Autonomous Traffic Slot Allocation for Congestion Prevention
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current traffic control systems for autonomous vehicles are inadequate as they do not consider the arrival time, destination, speed, and path of vehicles on entry and exit ramps, and lack communication between vehicles and infrastructure, leading to inevitable traffic congestion rather than proactive prevention.
Innovation Solution
A method for traffic control that determines uncommitted demand, aggregates supply patterns, allocates remaining downstream supply based on demand weights, and configures pattern shifts to avoid slot position conflicts, ensuring fair and efficient traffic flow by leveraging communication between vehicles and infrastructure.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If statistical techniques are used to estimate future flow and predict traffic, then traffic prediction capability is improved, but the system remains unaware of specific vehicle parameters (arrival time, destination, speed, path) and cannot prevent congestion proactively
Solution Approach 1:
The patent segments traffic information into two categories: statistical aggregate data (for flow estimation) and individual vehicle parameter data (for proactive control). This segmentation allows the system to maintain both statistical prediction capabilities and detailed vehicle-level awareness, resolving the contradiction between measurement precision and information loss.
Solution Approach 2:
The system performs preliminary actions by using detailed vehicle parameter information (arrival time, destination, speed, path) to proactively allocate traffic slots and prevent congestion before it occurs, rather than merely predicting traffic conditions. This preliminary allocation based on specific vehicle data enables the system to act in advance to avoid congestion.
2Device complexity
If current traffic control systems operate without vehicle-infrastructure communication, then system simplicity is maintained, but congestion prevention capability is lost
Solution Approach 1:
The patent implements a feedback mechanism where vehicles communicate their parameters (arrival time, destination, speed, path) to the traffic control system, and the system responds by allocating specific slots and providing proactive traffic management. This feedback loop enables congestion prevention while maintaining reasonable system complexity through standardized communication protocols.
3Ease of manufacture
If reactive traffic management is used to reduce negative impact of congestion, then implementation simplicity is improved, but proactive congestion avoidance capability deteriorates
Solution Approach 1:
The system performs preliminary slot allocation based on vehicle parameters before vehicles arrive at congestion points. By predicting future traffic conditions and pre-allocating slots, the system proactively prevents congestion rather than reacting to it, thereby reducing travel time losses while maintaining implementation feasibility through automated control algorithms.
Data Source
AI summary
Embodiments of the present invention disclose a method, computer program product, and computer system for traffic control. A computer determines an uncommitted demand and receives a supply pattern from an adjacent downstream node. The computer aggregates the supply pattern with its own supply pattern before propagating the supply pattern to an adjacent upstream node. Moreover, the computer receives a committed demand and demand weight pattern from an upstream node and aggregates the committed demand and demand weight patterns with its own before propagating the aggregated committed demand and demand weight patterns to an adjacent downstream node. The computer further allocates a remaining downstream supply to the uncommitted demand based on weight and detects pending slot position conflicts. Based on detecting a pending slot position conflict, the computer configures a pattern shift and commits the available downstream supply as upstream committed demand. Lastly, the computer weights the unmet uncommitted demand.