Dynamic Vehicle Dispatch Scheduling via Demand Prediction
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Solution Overview
Problem
Conventional fixed schedules for vehicle dispatch in transportation systems are inadequate in addressing varying public demand due to time-dependent and event-related fluctuations, leading to inefficiencies and unrealized demand.
Innovation Solution
A dynamic method and system that generates and updates vehicle dispatch schedules based on real-time demand predictions using sensors and processors, adjusting the number of vehicles dispatched at each station to match changing demand, and transmitting updated schedules to vehicles and users.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a fixed schedule for vehicle dispatch is used based on historical demand, then the dispatch schedule is simple to manage and implement, but it cannot adequately address varying public demand due to time-dependent and event-related fluctuations
Solution Approach 1:
The dispatch schedule transitions from a static fixed timetable to a dynamic system that automatically adjusts vehicle counts based on real-time demand predictions. The system uses sensors to collect current demand data, processes this information through algorithms that consider time of day, day of week, and special events, then dynamically generates updated dispatch schedules. This dynamic approach resolves the contradiction by making the system adaptable to varying demand while managing complexity through automated processing rather than manual schedule creation.
Solution Approach 2:
The system implements a feedback loop where sensors continuously monitor current demand at stations, this information is fed back to the processing system which compares actual demand against the fixed schedule, and then generates corrected dispatch instructions. This feedback mechanism enables the system to adapt to varying public demand in real-time while the automated feedback processing manages the complexity of continuously adjusting schedules based on multiple demand parameters.
2Productivity
If real-time demand prediction and dynamic schedule updates are implemented, then service efficiency and demand matching are improved, but system complexity and computational requirements increase
Solution Approach 1:
The system segments the dispatch management into distinct functional modules: sensor units at individual stations for demand detection, processing units that handle prediction algorithms and schedule generation, and communication units for transmitting updates to vehicles. This segmentation improves service efficiency by enabling specialized processing at each stage while managing system complexity through modular design, where each segment can be independently optimized and maintained.
Solution Approach 2:
The system introduces an intermediary processing layer between the fixed schedule and the actual vehicle dispatch. This intermediary component receives the rigid fixed schedule, overlays real-time demand predictions from sensors, processes the combined information through prediction algorithms, and generates the final dynamic dispatch instructions. This intermediary approach improves service efficiency by enabling demand-responsive dispatch while managing complexity by centralizing the computational burden in a dedicated processing layer rather than distributing it across all system components.
3Reliability
If the dispatch schedule is updated frequently based on current demand, then unrealized demand is minimized, but the frequency of schedule changes may cause operational disruptions
Solution Approach 1:
The system performs preliminary demand prediction by analyzing current demand patterns and forecasting future demand before actually generating schedule updates. Sensors detect current demand, the system predicts near-future demand trends, and only then generates dispatch schedule adjustments. This preliminary action approach improves demand fulfillment reliability by proactively preparing schedules based on predicted demand while managing adjustment time by only triggering updates when predictions indicate significant demand changes, rather than continuously updating regardless of demand variability.
Data Source
AI summary
The disclosed embodiments illustrate method and system for managing a dispatch of vehicles based on generation of a dispatch schedule. The method includes generating a dispatch schedule for one or more vehicles at one or more time instants based on a first demand for the one or more vehicles along a route. For a current time instant, the method includes predicting a second demand for the one or more vehicles at each of the one or more stations, at time instants subsequent to the current time instant, based on a current demand. The method further includes updating the dispatch schedule by varying the count of vehicles to be dispatched from the first station based at least on the second demand. Further, the method includes transmitting a notification to a computing device installed in each of the one or more vehicles.