Transporter Network Mapping for Predictive Connectivity Coverage
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Solution Overview
Problem
Existing network connectivity mapping methods, particularly for transporters, are inaccurate and imprecise, leading to delays and inefficiencies in item or person transportation due to poor network coverage, which current mobile network operator maps cannot effectively address.
Innovation Solution
A server computer collects transporter data at regular intervals to create a network connectivity map, utilizing machine learning models to predict network coverage based on transporter location and device type, enabling proactive measures to minimize connectivity issues.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing network connectivity mapping methods are used, then network coverage information is available, but the accuracy and precision of the connectivity map is insufficient
Solution Approach 1:
The system uses transporter devices themselves to collect network connectivity data during their normal operations. The transporters' own devices measure and report connectivity status, turning the transport fleet into a distributed measurement network that continuously maps connectivity without requiring separate dedicated measurement infrastructure.
Solution Approach 2:
The system continuously collects connectivity data from transporters and feeds it back to update the network connectivity map in real-time. This feedback loop allows the system to learn from actual transporter experiences and improve the accuracy of connectivity predictions, which then helps prevent future connectivity issues through proactive routing adjustments.
2Reliability
If proactive measures are taken to mitigate connectivity issues, then delivery reliability improves, but additional time and resources are required for monitoring and adjustment
Solution Approach 1:
The system proactively identifies areas with poor network connectivity using the connectivity map and takes preventive actions before transporters actually enter those problem areas. By predicting connectivity issues in advance and adjusting routes or notifying transporters beforehand, the system prevents delivery delays rather than reacting to them after they occur.
Solution Approach 2:
The system replaces manual monitoring and reactive problem-solving with an automated computational system that continuously analyzes connectivity data, updates the connectivity map, and generates proactive recommendations. This automated information processing system substitutes for human monitoring efforts and enables faster, more scalable connectivity management.
3Measurement precision
If transporter data is collected at regular intervals, then network connectivity patterns are accurately captured, but data processing load increases
Solution Approach 1:
The system uses a universal data collection approach where all transporter devices follow the same regular sampling interval and data reporting protocol. This standardized multi-functional framework allows the same data collection mechanism to serve multiple purposes: mapping connectivity, analyzing patterns, and generating predictions, thereby managing complexity through uniformity rather than customization.
Data Source
AI summary
A method is disclosed. The method includes receiving transporter data associated with a plurality of transporters. The transporter data comprises time and location data of the transporters at time intervals as the transporters deliver items to end users. The method also includes creating a network connectivity map based upon the transporter data, and then storing the network connectivity map. The network connectivity map is then used to assist other transporters when transporting items.