ETA Calculation via Sparse Feature Machine Learning
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Solution Overview
Problem
Existing digital route planning systems for on-demand transportation services, such as online taxi-hailing, face challenges in accurately determining the estimated time of arrival (ETA) due to varying factors like traffic conditions and historical data complexity, leading to suboptimal route planning.
Innovation Solution
The system employs a machine learning model generated from historical data to determine a high-dimensional sparse feature, which is used to calculate an ETA for a target route, incorporating factors like traffic status, road conditions, and driver behavior, and updates the model dynamically with new data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional route planning methods are used, then the system is simple to implement, but the ETA calculation accuracy is insufficient due to varying traffic conditions and historical data complexity
Solution Approach 1:
The patent introduces a machine learning model as an intermediary component between the route planning system and the historical data/traffic conditions. This model processes complex historical data and real-time traffic information to generate accurate ETA predictions, resolving the contradiction by adding a specialized intermediary layer that handles the complexity while providing precise measurements.
Solution Approach 2:
The system dynamically changes parameters by using a machine learning model that adapts to varying traffic conditions, road types, and historical patterns. The model adjusts its predictions based on multiple input parameters including traffic status, road conditions, and driver behavior, thereby improving ETA accuracy without requiring a complete system redesign.
2Measurement precision
If a machine learning model with high-dimensional sparse features is used, then the ETA calculation accuracy improves, but the model training and processing time increases
Solution Approach 1:
The patent applies preliminary action by pre-training the machine learning model offline using historical data to establish high-dimensional sparse feature representations. This preprocessing step creates a ready-to-use model that can then quickly make predictions during runtime, separating the time-consuming training phase from the time-critical prediction phase.
Solution Approach 2:
The system implements dynamics by using a machine learning model that can adapt and update its parameters based on new data while maintaining efficient inference. The model dynamically adjusts to changing traffic patterns and conditions, providing accurate predictions without requiring complete retraining, thus balancing accuracy with processing time.
3Measurement precision
If real-time traffic conditions and historical data are comprehensively analyzed, then the ETA accuracy improves, but the computational complexity and data processing requirements increase
Solution Approach 1:
The patent extracts only the most relevant features from comprehensive historical data and real-time traffic conditions to create high-dimensional sparse feature representations. By selecting and extracting only the critical parameters needed for prediction, the system achieves high accuracy while reducing the complexity of data processing and computational requirements.
Solution Approach 2:
The system applies local quality by focusing computational resources on analyzing specific, relevant features and data points that have the greatest impact on ETA accuracy. Rather than uniformly processing all available data, the machine learning model identifies and prioritizes locally important features such as traffic status, road conditions, and driver behavior patterns.
Data Source
AI summary
A method for determining an estimated time of arrival (ETA) includes receiving a start location and a destination from a user device via a network. The method also includes obtaining a machine learning model for determining an ETA, which is generated according to a process including: obtaining historical data related to an on-demand service order; determining a high-dimensional sparse feature based on the historical data; and determining a machine learning model based on the high-dimensional sparse feature. The method further includes determining an ETA for a target route based on the machine learning model, the start location, and the destination, and transmitting the determined ETA to the user device via the network.