Waypoint Determination Using Historical Delivery Data
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Solution Overview
Problem
Current navigation applications fail to efficiently route users for trips requiring multiple modes of travel or temporary stopping points, leading to delays, unnecessary network resource consumption, and potential environmental and safety issues due to the inability to quickly locate suitable stopping points.
Innovation Solution
A method using historical data and machine learning clustering to determine temporary transporter locations and waypoint locations, allowing transporters to temporarily stop and change travel modes efficiently, by analyzing location and motion data from previous deliveries.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If current navigation applications are used for trips requiring multiple modes of travel, then basic navigation is provided, but delays occur and network resources are consumed unnecessarily
Solution Approach 1:
The system performs preliminary actions by determining waypoint locations in advance based on historical data before the actual delivery trip. This allows transporters to pre-plan temporary stopping points where they can switch between different modes of travel, eliminating delays during the actual delivery process.
Solution Approach 2:
The system dynamically adapts navigation routes by incorporating multiple modes of travel and temporary stopping points based on historical transporter behavior data. The routing is not static but adjusts to accommodate real-world delivery complexities including mode transitions and local accessibility constraints.
2Ease of operation
If transporters search for suitable stopping points during delivery, then temporary stops can be made, but network resources are consumed and time is lost
Solution Approach 1:
Waypoint locations are determined in advance using historical data before the delivery trip begins. Transporters receive pre-calculated stopping points with their coordinates, eliminating the need to search for suitable locations during the actual delivery and reducing network resource consumption.
Solution Approach 2:
The system uses historical data from previous successful deliveries to create representative waypoint locations. Instead of requiring transporters to discover stopping points through trial and error, the system copies proven effective locations from historical records and provides them as recommendations.
3Productivity
If transporters cannot quickly locate suitable stopping points, then delivery can proceed, but safety and legal issues may arise
Solution Approach 1:
The system pre-determines waypoint locations that are known to be suitable for temporary stopping based on historical data from successful deliveries. This ensures that transporters can quickly access pre-validated stopping points that are legally and safely appropriate, avoiding safety and legal issues.
Solution Approach 2:
The system incorporates feedback from historical delivery data to continuously improve waypoint recommendations. By analyzing which stopping points have been successful in the past, the system learns and provides increasingly reliable recommendations that balance delivery speed with safety and legal compliance.
Data Source
AI summary
A method includes receiving, by a computer, a destination address. The computer can obtain historical fulfillment data for the destination address. The computer can then determine one or more temporary transporter locations based on the historical fulfillment data for the destination address. The computer can determine a waypoint location from the one or more temporary transporter locations. The computer can provide the waypoint location.