Shared Vehicle Availability Prediction From Parking Search Behavior
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Solution Overview
Problem
Service providers face challenges in accurately determining the availability of shared vehicles in real-time, particularly in areas where vehicles are scarce, as traditional methods rely on post-parking data and do not estimate availability before vehicles are parked, leading to inefficiencies in user convenience and resource utilization.
Innovation Solution
A system that detects parking search behaviors of vehicles using trajectory data and sensors, allowing for the estimation of vehicle availability before parking, by identifying irregular driving patterns and predicting when vehicles will become available for use by another user, thereby enhancing the precision of shared vehicle availability information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional post-parking data methods are used to determine vehicle availability, then data collection is simpler, but vehicle availability information is delayed and less accurate
Solution Approach 1:
The system performs preliminary detection of parking search behaviors before vehicles are actually parked, using trajectory data and sensor information to predict when vehicles will become available. This advance detection enables the system to provide availability information before traditional post-parking methods would detect it, resolving the contradiction between accuracy and complexity by establishing a predictive monitoring framework
Solution Approach 2:
The system introduces trajectory data and sensor information as intermediary elements between the vehicle and the availability detection system. These intermediaries carry information about parking search behaviors, enabling accurate prediction of vehicle availability without requiring direct post-parking detection, thus improving accuracy while managing complexity through indirect observation
2Productivity
If real-time parking search behavior detection is implemented, then vehicle availability prediction is improved, but data processing resources are increased
Solution Approach 1:
The system extracts only the essential features from trajectory data and sensor information that are relevant to detecting parking search behaviors. By filtering and selecting only the critical data elements needed for availability prediction, the system achieves real-time detection capability while minimizing data processing resource consumption, resolving the contradiction between productivity and energy use
3Loss of time
If parking search behavior data is collected continuously, then vehicle availability information is more timely, but data storage requirements are increased
Solution Approach 1:
The system extracts and stores only the essential parking search behavior features from continuous data streams, such as trajectory patterns and sensor readings that indicate parking intent. By filtering out redundant information and retaining only critical data elements, the system achieves timely vehicle availability information while minimizing data storage requirements, resolving the contradiction between time loss and quantity of data
Data Source
AI summary
An approach is provided for shared vehicle availability detection based on vehicle trajectory information. A vehicle sharing platform determines that a vehicle is engaged in a parking search behavior, wherein the vehicle is in use by a current user. The vehicle sharing platform further computes a probability that the vehicle will become available for use by another user based on the parking search behavior. The vehicle sharing platform further designates the availability of the vehicle for use by the another user based on the probability.