scholarly journals Applications of Internet of Things

2018 ◽  
Vol 7 (9) ◽  
pp. 334
Author(s):  
Chi-Hua Chen ◽  
Kuen-Rong Lo
Keyword(s):  
Internet Of Things ◽  
Intelligent Transportation Systems ◽  
Traffic Congestion ◽  
Location Privacy ◽  
High Efficiency ◽  
Transportation Systems ◽  
Location Based Services ◽  
Vehicle Operation ◽  
Vehicle Positioning ◽  
Coverage Estimation

This editorial introduces the special issue entitled “Applications of Internet of Things”, of ISPRS International Journal of Geo-Information. Topics covered in this issue include three main parts: (I) intelligent transportation systems (ITS), (II) location-based services (LBS), and (III) sensing techniques and applications. Three papers on ITS are as follows: (1) “Vehicle positioning and speed estimation based on cellular network signals for urban roads,” by Lai and Kuo; (2) “A method for traffic congestion clustering judgment based on grey relational analysis,” by Zhang et al.; and (3) “Smartphone-based pedestrian’s avoidance behavior recognition towards opportunistic road anomaly detection,” by Ishikawa and Fujinami. Three papers on LBS are as follows: (1) “A high-efficiency method of mobile positioning based on commercial vehicle operation data,” by Chen et al.; (2) “Efficient location privacy-preserving k-anonymity method based on the credible chain,” by Wang et al.; and (3) “Proximity-based asynchronous messaging platform for location-based Internet of things service,” by gon Jo et al. Two papers on sensing techniques and applications are as follows: (1) “Detection of electronic anklet wearers’ groupings throughout telematics monitoring,” by Machado et al.; and (2) “Camera coverage estimation based on multistage grid subdivision,” by Wang et al.

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.

scholarly journals A Multi-Loop Vehicle-Counting Method under Gray Mode and RGB Mode

2021 ◽  
Vol 11 (15) ◽  
pp. 6831
Author(s):  
Yue Chen ◽  
Jian Lu
Keyword(s):  
Intelligent Transportation Systems ◽  
High Efficiency ◽  
Road Traffic ◽  
Low Cost ◽  
Rapid Development ◽  
Intelligent Transportation ◽  
Transportation Systems ◽  
Counting Method ◽  
Moving Vehicle ◽  
Available Information

With the rapid development of road traffic, real-time vehicle counting is very important in the construction of intelligent transportation systems (ITSs). Compared with traditional technologies, the video-based method for vehicle counting shows great importance and huge advantages in its low cost, high efficiency, and flexibility. However, many methods find difficulty in balancing the accuracy and complexity of the algorithm. For example, compared with traditional and simple methods, deep learning methods may achieve higher precision, but they also greatly increase the complexity of the algorithm. In addition to that, most of the methods only work under one mode of color, which is a waste of available information. Considering the above, a multi-loop vehicle-counting method under gray mode and RGB mode was proposed in this paper. Under gray and RGB modes, the moving vehicle can be detected more completely; with the help of multiple loops, vehicle counting could better deal with different influencing factors, such as driving behavior, traffic environment, shooting angle, etc. The experimental results show that the proposed method is able to count vehicles with more than 98.5% accuracy while dealing with different road scenes.

The convergence of Internet of Things, Blockchain and Connected Vehicles: Conceptual Advantages and Disadvantages of a new Cooperative Intelligent Transportation System

2021 ◽  
Vol 03 (01) ◽  
pp. 33-41
Author(s):  
Vittorio Astarita ◽  
Vincenzo Pasquale Giofrè ◽  
Giuseppe Guido ◽  
Alessandro Vitale
Keyword(s):  
Internet Of Things ◽  
Intelligent Transportation Systems ◽  
Intelligent Transportation System ◽  
Main Idea ◽  
Intelligent Transportation ◽  
Transportation Systems ◽  
Connected Vehicles ◽  
City Management ◽  
Advantages And Disadvantages ◽  
Blockchain Technology

