Traffic Flow Hidden Measure and Assignment Model for the Uncertain Direction Military Traffic Network

2012 ◽  
Vol 38 (2) ◽  
pp. 315-320 ◽  
Author(s):  
Wei ZHOU ◽  
Jian-Min HE ◽  
De-Jian YU
Keyword(s):  
Traffic Flow ◽  
Traffic Network ◽  
Assignment Model

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.

CAPACITY ASSIGNMENT MODEL TO DEFENSE CASCADING FAILURES

2009 ◽  
Vol 20 (07) ◽  
pp. 991-999 ◽  
Author(s):  
J. J. WU ◽  
H. J. SUN ◽  
Z. Y. GAO
Keyword(s):  
Traffic Flow ◽  
Cascading Failures ◽  
Traffic Networks ◽  
Practical Application ◽  
Assignment Rule ◽  
Scale Free ◽  
Capacity Assignment ◽  
Assignment Model ◽  
Equilibrium Assignment ◽  
Theoretical Significance

How to alleviate the damages of cascading failures triggered by the overload of edges/nodes is common in complex networks. To describe the whole cascading failures process from edges overloading to nodes malfunctioning and the dynamic spanning clustering with the evolvement of traffic flow, we propose a capacity assignment model by introducing an equilibrium assignment rule of flow in artificially created scale-free traffic networks. Additionally, the capacity update rule of node is given in this paper. We show that a single failed edge may undergo the cascading failures of nodes, and a small failure has the potential to trigger a global cascade. It is suggested that enhancing the capacity of node is particularly important for the design of any complex network to defense the cascading failures. Meanwhile, it has very important theoretical significance and practical application worthiness in the development of effective methods to alleviate the damage of one or some failed edges/nodes.

An Evaluation Model for Traffic Guidance Measures of the Urban Central Commercial District and its Application

2012 ◽  
Vol 253-255 ◽  
pp. 1695-1699
Author(s):  
Miao Cui ◽  
Tie Xin Cheng ◽  
Jing Zhu Chen
Keyword(s):  
Traffic Flow ◽  
Evaluation Model ◽  
Heavy Traffic ◽  
Simulation Software ◽  
Left Turn ◽  
Traffic Jam ◽  
The Road ◽  
Dynamic Assignment ◽  
Assignment Model ◽  
Traffic Guidance

As the city’s economic and cultural center, the urban Central Commercial Districts (CCDs) are rapidly developing, which always brings the heavy traffic jam in the city. In this paper, the traffic flow of CCDs was analyzed, and the traffic flow guidance evaluation model was established. Firstly, according to the travel time function, traffic flow was allocated to the road network of the CCDs by the dynamic assignment model, and the heavy-traffic roads were identified. Then, traffic flow guidance measures mainly including forbidding straight, left turn, or right turn were given, and the evaluation model above was applied to select the optimal measure. Finally, with VISSIM simulation software, the model was applied to the Tianjin Binjiangdao CCD, which illustrated that the satisfied guidance measure can be selected effectively.

scholarly journals MAS architecture and knowledge model for vehicles data communication

2013 ◽  
Vol 1 (1) ◽  
pp. 23-31
Author(s):  
Emmanuel Adam ◽  
Emmanuelle Grislin-Le Strugeon ◽  
René Mandiau
Keyword(s):  
Mobile Robots ◽  
Traffic Flow ◽  
Mobile Agents ◽  
Autonomous Vehicles ◽  
Data Communication ◽  
Road Accident ◽  
Traffic Network ◽  
Knowledge Model ◽  
New Knowledge ◽  
Knowledge Communication

Completely autonomous vehicles in traffic should allow to decrease the number of road accident victims greatly, and should allow gains in terms of performance and economy. Modelling the vehicles interaction, and especially knowledge sharing, is one of the main challenges to optimize traffic flow with autonomous vehicles. We propose in this paper a model of knowledge communication between mobile agents on a traffic network. The model of knowledge and of interaction enables to propagate new knowledge without overloading the system with a too large number of communications. For that, only the new knowledge is communicated, and two agents communicate the same knowledge only once. Moreover, in order to allow agents to update their knowledge (perceived or created), a notion of degradation is used. A simulator has been built to evaluate the proposal, before to implement it in mobile robots. Some results of the simulator are proposed in this article.

scholarly journals Optimal Control Design for Traffic Flow Maximization Based on Data-Driven Modeling Method

Energies ◽  
2021 ◽  
Vol 15 (1) ◽  
pp. 187
Author(s):  
Balázs Németh ◽  
Dániel Fényes ◽  
Zsuzsanna Bede ◽  
Péter Gáspár
Keyword(s):  
Traffic Flow ◽  
Optimization Problem ◽  
Control Design ◽  
Prediction Method ◽  
Data Driven ◽  
Traffic Network ◽  
Control Methods ◽  
Prediction And Control ◽  
Data Driven Modeling ◽  
And Control

This paper proposes enhanced prediction and control design methods for improving traffic flow with human-driven and automated vehicles. To achieve accurate prediction for the entire time horizon, data-driven and model-based prediction methods were integrated. The goal of the integration was to accurately predict the outflow of the traffic network, which was selected as the highway section in this paper. The proposed novel prediction method was used in the optimal design for calculating controlled inflows on highway ramps. The goal of the design was to reach the maximum outflow of the traffic network, even against disturbances on uncontrolled inflows of the network. The control design leads to an optimization problem based on the min–max principle, i.e., the traffic outflow is considered to be maximized by controlled inflows and to be minimized by uncontrolled inflows. The effectiveness of the prediction and the control methods through simulation examples are illustrated, i.e., traffic outflow can be maximized by the control system under various uncontrolled inflow values.

Traffic Flow Management Modeling with the Time Assignment Model

1993 ◽  
Vol 1 (3) ◽  
pp. 255-276 ◽  
Author(s):  
Kenneth S. Lindsay ◽  
E. Andrew Boyd ◽  
Rusty Burlingame
Keyword(s):  
Traffic Flow ◽  
Flow Management ◽  
Assignment Model ◽  
Traffic Flow Management
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