Conditional Contraction Hierarchies for Real-Time Vehicle Routing
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Solution Overview
Problem
Existing routing systems utilizing contraction hierarchies for vehicle navigation consume excessive computing resources and fail to provide accurate navigational routes that accommodate real-time road network changes, especially when dealing with conditional constraints.
Innovation Solution
A routing system generates conditional customizable contraction hierarchies (CCCH) by constructing a node-based graph, identifying additional arcs for connectivity, determining arc configurations, and filtering sub-optimal configurations to optimize routing while preserving network interconnectivity and adhering to link priority ordering.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If traditional contraction hierarchies are used for vehicle navigation, then routing computation speed is improved, but computing resource consumption increases and adaptability to real-time changes deteriorates
Solution Approach 1:
The routing system segments the contraction hierarchy into multiple levels or layers, allowing computations to be performed on relevant segments only rather than the entire graph. This reduces computing resource consumption while maintaining fast routing speed by focusing processing power on localized areas of the network.
Solution Approach 2:
The system implements dynamic contraction hierarchies that can be updated and reconfigured in real-time based on changing road conditions, traffic patterns, and vehicle parameters. This allows the routing computation to adapt to real-time changes without requiring complete recomputation, reducing overall computing resource consumption while maintaining speed.
2Productivity
If traditional contraction hierarchies are used, then routing efficiency is improved, but adaptability to conditional constraints and real-time changes deteriorates
Solution Approach 1:
The contraction hierarchy is made dynamic and configurable, allowing the system to adjust the hierarchy structure and parameters based on specific vehicle characteristics, cargo types, and real-time road conditions. This enables the system to maintain high routing efficiency while adapting to various conditional constraints such as vehicle dimensions, weight limits, and restricted access roads.
Solution Approach 2:
The system allows modification of contraction hierarchy parameters such as node contraction thresholds, edge weight calculations, and priority assignments based on conditional constraints. By dynamically changing these parameters, the system maintains routing efficiency while becoming adaptable to different vehicle types, cargo conditions, and real-time road network changes.
3Measurement precision
If extensive searching is performed to establish cost-effective paths with conditional constraints, then routing accuracy is improved, but computing resource consumption increases
Solution Approach 1:
The system applies local quality by performing extensive searching and accuracy checks only in specific regions or contexts where conditional constraints are relevant, rather than uniformly across the entire road network. This allows high routing accuracy for constraint-sensitive routes while reducing computing resource consumption in areas where constraints do not apply.
Solution Approach 2:
The system dynamically adjusts searching parameters such as search depth, expansion factors, and constraint checking intensity based on the specific routing context and vehicle characteristics. This enables accurate route calculation for cost-effective paths with conditional constraints while optimizing computing resource consumption by reducing unnecessary searches in low-priority areas.
Data Source
AI summary
A device may receive traffic data associated with a vehicle, may generate a node-based graph based on the traffic data, and may generate an edge-based graph, a vertex order, and original arcs based on the node-based graph. The device may identify additional arcs that facilitate connectivity between all pairs of links of the edge-based graph, and may combine the additional arcs and the original arcs to generate CCH arcs. The device may determine arc configurations for the CCH arcs based on the vertex order, and may calculate parameters for the arc configurations. The device may combine the node-based graph, a priority order of links in the node-based graph, the original arcs, the CCH arcs, and the arc configurations to generate a CCCH, and may implement the CCCH for the vehicle.