This paper intends to explore the convergence of some technological innovations that could lead to new cooperative Intelligent Transportation Systems (ITS). The technologies that might soon converge and lead to some new developments are: the Blockchain Technology (BT) concept, Internet of Things (IoT) and Connected and Automated Vehicles (CAV). Advantages and disadvantages of the new concepts founding a new ITS system are discussed in this conceptual paper. Blockchain technology has been recently introduced and many research ideas have been presented for application in the transportation sector. In this paper, we discuss a system that is based on a dedicated blockchain, able to involve both drivers and city administrations in the adoption of promising and innovative technologies that will create cooperation among connected vehicles. The proposed blockchain-based system can allow city administrators to reward drivers when they are willing to share travel data. The system manages in a special way the creation of rewards which are assigned to drivers and institutions participating actively in the system. Moreover, the system allows keeping a complete track of all transactions and interactions between drivers and city management on a completely open and shared platform. The main idea is to combine connected vehicles with BT to promote Cooperative ITS use, a better use of infrastructures and a more sustainable eco-system of cryptocurrencies. A short description of BT is introduced to evidence energy problems of sustainability in the implementation of Proof of Work (PoW) that is adopted by many blockchains.

Smart Traffic Management for Collision Avoidance Using V2V

2021 ◽  
Vol 11 (1) ◽  
pp. 01-07
Author(s):  
V. Naren Thiruvalar ◽  
E. Vimal
Keyword(s):  
Intelligent Transportation Systems ◽  
Traffic Congestion ◽  
Traffic Management ◽  
Emergency Situation ◽  
Transportation Systems ◽  
Pollution Monitoring ◽  
Inappropriate Use ◽  
V2v Communication ◽  
Use Of Resources ◽  
Smart Traffic

The main objective of this project is to connect the vehicles together and avoid accidents by using V2V Communication. The vehicles are to be connected together by means of DSRC algorithm which is used for transceiving alert messages among the connected vehicles, in case of any emergency situation such as accidents. The Vehicle-to-Vehicle (V2V) and Vehicle-to- Infrastructure (V2I) technologies are specific cases of IoT and key enablers for Intelligent Transportation Systems (ITS). V2V and V2I have been widely used to solve different problems associated with transportation in cities, in which the most important is traffic congestion. A high percentage of congestion is usually presented by the inappropriate use of resources in vehicular infrastructure. In addition, the integration of traffic congestion in decision making for vehicular traffic is a challenge due to its high dynamic behaviour. An increase in the infrastructure growth is a possible solution but turns out to be costly in terms of both time and effort. Various applications that target transport efficiency could make use of the vast information collected by vehicles: safety, traffic management, pollution monitoring, tourist information, etc.

Parametric Evaluation of Internet of Things Applications to Freight Transportation Using Model-Based Systems Engineering

2021 ◽  
pp. 036119812110491
Author(s):  
Aldo D. Fabregas ◽  
Paul Crawford ◽  
Rodrigo Mesa ◽  
Agustina Calatayud
Keyword(s):  
Internet Of Things ◽  
Systems Engineering ◽  
Intelligent Transportation Systems ◽  
Management Plan ◽  
Transportation Systems ◽  
Interface Control ◽  
Current Trends ◽  
Industrial Internet ◽  
Proposed Model ◽  
Model Based Systems Engineering

The intended purpose of an intelligent transportation systems (ITS) project is to automate operations through device-to-device connectivity. These devices generally represent stakeholder’s endpoints and expose interfaces to automated operations. Current trends in communication allow more applications and devices to perform functions traditionally allocated to the transportation ITS infrastructure. This connected environment of industrial internet of things presents design challenges because of the diversity of stakeholders, interfaces, and the messages among them. This may require new ways in which ITS planning can handle the scale and complexity of these highly connected systems. This work focuses on the modeling of the architecture of a freight-focused ITS application, including maritime ports. The proposed model integrates stakeholders, behaviors, and messages using the Systems Modeling Language (SysML). The key contribution of this work is to demonstrate the creation of an executable SysML model for an ITS application without sacrificing the typical Systems Engineering Management Plan artifacts (e.g., requirements traceability matrices and interface control documents). At the same time, the proposed model provides a re-usable pattern to support parametric analysis of candidate architectures with respect to any measure of effectiveness. This allows establishing a single source of architecture definition and having multiple architecture specializations depending on the measure of effectiveness being evaluated. Recommendations for implementation and integration with existing ITS tools are provided.

scholarly journals Deep Autoencoder Neural Networks for Short-Term Traffic Congestion Prediction of Transportation Networks

Sensors ◽  
2019 ◽  
Vol 19 (10) ◽  
pp. 2229 ◽  
Author(s):  
Sen Zhang ◽  
Yong Yao ◽  
Jie Hu ◽  
Yong Zhao ◽  
Shaobo Li ◽  
...  
Keyword(s):  
Neural Network ◽  
Intelligent Transportation Systems ◽  
Traffic Congestion ◽  
Large Scale ◽  
Transportation Networks ◽  
Transportation Network ◽  
Transportation Systems ◽  
Neural Network Models ◽  
Computation Efficiency ◽  
Congestion Prediction

