate travelers to minimize waiting times and travel times while ensuring affordable and convenient point-to-point transit.
Innovation Solution
A computer-implemented system that analyzes travel requests and calculates optimal routes for multiple vehicles based on pickup and destination locations, traffic conditions, and traveler numbers, optimizing aggregation to minimize waiting and travel times, and provides real-time vehicle information to passengers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If multiple vehicles are used to transport large numbers of travelers, then transportation capacity and coverage area increase, but system complexity and coordination difficulty increase
Solution Approach 1:
The system divides the transportation problem into individual vehicle routes, calculating optimal paths for each vehicle separately while considering all travelers. This segmentation allows complex multi-vehicle coordination to be broken down into manageable route optimization problems, reducing overall system complexity while maintaining high transportation capacity
Solution Approach 2:
A centralized computer system acts as an intermediary between travelers and vehicles, automatically calculating optimal routes and coordinating assignments. This intermediary handles the complexity of coordinating multiple vehicles and travelers, eliminating the need for manual coordination and reducing the perceived system complexity from the user perspective
2Productivity
If routes are optimized to minimize travel time, then transportation efficiency increases, but waiting time for travelers may increase due to vehicle aggregation
Solution Approach 1:
The system performs preliminary route calculations and vehicle aggregations before travelers need to be transported. By pre-optimizing routes and grouping travelers with compatible destinations, the system minimizes waiting time while maintaining efficiency. Travelers are assigned to vehicles in advance based on optimized criteria, reducing on-site coordination delays
Solution Approach 2:
The route optimization system dynamically adjusts vehicle routes and assignments in real-time based on traveler locations, destinations, and vehicle capacities. This dynamic approach allows the system to balance between aggregation benefits and waiting time penalties, adapting to changing conditions to minimize overall time loss while maintaining high efficiency
3Object-generated harmful factors
If travelers are aggregated on fewer vehicles, then cost and emissions decrease, but travel time and waiting time increase
Solution Approach 1:
The system changes multiple parameters simultaneously including vehicle capacity, route length, aggregation level, and timing to optimize the balance between emissions reduction and travel time minimization. By adjusting these parameters dynamically, the system can achieve lower emissions with acceptable travel times rather than fixed constraints
Solution Approach 2:
The optimization considers additional dimensions such as temporal scheduling, geographic distribution, and traveler preferences alongside the basic route aggregation. By adding these dimensions to the optimization problem, the system can distribute travelers across fewer vehicles without significantly increasing travel time, as time and space are optimized simultaneously rather than sequentially
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.