Dynamic Vehicle Deployment Region via Graph Partitioning
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Solution Overview
Problem
Current methods for deploying service providers, such as law enforcement and medical professionals, in urban areas are inefficient due to lack of real-time traffic and incident pattern awareness, leading to increased response times and inefficient route planning.
Innovation Solution
The use of street-level data and graph data structures, combined with Dijkstra's algorithm, to determine vehicle deployment regions based on a specified maximum cost, such as response time, allowing for dynamic adjustments to improve service coverage and reduce gaps or overlaps.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If service providers are deployed based on historical traffic and incident patterns, then deployment can be planned in advance, but response times increase due to lack of real-time awareness
Solution Approach 1:
The patent implements dynamic deployment regions that automatically adjust based on real-time traffic conditions and incident patterns. Instead of static historical-based deployment, the system continuously updates service provider assignments and deployment boundaries to reflect current conditions, enabling faster response times while managing complexity through automated real-time data processing
Solution Approach 2:
The system incorporates real-time feedback loops where traffic data, incident reports, and service provider locations are continuously monitored and fed back into the deployment algorithm. This feedback mechanism allows the system to adapt deployment regions dynamically, reducing response times by routing providers through current optimal paths while using automated algorithms to manage system complexity
2Reliability
If vehicles are scheduled to patrol given areas at specific time periods, then coverage can be ensured, but inefficiencies occur due to route or region overlaps
Solution Approach 1:
The patent segments the service area into dynamic deployment regions that are continuously adjusted based on real-time conditions. Each region is assigned to specific service providers, and boundaries are modified to eliminate overlaps while ensuring complete coverage. This segmentation approach maintains reliable service availability while improving deployment efficiency by assigning providers to non-overlapping optimal zones
Solution Approach 2:
The system uses dynamic region boundaries that automatically adjust to prevent overlaps and gaps in coverage. Instead of fixed patrol routes, deployment regions are continuously recalculated based on real-time traffic, incident density, and provider locations, ensuring reliable coverage while maximizing productivity by eliminating redundant patrols in low-demand areas
3Productivity
If deployment approaches do not account for dynamic or real-time changes in traffic or incident patterns, then system complexity is reduced, but response times increase and efficiency decreases
Solution Approach 1:
The patent implements comprehensive feedback mechanisms that continuously collect real-time traffic data, incident reports, and provider location information. This feedback drives automated adjustments to deployment regions and provider assignments, significantly improving service provider efficiency by routing them through current optimal paths while using centralized algorithms to manage the complexity of processing and responding to multiple real-time data streams
4Loss of time
If deployment approaches do not account for vehicle accessibility changes due to road construction or accidents, then system complexity is reduced, but response times increase due to inaccessible routes
Solution Approach 1:
The system dynamically adjusts deployment regions and provider assignments in response to accessibility changes caused by road construction, accidents, or other obstacles. Real-time traffic data and incident reports trigger automatic recalculation of optimal routes and region boundaries, ensuring providers are routed through accessible paths while using automated algorithms to manage the complexity of continuously monitoring and responding to accessibility changes
Data Source
AI summary
Computer-implemented systems and methods are disclosed for distance and congestion-aware resource deployment. In some embodiments, a method is provided to estimate a vehicle deployment region. The method includes constructing a graph data structure using at least in part a single invocation of a form of Dijkstra's algorithm. The method additionally includes partitioning an angular space centered on a vehicle location into a plurality of angular space regions, the vehicle location corresponding to a current or potential location of the vehicle. The method also includes selecting, for at least three of the plurality of the partitioned angular space regions, a geographical location within the angular space region, wherein each selected geographical location in an angular space region is a geographical location furthest from the vehicle location, and wherein each selected geographical location was determined, using the graph data structure, to be reachable from the vehicle location within a specified maximum cost. The method further includes forming a polygon representing a vehicle deployment region from the selected geographical locations.