Ride Route Prediction Using Historical Driver Behavior
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Solution Overview
Problem
Existing vehicle routing systems require significant processing power and incur high costs due to frequent re-routing requests, as drivers often deviate from suggested routes, necessitating recalculations by the server computer.
Innovation Solution
A system that utilizes historical route information and machine learning to identify the most likely route for a vehicle to take, reducing the need for re-routing requests by predicting driver behavior and optimizing routes based on probability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If the server computer calculates the shortest path based on distance and speed limits, then the routing efficiency is improved, but the number of API calls increases when drivers deviate from the suggested route
Solution Approach 1:
The system performs preliminary actions by collecting historical route data in advance and pre-calculating probability metrics for multiple potential routes. When a routing request is received, the system has already prepared probability assessments for various routes based on historical driver behavior, allowing it to quickly identify the most likely route without requiring real-time recalculations when drivers deviate.
Solution Approach 2:
The system implements feedback by continuously collecting historical route data from actual driver behavior and using this feedback to improve future route suggestions. The historical data includes information about which routes drivers actually take versus which routes are suggested, allowing the system to learn and adjust its probability calculations to better predict driver preferences and reduce deviations.
2Measurement precision
If the server computer recalculates the route in real-time when drivers deviate, then the routing accuracy is improved, but the processing power requirement increases
Solution Approach 1:
The system performs preliminary calculations by pre-assessing the probability of drivers taking various routes based on historical data before real-time routing decisions are needed. This preliminary probability assessment allows the system to have multiple pre-evaluated route options ready, reducing the need for intensive real-time recalculations when drivers deviate from the suggested route.
3Ease of operation
If the system provides turn-by-turn navigation, then the driver guidance is improved, but the frequency of re-routing requests increases when drivers fail to follow the route
Solution Approach 1:
The system performs preliminary analysis of historical route data to identify the most probable route that drivers will actually follow, rather than just the mathematically optimal route. By preparing probability assessments in advance for multiple potential routes, the system can provide turn-by-turn navigation guidance that aligns with driver preferences and habits, reducing the likelihood of drivers deviating and the need for re-routing requests.
4Speed
If the server computer uses traditional shortest path algorithms, then the routing speed is improved, but the driver compliance decreases
Solution Approach 1:
The system performs preliminary calculations of driver behavior probabilities using historical data before providing routing suggestions. By pre-assessing which routes drivers are most likely to follow based on past behavior patterns, the system can suggest routes that are not only fast but also align with driver preferences and habits, thereby improving compliance without sacrificing routing speed.
Solution Approach 2:
The system implements feedback loops where historical data about actual driver behavior is continuously collected and used to refine probability models. This feedback mechanism allows the system to learn from past driver decisions and improve its ability to predict future driver choices, leading to better route suggestions that drivers are more likely to follow while maintaining efficient routing.
Data Source
AI summary
A server computer receives a routing request for providing a ride service by a vehicle and retrieves a start location and a destination location from the routing request. The server computer further identifies a plurality of routes connecting the start location and the destination among historical route information collected over a period of time from one or more vehicles. The plurality of routes are traveled by the one or more vehicles and reported to the server computer during the period of time to get form the start location to the destination. The server computer identifies a selected route among the plurality of routes based on the selected route being more likely to be taken than remaining routes. The server computer transmits routing navigation information for the selected route to a device associated with the vehicle.