Vehicle Dispatching via Markov Decision Process for Passenger Waiting Minimization
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current Reinforcement Learning (RL) frameworks for optimizing vehicle dispatch in public transportation networks do not effectively utilize the known structure of optimal policies to minimize regret, and often prioritize long-range average reward over regret minimization, which can be disadvantageous, especially when transition probabilities and reward values are unknown.
Innovation Solution
A system that employs a Markov Decision Process (MDP) model to monitor passenger numbers at transportation stops, using camera-based image processing and token readers to determine optimal dispatch decisions, applying decision rules to minimize waiting times and dispatch reserve vehicles based on calculated scores and transition probabilities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If Reinforcement Learning frameworks optimize for long-range average reward, then long-term performance is improved, but regret minimization deteriorates
Solution Approach 1:
The system dynamically switches between different RL objectives (average reward vs. regret minimization) based on current system state and conditions. The dispatch policy adapts its optimization target in real-time, selecting whichever objective is more appropriate for the current situation rather than committing to a single fixed objective.
2Adaptability or versatility
If transition probabilities and reward values are unknown, then system adaptability is improved, but decision-making accuracy deteriorates
Solution Approach 1:
The system continuously monitors actual outcomes and uses this feedback to update its estimates of transition probabilities and reward values. By incorporating real-world feedback loops, the system progressively improves its decision-making accuracy while maintaining adaptability to changing conditions through ongoing learning and adjustment.
3Loss of time
If reserve vehicles are dispatched frequently to minimize waiting times, then passenger service quality is improved, but operational cost increases
Solution Approach 1:
The system changes the threshold parameters for reserve vehicle dispatching based on learned patterns and current conditions. By dynamically adjusting these parameters, the system optimizes the balance between passenger waiting time and operational costs, dispatching reserve vehicles only when the expected benefit justifies the expense.
Data Source
AI summary
A system for dispatching vehicles in a public transportation network may include a passenger monitoring system configured to monitor a number of passengers waiting at a stop in the transportation network, a vehicle dispatching system and a processing device. The processing device may apply a Markov Decision Process (MDP) model to determine a score for each of multiple decision rules, in which each score represents a number of passengers waiting at the stop at the end of a time interval, and use the scores to identify a number of waiting passengers at which a reserve vehicle should be dispatched. The system may use information received from the passenger monitoring system to determine a state at an instant of time, and determine whether a reserve vehicle should be dispatched based on the MDP model and cause the vehicle dispatch system to dispatch a reserve vehicle or retain a nominal vehicle.