System-Optimal Traffic Routing Through Spatial-Temporal Batching
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Solution Overview
Problem
Existing traffic routing algorithms, particularly those employed by online mapping services and mobility-as-a-service providers, prioritize user-optimal routes, leading to sub-optimal network conditions and increased congestion, emissions, and safety issues, which can be mitigated by adopting system-optimal routing strategies.
Innovation Solution
A method and system for system-optimal traffic routing that involves obtaining a traffic network state, identifying active agents, applying a batching function to create sub-batches based on spatial and temporal relationships, generating diverse alternative paths, and assigning optimal paths using a Monte Carlo tree search to minimize total travel time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If user-optimal routing algorithms are employed to minimize individual travel time, then individual agent utility is improved, but network congestion increases and overall system efficiency deteriorates
Solution Approach 1:
The patent segments the routing problem by creating sub-batches of agents based on spatial and temporal relationships. This allows the system to handle large-scale routing optimization in manageable portions, enabling system-optimal solutions without overwhelming computational complexity. The segmentation principle resolves the contradiction by allowing individual agent optimization within sub-batches while coordinating across batches to achieve overall network efficiency.
Solution Approach 2:
The patent implements dynamic routing by continuously updating routes based on changing network conditions. The system optimizes routes in real-time based on current traffic states, allowing the routing strategy to adapt from static to dynamic. This dynamic approach enables the system to achieve system-optimal solutions that respond to changing conditions, resolving the contradiction between individual optimization and overall network efficiency.
2Productivity
If system-optimal routing is implemented to improve network efficiency, then overall travel time is reduced, but computational complexity increases
Solution Approach 1:
The patent divides the routing computation into sub-batches based on spatial and temporal clustering of agents. This segmentation reduces the computational complexity by processing smaller groups of agents simultaneously rather than optimizing all agents at once. The system creates sub-batches that can be processed in parallel or sequentially, making the computationally intensive system-optimal routing feasible for large-scale networks.
Solution Approach 2:
The patent applies partial optimization by focusing computational resources on specific sub-batches of agents rather than optimizing the entire network simultaneously. This partial action approach allows the system to achieve system-optimal solutions for critical portions of the network while managing computational complexity. The system can prioritize sub-batches based on congestion levels or spatial proximity, applying optimization where it matters most.
3Loss of time
If batch processing is used to manage computational load, then processing time is reduced, but solution optimality may be compromised
Solution Approach 1:
The patent segments agents into sub-batches based on spatial and temporal relationships, allowing the system to process routing optimization in manageable time windows. This segmentation enables the system to maintain solution optimality within each sub-batch while processing multiple batches in sequence or parallel. The temporal clustering ensures that agents with similar routing needs are processed together, maintaining optimality without excessive processing time.
Solution Approach 2:
The patent implements periodic routing updates where the system reoptimizes routes at regular intervals or triggered by specific events. This periodic action allows the system to balance processing time with solution optimality by updating routes periodically rather than continuously. The system can perform comprehensive optimization at strategic intervals while using lighter processing for routine updates, maintaining both time efficiency and routing quality.
Data Source
AI summary
According to some aspects of the present disclosure, a method for system-optimal traffic routing is disclosed. In one embodiment, the method includes: obtaining a current state of a traffic network, determining a set of active agents in the traffic network that are configured to receive a system optimal route, where each of the active agents corresponds to a vehicle of a set of vehicles, applying a batching function to the set of vehicles to create a batch of vehicles for replanning; creating sub-batches based on a spatial relationship between the vehicles and temporal relationships between the vehicles, generating a plurality of alternative paths for each of the sub-batches, generating a batch assignment for each of the sub-batches, assigning an alternative path to each agent in the set of active agents based on the batch assignment function, and transmitting the assigned alternative paths to the active agents.