Parking Route Optimization via Probability-Weighted Cost Functions
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Solution Overview
Problem
Current navigation systems face challenges in efficiently generating parking routes that maximize the probability of finding a vacant parking space, particularly in congested urban areas, leading to time-consuming searches and increased fuel consumption.
Innovation Solution
A method and system that determine a sub-network of roads within a predetermined walking distance of a destination location, associating each segment with a probability of having a vacant parking space and calculating costs for candidate parking routes using a search algorithm that considers the probability of finding a space and cumulative travel and walking time, to select the most optimal route.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If traditional navigation systems are used to search for parking spaces, then the system can provide basic route guidance, but the time spent searching for parking increases and fuel consumption rises
Solution Approach 1:
The patent transforms the traditional navigation approach by changing the optimization parameter from simple distance or time to a composite cost function that incorporates parking probability. Each road segment is assigned a probability value indicating the likelihood of finding a vacant parking space, and routes are optimized based on this new parameter rather than conventional metrics alone.
Solution Approach 2:
The system pre-calculates and stores probability values for parking space availability on different road segments before the user initiates a parking search. This preliminary action allows the navigation system to quickly evaluate multiple potential routes and their associated parking probabilities without requiring real-time data collection during the search process.
2Reliability
If the navigation system explores more road segments to find parking spaces, then the probability of finding a vacant space increases, but the cumulative travel time and fuel consumption increase
Solution Approach 1:
The cost function provides feedback by combining the probability of finding parking with the expected cumulative time to reach a parking space. This feedback mechanism allows the system to evaluate routes not just by parking probability alone, but by the product of probability and time, automatically penalizing routes that require excessive travel time even if they have high parking availability.
Solution Approach 2:
The patent introduces a new cost parameter that is the product of parking probability and cumulative time, transforming the optimization criterion from single-parameter (distance or time) to a multi-parameter cost function that simultaneously considers both the likelihood of finding parking and the time investment required.
3Ease of operation
If the navigation system calculates multiple candidate routes with different probabilities, then the quality of route selection improves, but the computational complexity increases
Solution Approach 1:
The system generates a limited set of candidate routes (e.g., top 5 or 10) rather than exhaustively exploring all possible paths through the road network. This partial action approach provides sufficiently high-quality route options for user selection without requiring computationally expensive complete enumeration of all possible routes.
Solution Approach 2:
The patent divides the road network into discrete segments, each with assigned probability values for parking availability. This segmentation allows the system to efficiently calculate costs for multiple candidate routes by summing segment probabilities along each path, rather than evaluating entire routes as monolithic entities.
Data Source
AI summary
A method of determining a parking route for a vehicle travelling on a road network within a geographic area is disclosed. A sub-network is determined comprising a subset of segments of an electronic map that are representative of roads within a predetermined walking time or distance of a destination location. Data indicative of a walking time or distance from the segment to the destination location is associated, at least with the segments of the sub-network representing roads having at least one associated parking space. A search algorithm having an associated cost function is used to explore the segments of the sub-network from an origin location to identify a plurality of candidate parking routes, wherein the cost for a given parking route is based on the probability of the vehicle successfully finding a parking space on the parking route and the expected cumulative travel and walking time or distance to the destination location should a parking space be found along the parking route.