Methods for creating Intelligent transportation systems using dynamic microlevel model of the city based on a qualitative correspondence matrix

2020 ◽  
Author(s):  
Kobdikova Shamsigul ◽  
Khalilev Rustam ◽  
Arimbekova Perizat
Keyword(s):  
Intelligent Transportation Systems ◽  
Intelligent Transportation ◽  
Transportation Systems ◽  
The City

scholarly journals Edge Computing and IoT Analytics for Agile Optimization in Intelligent Transportation Systems

Energies ◽  
2021 ◽  
Vol 14 (19) ◽  
pp. 6309
Author(s):  
Mohammad Peyman ◽  
Pedro J. Copado ◽  
Rafael D. Tordecilla ◽  
Leandro do C. Martins ◽  
Fatos Xhafa ◽  
...  
Keyword(s):  
Intelligent Transportation Systems ◽  
Smart Cities ◽  
Intelligent Transportation ◽  
Transportation System ◽  
Transportation Systems ◽  
Edge Computing ◽  
Analytical Models ◽  
Current Status ◽  
Ride Sharing ◽  
The City

With the emergence of fog and edge computing, new possibilities arise regarding the data-driven management of citizens’ mobility in smart cities. Internet of Things (IoT) analytics refers to the use of these technologies, data, and analytical models to describe the current status of the city traffic, to predict its evolution over the coming hours, and to make decisions that increase the efficiency of the transportation system. It involves many challenges such as how to deal and manage real and huge amounts of data, and improving security, privacy, scalability, reliability, and quality of services in the cloud and vehicular network. In this paper, we review the state of the art of IoT in intelligent transportation systems (ITS), identify challenges posed by cloud, fog, and edge computing in ITS, and develop a methodology based on agile optimization algorithms for solving a dynamic ride-sharing problem (DRSP) in the context of edge/fog computing.These algorithms allow us to process, in real time, the data gathered from IoT systems in order to optimize automatic decisions in the city transportation system, including: optimizing the vehicle routing, recommending customized transportation modes to the citizens, generating efficient ride-sharing and car-sharing strategies, create optimal charging station for electric vehicles and different services within urban and interurban areas. A numerical example considering a DRSP is provided, in which the potential of employing edge/fog computing, open data, and agile algorithms is illustrated.

IMPROVING ROAD TRAFFIC ORGANIZATION THROUGH THE APPLICATION OF INTELLIGENT TRANSPORTATION SYSTEMS

2021 ◽  
Vol 74 (3) ◽  
pp. 80-86
Author(s):  
L.E. KUSHCHENKO ◽  
◽  
A.S. KAMBUR ◽  
A.A. PEKHOV ◽  
◽  
...  
Keyword(s):  
Travel Time ◽  
Russian Federation ◽  
Intelligent Transportation Systems ◽  
Road Traffic ◽  
Intelligent Transportation ◽  
Transportation Systems ◽  
Environmental Situation ◽  
The Russian Federation ◽  
Traffic Organization ◽  
The City

Examples of the use of ITS in various countries are given, improvements in traffic manage-ment, methods of reducing delays, travel time, as well as improving the environmental situation when using systems are considered. The system «Auto-Intellect», used in the territory of the Russian Federation, is presented. On the example of the city of Belgorod, a method of using ITS is pro-posed, by prohibiting the entry of cars into the city, taking into account certain state license plates.

scholarly journals Building Eco-Cities of the Future: The Example of Masdar City

2015 ◽  
Vol 12 (1) ◽  
Keyword(s):  
Intelligent Transportation Systems ◽  
Fossil Fuels ◽  
Policy Decision ◽  
Intelligent Transportation ◽  
Green Buildings ◽  
Transportation Systems ◽  
Lessons Learned ◽  
The Uk ◽  
Coordination And Collaboration ◽  
The City

Global warming and increased population growth are putting more pressure on policy decision makers to adapt more sustainable approach to planning and designing future cities. This has led to the rise of Eco-Cities that have smart and sustainable infrastructures such as green buildings; intelligent transportation systems; and efficient electricity, water, wastewater, and solid waste networks. In addition these cities should be less dependent on fossil fuels and ensure healthier life and comfort. This paper gives a brief overview on the sustainable design concept of six Eco-cities from around the world such as Vauban in Germany, BedZed in the UK, Sonoma Mountain in California, Dongtan and Tianjin in China, and Sondgo in Korea. Masdar City is discussed in more details including the green buildings, intelligent transportation systems, and other important infrastructure systems. This endeavor requires the managing of complex systems which necessitates the coordination and collaboration of all the stakeholders that are involved designing, constructing, and operating the city. The paper concludes with lessons learned so far from Masdar City.

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.

Quality indicator of the smart city transportation and logistics system

2020 ◽  
Vol 19 (11) ◽  
pp. 2116-2135
Author(s):  
G.V. Savin
Keyword(s):  
Intelligent Transportation Systems ◽  
Smart City ◽  
Urban Areas ◽  
Human Life ◽  
Life Quality ◽  
Intelligent Transportation ◽  
Transportation Systems ◽  
Logistics System ◽  
Organization And Management

Subject. The article considers functioning and development of process flows of transportation and logistics system of a smart city. Objectives. The study identifies factors and dependencies of the quality of human life on the organization and management of stream processes. Methods. I perform a comparative analysis of previous studies, taking into account the uniquely designed results, and the econometric analysis. Results. The study builds multiple regression models that are associated with stream processes, highlights interdependent indicators of temporary traffic and pollution that affect the indicator of life quality. However, the identified congestion indicator enables to predict the time spent in traffic jams per year for all participants of stream processes. Conclusions. The introduction of modern intelligent transportation systems as a component of the transportation and logistics system of a smart city does not fully solve the problems of congestion in cities at the current rate of urbanization and motorization. A viable solution is to develop cooperative and autonomous intelligent transportation systems based on the logistics approach. This will ensure control over congestion, the reduction of which will contribute to improving the life quality of people in urban areas.

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