Traffic Signal Optimization via Wireless Sensor Networks
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Solution Overview
Problem
Current methods for retiming traffic signals are inconvenient and costly due to manual processes, and existing systems lack efficient methods for optimizing transportation networks, leading to suboptimal traffic flow and increased delays.
Innovation Solution
An automated system that uses wireless networks and cameras to collect and process data from intersections, updating models of transportation networks to optimize traffic signal timing across multiple intersections, leveraging existing broadband wireless networks and cloud computing for cost-effective and scalable solutions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If manual processes are used for retiming traffic signals, then implementation is simple, but costs are high and efficiency is low
Solution Approach 1:
The system enables automated traffic signal optimization where the network self-monitors and self-optimizes through embedded sensors, processors, and algorithms. Traffic controllers automatically adjust signal timing based on real-time data without manual intervention, making the system service itself while dramatically improving efficiency over manual methods
Solution Approach 2:
The patent replaces manual mechanical processes with electronic and software-based systems. Automated algorithms process traffic data and generate optimization instructions electronically, substituting the manual mechanical approach of physically adjusting timers and switches with automated digital control systems
2Measurement precision
If comprehensive traffic data collection is implemented across multiple intersections, then optimization accuracy improves, but system complexity increases
Solution Approach 1:
The system divides the transportation network into discrete segments or intersections, each equipped with its own sensors and processors. This segmentation allows comprehensive data collection at multiple points while managing complexity through modular architecture, where each intersection operates semi-independently and reports to the central optimization system
Solution Approach 2:
The patent employs universal traffic controllers and sensors that can be deployed across multiple intersections with the same hardware platform. These multi-functional devices handle data collection, processing, and control tasks at each intersection, reducing overall system complexity through standardization while maintaining comprehensive monitoring capability
3Productivity
If real-time optimization is implemented, then traffic flow improves and delays reduce, but computational requirements and costs increase
Solution Approach 1:
The system performs preliminary computations by pre-calculating optimization strategies and storing them in traffic controllers. During real-time operation, the system retrieves and applies pre-computed instructions rather than performing full optimization calculations from scratch, reducing real-time computational energy consumption while maintaining traffic flow efficiency
Solution Approach 2:
The patent implements partial optimization by focusing computational resources on critical intersections or time periods rather than optimizing the entire network simultaneously. This selective approach reduces overall computational energy requirements while still achieving significant traffic flow improvements in key areas
Data Source
AI summary
A method and system are provided for modeling and optimizing the performance of transportation networks, e.g. for traffic signal retiming. The modeling and optimization may be implemented by obtaining a video signal from a camera at a first intersection; processing data from the video signal to determine at least one value indicative of a corresponding traffic flow through the first intersection; sending the at least one value to a remote processing entity via a wireless network to enable the remote processing entity to update a model of the transportation network, the transportation network comprising the first intersection and at least a second intersection; receiving from the remote processing entity, an instruction for a controller at the first intersection, the instruction having been determined from an update of the model based on data from at least the second intersection; and having the instruction implemented by the controller at the first intersection to optimize at least a portion of the transportation network.