ETA Prediction via Spatial-Temporal Clustering and Model Selection
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Solution Overview
Problem
Current systems for determining the estimated time of arrival (ETA) in location-based services, such as web mapping and online transportation services, face challenges in providing accurate and real-time ETAs due to the complexity of routing and traffic conditions.
Innovation Solution
The system clusters service orders based on temporal and spatial features, trains ETA models for each subset, and selects the most accurate model to predict ETAs for service requests, allowing for weighted combinations of candidate ETAs to improve accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a single ETA model is used for all service orders, then the system complexity is low, but the prediction accuracy decreases due to diverse routing and traffic conditions
Solution Approach 1:
The patent divides the service orders into multiple subsets based on temporal and spatial features. Each subset is trained with a separate ETA model, allowing predictions to be tailored to specific conditions (e.g., morning rush hour in urban areas vs. evening in suburban areas). This segmentation resolves the contradiction by improving accuracy through condition-specific models while managing complexity through organized modular structure.
Solution Approach 2:
The system dynamically selects which ETA model to use based on the characteristics of the current service request. Instead of using a static single model, the system adapts by choosing the most appropriate model from multiple trained models according to temporal and spatial conditions. This dynamic approach improves accuracy while keeping the system manageable through intelligent selection rather than exhaustive processing.
2Measurement precision
If multiple ETA models are trained for different subsets, then the prediction accuracy improves, but the training time and computational resources increase
Solution Approach 1:
By segmenting the training data into meaningful subsets (temporal and spatial), the system trains smaller, focused models rather than one large comprehensive model. This allows parallel training of multiple smaller models, reducing total training time while improving accuracy through specialized training data for each model.
Solution Approach 2:
The system performs preliminary clustering of service orders into subsets before model training. This preliminary organization of data enables more efficient training by pre-grouping similar cases together, reducing the computational burden during the actual training phase and allowing for faster model development and deployment.
3Adaptability or versatility
If the system uses clustering and multiple models, then the adaptability to different conditions improves, but the device complexity increases
Solution Approach 1:
The system segments service conditions into distinct temporal and spatial categories, creating specialized models for each segment. This segmentation provides adaptability to different conditions while managing complexity through clear organizational boundaries and modular model structures that can be independently developed and maintained.
Solution Approach 2:
The patent introduces an intermediary layer (the model selection mechanism) that bridges the gap between diverse service conditions and appropriate ETA models. This intermediary automatically determines which model to use based on input characteristics, providing adaptability without requiring complex manual configuration or user intervention, thus managing system complexity.
Data Source
AI summary
Systems and methods for processing at least one service request for an on-demand service are provided. The systems may perform the methods to obtain a plurality of example service orders, each example service order of the plurality of example service orders may include a feature associated with a section of a plurality of sections of a measurement; cluster the plurality of example service orders into a plurality of subsets of example service orders, the feature of each example service order in a same subset of example service orders may be associated with a same section of the measurement; for each of the plurality of subsets of example service orders, train a first model of estimated time of arrival (first ETA model) using the subset of example service orders; and store structured data in the at least one storage medium encoding the plurality of first ETA models.