Division of Road Traffic Network for Distributed Simulation Performed on Heterogeneous Clusters

2012 ◽  
Author(s):  
Tomas Potuzak
Keyword(s):  
Road Traffic ◽  
Distributed Simulation ◽  
Traffic Network ◽  
Heterogeneous Clusters

scholarly journals Methods for division of road traffic network for distributed simulation performed on heterogeneous clusters

2013 ◽  
Vol 10 (1) ◽  
pp. 321-348 ◽  
Author(s):  
Tomas Potuzak
Keyword(s):  
Road Traffic ◽  
Distributed Simulation ◽  
Computation Time ◽  
Traffic Network ◽  
Traffic Networks ◽  
Computing Environment ◽  
Heterogeneous Cluster ◽  
Heterogeneous Clusters ◽  
The Road ◽  
Road Traffic Networks

The computer simulation of road traffic is an important tool for control and analysis of road traffic networks. Due to their requirements for computation time (especially for large road traffic networks), many simulators of the road traffic has been adapted for distributed computing environment where combined power of multiple interconnected computers (nodes) is utilized. In this case, the road traffic network is divided into required number of sub-networks, whose simulation is then performed on particular nodes of the distributed computer. The distributed computer can be a homogenous (with nodes of the same computational power) or a heterogeneous cluster (with nodes of various powers). In this paper, we present two methods for road traffic network division for heterogeneous clusters. These methods consider the different computational powers of the particular nodes determined using a benchmark during the road traffic network division.

scholarly journals Traffic Inequality and Relations in Maritime Silk Road: A Network Flow Analysis

2021 ◽  
Vol 10 (1) ◽  
pp. 40
Author(s):  
Naixia Mou ◽  
Haonan Ren ◽  
Yunhao Zheng ◽  
Jinhai Chen ◽  
Jiqiang Niu ◽  
...  
Keyword(s):  
Traffic Flow ◽  
Network Flow ◽  
Road Traffic ◽  
Flow Analysis ◽  
Silk Road ◽  
Identification System ◽  
Traffic Network ◽  
Trajectory Data ◽  
Maritime Traffic ◽  
Maritime Silk Road

Maritime traffic can reflect the diverse and complex relations between countries and regions, such as economic trade and geopolitics. Based on the AIS (Automatic Identification System) trajectory data of ships, this study constructs the Maritime Silk Road traffic network. In this study, we used a complex network theory along with social network analysis and network flow analysis to analyze the spatial distribution characteristics of maritime traffic flow of the Maritime Silk Road; further, we empirically demonstrate the traffic inequality in the route. On this basis, we explore the role of the country in the maritime traffic system and the resulting traffic relations. There are three main results of this study. (1) The inequality in the maritime traffic of the Maritime Silk Road has led to obvious regional differences. Europe, west Asia, northeast Asia, and southeast Asia are the dominant regions of the Maritime Silk Road. (2) Different countries play different maritime traffic roles. Italy, Singapore, and China are the core countries in the maritime traffic network of the Maritime Silk Road; Greece, Turkey, Cyprus, Lebanon, and Israel have built a structure of maritime traffic flow in the eastern Mediterranean Sea, and Saudi Arabia serves as a bridge for maritime trade between Asia and Europe. (3) The maritime traffic relations show the characteristics of regionalization; countries in west Asia and the European Mediterranean region are clearly polarized, and competition–synergy relations have become the main form of maritime traffic relations among the countries in the dominant regions. Our results can provide a scientific reference for the coordinated development of regional shipping, improvement of maritime competition, cooperation strategies for countries, and adjustments in the organizational structure of ports along the Maritime Silk Road.

scholarly journals PATTERN RECOGNITION BASED SPEED FORECASTING METHODOLOGY FOR URBAN TRAFFIC NETWORK

Transport ◽  
2018 ◽  
Vol 33 (4) ◽  
pp. 959-970 ◽  
Author(s):  
Tamás Tettamanti ◽  
Alfréd Csikós ◽  
Krisztián Balázs Kis ◽  
Zsolt János Viharos ◽  
István Varga
Keyword(s):  
Neural Network ◽  
Input Data ◽  
Road Traffic ◽  
Urban Traffic ◽  
Traffic Network ◽  
Data Handling ◽  
Urban Road ◽  
Traffic Sensors ◽  
Network Training ◽  
Artificial Neural Network Ann

A full methodology of short-term traffic prediction is proposed for urban road traffic network via Artificial Neural Network (ANN). The goal of the forecasting is to provide speed estimation forward by 5, 15 and 30 min. Unlike similar research results in this field, the investigated method aims to predict traffic speed for signalized urban road links and not for highway or arterial roads. The methodology contains an efficient feature selection algorithm in order to determine the appropriate input parameters required for neural network training. As another contribution of the paper, a built-in incomplete data handling is provided as input data (originating from traffic sensors or Floating Car Data (FCD)) might be absent or biased in practice. Therefore, input data handling can assure a robust operation of speed forecasting also in case of missing data. The proposed algorithm is trained, tested and analysed in a test network built-up in a microscopic traffic simulator by using daily course of real-world traffic.

scholarly journals PARTICULARITIES OF AUTOMATED TRAFFIC CONTROL SYSTEMS IN THE CITIES OF UKRAINE

2013 ◽  
Vol 5 (5) ◽  
pp. 572-577 ◽  
Author(s):  
A. Stepanchuk ◽  
A. Bieliatynskyi ◽  
A. Pylypenko
Keyword(s):  
Control Systems ◽  
Traffic Control ◽  
Road Traffic ◽  
Traffic Network ◽  
Basic Concepts ◽  
Further Development

The article considers the basic concepts concerningthe possibility of increasing the efficiency and capacity of theroad traffic network in the cities of Ukraine. The paper alsoanalyzes some of the measures to improve road traffic managementthrough the further development of an automated trafficcontrol system.

A Novel Self-Organizing Approach to Automatic Traffic Light Management System for Road Traffic Network

2019 ◽  
Vol 110 (3) ◽  
pp. 1303-1321
Author(s):  
Amita Jain ◽  
Sudesh Yadav ◽  
Sonakshi Vij ◽  
Yogesh Kumar ◽  
Devendra Kumar Tayal
Keyword(s):  
Management System ◽  
Road Traffic ◽  
Traffic Network ◽  
Traffic Light ◽  
Light Management ◽  
Self Organizing

scholarly journals Road traffic network state prediction based on a generative adversarial network

2020 ◽  
Vol 14 (10) ◽  
pp. 1286-1294
Author(s):  
Dongwei Xu ◽  
Peng Peng ◽  
Chenchen Wei ◽  
Defeng He ◽  
Qi Xuan
Keyword(s):  
Road Traffic ◽  
Traffic Network ◽  
Generative Adversarial Network ◽  
Network State ◽  
Adversarial Network ◽  
State Prediction
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