Cloud Traffic Signal Phase Timing Control
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Solution Overview
Problem
Traffic light signal phase and timing systems struggle to adapt to changes in traffic flow caused by anomalies such as pedestrian crossings, emergency vehicles, or adverse weather, leading to inefficiencies and safety concerns at intersections.
Innovation Solution
A cloud-based traffic management system that analyzes probe and sensor data to determine revised signal phase and timing for traffic lights, incorporating safety buffers and adapting to real-time conditions, allowing for centralized control of traffic lights across a network.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traffic light timing is fixed and standardized, then system reliability and predictability are improved, but adaptability to changing traffic conditions and anomalies deteriorates
Solution Approach 1:
The traffic light control system transitions from fixed timing to dynamic adaptive timing by incorporating real-time probe data and sensor data. The system continuously monitors traffic conditions, detects anomalies such as emergency vehicles or accidents, and adjusts signal phases and timings dynamically to respond to changing conditions while maintaining overall system reliability through structured control protocols.
Solution Approach 2:
The system implements feedback mechanisms by collecting real-time traffic data from probes and sensors, analyzing current traffic conditions, and using this information to adjust traffic light timing. The feedback loop enables the system to detect anomalies and modify signal phases accordingly, bridging the gap between reliable fixed timing and adaptive response to changing conditions.
2Productivity
If centralized cloud-based control is implemented, then system coordination and traffic flow efficiency are improved, but device complexity and infrastructure requirements worsen
Solution Approach 1:
A centralized cloud-based traffic management system serves as an intermediary between distributed traffic lights, probes, and sensors. This mediator coordinates signal phases across multiple intersections, analyzes aggregate traffic data, and optimizes traffic flow efficiency while managing system complexity through centralized processing and standardized communication protocols.
Solution Approach 2:
The centralized cloud-based control system performs multiple functions including real-time traffic monitoring, anomaly detection, signal phase coordination, and adaptive timing optimization. By consolidating these diverse functions into a single multi-functional platform, the system improves traffic flow efficiency while avoiding the need for multiple separate complex systems.
3Adaptability or versatility
If real-time data analysis is performed, then responsiveness to traffic anomalies is improved, but processing time and computational requirements worsen
Solution Approach 1:
The system performs preliminary actions by continuously collecting and pre-processing traffic data from probes and sensors before anomalies occur. Historical traffic patterns are analyzed in advance to establish baseline conditions, enabling the system to quickly detect deviations and respond to anomalies with minimal processing delay when they occur.
Solution Approach 2:
When anomalies are detected, the system skips unnecessary processing steps by directly triggering pre-defined response protocols. The streamlined anomaly response pathway rushes through critical processing stages to adjust traffic light timing immediately, minimizing the time loss associated with real-time data analysis while maintaining rapid responsiveness to traffic conditions.
Data Source
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AI summary
A method is provided for controlling traffic lights of a road geometry network using a cloud-based traffic control system. Methods may include: receiving map data including road network geometry and traffic light locations relative to intersections of the road network geometry; receiving signal phase and timing of traffic lights at the traffic light locations; receiving probe and sensor data from a plurality of probes traversing the road network geometry; analyzing the received probe and sensor data from the plurality of probes relative to the road network geometry and the traffic light locations; determining revised signal phase and timing for at least one traffic light within the road network geometry based on the analyzed probe and sensor data relative to the road network geometry and the traffic light locations; and providing revised signal phase and timing to the at least one traffic light within the road network geometry.