Travel Time Prediction Model Using Spatio-Temporal Data
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Solution Overview
Problem
Conventional travel time prediction systems fail to accurately reflect real-time temporal and spatial characteristics, leading to inaccurate predictions due to reliance on past statistical data without considering current variables.
Innovation Solution
A method and apparatus that generate multiple prediction models using varying types of traffic data, including travel speed history, weather information, time zone data, and traffic amount variations, to predict travel times more accurately by continuously modifying and updating these models with new data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional statistical prediction methods using past traffic data are used, then the prediction system is simple to implement, but the prediction accuracy is insufficient because real-time temporal and spatial characteristics are not reflected
Solution Approach 1:
The patent implements dynamic prediction models that are continuously updated with real-time traffic data, weather information, and temporal characteristics. The system transitions from static historical statistical models to dynamic models that adapt to changing conditions, allowing the prediction accuracy to improve as more real-time data is incorporated without requiring complete model replacement
Solution Approach 2:
The prediction system is divided into multiple independent modules: temporal characteristic analysis module, spatial characteristic analysis module, weather data integration module, and prediction model generation module. Each module processes specific aspects of traffic prediction independently, allowing the complex prediction task to be managed through modular components that can be developed and maintained separately
2Reliability
If multiple types of real-time traffic data are collected and processed, then the prediction accuracy improves by reflecting current conditions, but the data processing complexity and computational requirements increase
Solution Approach 1:
The patent creates a universal prediction framework that can process multiple types of data (traffic flow, weather, temporal patterns, spatial characteristics) through a single integrated model structure. This multi-functional system handles diverse data inputs using common processing mechanisms, reducing the need for separate specialized systems for each data type and thereby managing complexity while maintaining reliability
3Adaptability or versatility
If recursive prediction models with continuous data updates are implemented, then the system adapts to real-time changes effectively, but the computational resources and processing time required increase
Solution Approach 1:
The system performs preliminary processing of traffic data, weather data, and spatial-temporal characteristics in advance to pre-compute baseline patterns and historical trends. By preparing prediction models with pre-processed data and pre-established relationships, the system reduces the computational burden during real-time prediction, allowing rapid adaptation to new conditions without excessive processing delays
Data Source
AI summary
According to the present invention, a method for a travel time predicting apparatus to predict a travel time by using a travel time prediction model, including generating a first prediction model for prediction of a travel time of a vehicle that will pass through a specific area by using first traffic data that are related to a travel speed at the specific area; generating a second prediction model by modifying the first prediction model that relates to the travel speed at the specific area and is different from the first traffic data; and predicting a travel time of the vehicle that will pass through the specific area by using the second prediction model can be provided so that a travel time at which a specific vehicle passes a specific area can be more precisely predicted compared to a conventional art and predicted travel time information can be promptly and precisely provided to the user.