Dynamic Patrol Scheduling via Demand Prediction Algorithms
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Solution Overview
Problem
Law enforcement agencies face challenges in dynamically forecasting demand for police services and optimally assigning patrol officers due to resource constraints and the inability to extract timely, actionable information from large databases of historic incidents, leading to inefficient patrol plans and prolonged response times.
Innovation Solution
A system that applies big-data machine-learning techniques to predict police service demand using historic and correlative data, generating optimized patrol schedules by combining expectation-maximization clustering algorithms, assignment algorithms, and Monte Carlo simulations to ensure effective resource allocation and real-time scheduling.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If patrol officers are assigned using traditional static scheduling methods, then administrative simplicity is maintained, but response times to service calls are prolonged and resource utilization is inefficient
Solution Approach 1:
The system performs preliminary actions by analyzing historical demand data and generating optimized patrol schedules in advance before service calls occur. The scheduling system proactively determines optimal officer assignments and routes based on predicted demand patterns, rather than reactively assigning officers after calls are received. This advance planning reduces response times while maintaining manageable system complexity through automated algorithms.
Solution Approach 2:
The patent replaces manual, mechanical scheduling methods with an automated computational system that uses historical data analysis and optimization algorithms. Instead of administrators manually creating schedules based on experience and availability, the system automatically generates optimized schedules by processing historical demand data, calculating optimal routes, and assigning officers using computational algorithms. This substitution dramatically improves response time efficiency.
2Productivity
If more patrol officers are deployed to reduce response times, then service quality improves, but operational costs and resource requirements increase
Solution Approach 1:
The system changes the parameter of officer assignment from static, uniform distribution to dynamic, demand-based allocation. By analyzing historical demand data, the system identifies high-demand areas and time periods, then adjusts officer assignments to concentrate resources where needed most. This parameter change allows the same number of officers to provide higher service quality by being strategically positioned rather than uniformly distributed.
Solution Approach 2:
The patent applies local quality by tailoring patrol assignments to specific geographic areas and time periods based on historical demand patterns. Different regions receive different levels of patrol coverage according to their specific needs identified through data analysis. High-demand areas receive concentrated patrol resources during peak periods, while low-demand areas receive appropriate baseline coverage. This localized optimization improves overall service quality without requiring uniform increases in total officer numbers.
3Measurement precision
If historical incident data is analyzed in detail to improve demand forecasting accuracy, then prediction precision improves, but data processing time and computational resources increase
Solution Approach 1:
The system extracts only the most relevant features and patterns from historical incident data that are necessary for accurate demand forecasting. Rather than processing every detail of historical records, the system identifies and extracts key variables such as incident types, locations, times, and frequencies that most strongly correlate with future demand. This selective extraction maintains high forecasting accuracy while significantly reducing computational time and resource requirements.
Solution Approach 2:
The patent applies partial action by focusing computational efforts on the most critical data elements and time periods that have the greatest impact on forecasting accuracy. The system may prioritize recent historical data or specific incident types that are most predictive of future demand, rather than equally processing all historical records. This partial processing approach achieves sufficient accuracy for operational decision-making while avoiding the excessive computational burden of analyzing every historical detail.
Data Source
AI summary
Systems and methods are described herein for dynamically generating and updating a patrol schedule for a shift based on historic demand event data, and a predicted-demand model based on the historic demand event data. The systems and methods also receive information associated with patrol officers assigned to a shift and constraints on the officers' availability, and generate a patrol schedule comprising patrol assignments for the assigned patrol officers optimized based on at least one policing objective. The patrol schedule may be dynamically updated based on changing information and provided to the patrol officer via mobile device or display in the patrol officer's vehicle.