Digital Infochemicals for Traffic Light Scheduling
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Solution Overview
Problem
Intelligent traffic lights face challenges in predicting traffic flows and stabilizing the green/Cycle ratio due to the unpredictability and suddenness of traffic flows, leading to severe vibrations in the system.
Innovation Solution
The method employs Digital Infochemicals (DIs) as a medium to predict traffic flow by collecting, aggregating, evaporating, and propagating DIs between traffic light controllers and traffic flow, smoothing the green/Cycle ratio through an infinite loop of data collection and adjustment processes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If traditional traffic lights adjust g/C ratio directly based on real-time traffic flow data, then the system responds quickly to traffic changes, but the green light length experiences tremendous changes due to unpredictability and suddenness of traffic flow
Solution Approach 1:
The patent introduces Digital Infochemicals (DIs) as an intermediary layer between traffic flow sensors and traffic light controllers. DIs are virtual particles that propagate through the road network, carrying traffic flow information. Instead of directly using raw traffic flow data to adjust green/Cycle ratios, the system collects DIs from multiple sources, allowing the intermediary to smooth out sudden fluctuations while maintaining responsive adjustment capability.
Solution Approach 2:
The system performs preliminary action by having vehicles leave DIs on the passed road in advance, and the traffic light system collects and aggregates these DIs before making scheduling decisions. This preliminary collection and aggregation process allows the system to predict upcoming traffic changes rather than merely reacting to them, stabilizing the green light length adjustments.
2Productivity
If intelligent traffic systems dynamically adjust green/Cycle ratio according to traffic flow, then traffic efficiency improves, but severe vibration of g/C ratio occurs due to unpredictability of traffic flows
Solution Approach 1:
The patent implements a feedback mechanism where the traffic light system continuously collects DIs from the road network, aggregates them, and uses this accumulated information to adjust green/Cycle ratios. The system monitors the effects of previous adjustments and incorporates this feedback into subsequent scheduling decisions, creating a stable yet adaptive control loop that maintains traffic efficiency while reducing oscillations.
Solution Approach 2:
By having vehicles leave DIs in advance and the system aggregating this information before making scheduling decisions, the system performs preliminary analysis of traffic patterns. This allows for smoother, more predictable adjustments to green/Cycle ratios that maintain productivity while avoiding severe vibrations.
3Adaptability or versatility
If traffic light system uses real-time traffic flow data for scheduling, then the scheduling is adaptive to current conditions, but the system cannot predict future traffic flows
Solution Approach 1:
The system performs preliminary action by collecting DIs from vehicles as they traverse the road network. These DIs carry information about vehicle positions, speeds, and trajectories. By aggregating this information in advance and allowing DIs to propagate through the network, the system builds a predictive model of future traffic conditions, enabling it to anticipate rather than merely react to traffic changes.
Solution Approach 2:
The system uses feedback from the accumulated DI information to continuously refine its predictions of future traffic flows. By monitoring the patterns in DI accumulation and propagation, the system adapts its scheduling decisions to anticipated traffic conditions while maintaining responsiveness to actual current conditions, thus preserving both adaptability and prediction capability.
Data Source
AI summary
A method to schedule intelligent traffic lights in real time based on digital infochemicals (DIs) is disclosed. The method takes advantage of DIs as medium to both predicate traffic flow and smooth the green/Cycle (g/C) ratio. First collect DIs, then update DIs by three actions including aggregation, evaporation, and propagation. After that, adjust the g/C ratio of the traffic light. DIs have the function of prediction due to the propagation that allows DIs reach the traffic earlier than the real traffic flow. On the other hand, DIs have the function of memory due to the evaporation that remembers the information of the historical traffic flow. The prediction and memory of DIs, as the reason why DIs are superior to the pure traffic flow, give the DI-based intelligent traffic light compelling advantages over the pure traffic based intelligent traffic light.