Ridesharing Simulation for Fleet Optimization
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Solution Overview
Problem
Current vehicle ridesharing systems face challenges in efficiently managing large fleets of vehicles and optimizing routes to minimize costs and reduce air pollution, particularly in handling multiple ride requests and varying user preferences for pick-up and drop-off locations.
Innovation Solution
A computer-implemented method and system that simulates ridesharing scenarios to determine performance levels of virtual vehicles, allowing for real-time demand management and recommendation of optimal service parameters, including the ability to adjust pick-up and drop-off locations based on user inputs and traffic data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a large fleet of ridesharing vehicles is deployed to meet increasing demand, then service coverage and user satisfaction improve, but system complexity and operational management difficulty increase
Solution Approach 1:
The system segments the large fleet into multiple zones or regions, each managed independently with localized dispatch algorithms. This divides the complex global management problem into smaller, more manageable regional problems, reducing overall system complexity while maintaining comprehensive service coverage.
Solution Approach 2:
The system performs preliminary routing calculations and vehicle assignments before peak demand periods. By pre-positioning vehicles and pre-calculating routes based on historical data and predicted demand, the system reduces real-time computational complexity while ensuring adequate service coverage.
2Ease of operation
If vehicles transport passengers to exact desired destinations, then user satisfaction improves, but travel time and fuel consumption increase due to multiple pick-up and drop-off locations
Solution Approach 1:
Instead of routing vehicles to passengers' exact desired destinations, the system inverts the approach by directing passengers to locations near their destinations that are more accessible to the vehicle fleet. This may involve designating specific drop-off zones or hub locations where multiple passengers can be consolidated, reducing total travel time while maintaining satisfactory service.
Solution Approach 2:
The system implements partial destination fulfillment by transporting passengers to locations that are sufficiently close to their desired destinations (e.g., within a walking distance threshold). This partial action approach allows vehicles to optimize routes without making unnecessary detours to exact destinations, reducing travel time while still meeting user needs.
3Object-generated harmful factors
If real-time route optimization is performed for all vehicles, then fuel efficiency and pollution reduction improve, but computational load and processing time increase
Solution Approach 1:
The system merges route optimization calculations by grouping vehicles into clusters that share common destinations or route segments. By optimizing routes at a cluster level rather than individually for each vehicle, the system reduces total computational load while still achieving significant fuel efficiency and pollution reduction benefits.
Solution Approach 2:
Instead of continuous real-time optimization for all vehicles, the system implements periodic optimization cycles. Routes are recalculated at predetermined intervals or when significant changes in demand patterns occur, rather than continuously. This periodic approach maintains pollution reduction benefits while dramatically reducing computational load and processing requirements.
Data Source
AI summary
The present disclosure relates to systems and methods for planning transportation routes. In one implementation, a method for simulating vehicle ridesharing is provided. The method may include receiving a first input of a geographical area and accessing map information of roadways in the geographical area. The method may also include receiving a second input indicative of at least one scenario of ridesharing demand in the geographical area and receiving a third input indicative of virtual vehicles designated to transport virtual passengers associated with the scenario. The method may further include initiating a transportation simulation of scenario to simulate rides of the virtual vehicles transporting the virtual passengers along the roadways. The method may also include determining, based on the transportation simulation, a performance level associated with the virtual vehicles and providing an output representative of the determined performance level.