Dynamic Boundary Vehicle Location Discrepancy Detection
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Solution Overview
Problem
Current solutions for car-sharing services do not effectively mitigate 'no-show' situations where vehicles are not found at the prescribed pickup location, as they only provide real-time vehicle location updates without proactive measures to resolve the issue.
Innovation Solution
A vehicle location mechanism that uses real-time GPS data and dynamic boundaries based on road, traffic, and weather conditions to detect and mitigate location discrepancies by identifying four states of location discrepancy (Safe, Recoverable, Non-recoverable, and Ready) and providing corresponding notifications and assistance to users.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If real-time vehicle location updates are provided, then users can track vehicle position, but no proactive measures are taken to resolve location discrepancies
Solution Approach 1:
The system performs preliminary actions by generating dynamic boundaries around the prescribed pickup location before the pickup time arrives. It proactively monitors vehicle location and predicts potential no-show situations by determining whether the vehicle will be at the pickup location at the prescribed time, allowing preventive notifications to be sent to users before the pickup deadline is missed.
Solution Approach 2:
The system implements feedback by continuously monitoring vehicle location data and comparing it against dynamic boundaries and predicted pickup status. Based on this feedback, the system automatically determines the vehicle's state (on-time, early, late, or no-show) and triggers appropriate notifications to users, creating a closed-loop system that responds to location discrepancies in real-time.
2Reliability
If dynamic boundaries and state detection are implemented, then proactive mitigation of no-show situations is achieved, but system complexity increases
Solution Approach 1:
The system segments the pickup location space by generating dynamic boundaries around the prescribed pickup location. These boundaries divide the geographic area into zones that help determine vehicle state. The system also segments the vehicle status into distinct states (on-time, early, late, no-show), allowing complex location monitoring to be broken down into manageable classification categories.
Solution Approach 2:
The system introduces dynamic boundaries as an intermediary construct between the vehicle location data and the final pickup status determination. Rather than directly comparing vehicle position to pickup requirements, the boundaries serve as a mediating layer that simplifies the assessment of whether the vehicle is on track for timely pickup.
3Measurement precision
If multiple states of location discrepancy are identified, then precise vehicle status monitoring is achieved, but notification and response time requirements increase
Solution Approach 1:
The system performs preliminary determination of predicted pickup status before the actual pickup time is reached. By calculating whether the vehicle will be at the pickup location at the prescribed time based on current location and dynamic boundaries, the system can send notifications in advance, giving users sufficient time to respond without waiting until the last moment.
Solution Approach 2:
The system uses dynamic boundaries that can adapt and change over time, allowing the monitoring system to adjust to different scenarios. The boundaries and state classifications enable flexible response times, where the system can notify users at appropriate intervals based on the vehicle's current state and trajectory toward the pickup location.
Data Source
AI summary
A mechanism is provided for detecting and mitigating vehicle location discrepancies. A set of dynamic boundaries are generated around a prescribed pickup location agreed upon with a next renter of a vehicle for a prescribed pickup time. A location of the vehicle is also determined. Based on the location of the vehicle, a determination is made of a boundary in the set of dynamic boundaries with which the vehicle is associated. One state from a set of states is identified based on the location of the vehicle and the identified boundary with which the vehicle is located. Based on the identified state, an associated action is performed.