Large-Scale Transportation Network Congestion Evolution Prediction Using Deep Learning Theory

Summary

Understanding how congestion at one location can cause ripples throughout large-scale transportation network is vital for transportation researchers and practitioners to pinpoint traffic bottlenecks for congestion mitigation. Traditional studies rely on either mathematical equations or simulation techniques to model traffic congestion dynamics. However, most of the approaches have limitations, largely due to unrealistic assumptions and cumbersome parameter calibration process. With the development of Intelligent Transportation Systems (ITS) and Internet of Things (IoT), transportation data become more and more ubiquitous. This triggers a series of data-driven research to investigate transportation phenomena. Among them, deep learning theory is considered one of the most promising techniques to tackle tremendous high-dimensional data. This study attempts to extend deep learning theory into large-scale transportation network analysis. A deep Restricted Boltzmann Machine and Recurrent Neural Network architecture is utilized to model and predict traffic congestion evolution based on Global Positioning System (GPS) data from taxi. A numerical study in Ningbo, China is conducted to validate the effectiveness and efficiency of the proposed method. Results show that the prediction accuracy can achieve as high as 88% within less than 6 minutes when the model is implemented in a Graphic Processing Unit (GPU)-based parallel computing environment. The predicted congestion evolution patterns can be visualized temporally and spatially through a map-based platform to identify the vulnerable links for proactive congestion mitigation.

Key Points

• 1Deep learning techniques provide a promising solution for modeling complex traffic patterns in large transportation networks
• 2A numerical study conducted in Ningbo, China, achieved prediction accuracy of up to 88%
• 3The methodology addresses limitations of traditional traffic congestion modeling methods
• 4The research emphasizes the importance of data-driven approaches in transportation analysis
• 5Visualization of predicted congestion patterns aids in proactive congestion mitigation strategies.

Related articles

• CENGR 2220: Geology for Civil Engineers - Structural Geology and Rock Mechanics

  This document is a lesson plan for a geology course aimed at civil engineering students, focusing on the concepts of structural geology, including attitudes of beds, types of outcrops, and geological maps.

• Operating Systems

  This document provides an overview of operating systems, including their functions, types, and the essential role they play in managing computer hardware and software resources. It outlines the basic operations of an operating system and explains how they serve as an interface between the user and computer hardware.

• Force, Motion, and Energy: Comprehensive Guide to Mechanics and Electricity

  This document provides a detailed overview of fundamental concepts in mechanics and electricity, covering topics such as the laws of motion, force, energy, and electrical circuits. It illustrates these principles with practical examples and problems, making it suitable for educational purposes.

• Introduction to Word Processing

  This document provides an overview of word processing, its historical development, and the primary functions of software like Microsoft Word. It discusses key features, objectives for users, and various applications in business, education, and creative writing.

• Wheel-INS2: Multiple MEMS IMU-based Dead Reckoning System with Different Configurations for Wheeled Robots

  This document presents a multiple IMUs-based dead reckoning solution for wheeled robots, improving navigation through the integration of IMUs mounted in various positions on the vehicle to enhance accuracy and robustness.