Predictive Traffic Signal System for Vehicle Congestion Management
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Solution Overview
Problem
Existing vehicle navigation systems fail to effectively predict and manage high traffic densities at traffic nodes, leading to inefficiencies and potential congestion, especially for driverless or partially automated vehicles.
Innovation Solution
A method and device that detect traffic density at exit points of traffic nodes and provide predictive signals to vehicles approaching these nodes, allowing for informed decision-making on entry, deceleration, or stopping to avoid congestion and improve throughput.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If vehicles enter traffic nodes without predictive traffic density information, then vehicles can maintain their intended routing, but traffic congestion and throughput inefficiency occur at exit points
Solution Approach 1:
The system performs preliminary detection of traffic density at exit points before vehicles enter the traffic node. This advance information allows vehicles to adjust their entry timing or routing decisions proactively, preventing congestion before it occurs and improving overall throughput without sacrificing vehicle autonomy
Solution Approach 2:
The system establishes a feedback loop where traffic density information is continuously monitored at exit points and fed back to approaching vehicles. This real-time feedback enables dynamic adjustment of vehicle entry decisions, creating an adaptive system that optimizes throughput while minimizing waiting time
2Loss of energy
If vehicles enter traffic nodes without predictive signals, then navigation simplicity is maintained, but congestion and energy inefficiency occur
Solution Approach 1:
The patent introduces an intermediary signal transmission system that bridges traffic density detection at exit points and vehicle navigation decisions. This intermediary layer provides processed traffic information to vehicles without requiring complex onboard analysis, reducing energy consumption while enabling informed routing decisions
Solution Approach 2:
The system pre-processes traffic density information and delivers it to vehicles before they reach traffic nodes. This preliminary provision of navigation-critical information allows vehicles to make energy-efficient routing decisions without requiring complex real-time analysis systems onboard
3Reliability
If real-time traffic density monitoring is implemented, then congestion prediction is improved, but system complexity and implementation cost increase
Solution Approach 1:
The system extracts only the critical traffic density parameter from complex traffic conditions and uses this single key metric for congestion prediction. By focusing on extracting and utilizing this specific information, the system achieves reliable congestion prediction without requiring complex monitoring of all possible traffic variables
Solution Approach 2:
The system monitors traffic density as a changeable parameter at exit points and uses this dynamic parameter information to predict congestion. By tracking this specific parameter over time and space, the system achieves reliable predictions while maintaining relatively simple implementation compared to comprehensive traffic modeling
Data Source
AI summary
A method for supplying a signal for at least one vehicle that is located in front of an entry point into a traffic node, it is provided that the method includes a step of detecting a traffic density at an exit point of the traffic node, as well as a step of supplying a signal as a function of the traffic density at an exit point of the traffic node.