Trip Duration Estimation Using Hybrid Machine Learning Models
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Solution Overview
Problem
Existing automated methods for generating trip duration estimates are not always accurate, as they rely on incomplete or non-existent graphs, assume specific routes, and fail to account for extra travel time at intersections or between road classes, and do not consider real-time traffic conditions.
Innovation Solution
A computer-implemented method that combines real-time trip data and historical trip data using machine learning to estimate trip durations, where a real-time model and a historical model are trained and weighted based on trip characteristics to provide a final estimated trip duration, and a selector model determines the most accurate prediction between the two.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional graph-based methods are used for trip duration estimation, then the estimation process is simple and fast, but the accuracy is poor due to incomplete graphs and failure to account for real-time conditions
Solution Approach 1:
The system segments the trip duration estimation problem into multiple independent models: a historical model that captures long-term patterns, a real-time model that captures current traffic conditions, and a selector model that determines which model to use. Each model focuses on specific aspects of the estimation problem, improving overall accuracy while maintaining manageable complexity through modular design.
Solution Approach 2:
The system dynamically changes the parameter being estimated by selecting different models based on current conditions. The selector model evaluates trip characteristics and switches between historical and real-time models, effectively changing the estimation approach to match the situation, thereby improving accuracy without requiring a single complex model to handle all scenarios.
2Adaptability or versatility
If a single model is used for trip duration estimation, then the system is simple to implement, but it cannot adapt to varying traffic conditions and trip characteristics
Solution Approach 1:
The system implements dynamics by making the estimation approach adaptive rather than static. The selector model dynamically chooses between historical and real-time models based on current trip characteristics and traffic conditions. This allows the system to adapt to varying situations without requiring manual reconfiguration, improving versatility while keeping the decision logic centralized in the selector.
Solution Approach 2:
The selector model acts as an intermediary between the historical model, real-time model, and the final estimation output. It receives inputs from both models, evaluates their suitability for the current situation, and determines which model's output to use. This intermediary layer simplifies the overall system architecture by providing a single decision point for model selection.
3Reliability
If historical data alone is used for estimation, then the model is stable and easy to train, but it fails to capture real-time traffic patterns and recent changes
Solution Approach 1:
The historical model is trained in advance on extensive historical data to capture long-term traffic patterns and relationships. This preliminary training establishes a reliable baseline estimation capability that does not require real-time data processing. The model is ready to provide stable predictions immediately, while the real-time model handles current condition adjustments.
Solution Approach 2:
The system maintains continuous useful action by having both historical and real-time models operating in parallel, with the selector determining which to use. The historical model provides continuous stable baseline predictions, while the real-time model continuously processes current data. This continuity ensures that the system always has a reliable estimation capability without interruption for model switching or retraining.
4Measurement precision
If real-time data only is used for estimation, then the predictions reflect current conditions, but the model lacks the benefit of historical patterns and trends
Solution Approach 1:
The system segments the data processing task by assigning different data requirements to different models. The real-time model processes only current traffic data to capture immediate conditions, while the historical model processes historical data to capture long-term patterns. This segmentation allows each model to focus on specific data types, improving real-time estimation accuracy without requiring one model to process all data types.
Solution Approach 2:
The selector model serves as an intermediary that determines when to use real-time data processing versus historical data patterns. It evaluates whether current conditions warrant real-time model usage, reducing unnecessary real-time data processing while maintaining accuracy. This intermediary layer optimizes data processing requirements by activating real-time analysis only when beneficial.
Data Source
AI summary
A system uses machine models to estimate trip durations or distance. The system trains a historical model to estimate trip duration using characteristics of past trips. The system trains a real-time model to estimate trip duration using characteristics of recently completed trips. The historical and real-time models may use different time windows of training data to predict estimates, and may be trained to predict an adjustment to an initial trip estimate. A selector model is trained to predict whether the historical model, the real-time model, or a combination of the historical and real-time models will more accurately estimate a trip duration, given features associated with a trip duration request, and the system accordingly uses the models to estimate a trip duration. In some embodiments, the real-time model and the selector may be trained using batch machine learning techniques which allow the models to incorporate new trip data as trips complete.