Travel Time Prediction Model Using Quantile Regression
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Solution Overview
Problem
Current delivery services face challenges in efficiently estimating and managing travel time for physical item deliveries, particularly in real-time scenarios like on-demand food delivery, where rapid service is crucial to preserve freshness and meet arrival deadlines, while also optimizing vehicle and personnel usage.
Innovation Solution
A computer-implemented travel time estimation server uses machine learning to build a digital model that predicts travel time and provides a prediction interval by analyzing various trip attributes, such as location, route, and transportation type, employing quantile regression to improve operational efficiency and flexibility.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If real-time data collection and machine learning models are used to predict travel time, then measurement precision of travel time is improved, but device complexity and computational resources increase
Solution Approach 1:
The system pre-collects training data from multiple sources (GPS, traffic sensors, weather stations) and pre-trains machine learning models offline before deployment. This preliminary action allows the complex computational work to be done in advance, reducing real-time processing requirements while maintaining high prediction accuracy.
Solution Approach 2:
The travel time prediction system is divided into multiple independent components: data collection modules, feature extraction modules, model training modules, and prediction modules. Each component processes specific aspects independently, making the overall complex system more manageable and deployable across distributed devices.
2Measurement precision
If multiple data sources and features are integrated into the model, then measurement precision improves, but loss of energy increases due to higher computational requirements
Solution Approach 1:
The machine learning model is trained offline using extensive multi-source data (traffic patterns, weather, historical GPS data) before deployment. This preliminary training action consumes energy in advance, allowing the deployed model to make accurate predictions with minimal real-time computational energy expenditure.
Solution Approach 2:
The system dynamically adjusts model complexity and data processing intensity based on available computational resources and urgency requirements. For routine predictions, a lighter model configuration is used, while critical time-sensitive predictions may activate more computationally intensive processing when energy is available.
3Measurement precision
If comprehensive trip attributes are analyzed for prediction, then measurement precision improves, but loss of time in data processing increases
Solution Approach 1:
The system pre-processes and stores historical trip data, traffic patterns, and route characteristics in structured formats during off-peak periods. This preliminary data preparation action enables rapid retrieval and analysis during actual prediction requests, maintaining high accuracy without adding processing delay.
Solution Approach 2:
The model prioritizes processing of locally relevant features (current location, immediate traffic conditions, recent historical data) over distant or less relevant features. This selective focus on locally important data quality aspects reduces processing time while maintaining prediction accuracy for the specific context.
4Measurement precision
If high-fidelity travel time information is provided, then measurement precision improves, but network bandwidth usage increases
Solution Approach 1:
The system dynamically adjusts the precision and detail level of prediction outputs based on application requirements. For standard delivery scheduling, rounded estimates with confidence intervals are provided, while for critical time-sensitive deliveries, more detailed breakdowns are supplied only when necessary, optimizing network bandwidth utilization.
Data Source
AI summary
A computer-implemented method comprises receiving training data corresponding to a plurality of trips, the training data including at least a value for a set of attributes for each of the plurality of trips, the set of attributes including an indication of when in a week a trip is taken or in which of a plurality of meal-based time zones in a day the trip is taken, the training data including an actual travel time for each of the plurality of trips. The method further comprises creating and storing, in computer memory, a digital model that is configured to predict a travel time for a future trip based on the training data, the digital model including a set of parameters corresponding to the set of attributes, the digital model including a plurality of sets of values for the set of parameters. In addition, the method comprises receiving specific data for a specific trip, the specific data including an indication when in a week the specific trip will be taken or in which of a plurality of meal-based time zones of a day the specific trip will be taken; calculating a specific travel time for the specific trip using the digital model; determining a specific prediction interval for the specific travel time based on the plurality of sets of values for the set of parameters; and causing display of the specific travel time and the specific prediction interval.