Vehicle Dispatch System Using Predictive Demand Analysis
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Solution Overview
Problem
Existing vehicle dispatch systems fail to effectively manage vehicle operations in response to changes in ride demand, as they do not consider predictive analytics for demand fluctuations.
Innovation Solution
An operation management apparatus that acquires performance and status data to predict ride demand, determining whether to operate vehicles as regularly scheduled or non-scheduled services, and introduces additional vehicles when demand exceeds thresholds, facilitating dynamic adjustment based on predicted demand.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If vehicles operate as non-scheduled vehicles responding to individual requests, then vehicle utilization rate improves, but response time to ride demand increases
Solution Approach 1:
The system performs preliminary actions by predicting ride demand in advance and pre-dispatching vehicles before actual requests occur. The controller predicts future ride demand based on historical data and proactively schedules vehicles, ensuring they are already positioned and ready when demand arises, thus eliminating response time delays while maintaining high vehicle utilization.
Solution Approach 2:
The system dynamically adjusts vehicle operation modes between scheduled and non-scheduled based on real-time predicted demand. The controller continuously monitors predicted ride demand and flexibly switches vehicles between regular scheduled operation and on-demand dispatch, optimizing both response time and utilization rate through adaptive control rather than fixed operational patterns.
2Reliability
If additional vehicles are introduced onto the route, then ride demand satisfaction improves, but operational complexity increases
Solution Approach 1:
The system implements feedback mechanisms where the controller continuously monitors actual ride demand, compares it with predicted demand, and adjusts vehicle dispatch decisions accordingly. This closed-loop control ensures vehicles are added only when necessary based on real demand conditions, maintaining high ride demand satisfaction while preventing unnecessary operational complexity through demand-driven decisions.
Solution Approach 2:
The system changes operational parameters dynamically by adjusting the number of active vehicles, their schedules, and dispatch timing based on predicted and actual demand patterns. The controller modifies these parameters (vehicle count, schedule frequency, dispatch timing) in response to demand conditions, enabling flexible adaptation to varying ride demands without creating permanent operational complexity.
Data Source
AI summary
An operation management apparatus includes a controller that acquires performance data indicating a usage record of vehicles, predicts, based on the performance data, a ride demand, determines to operate each vehicle as a regularly scheduled vehicle during the time slot when a first predicted value of the ride demand is higher than a first threshold, determines to operate each vehicle as a non-scheduled vehicle during the time slot when the first predicted value is not higher than the first threshold, acquires status data indicating an existence status of a user when each vehicle is operated as a regularly scheduled vehicle during the time slot, predicts a ride demand during a remaining time of the time slot based on the status data, and determines to introduce an additional vehicle onto the route for the remaining time when a second predicted value of the ride demand is higher than a second threshold.