scholarly journals MOBDA: Microservice-Oriented Big Data Architecture for Smart City Transport Systems

2020 ◽  
Vol 4 (3) ◽  
pp. 17 ◽  
Author(s):  
Suriya Priya R. Asaithambi ◽  
Ramanathan Venkatraman ◽  
Sitalakshmi Venkatraman
Keyword(s):  
Big Data ◽  
Intelligent Transportation Systems ◽  
Consumer Satisfaction ◽  
Smart Cities ◽  
Transportation Systems ◽  
Transport Systems ◽  
Hybrid Architecture ◽  
Smart Transportation ◽  
Transport Services ◽  
Data Architecture

Highly populated cities depend highly on intelligent transportation systems (ITSs) for reliable and efficient resource utilization and traffic management. Current transportation systems struggle to meet different stakeholder expectations while trying their best to optimize resources in providing various transport services. This paper proposes a Microservice-Oriented Big Data Architecture (MOBDA) incorporating data processing techniques, such as predictive modelling for achieving smart transportation and analytics microservices required towards smart cities of the future. We postulate key transportation metrics applied on various sources of transportation data to serve this objective. A novel hybrid architecture is proposed to combine stream processing and batch processing of big data for a smart computation of microservice-oriented transportation metrics that can serve the different needs of stakeholders. Development of such an architecture for smart transportation and analytics will improve the predictability of transport supply for transport providers and transport authority as well as enhance consumer satisfaction during peak periods.

Fruitful Synergy Model of Artificial Intelligence and Internet of Thing for Smart Transportation System

2020 ◽  
Vol 4 (1) ◽  
pp. 43-57 ◽  
Author(s):  
Vikram Puri ◽  
Chung Van Le ◽  
Raghvendra Kumar ◽  
Sandeep Singh Jagdev
Keyword(s):  
Artificial Intelligence ◽  
Intelligent Transportation Systems ◽  
Smart Cities ◽  
Urban Transportation ◽  
Fall Detection ◽  
Transportation System ◽  
Transportation Systems ◽  
Support Vector ◽  
Smart Transportation ◽  
Artificial Neural Network Ann

In urban transportation systems, bicycle sharing systems are majorly deployed in major cities of both developed and developing countries. The recent boom of bicycle sharing system along with its upgraded technology have opened new opportunities towards urban transportation system. With the enlargement of intelligent transportation systems (ITS's), smart bicycle sharing schemes are more popular to smart cities as a green transportation mode. In this article, the Internet of Things (IoT) and artificial intelligence-based monitoring devices have been proposed for the bicycles. This system contains a harmful exhaust gas sensor, wireless module, and a GPS receiver and camera that are capable to send data with time and date stamping. In addition, sensor also integrated on the bicycle for the fall detection. An artificial neural network (ANN) and support vector machine (SVM) applied to the data collected at central server is designed to analyze the root mean square error (RMSE), and coefficient of correlation (R2). Result shows that ANN performance is better when compared to SVM.

scholarly journals Smart Cities and Data Analytics for Intelligent Transportation Systems: An Analytical Model for Scheduling Phases and Traffic Lights at Signalized Intersections

2021 ◽  
Vol 11 (15) ◽  
pp. 6816
Author(s):  
Fatih Gunes ◽  
Selim Bayrakli ◽  
Abdul Halim Zaim
Keyword(s):  
Traffic Control ◽  
Intelligent Transportation Systems ◽  
Smart Cities ◽  
Transportation Systems ◽  
Signalized Intersections ◽  
Effective Control ◽  
Main Role ◽  
Smart Transportation ◽  
Traffic Lights ◽  
Service Rates

