Pickup Location Scoring for Faster Ride Matching and Routing
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Solution Overview
Problem
On-demand service systems face inefficiencies due to suboptimal pickup locations, leading to longer routes, increased cancellations, and resource mismanagement, as providers struggle to locate requestors at difficult locations, causing delays and unnecessary system resource usage.
Innovation Solution
A dynamic transportation matching system determines alternate request locations based on curb segment data and pickup location scores, using historical ride data to identify efficient pickup points, optimizing routes and reducing delays.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If providers are directed to request locations based on simple geographic proximity, then matching speed is improved, but travel time and route efficiency deteriorate due to suboptimal pickup locations
Solution Approach 1:
The system pre-calculates and stores pickup location scores for various locations before they are needed for matching. Historical ride data is analyzed in advance to determine which locations have poor pickup experiences (requiring provider to walk, difficult to find, etc.). When a request comes in, the system can quickly retrieve pre-computed scores and select an optimized pickup location, rather than calculating everything in real-time. This preliminary preparation resolves the contradiction by having matching speed from pre-computed data while ensuring travel time efficiency through optimized location selection.
Solution Approach 2:
The patent introduces an intermediary pickup location score as a mediator between the simple geographic matching and the actual travel efficiency. Instead of directly matching providers to request locations based solely on proximity, the system uses the pickup location score (derived from historical data about walking requirements, accessibility, etc.) as an intermediate factor to select the optimal pickup location. This intermediary metric allows the system to balance quick matching with efficient routing by filtering and ranking potential pickup locations based on their historical performance.
2Adaptability or versatility
If providers are sent to difficult-to-access request locations, then service coverage is improved, but resource allocation efficiency deteriorates due to delays and cancellations
Solution Approach 1:
The system uses historical ride data to self-identify problematic pickup locations and automatically adjust future matching decisions. By analyzing past experiences where providers had to walk to pickup locations or where requests were canceled due to accessibility issues, the system builds an automated knowledge base that guides future location selections. This self-learning mechanism allows the system to maintain broad service coverage while automatically avoiding inefficient locations, thus preserving resource allocation efficiency without manual intervention.
Solution Approach 2:
The system implements a feedback loop where actual pickup experiences (whether the provider had to walk, how long it took to locate the requestor, whether the request was canceled) are fed back into the historical data. This feedback continuously refines the pickup location scores, allowing the system to learn from past mistakes and improve future matching decisions. The feedback mechanism enables the system to maintain service coverage by knowing which locations are problematic, while improving resource efficiency by avoiding those locations in future matches.
3Loss of time
If the system uses detailed historical ride data to optimize pickup locations, then route efficiency is improved, but system complexity and processing overhead increase
Solution Approach 1:
The patent segments the complex task of pickup location optimization into distinct components: (1) collecting historical ride data, (2) analyzing that data to determine pickup location characteristics (whether provider walked, location accessibility, etc.), (3) computing pickup location scores based on segmented factors, and (4) using those scores for matching decisions. This segmentation allows the system to manage complexity by breaking down the overall problem into manageable modules that can be developed, maintained, and processed independently, while still achieving route efficiency through the cumulative effect of these segmented analyses.
Solution Approach 2:
The system performs preliminary analysis of historical ride data to pre-compute pickup location scores before they are needed for actual matching operations. By doing the heavy lifting of data analysis and score computation in advance (as a preliminary action), the system reduces the complexity and processing overhead during real-time matching. The pre-computed scores are stored and can be quickly retrieved and applied during matching, separating the complex analytical work from the time-critical matching operation.
4Ease of operation
If providers must walk to pickup locations due to poor location selection, then service accessibility is maintained, but interaction time and cancellation risk increase
Solution Approach 1:
The patent uses the pickup location score as an intermediary metric that captures the relationship between service accessibility and interaction success. The score is derived from historical data about whether providers had to walk to pickup locations and whether requests were canceled. By using this intermediary score, the system can select pickup locations that balance accessibility (maintaining service availability) with interaction success (reducing cancellations and delays), without having to directly optimize for either extreme.
Data Source
AI summary
Embodiments provide techniques, including systems and methods, for determining alternate request locations based on a pickup location score (PLoS) of a location associated with transportation request information. A pickup location score may include an objective quantitative measurement of the fitness of a location for a pickup by a provider. For example, embodiments may receive transport request information associated with a requestor computing device including a request location, determine a modified request location based at least on a location score for each of one or more alternate request locations that are within a threshold distance of the request location, and send modified transport request information associated with the modified request location and the first requestor computing device to a provider computing device associated with a matched provider for the transport request information.