Traffic Signal Coordination via Intensity Distribution Matching
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Solution Overview
Problem
Current methods for coordinating traffic signal-controlled nodes in road networks fail to accurately simulate real vehicle group distributions, leading to suboptimal coordination results due to inadequate modeling of intensity distributions before and after signal groups, resulting in inefficient traffic flow and increased stops and waiting times.
Innovation Solution
A generic coordination method that evaluates the matching of intensity distributions for vehicle groups approaching and departing from signal groups, considering the coordinated nodes' signal schedules and phase sequences, to simulate more realistic vehicle group formations and optimize offset times and phase sequences, thereby improving traffic flow coordination.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If simplified rule-based methods are used to determine offset times, then the complexity of the coordination system is reduced, but the accuracy of traffic flow simulation and coordination results deteriorates
Solution Approach 1:
The patent transforms the coordination approach by changing the fundamental parameter being optimized: instead of directly optimizing offset times using simplified rules, the system optimizes intensity distributions that describe vehicle group characteristics. This parameter transformation enables more accurate traffic flow simulation while maintaining computational feasibility through iterative optimization of distribution parameters rather than direct offset time calculation.
Solution Approach 2:
The patent creates a virtual copy of the traffic system through intensity distribution models that replicate real vehicle group behaviors. By working with these modeled representations (copies) of traffic flows rather than actual traffic data, the system achieves accurate simulation without requiring complex real-time data processing, thus resolving the contradiction between simplicity and accuracy.
2Reliability
If heuristic optimization methods are used to avoid local optima, then the coordination quality improves, but the computational complexity and time required for optimization increases
Solution Approach 1:
The patent performs preliminary actions by pre-defining the structure and parameters of intensity distributions before the actual optimization process. By establishing the functional form and key parameters of vehicle group intensity distributions in advance, the system reduces the computational search space, enabling faster convergence to optimal solutions without requiring extensive iterative heuristic searches.
Solution Approach 2:
The patent introduces dynamics by making intensity distribution parameters adaptive and adjustable during the optimization process. Rather than using fixed heuristic rules, the system dynamically adjusts distribution parameters based on traffic conditions and optimization progress, allowing the model to evolve toward optimal coordination while maintaining computational efficiency through structured parameter updates.
3Productivity
If the intensity distributions are not accurately modeled, then the computational process is simpler, but the coordination results become suboptimal with increased stops and waiting times
Solution Approach 1:
The patent applies segmentation by dividing the traffic flow into distinct vehicle groups with individual intensity distributions. Instead of treating traffic as a continuous stream, the system segments it into discrete groups characterized by specific intensity parameters, allowing for more precise modeling of real traffic patterns while keeping each individual distribution model relatively simple and computationally manageable.
Data Source
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AI summary
The method involves defining an optimization direction (OPT) in which pre-nodes and a main node are coordinated. An optimal offset time between signal time plans (SZPh1-SZPh3) of the main node and one of the pre-nodes is determined. Determination of the optimal offset time for the main node is evaluated to find matching of an intensity distribution, which is modeled for a vehicle block adjacent to a signal group of the main node in the direction, with another intensity distribution that is modeled during coordination of adjacent nodes for another vehicle block routed by the signal group.