Predictive Stop Location Routing Using Historical Data
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Solution Overview
Problem
Current fleet management systems face challenges in optimizing vehicle routes and stop locations, leading to logistical issues such as late arrivals, inefficient navigation, and increased costs due to the lack of precise stop location identification and driver knowledge transfer.
Innovation Solution
A system utilizing a server computer with a processor and memory to generate route service requests, identify start and stop locations based on historical data, determine probabilities of stop location usage, and transmit optimized routing navigation instructions to vehicles, incorporating machine learning algorithms to select and assign the most likely stop locations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If conventional address-based routing is used, then drivers can be provided with simple stop addresses, but stop location precision is poor and logistical issues arise
Solution Approach 1:
The patent replaces the mechanical/manual method of drivers memorizing and identifying stop locations with an electronic system that uses historical data, machine learning algorithms, and GPS technology to automatically determine and communicate precise stop locations to drivers, eliminating the need for human memory while improving precision
Solution Approach 2:
The patent creates a virtual model of stop locations by analyzing historical routing data and driver behavior patterns, generating predicted stop locations that replicate the knowledge of experienced drivers without requiring them to manually transfer their expertise
2Device complexity
If driver knowledge of stop locations is relied upon, then routing can be performed without complex systems, but the system becomes vulnerable to driver replacement and knowledge loss
Solution Approach 1:
The patent implements a feedback mechanism where historical routing data from actual driver behavior is continuously collected and used to train machine learning models, which then provide predicted stop locations back to the routing system, creating a self-improving system that maintains reliability independent of individual drivers
Solution Approach 2:
The system performs self-learning by automatically analyzing historical data and generating routing predictions without requiring manual intervention or knowledge transfer from drivers, making the system self-sufficient and immune to personnel changes
3Productivity
If machine learning algorithms are implemented to predict stop locations, then routing precision and efficiency are improved, but computational requirements and system complexity increase
Solution Approach 1:
The patent performs preliminary action by pre-training machine learning algorithms on historical routing data during off-peak times, so that when routing decisions are needed, the system can quickly query pre-computed predictions rather than performing complex calculations in real-time, reducing computational burden during operational periods
Data Source
AI summary
A system and method include server computer which may generate a route service request to service a route with a vehicle and identify a start location for the route and one or more stop locations of the route based on historical data associated at least with one of the route or the vehicle. The server computer may proceed to determine a probability that each of the one or more stop locations for the route will be serviced by the vehicle for the route and automatically select, based on the probability associated with each one of the one or more stop locations for the route, a subset of stop locations among the one or more stop locations. The server computer may then assign the subset of stop locations to the route, and transmit routing navigation instructions to a device associated with the vehicle.