Adaptive Ride Route Selection for Pickup Point Trade-Offs
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Solution Overview
Problem
Ride-hailing platforms face challenges in enabling users to efficiently identify optimal pick-up and drop-off points that minimize travel time and cost, as manual comparison of all possible pairs is infeasible.
Innovation Solution
A method and system that determine one or more pick-up and drop-off points based on historical data, calculate routes between them, and adaptively identify the optimal route based on criteria such as cheapest, fastest, or shortest distance, using a route server and associated databases.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If users manually compare prices of different pick-up and drop-off point pairs to find optimal routes, then users can identify cost-effective options, but the process becomes infeasibly complex and time-consuming when considering all possible pairs
Solution Approach 1:
The system performs automated route optimization and price comparison itself, eliminating the need for users to manually evaluate multiple pick-up and drop-off point pairs. The server automatically determines optimal routes based on historical data and presents pre-compared options to users.
Solution Approach 2:
The server acts as an intermediary between users and route options, automatically processing the complex comparison of multiple pick-up and drop-off point pairs. Users interact with the simplified interface while the server handles the computationally intensive route analysis in the background.
2Adaptability or versatility
If the system determines multiple pick-up and drop-off points based on historical data and calculates all possible routes between them, then comprehensive route options are provided to users, but the computational complexity and processing time increase significantly
Solution Approach 1:
The system pre-determines likely pick-up and drop-off points based on historical data before users need to compare routes. By anticipating user needs and pre-calculating probable route options, the system reduces real-time computational requirements while maintaining comprehensive coverage.
Solution Approach 2:
The system dynamically adjusts the number and selection of pick-up and drop-off points to be evaluated based on historical patterns. By changing parameters such as the number of candidate points considered and the criteria for selection, the system balances comprehensiveness with computational efficiency.
Data Source
AI summary
The present disclosure provides methods and systems for adaptively identifying an optimal route. In some examples, there is provided a method comprising: determining, by a processor, one or more pick up points based on historical data relating to a starting location, the starting location being one that is indicated in a request for a ride from the starting location to a destination location; determining, by the processor, one or more routes between each of the determined one or more pick up points and the destination location indicated in the ride request; and adaptively identifying, by the processor, an optimal route from the one or more routes based on at least one of a cheapest route, fastest route, shortest route and shortest walking distance.