Decentralized Traffic Signal Control Using Single-Machine Scheduling
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Solution Overview
Problem
Urban traffic congestion is exacerbated by poorly timed traffic signals that fail to respond to real-time traffic patterns, leading to inefficiencies and increased emissions, particularly in networks with multiple competing flows and densely spaced intersections.
Innovation Solution
A decentralized urban traffic control system, SURTRAC, where each intersection independently solves a single-machine scheduling problem to allocate green time and communicates planned outflows to neighbors, enabling real-time adaptation to changing traffic conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If centralized approaches (e.g., SCATS, SCOOT, ACS-Lite) are used to adjust traffic signal timings, then coordination between intersections is improved, but the system cannot respond quickly enough to locally changing traffic patterns due to parametric adjustment restrictions
Solution Approach 1:
The patent divides the traffic signal control system into autonomous intersection agents that independently make control decisions. Each intersection agent segments the overall control problem and solves it locally using real-time traffic data, enabling rapid response to local conditions while maintaining network-wide coordination through information exchange.
Solution Approach 2:
The system dynamically adjusts signal timings at each intersection based on real-time traffic conditions rather than using fixed parametric adjustments. The decentralized agents continuously update their control strategies using current traffic flow data, allowing the system to adapt quickly to changing patterns while maintaining coordination through inter-agent communication.
2Speed
If model-based optimization approaches (e.g., ALLONS-D, PRODYN, OPAC) are used to optimize actual traffic flows, then real-time responsiveness is improved, but computational complexity becomes intractable for real-time operation in realistic planning horizons
Solution Approach 1:
The patent segments the complex network optimization problem into independent single-intersection scheduling problems. Each intersection agent solves a simplified scheduling problem locally rather than optimizing the entire network simultaneously, dramatically reducing computational complexity while maintaining real-time responsiveness through distributed decision-making.
Solution Approach 2:
Each intersection agent focuses on optimizing local traffic flow at its specific intersection rather than attempting to optimize the entire network. This partial action approach reduces computational burden significantly while the collective effect of all agents working independently achieves near-optimal network-wide performance.
3Speed
If decentralized operations are used at individual intersections, then real-time local optimization is achieved, but coordination between adjacent intersections deteriorates due to lack of network-wide synchronization
Solution Approach 1:
The patent implements feedback mechanisms where intersection agents exchange traffic state information and coordination messages with neighboring agents. This feedback loop enables decentralized agents to maintain network-wide coordination by being aware of and responding to the actions and conditions of adjacent intersections, preventing suboptimal local decisions from degrading overall network performance.
Solution Approach 2:
Each intersection agent is designed as a universal entity that performs multiple functions: local traffic optimization, information gathering, message passing to neighbors, and adaptation to network conditions. This multi-functionality enables decentralized operation while maintaining coordination, as each agent simultaneously optimizes locally and contributes to network-wide synchronization through its communication and decision-making processes.
Data Source
AI summary
Scalable urban traffic control system has been developed to address current challenges and offers a new approach to real-time, adaptive control of traffic signal networks. The methods and system described herein exploit a novel conceptualization of the signal network control problem as a decentralized process, where each intersection in the network independently and asynchronously solves a single-machine scheduling problem in a rolling horizon fashion to allocate green time to its local traffic, and intersections communicate planned outflows to their downstream neighbors to increase visibility of future incoming traffic and achieve coordinated behavior. The novel formulation of the intersection control problem as a single-machine scheduling problem abstracts flows of vehicles into clusters, which enables orders-of-magnitude speedup over previous time-based formulations and is what allows truly real-time (second-by-second) response to changing conditions.