Traffic Congestion

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Yun Jang - One of the best experts on this subject based on the ideXlab platform.

  • Visual Cause Analytics for Traffic Congestion.
    IEEE transactions on visualization and computer graphics, 2021
    Co-Authors: Hanbyul Yeon, Hyesook Son, Yun Jang
    Abstract:

    Urban Traffic Congestion has become an important issue not only affecting our daily lives, but also limiting economic development. The primary cause of urban Traffic Congestion is that the number of vehicles is higher than the permissible limit of the road. Previous studies have focused on dispersing Traffic volume by detecting urban Traffic Congestion zones and predicting future trends. However, to solve the fundamental problem, it is necessary to discover the cause of Traffic Congestion. Nevertheless, it is difficult to find a research which presents an approach to identify the causes of Traffic Congestion. In this paper, we propose a technique to analyze the cause of Traffic Congestion based on the Traffic flow theory. We extract vehicle flows from Traffic data, such as GPS trajectory and Vehicle Detector data. We detect vehicle flow changes utilizing the entropy from the information theory. Then, we build cumulative vehicle count curves (N-curve) that can quantify the flow of the vehicles in the Traffic Congestion area. The N-curves are classified into four different Traffic Congestion patterns by a convolutional neural network. Analyzing the causes and influence of Traffic Congestion is difficult and requires considerable experience and knowledge. Therefore, we present a visual analytics system that can efficiently perform a series of processes to analyze the cause and influence of Traffic Congestion. Through case studies, we have evaluated that our system can classify the causes of Traffic Congestion and can be used efficiently in road planning.

Oliver W. W. Yang - One of the best experts on this subject based on the ideXlab platform.

  • LCN - Vehicle Traffic Congestion management in vehicular ad-hoc networks
    2009 IEEE 34th Conference on Local Computer Networks, 2009
    Co-Authors: Brijesh Kadri Mohandas, Ramiro Liscano, Oliver W. W. Yang
    Abstract:

    Vehicle Traffic Congestion is reflected as delays while traveling. Traffic Congestion has a number of negative effects and is a major problem in today's society. Several techniques have been deployed to deal with this problem. In this paper, we have proposed an innovative approach to deal with the problem of Traffic Congestion using the characteristics of vehicular ad-hoc networks (VANET). We have used the Adaptive Proportional Integral rate controller, a Congestion control algorithm designed for the Internet, to deal with the problem of vehicle Traffic Congestion in vehicular networks. The adaptive PI rate controller is a rate based controller that employs control theory to manage the problem of data Traffic Congestion in computer networks. Using simulations we have demonstrated the applicability of the algorithm in the domain of vehicle Traffic Congestion in a VANET.

Chris Gruyter - One of the best experts on this subject based on the ideXlab platform.

  • Traffic Congestion relief associated with public transport: state-of-the-art
    Public Transport, 2020
    Co-Authors: Duy Q. Nguyen-phuoc, William Young, Graham Currie, Chris Gruyter
    Abstract:

    Public transport (PT) influences the urban road system in many ways, including Traffic Congestion, environment, society, safety and land use impacts. While there are many studies focusing on the benefits of PT, research on Congestion impacts, a fundamental component of any analysis of transport performance, associated with PT has received little attention. This paper aims to review the Traffic Congestion impacts of PT and how they are assessed. Traffic Congestion is most commonly related to vehicle travel; yet, the real measure of Congestion in transport systems is people travel. This paper looks at the appropriateness of existing Traffic Congestion measures and how suitable they are for measuring the impact of an existing PT system in the short-term. The literature review indicates that most studies relating to the Congestion impacts of PT have used vehicle-based Congestion measures. People-based measures may be more appropriate in assessing PT impacts. The paper also proposes a new framework for looking at the short-term effects of an existing PT system on Traffic Congestion. It suggests a few areas where further work can be undertaken to improve our understanding of Traffic Congestion incorporating PT such as exploring the mode shift from PT to car, estimating network-wide PT Congestion creation impacts and determining the net Congestion impact of PT.

Hanbyul Yeon - One of the best experts on this subject based on the ideXlab platform.

  • Visual Cause Analytics for Traffic Congestion.
    IEEE transactions on visualization and computer graphics, 2021
    Co-Authors: Hanbyul Yeon, Hyesook Son, Yun Jang
    Abstract:

    Urban Traffic Congestion has become an important issue not only affecting our daily lives, but also limiting economic development. The primary cause of urban Traffic Congestion is that the number of vehicles is higher than the permissible limit of the road. Previous studies have focused on dispersing Traffic volume by detecting urban Traffic Congestion zones and predicting future trends. However, to solve the fundamental problem, it is necessary to discover the cause of Traffic Congestion. Nevertheless, it is difficult to find a research which presents an approach to identify the causes of Traffic Congestion. In this paper, we propose a technique to analyze the cause of Traffic Congestion based on the Traffic flow theory. We extract vehicle flows from Traffic data, such as GPS trajectory and Vehicle Detector data. We detect vehicle flow changes utilizing the entropy from the information theory. Then, we build cumulative vehicle count curves (N-curve) that can quantify the flow of the vehicles in the Traffic Congestion area. The N-curves are classified into four different Traffic Congestion patterns by a convolutional neural network. Analyzing the causes and influence of Traffic Congestion is difficult and requires considerable experience and knowledge. Therefore, we present a visual analytics system that can efficiently perform a series of processes to analyze the cause and influence of Traffic Congestion. Through case studies, we have evaluated that our system can classify the causes of Traffic Congestion and can be used efficiently in road planning.

Chen Xiaorong - One of the best experts on this subject based on the ideXlab platform.