Pickup Location Model Filtering for Ride-Hailing Accuracy
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional on-demand transportation systems face inaccuracies in determining pickup locations and inefficiencies in computation, often requiring excessive processing power and time due to the brute force approach in selecting pickup locations.
Innovation Solution
The system intelligently generates potential pickup locations within a walkable radius using a pickup location model and filtering techniques to remove impractical and inefficient locations, such as those on medians or side streets, thereby determining more accurate and efficient pickup points.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional systems process large amounts of data using brute force approach to generate potential pickup locations, then comprehensive location coverage is achieved, but excessive computation power and computation time are required
Solution Approach 1:
The system performs preliminary actions by pre-identifying and storing geographic features (intersections, medians, venues, side streets) and their spatial relationships in a database before pickup location determination is needed. When a transportation request arrives, the system quickly queries pre-computed data rather than processing raw geographic data in real-time, significantly reducing computation time while maintaining comprehensive location accuracy
Solution Approach 2:
The system segments the pickup location determination process into distinct phases: generating door points from provider location, filtering door points using pre-defined geographic features, and selecting final pickup locations. This segmentation allows each phase to be optimized independently, with filtering rules efficiently eliminating impractical locations without requiring exhaustive analysis of all possible pickup points
2Measurement precision
If conventional systems generate pickup locations without filtering, then all potential locations are considered, but impractical and dangerous locations (medians, intersections, buildings) are included
Solution Approach 1:
The system applies local quality by associating specific filtering rules with specific geographic features. Different locations require different filtering treatments: medians are completely excluded, intersections require venue proximity checks, side streets have specific distance thresholds. This localized filtering approach improves pickup location accuracy by tailoring constraints to each location type's characteristics without requiring a single complex universal filter
Solution Approach 2:
The system introduces an intermediary filtering layer between door point generation and final pickup location selection. This intermediary layer uses pre-computed geographic features (intersections, medians, venues) as mediators to evaluate and filter door points. The intermediary filtering logic simplifies the overall system by providing a structured, rule-based approach that prevents impractical locations without requiring complex optimization algorithms
3Reliability
If the system filters out locations using multiple criteria (proximity to venues, medians, side streets), then pickup location practicality is improved, but computational overhead increases
Solution Approach 1:
The system performs preliminary actions by pre-identifying and storing geographic features (intersections, medians, venues, side streets) and their spatial relationships in a database before pickup location determination is needed. When a transportation request arrives, the system quickly queries pre-computed data rather than processing raw geographic data in real-time, significantly reducing computation time while maintaining comprehensive location accuracy
Solution Approach 2:
The system applies partial filtering by focusing computational resources on the most critical filtering criteria that eliminate the majority of impractical locations. Rather than applying all possible filters equally, the system prioritizes filters that provide the greatest practical benefit (such as excluding medians and dangerous locations) while using less stringent filters for less critical cases, optimizing the balance between reliability and computational efficiency
Data Source
AI summary
This disclosure describes a pickup location determination system that determines a pickup location for a received transportation request by filtering out door points based on various factors and by utilizing a pickup location model to select a pickup location from the filtered door points. For example, the disclosed systems generate door points relative to intersection points within a request radius of a request location associated with a received transportation request. The disclosed systems generate potential pickup locations by filtering out door points that are impractical and/or inefficient based on proximity to other door points, locations relative to venues, locations relative to side streets, and/or locations between parallel road segments (e.g., medians). The disclosed systems further utilize a pickup location model to select a pickup location from the potential pickup locations.