Traffic Forecasting System Using Queue Length Estimation
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Solution Overview
Problem
Conventional traffic information systems fail to precisely forecast real-time traffic conditions based on historical data by road segment and cannot optimize signal cycles effectively, especially at city block units.
Innovation Solution
A system that uses vehicle queue length estimation, traffic density calculation, and data mining to correct traffic data based on historical data stored in a cloud server, optimizing signal cycles by applying machine learning and pattern matching to provide real-time traffic condition forecasts and turning rates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional traffic information systems use electronic flow-type vehicle detectors and CCTV cameras to collect real-time traffic information, then real-time traffic monitoring is achieved, but the ability to precisely forecast future traffic conditions based on historical data by road segment is insufficient
Solution Approach 1:
The system performs preliminary actions by collecting and storing historical traffic data by road segment in advance, then uses this pre-collected data to forecast future traffic conditions. The traffic information collection units continuously gather historical data which is stored in the database, enabling the forecasting unit to make accurate predictions without needing to collect data at the moment of prediction.
Solution Approach 2:
The system implements feedback by comparing forecasted traffic conditions with actual measured traffic information. The evaluation unit assesses the accuracy of forecasts by comparing predicted values with real-time data from detectors and cameras, then uses this feedback to improve future forecasting accuracy and optimize signal control strategies.
2Productivity
If traffic signal cycles are optimized based on real-time traffic information only, then real-time traffic control is improved, but the optimization capability at city block unit level and long-term traffic pattern optimization cannot be achieved
Solution Approach 1:
The system merges real-time traffic control with historical data analysis and forecasting functions into a unified traffic management system. The signal control unit integrates both real-time detector data and forecasted future traffic patterns to optimize signal cycles, while the system also combines individual road segment data with city block unit-level aggregation to achieve multi-scale optimization simultaneously.
Solution Approach 2:
The system segments the traffic management function into distinct components: individual road segment monitoring units, city block unit aggregation functions, and forecasting units. This segmentation allows each component to operate independently at its optimal level while contributing to the overall system performance, managing complexity through functional decomposition.
3Measurement precision
If more traffic detection equipment is deployed to improve data collection accuracy, then measurement precision is improved, but system complexity and cost increase
Solution Approach 1:
The traffic information collection units are designed with multi-functionality, serving both as real-time traffic detectors and as historical data collection points. The same detectors and cameras used for immediate traffic monitoring also continuously feed data to the database for historical analysis and forecasting, eliminating the need for separate dedicated historical data collection systems.
Data Source
AI summary
The present invention relates to a system for forecasting the traffic condition pattern by an analysis of traffic data. The forecasting system thereof according to Example 1 of the present invention comprises a queue length estimation unit, which receives from a cloud server the information about vehicle passage time and speed of a first intersection or a second intersection, measured by a first beacon installed in the first intersection or a second beacon installed in the second intersection adjacent to the first intersection, and from the vehicle passage time and speed information of the first intersection or the second intersection, estimates the queue length of vehicles that entered the first intersection but did not pass the second intersection; a traffic estimation unit that uses the estimated queue length to calculate the traffic density by road segment between the first intersection and the second intersection, to thereby estimate the traffic; a traffic correction unit that corrects the estimated traffic data based on the historical data by road segment stored in the cloud server; and a traffic condition information calculation unit that applies data mining and pattern matching method to the corrected traffic data, to thereby calculate the traffic condition pattern and the traffic turning rate by time period and road segment.