Pickup Order ETA Prediction Using Location-Specific Machine Learning
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Solution Overview
Problem
Existing ETA calculation methods for pickup orders fail to accurately consider factors such as traffic, parking lot congestion, and business popularity, leading to significant inaccuracies that impact logistical efficiency and customer satisfaction.
Innovation Solution
Implementing machine learning models, particularly deep neural networks, to process and update ETAs by incorporating real-time location data, traffic conditions, and business-specific metrics, ensuring precise arrival time predictions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional ETA calculation methods are used, then the calculation process is simple, but the accuracy of ETA is poor and cannot consider multiple metrics such as traffic, parking lot congestion, and business popularity
Solution Approach 1:
The patent replaces conventional mechanical calculation methods with machine learning models (neural networks) to predict ETA. The system uses trained models that process multiple input features (traffic conditions, parking lot congestion, business popularity, historical data) to generate accurate ETA predictions, substituting complex mathematical computations with learned patterns from data.
Solution Approach 2:
The patent transforms the ETA calculation from a simple time-distance computation to a multi-parameter prediction problem. The system incorporates numerous parameters including traffic conditions, parking lot congestion levels, business popularity metrics, historical pickup data, and real-time location information, changing the fundamental parameters used in the calculation.
2Reliability
If machine learning models are implemented to consider multiple metrics, then the ETA accuracy is improved, but the computational complexity and data processing requirements increase
Solution Approach 1:
The patent performs preliminary actions by pre-training machine learning models using historical data before deployment. The system collects and processes historical pickup data, traffic patterns, and business metrics to train neural networks in advance, so that when the model is deployed, it can make accurate predictions without requiring complex real-time computations for each individual ETA calculation.
Solution Approach 2:
The machine learning model performs self-service by automatically learning patterns from data and improving its predictions over time. The system continuously processes new data to refine its models, reducing the need for manual tuning and complex intervention, while maintaining high reliability in ETA predictions.
3Measurement precision
If real-time location data and multiple metrics are processed, then the ETA accuracy is enhanced, but the data processing time and computational resources increase
Solution Approach 1:
The system performs preliminary data processing by pre-processing and feature-engineering historical data before model training. Real-time location data and metrics are processed using pre-trained models that have already learned the relationships between various parameters, enabling fast predictions without requiring complex real-time analysis of all individual factors.
Solution Approach 2:
The patent replaces time-consuming mechanical data processing and analysis with machine learning inference. Once models are trained, they can rapidly process new input data and generate ETA predictions in real-time, substituting slow computational analysis with efficient pattern recognition based on learned representations.
Data Source
AI summary
Techniques for determining an estimated time of arrival associated with a pickup location are described herein. For example, a customer can order a good or service for pickup at a pickup location. In some examples, order data can include a pickup location intent, such as a drive-through location, a pickup window, or a parking location. A location of a user computing device can be determined and can be input to a machine-learning model trained to determine estimated times of arrival for a particular location. In some examples, the machine-learning model can be trained with ground truth data specific to the location for which the model is to determine estimated times of arrival. The machine-learning model can output the estimated time of arrival and can update the time at any regular or irregular intervals, which can be used to prioritize an order in an order queue.