Traffic congestion prediction is critical for implementing intelligent transportation systems for improving the efficiency and capacity of transportation networks. However, despite its importance, traffic congestion prediction is severely less investigated compared to traffic flow prediction, which is partially due to the severe lack of large-scale high-quality traffic congestion data and advanced algorithms. This paper proposes an accessible and general workflow to acquire large-scale traffic congestion data and to create traffic congestion datasets based on image analysis. With this workflow we create a dataset named Seattle Area Traffic Congestion Status (SATCS) based on traffic congestion map snapshots from a publicly available online traffic service provider Washington State Department of Transportation. We then propose a deep autoencoder-based neural network model with symmetrical layers for the encoder and the decoder to learn temporal correlations of a transportation network and predicting traffic congestion. Our experimental results on the SATCS dataset show that the proposed DCPN model can efficiently and effectively learn temporal relationships of congestion levels of the transportation network for traffic congestion forecasting. Our method outperforms two other state-of-the-art neural network models in prediction performance, generalization capability, and computation efficiency.

scholarly journals Blockchain-based Reputation for Intelligent Transportation Systems

Sensors ◽  
2020 ◽  
Vol 20 (3) ◽  
pp. 791 ◽  
Author(s):  
Liviu-Adrian Hîrţan ◽  
Ciprian Dobre ◽  
Horacio González-Vélez
Keyword(s):  
Internet Of Things ◽  
San Francisco ◽  
Intelligent Transportation Systems ◽  
Routing Algorithm ◽  
Intelligent Transportation ◽  
Transportation Systems ◽  
Traffic Information ◽  
Disruptive Technology ◽  
Average Speed ◽  
The Impact

A disruptive technology often used in finance, Internet of Things (IoT) and healthcare, blockchain can reach consensus within a decentralised network—potentially composed of large amounts of unreliable nodes—and to permanently and irreversibly store data in a tamper-proof manner. In this paper, we present a reputation system for Intelligent Transportation Systems (ITS). It considers the users interested in traffic information as the main actors of the architecture. They securely share their data which are collectively validated by other users. Users can choose to employ either such crowd-sourced validated data or data generated by the system to travel between two locations. The data saved is reliable, based on the providers’ reputation and cannot be modified. We present results with a simulation for three cities: San Francisco, Rome and Beijing. We have demonstrated the impact of malicious attacks as the average speed decreased if erroneous information was stored in the blockchain as an implemented routing algorithm guides the honest cars on other free routes, and thus crowds other intersections.

scholarly journals Vehicular Ad hoc Network for Intelligent Transport System: A review

2018 ◽  
Vol 7 (4.36) ◽  
pp. 350
Author(s):  
Mohammed Saad Talib ◽  
Aslinda Hassan ◽  
Burairah Hussin ◽  
Ali Abdul-Jabbar Mohammed ◽  
Ali Abdulhussian Hassan ◽  
...  
Keyword(s):  
Traffic Safety ◽  
Intelligent Transportation Systems ◽  
Vehicular Ad Hoc Networks ◽  
Ad Hoc ◽  
High Efficiency ◽  
New Technologies ◽  
Negative Impact ◽  
Transportation Systems ◽  
Transport Systems ◽  
Intelligent Transport System

the numbers of accidents are increasing in an exponential manner with the growing of vehicles numbers on roads in recent years.  This huge number of vehicles increases the traffic congestion rates. Therefore, new technologies are so important to reduce the victims in the roads and improve the traffic safety. The Intelligent Transportation Systems (ITS) represents an emerging technology to improve the road's safety and traffic efficiency. ITS have various safety and not safety applications. Numerous methods are intended to develop the smart transport systems. The crucial form is the Vehicular Ad hoc Networks (VANET). VANET is becoming the most common network in ITS. It confirms human’s safety on streets by dissemination protection messages among vehicles. Optimizing the traffic management operations represent an urgent issue in this era a according to the massive growing in number of circulating vehicles, traffic congestions and road accidents. Street congestions can have significant negative impact on the life quality, passenger's safety, daily activities, economic and environmental for citizens and organizations. Current progresses in communication and computing paradigms fetched the improvement of inclusive intelligent devices equipped with wireless communication capability and high efficiency processors.  

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