This paper is intended to improve the performance of signalized intersections, one of the most important systems of traffic control explained within the scope of smart transportation systems. These structures, which have the main role in ensuring the order and flow of traffic, are alternative systems depending on the different methods and techniques used. Determining the operation principles of these systems requires an extremely careful and planned study, considering their important role. Performance outputs obtained from the queue analyses made in previous studies formed the input of this study. The most important techniques are used in the effective control of intersections, such as signal timing: in particular, the use of effective green time and order of the transitions between phases. In this research, a traffic-sensitive signalized intersection control system was designed with the suggested methods against these two problems. The sample intersections were selected from three cities with the highest population density as the case study area. In the analysis of the performance of the connection arms of the selected intersections, flow intensity data were taken into consideration, as well as the arrival and service rates. Based on this, the outputs of the two proposed models regarding the use of phase transitions and effective green durations were compared with two other adaptive control systems and their positive results were shared. The results showed that signalized intersections, operating with a well-planned and correctly chosen technique, better regulate density and queuing.

scholarly journals Transport Inequalities and the Adoption of Intelligent Transportation Systems in Africa: A Research Landscape

2021 ◽  
Vol 13 (22) ◽  
pp. 12891
Author(s):  
Olasupo O. Ajayi ◽  
Antoine B. Bagula ◽  
Hloniphani C. Maluleke ◽  
Isaac A. Odun-Ayo
Keyword(s):  
Systematic Review ◽  
Information And Communication Technologies ◽  
Intelligent Transportation Systems ◽  
Smart Cities ◽  
Intelligent Transportation ◽  
Transportation Systems ◽  
Western World ◽  
Smart Transportation ◽  
African Cities ◽  
The Impact

Intelligent Transportation Systems (ITS), also known as Smart Transportation, is an infusion of information and communication technologies into transportation. ITS are a key component of smart cities, which have seen rapid global development in the last few decades. This has in turn translated to an increase in the deployment and adoption of ITS, particularly in countries in the Western world. Unfortunately, this is not the case with the developing countries of Africa and Asia, where dilapidated road infrastructure, poorly maintained public/mass transit vehicles and poverty are major concerns. However, the impact of Westernization and “imported technologies” cannot be overlooked; thus, despite the aforementioned challenges, ITS have found their way into African cities. In this paper, a systematic review was performed to determine the state of the art of ITS in Africa. The output of this systematic review was then fed into a hybrid multi-criteria model to analyse the research landscape, identify connections between published works and reveal research gaps and inequalities in African ITS. African peculiarities inhibiting the widespread implementation of ITS were then discussed, followed by the development of a conceptual architecture for an integrated ITS for African cities.

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.

A Comprehensive Study on Intelligent Transportation Systems

2018 ◽  
Vol 4 (10) ◽  
pp. 10
Author(s):  
Ankur Mishra ◽  
Aayushi Priya
Keyword(s):  
Intelligent Transportation Systems ◽  
Traffic Congestion ◽  
Transportation Systems ◽  
Environmental Conservation ◽  
Integrated System ◽  
Transport Systems ◽  
Transport Sector ◽  
Intelligent Transport System ◽  
Intelligent Transport ◽  
Transportation Sector

Transportation or transport sector is a legal source to take or carry things from one place to another. With the passage of time, transportation faces many issues like high accidents rate, traffic congestion, traffic & carbon emissions air pollution, etc. In some cases, transportation sector faced alleviating the brutality of crash related injuries in accident. Due to such complexity, researchers integrate virtual technologies with transportation which known as Intelligent Transport System. Intelligent Transport Systems (ITS) provide transport solutions by utilizing state-of-the-art information and telecommunications technologies. It is an integrated system of people, roads and vehicles, designed to significantly contribute to improve road safety, efficiency and comfort, as well as environmental conservation through realization of smoother traffic by relieving traffic congestion. This paper aims to elucidate various aspects of ITS - it's need, the various user applications, technologies utilized and concludes by emphasizing the case study of IBM ITS.

