Machine-Learned Parking Model for Online Concierge Systems
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Solution Overview
Problem
Concierge systems face challenges in optimizing parking locations for assistants at retail stores, leading to parking scarcity and traffic congestion, as they often arrive at busy locations without prior requests, aiming to be positioned for potential tasks.
Innovation Solution
An online concierge system employs a machine-learned parking model that evaluates the suitability of parking locations based on features like location description, time, retail demand, wait times, parking capacity, and safety, suggesting alternative parking spots to balance assistant efficiency and community needs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If assistants arrive at busy retail locations before receiving customer requests to be positioned for potential tasks, then assistant response time is reduced, but parking scarcity and traffic congestion worsen
Solution Approach 1:
The system performs preliminary actions by having assistants arrive at retail locations before customer requests are received, positioning them in advance to quickly fulfill orders. However, it applies this principle selectively by using the machine-learned model to determine when pre-positioning is actually beneficial, avoiding unnecessary arrivals that would worsen parking and traffic conditions.
Solution Approach 2:
The machine-learned model changes the parameter of assistant positioning by providing dynamic parking location recommendations based on real-time conditions such as retail location demand, current parking availability, and historical data. This allows the system to optimize between assistant response time and minimizing negative impacts on public resources.
2Productivity
If multiple assistants park at the same retail location simultaneously, then assistant availability for orders is improved, but parking availability for other patrons deteriorates
Solution Approach 1:
The system applies local quality by providing customized parking recommendations to different assistants based on their specific contexts, including the retail location, current demand conditions, and individual assistant availability. This distributed approach with localized decisions prevents concentration of assistants at single locations, maintaining both assistant productivity and patron parking access.
Solution Approach 2:
The machine-learned model incorporates feedback loops by continuously monitoring retail location demand, current parking conditions, and assistant positioning data. This feedback enables the system to dynamically adjust recommendations, ensuring that assistant availability is optimized without creating excessive demand for parking at any single location.
3Speed
If assistants wait in restricted regions near retail locations, then order fulfillment speed is improved, but violations of parking restrictions may occur
Solution Approach 1:
The system introduces an intermediary mechanism by providing virtual parking recommendations through the machine-learned model that guide assistants to appropriate waiting locations. These recommendations act as intermediaries between the goal of quick order fulfillment and the requirement to comply with parking restrictions, directing assistants to legal alternative waiting areas.
Solution Approach 2:
The parking recommendations are dynamic and adapt based on changing conditions such as retail location demand, current parking availability, and restriction zones. This allows the system to maintain high order fulfillment speed by directing assistants to the most appropriate locations in real-time while ensuring continuous compliance with parking restrictions.
Data Source
AI summary
An online concierge system uses a machine-learned parking quality model to quantify the suitability of a particular parking location (e.g., a parking lot, or a street) for use when performing purchases at a retail location on behalf of customers. The parking quality model's output is determined according to input features related to parking at a candidate parking location, such as a current time, a current degree of demand for shoppers at the retail location, or a current average shopper wait time at the retail location before receiving an order. The online concierge system provides suggested alternate parking locations to a client device of the shopper, where they may be displayed, e.g., as part of an electronic map. Use of the suggested alternate parking locations helps to preserve parking availability in restricted areas such as retailer parking lots and to reduce traffic congestion in the area of the retailer.