Traffic Volume Prediction Using Probe Vehicle Trend Modeling
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Solution Overview
Problem
Existing traffic prediction systems struggle to accurately predict future traffic volume and travel time due to limitations in analyzing past traffic patterns.
Innovation Solution
A system and method that utilizes vehicles equipped with sensors and communication devices to transmit driving information to a server, which processes this data using a trend-based demand prediction model to forecast future traffic volume and calculate travel time, employing a Bureau of Public Roads (BPR) function and machine learning for accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a scheme predicting traffic volume uses past traffic patterns to analyze current traffic flow, then the system can process existing data, but it cannot accurately predict future traffic volume
Solution Approach 1:
The system performs preliminary actions by collecting and processing traffic data from multiple sources before the prediction is needed. It pre-processes data from probe vehicles, construction sites, and historical records to create a comprehensive dataset that can be used for future predictions, thereby enabling accurate forecastings without waiting for real-time data accumulation
Solution Approach 2:
The system introduces intermediary elements such as probe vehicles equipped with sensors, construction site information systems, and data processing servers that mediate between raw traffic data and prediction outputs. These intermediaries collect, validate, and transform data into usable formats for accurate future traffic volume prediction
2Measurement precision
If the system collects detailed traffic data from multiple vehicles and sources, then prediction accuracy improves, but data processing complexity increases
Solution Approach 1:
The system segments the complex data processing task into distinct modules: probe vehicle data collection, construction site data collection, data validation, trend analysis, and prediction generation. Each module handles specific data types and processing requirements independently, making the overall complex system manageable and maintainable while improving prediction accuracy through comprehensive data processing
3Measurement precision
If the system uses trend-based demand prediction models, then future traffic volume can be forecasted, but the model requires extensive historical data and computation
Solution Approach 1:
The system performs preliminary data processing and trend analysis using historical data before actual predictions are needed. It pre-processes traffic patterns, construction site information, and probe vehicle data to establish baseline trends and models, reducing the computational energy required during actual prediction operations while maintaining high forecast accuracy
Data Source
AI summary
A system and method for predicting traffic information are disclosed. The system includes a plurality of vehicles that transmits information obtained while traveling in a specified section, and a server that generates processed information based on the information received from the plurality of vehicles, predicts a traffic volume of the specified section at a first time point based on a traffic volume of the specified section and the processed information, and calculates a time required to travel the specified section based on the predicted traffic volume.