Crime-Aware Route Planning With User-Weighted Risk Tradeoffs
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing routing systems fail to adequately consider user preferences for crime avoidance, particularly in determining optimal routes based on crime rates and types relevant to the user.
Innovation Solution
A system and method that incorporates user-defined crime avoidance preferences, using a weighted selection triangle to balance crime avoidance with time, cost, and distance, and a crime database query to generate optimized routes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If standard routing algorithms are used to optimize travel time, distance, and cost, then routing efficiency is improved, but user safety regarding crime avoidance deteriorates
Solution Approach 1:
The patent extends the traditional routing algorithm by adding a new parameter (crime rate) to the existing parameters (travel time, distance, cost). This transforms the routing problem from a 3-parameter optimization to a 4-parameter optimization, allowing the system to balance efficiency with safety by weighting each parameter according to user preferences
Solution Approach 2:
The patent creates a composite routing metric that combines multiple factors (time, distance, cost, and crime rate) into a single weighted evaluation framework. This composite approach allows the routing system to simultaneously consider both efficiency and safety, producing routes that optimize the overall user experience rather than just one dimension
2Object-affected harmful factors
If crime avoidance is incorporated into routing algorithms, then user safety is improved, but routing complexity increases
Solution Approach 1:
The patent divides the routing problem into distinct segments: (1) data collection from crime databases, (2) crime rate calculation for each road segment, (3) weight assignment based on user preferences, and (4) weighted shortest path computation. This segmentation allows the complex problem to be solved through a series of manageable steps using existing algorithmic building blocks
Solution Approach 2:
The patent introduces an intermediary layer (the crime rate calculation module) that processes raw crime data and transforms it into a format suitable for routing algorithms. This intermediary computes crime rates for each road segment and provides them as input to the weighted shortest path algorithm, separating the data processing complexity from the routing logic
3Measurement precision
If comprehensive crime data is collected and analyzed, then route safety accuracy is improved, but data processing time increases
Solution Approach 1:
The patent extracts only the relevant crime data needed for routing decisions, rather than processing all available crime information. It specifically queries for crime incidents along potential route segments and calculates crime rates only for those areas, eliminating unnecessary data processing while maintaining accuracy for the routing purpose
Solution Approach 2:
The patent performs preliminary crime rate calculations for all road segments before executing the weighted shortest path algorithm. By pre-computing crime rates and storing them associated with each segment, the system avoids repeated data queries during route calculation, significantly reducing processing time while maintaining comprehensive crime data analysis
Data Source
AI summary
Systems and methods are provided for generating routes taking into consideration user preferences for crime avoidance, in addition to or alternatively to time, distance, and/or cost. User preferences for crime avoidance during a trip from one location to another can be quantified and used to augment standard routing methods by giving weight to crime avoidance. For example, a weighting triangle can be used, where a user chooses a balanced weighting of three factors, including crime avoidance, before a route from the starting location to the destination is generated. The crime rates considered can be limited to those of potentially significant impact on the user, considering the user's modality, demographics, and preferences. The relevant sub-types of crimes can be further assigned sub-weights reflecting the gravity of their potential impact on the user.