Big Data Processing and Mining for Next Generation Intelligent Transportation Systems

2013 ◽  
Vol 63 (3) ◽  
Author(s):  
Jelena Fiosina ◽  
Maxims Fiosins, Jörg P. Müller
Keyword(s):  
Big Data ◽  
Data Processing ◽  
Real World ◽  
Intelligent Transportation Systems ◽  
Social Impact ◽  
Intelligent Transportation ◽  
Transportation Systems ◽  
Optimization Techniques ◽  
Next Generation ◽  
Big Data Processing

The deployment of future Internet and communication technologies (ICT) provide intelligent transportation systems (ITS) with huge volumes of real-time data (Big Data) that need to be managed, communicated, interpreted, aggregated and analysed. These technologies considerably enhance the effectiveness and user friendliness of ITS, providing considerable economic and social impact. Real-world application scenarios are needed to derive requirements for software architecture and novel features of ITS in the context of the Internet of Things (IoT) and cloud technologies. In this study, we contend that future service- and cloud-based ITS can largely benefit from sophisticated data processing capabilities. Therefore, new Big Data processing and mining (BDPM) as well as optimization techniques need to be developed and applied to support decision-making capabilities. This study presents real-world scenarios of ITS applications, and demonstrates the need for next-generation Big Data analysis and optimization strategies. Decentralised cooperative BDPM methods are reviewed and their effectiveness is evaluated using real-world data models of the city of Hannover, Germany. We point out and discuss future work directions and opportunities in the area of the development of BDPM methods in ITS.

Potential Applications of Intelligent Transportation Systems to Road Freight Transport in Mexico

2000 ◽  
Vol 1707 (1) ◽  
pp. 81-85
Author(s):  
Alberto Mendoza ◽  
Antonio García
Keyword(s):  
Intelligent Transportation Systems ◽  
Intelligent Transportation ◽  
Freight Transport ◽  
Transportation Systems ◽  
Transport Systems ◽  
Environment Friendly ◽  
Generic Term ◽  
Road Freight ◽  
Potential Applications ◽  
Made In

In the past few years, tools have been developed based on different communication means with the purpose of achieving a safer, more efficient, and environment-friendly operation of vehicular flows in the transport systems. Some of the early means generally involved a very strong human participation. In the course of time and with the rapid progress made in electronics, telecommunications, and computer systems, such processes have become automated until generating a series of technologies that currently are incorporated into the single generic term of intelligent transportation systems (ITS). This research has multiple purposes. First, some characteristics of road freight transport in Mexico are presented. Then, with such characteristics under consideration, the ITS technologies with the largest potential for application to that transportation type are described. A vision of future implementation is shown. Finally, some conclusions are presented.

scholarly journals An Advanced Deep Learning Approach for Multi-Object Counting in Urban Vehicular Environments

2021 ◽  
Vol 13 (12) ◽  
pp. 306
Author(s):  
Ahmed Dirir ◽  
Henry Ignatious ◽  
Hesham Elsayed ◽  
Manzoor Khan ◽  
Mohammed Adib ◽  
...  
Keyword(s):  
Deep Learning ◽  
Intelligent Transportation Systems ◽  
Smart Cities ◽  
Weather Conditions ◽  
Research Area ◽  
Transportation Systems ◽  
Detection Accuracy ◽  
Adverse Weather ◽  
Object Counting ◽  
The Impact

Object counting is an active research area that gained more attention in the past few years. In smart cities, vehicle counting plays a crucial role in urban planning and management of the Intelligent Transportation Systems (ITS). Several approaches have been proposed in the literature to address this problem. However, the resulting detection accuracy is still not adequate. This paper proposes an efficient approach that uses deep learning concepts and correlation filters for multi-object counting and tracking. The performance of the proposed system is evaluated using a dataset consisting of 16 videos with different features to examine the impact of object density, image quality, angle of view, and speed of motion towards system accuracy. Performance evaluation exhibits promising results in normal traffic scenarios and adverse weather conditions. Moreover, the proposed approach outperforms the performance of two recent approaches from the literature.

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