Automated Ride Scheduling System for Optimal Driver Selection
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Solution Overview
Problem
Conventional carpooling systems lack an efficient mechanism to determine the driver for shared rides, which can lead to suboptimal choices in terms of carbon emissions, fuel efficiency, and other factors, contributing to increased traffic and environmental impact in metropolitan areas.
Innovation Solution
An automated ride scheduling system calculates and compares various variables such as carbon emission, driving time, and passenger-to-driver ratio to select the most optimal driver for a shared ride, using weighted sums and user-specific parameters to assign the driver based on predefined criteria.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Object-generated harmful factors
If conventional carpooling systems assign drivers without an efficient determination mechanism, then the system is simple to operate, but the carbon emissions and fuel efficiency are suboptimal
Solution Approach 1:
The system changes the parameters used for driver selection from simple arbitrary assignment to multiple quantifiable variables including carbon emission metrics, fuel efficiency data, driving time, distance, and passenger-to-driver ratios. This allows the system to optimize for environmental impact while maintaining operational efficiency through automated calculation and comparison of these parameters.
Solution Approach 2:
The system enables potential drivers to self-report their vehicle parameters, fuel efficiency data, and availability. This self-service approach reduces the complexity of data collection while still gathering the necessary information for optimized driver selection, allowing users to contribute to the optimization process without increasing system complexity.
2Object-generated harmful factors
If the system calculates and compares multiple variables to select the optimal driver, then carbon emissions are minimized, but the computational complexity increases
Solution Approach 1:
The system performs preliminary calculations by having potential drivers pre-report their vehicle parameters, fuel efficiency data, and availability before the matching process. This preliminary action reduces the computational burden during the actual driver selection by having data ready in advance, allowing the system to minimize environmental impact without excessive real-time computational complexity.
Solution Approach 2:
The system introduces an intermediary computational layer that processes multiple variables (carbon emissions, fuel efficiency, driving time, distance, passenger-to-driver ratios) and converts them into a standardized evaluation metric. This intermediary mechanism simplifies the comparison process by transforming complex multi-variable optimization into a single comparable value for each potential driver.
3Productivity
If the system uses weighted sums of multiple variables to determine the optimal driver, then ride scheduling is optimized, but the difficulty of detecting and measuring variables increases
Solution Approach 1:
The system leverages self-service by requiring potential drivers to self-report their vehicle parameters, fuel efficiency data, and availability preferences. This approach reduces the difficulty of detecting and measuring variables by obtaining data directly from the source without requiring complex sensing or measurement systems, while still enabling optimized ride scheduling through the weighted sum calculation.
Solution Approach 2:
The system employs a universal weighted sum formula that can evaluate multiple different variables (carbon emissions, fuel efficiency, driving time, distance, passenger-to-driver ratios) using a consistent mathematical framework. This multi-functional approach allows the system to handle diverse variable types uniformly, reducing the difficulty of measurement by applying the same evaluation methodology across all variables.
Data Source
AI summary
Values of a variable affecting the determination of the driver of a shared ride may be calculated. Each value may be associated with a respective potential driver. An optimal value from the calculated values may be selected. A potential driver associated with the selected optimal value may be assigned as the driver of the shared ride. The variable may be carbon emission, electricity consumption, passenger to driver role ratio, driving distance, driving time, vehicle size, fuel efficiency, electricity to gasoline usage ratio, accident occurrence, vehicle safety, vehicle comfort, or vehicle speed. The optimal value may be the lowest value or the highest value from the calculated values. Each value may be calculated based on parameters specified by the respective potential driver.