Probabilistic Traffic Signal Timing Optimization
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Solution Overview
Problem
Current traffic management systems struggle to effectively address vehicle traffic congestion, which is caused by factors such as vehicle counts exceeding road capacity, unpredictable human drivers, accidents, and timed traffic signals.
Innovation Solution
A probabilistically adaptive traffic management system that uses circuitry and a processor to calculate and estimate total probabilities of future traffic locations and time periods, allowing for real-time adjustments to traffic signal timings and other control measures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If timed traffic signals are used to control traffic flow, then traffic control structure is established, but road capacity is limited and congestion occurs
Solution Approach 1:
The patent implements dynamic traffic signal timing that adapts to real-time traffic conditions. The system continuously monitors traffic flow, queue lengths, and travel times, then adjusts signal phases and durations dynamically rather than using fixed timed cycles. This allows the traffic control structure to respond adaptively to changing conditions, maximizing road capacity utilization while maintaining orderly traffic control.
Solution Approach 2:
The system incorporates feedback loops where traffic sensors detect current conditions, the controller processes this information, and signal timing is adjusted based on the feedback. Travel time detectors provide feedback on actual traffic flow patterns, allowing the system to learn and adapt to recurring traffic patterns while responding to real-time variations, thereby increasing effective road capacity.
2Productivity
If traffic signal remains green to clear waiting queue, then vehicles can proceed through junction, but congestion occurs on road ahead
Solution Approach 1:
The system uses travel time detectors and traffic flow analysis to predict when vehicles will arrive at downstream locations. Before extending green phases to clear queues, the system checks predicted arrival times against downstream capacity. This preliminary assessment prevents green extensions that would cause downstream congestion, allowing the system to safely maximize throughput only when downstream roads can accommodate the additional traffic.
Solution Approach 2:
The system dynamically adjusts signal timing parameters based on real-time and predicted traffic conditions. When downstream congestion is detected or predicted, the system modifies green phase durations and timing parameters to prevent queue spillback. This adaptive parameter adjustment allows the system to optimize vehicle throughput while preventing downstream congestion through continuous parameter optimization.
3Productivity
If real-time traffic data processing is implemented, then traffic flow optimization is achieved, but system complexity increases
Solution Approach 1:
The system divides the traffic control function into separate modular components: traffic signal controllers at intersections, travel time detectors on road segments, central coordination system, and communication infrastructure. Each component performs a specific function and can be independently configured and maintained. This segmentation reduces overall system complexity while enabling sophisticated real-time optimization through coordinated operation of the distributed components.
Data Source
AI summary
The system includes circuitry to send and/or receive data, a processor to process data, memory for storing data to operate a traffic control algorithm. The system is configured to transmit processed data to traffic control devices. The traffic control algorithm includes at least one step of calculating and estimating total probabilities of future traffic locations and time periods, and selecting an action for the traffic control devices to perform during those time periods.