Adapted A* Algorithm for Parking Spot Routing
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Solution Overview
Problem
The increasing number of vehicles in urban areas leads to significant traffic congestion due to drivers' difficulty in finding parking spots efficiently, as existing navigation systems do not effectively account for parking probabilities and travel times.
Innovation Solution
A method utilizing an adapted A* algorithm for park spot routing that incorporates parking probabilities, travel times, and user-defined walking distances into a cost function to determine the most efficient route to available parking facilities, ensuring higher success rates and reduced travel time to parking spots.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If drivers search for parking spots using traditional navigation systems, then they can reach destination locations, but they cause significant traffic congestion due to random searching behavior
Solution Approach 1:
The system performs preliminary actions by pre-calculating parking probabilities for various locations around the destination and incorporating these into the routing algorithm before the driver starts searching, thereby eliminating random searching behavior and reducing time spent looking for parking
Solution Approach 2:
The invention changes the routing parameter from simple distance or time to a composite parameter that includes parking probability. The cost function is modified to include parking availability metrics, transforming the navigation problem from finding the shortest path to finding the path with highest probability of successful parking
2Reliability
If navigation systems use simple shortest path algorithms, then routes are calculated quickly, but they do not account for parking availability and probabilities
Solution Approach 1:
The invention merges multiple factors (travel time, distance, and parking probability) into a single composite cost function that the A* algorithm can process. This combining of heterogeneous parameters into a unified metric allows the system to consider parking availability without requiring a completely new algorithmic approach
Solution Approach 2:
The patent introduces an intermediary layer (the adapted cost function) that translates complex parking probability data into routing decisions. This intermediary processing layer allows the standard A* algorithm to work with probabilistic parking data without requiring fundamental changes to the algorithm's core logic
3Reliability
If drivers park farther from destination to ensure availability, then parking success rate increases, but travel time to destination increases
Solution Approach 1:
The system changes the optimization parameter from minimizing distance or time to maximizing the probability of successful parking within acceptable travel time. By adjusting the cost function to include parking probability as a primary factor, the algorithm naturally balances parking availability with travel efficiency without requiring manual intervention
Solution Approach 2:
The invention introduces dynamics by making the routing decision adaptive to real-time or near-real-time parking probability data. Rather than static routing based on fixed geography, the system dynamically adjusts routes based on current parking availability patterns, allowing it to optimize for both availability and speed under varying conditions
Data Source
Figure 1
AI summary
A method for park spot routing for a vehicle comprises: - providing a start position of the vehicle for the park spot routing, - providing a destination position of the vehicle for the park spot routing, - providing parking data which comprise information about parking facilities, - determining a park spot route based on the start position, the destination position and the parking data, wherein the park spot route is determined by means of an adapted shortest route algorithm.