Rideshare Dispatch Priority Scoring for Fleet Efficiency
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Solution Overview
Problem
Fleet management systems for personal transportation services face difficulties in efficiently prioritizing customer pickups during high usage periods with low vehicle availability, leading to undue wear on vehicles and frustration for customers due to long distances and wait times.
Innovation Solution
A computer-based system calculates a rideshare service priority variable for each customer based on weighted factors such as expected revenue, pickup time, journey time, charge expenditure, current wait time, and desirability of the destination, using the equation VT=α(RT)−β1(PT)−β2(JT)−γ(CT)+δ1(WT)+δ2(WT2)+ε(DT) to optimize vehicle routing and reduce wait times.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If customers are prioritized on a first-in-first-out basis, then customer service order is maintained, but vehicles must traverse unduly long distances causing wear and tear and long wait times
Solution Approach 1:
The system changes the prioritization parameter from simple first-in-first-out queue ordering to a multi-factor priority score that includes customer wait time, vehicle distance, and service efficiency metrics. This allows dynamic adjustment of service order based on current system state rather than rigid chronological ordering.
Solution Approach 2:
The dispatch system transitions from static first-in-first-out assignment to dynamic priority-based assignment where customer and vehicle priorities are continuously calculated and adjusted based on real-time conditions such as location, wait time, and fleet availability.
2Productivity
If vehicles traverse long distances to serve next-in-line customers, then all customers are served, but vehicle wear and tear increases
Solution Approach 1:
The system introduces vehicle priority parameters that account for distance traveled, wear accumulation, and operational efficiency. These parameters are used to dynamically adjust which vehicles are assigned to which customers, optimizing the balance between service coverage and vehicle preservation.
Solution Approach 2:
The central dispatch server acts as an intermediary that coordinates between customers and vehicles, calculating optimal matches that minimize total travel distance and vehicle wear while ensuring all customers are served. This intermediary optimization layer prevents direct long-distance assignments.
3Loss of time
If more vehicles are deployed to reduce wait times, then customer service improves, but fleet utilization efficiency decreases
Solution Approach 1:
The system dynamically adjusts vehicle-customer assignments based on real-time fleet availability and customer demand, optimizing the use of existing vehicles before deploying additional ones. This dynamic matching maximizes fleet utilization while keeping wait times acceptable through intelligent routing and priority assignment.
Data Source
AI summary
A rideshare system including: a memory including executable instructions; a controller to execute the executable instructions; one or more mobile computing devices configured to transmit pickup location data and drop-off location data to the controller; a rideshare vehicle configured to transmit vehicle location data to the controller; and where the executable instructions enable the controller to: receive the pickup location data from the one or more mobile computing devices; receive the drop-off location data from the one or more mobile computing devices; receive vehicle location data from the rideshare vehicle; based on the pickup location data, drop-off location data, and vehicle location data, calculate rideshare service priority variable data for each of the one or more mobile computing devices; transmit, to the rideshare vehicle, the rideshare service priority variable data, pickup location data, and drop-off location data for at least one of the one or more mobile computing devices.