Maritime traffic flow simulation in the Intelligent Transportation Systems theme

2014 ◽  
pp. 301-310
Keyword(s):  
Traffic Flow ◽  
Intelligent Transportation Systems ◽  
Flow Simulation ◽  
Intelligent Transportation ◽  
Transportation Systems ◽  
Maritime Traffic ◽  
Traffic Flow Simulation

Traffic flow guidance algorithm in intelligent transportation systems considering the effect of non-floating vehicle

2019 ◽  
Vol 23 (19) ◽  
pp. 9097-9110 ◽  
Author(s):  
Yu-Feng Chen ◽  
Zhan Gao ◽  
Hong Zhou ◽  
Yan Wang ◽  
Tao Zhang ◽  
...  
Keyword(s):  
Traffic Flow ◽  
Intelligent Transportation Systems ◽  
Intelligent Transportation ◽  
Transportation Systems ◽  
Guidance Algorithm

scholarly journals Building the Traffic Flow Network with Taxi GPS Trajectories and Its Application to Identify Urban Congestion Areas for Traffic Planning

2020 ◽  
Vol 13 (1) ◽  
pp. 266
Author(s):  
Jiayu Qin ◽  
Gang Mei ◽  
Lei Xiao
Keyword(s):  
Traffic Flow ◽  
Intelligent Transportation Systems ◽  
Traffic Congestion ◽  
Intelligent Transportation ◽  
Transportation Systems ◽  
Urban Traffic ◽  
Traffic Planning ◽  
Flow Network ◽  
Gps Trajectories ◽  
Congestion Problems

Traffic congestion is becoming a critical problem in urban traffic planning. Intelligent transportation systems can help expand the capacity of urban roads to alleviate traffic congestion. As a key concept in intelligent transportation systems, urban traffic networks, especially dynamic traffic networks, can serve as potential solutions for traffic congestion, based on the complex network theory. In this paper, we build a traffic flow network model to investigate traffic congestion problems through taxi GPS trajectories. Moreover, to verify the effectiveness of the traffic flow network, an actual case of identifying the congestion areas is considered. The results indicate that the traffic flow network is reliable. Finally, several key problems related to traffic flow networks are discussed. The proposed traffic flow network can provide a methodological reference for traffic planning, especially to solve traffic congestion problems.

scholarly journals The Application of Virtual Reality Technology on Intelligent Traffic Construction and Decision Support in Smart Cities

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Gongxing Yan ◽  
Yanping Chen
Keyword(s):  
Big Data ◽  
Traffic Flow ◽  
Intelligent Transportation Systems ◽  
Intelligent Transportation System ◽  
Smart Cities ◽  
Intelligent Transportation ◽  
Transportation System ◽  
Transportation Systems ◽  
The Other ◽  
The City

The core of smart city is to build intelligent transportation system.. An intelligent transportation system can analyze the traffic data with time and space characteristics in the city and acquire rich and valuable knowledge, and it is of great significance to realize intelligent traffic scheduling and urban planning. This article specifically introduces the extensive application of urban transportation infrastructure data in the construction and development of smart cities. This article first explains the related concepts of big data and intelligent transportation systems and uses big data to illustrate the operation of intelligent transportation systems in the construction of smart cities. Based on the machine learning and deep learning method, this paper is aimed at the passenger flow and traffic flow in the smart city transportation system. This paper deeply excavates the time, space, and other hidden features. In this paper, the traffic volume of the random sections in the city is predicted by using the graph convolutional neural network (GCNN) model, and the data are compared with the other five models (VAR, FNN, GCGRU, STGCN, and DGCNN). The experimental results show that compared with the other 4 models, the GCNN model has an increase of 8% to 10% accuracy and 15% fault tolerance. In forecasting morning and evening peak traffic flow, the accuracy of the GCNN model is higher than that of other models, and its trend is basically consistent with the actual traffic volume, the predicted results can reflect the actual traffic flow data well. Aimed at the application of intelligent transportation in an intelligent city, this paper proposes a machine learning prediction model based on big data, and this is of great significance for studying the mechanical learning of such problems. Therefore, the research of this paper has a good implementation prospect and academic value.

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