scholarly journals When Intelligent Transportation Systems Sensing Meets Edge Computing: Vision and Challenges

2021 ◽  
Vol 11 (20) ◽  
pp. 9680
Author(s):  
Xuan Zhou ◽  
Ruimin Ke ◽  
Hao Yang ◽  
Chenxi Liu
Keyword(s):  
Intelligent Transportation Systems ◽  
Intelligent Transportation ◽  
Transportation Systems ◽  
Research Community ◽  
Edge Computing ◽  
Data Driven ◽  
Future Directions ◽  
Critical Part ◽  
Modern Cities ◽  
Computing Vision

The widespread use of mobile devices and sensors has motivated data-driven applications that can leverage the power of big data to benefit many aspects of our daily life, such as health, transportation, economy, and environment. Under the context of smart city, intelligent transportation systems (ITS), such as a main building block of modern cities and edge computing (EC), as an emerging computing service that targets addressing the limitations of cloud computing, have attracted increasing attention in the research community in recent years. It is well believed that the application of EC in ITS will have considerable benefits to transportation systems regarding efficiency, safety, and sustainability. Despite the growing trend in ITS and EC research, a big gap in the existing literature is identified: the intersection between these two promising directions has been far from well explored. In this paper, we focus on a critical part of ITS, i.e., sensing, and conducting a review on the recent advances in ITS sensing and EC applications in this field. The key challenges in ITS sensing and future directions with the integration of edge computing are discussed.

Data-Driven Intelligent Transportation Systems: A Survey

2011 ◽  
Vol 12 (4) ◽  
pp. 1624-1639 ◽  
Author(s):  
Junping Zhang ◽  
Fei-Yue Wang ◽  
Kunfeng Wang ◽  
Wei-Hua Lin ◽  
Xin Xu ◽  
...  
Keyword(s):  
Intelligent Transportation Systems ◽  
Intelligent Transportation ◽  
Transportation Systems ◽  
Data Driven

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.

scholarly journals Security-aware Data-driven Intelligent Transportation Systems

2020 ◽  
pp. 1-1
Author(s):  
Jahanzaib Malik ◽  
Adnan Akhunzada ◽  
Iram Bibi ◽  
Muhammad Talha ◽  
Mian Ahmad Jan ◽  
...  
Keyword(s):  
Intelligent Transportation Systems ◽  
Intelligent Transportation ◽  
Transportation Systems ◽  
Data Driven

scholarly journals From Data to Actions in Intelligent Transportation Systems: A Prescription of Functional Requirements for Model Actionability

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1121
Author(s):  
Ibai Laña ◽  
Javier J. Sanchez-Medina ◽  
Eleni I. Vlahogianni ◽  
Javier Del Ser
Keyword(s):  
Adaptive Learning ◽  
Intelligent Transportation Systems ◽  
Data Science ◽  
Model Development ◽  
Intelligent Transportation ◽  
Transportation Systems ◽  
Data Driven ◽  
Information Flows ◽  
Functional Requirements ◽  
Model Learning

Advances in Data Science permeate every field of Transportation Science and Engineering, resulting in developments in the transportation sector that are data-driven. Nowadays, Intelligent Transportation Systems (ITS) could be arguably approached as a “story” intensively producing and consuming large amounts of data. A diversity of sensing devices densely spread over the infrastructure, vehicles or the travelers’ personal devices act as sources of data flows that are eventually fed into software running on automatic devices, actuators or control systems producing, in turn, complex information flows among users, traffic managers, data analysts, traffic modeling scientists, etc. These information flows provide enormous opportunities to improve model development and decision-making. This work aims to describe how data, coming from diverse ITS sources, can be used to learn and adapt data-driven models for efficiently operating ITS assets, systems and processes; in other words, for data-based models to fully become actionable. Grounded in this described data modeling pipeline for ITS, we define the characteristics, engineering requisites and challenges intrinsic to its three compounding stages, namely, data fusion, adaptive learning and model evaluation. We deliberately generalize model learning to be adaptive, since, in the core of our paper is the firm conviction that most learners will have to adapt to the ever-changing phenomenon scenario underlying the majority of ITS applications. Finally, we provide a prospect of current research lines within Data Science that can bring notable advances to data-based ITS modeling, which will eventually bridge the gap towards the practicality and actionability of such models.

scholarly journals Moving Beyond the Crypto-Currency Success of Blockchain: A Systematic Survey

2021 ◽  
Vol 22 (3) ◽  
pp. 321-346
Author(s):  
Mir Shahnawaz Ahmad ◽  
Shahid Mehraj Shah
Keyword(s):  
Intelligent Transportation Systems ◽  
Intelligent Transportation ◽  
Transportation Systems ◽  
Voting Systems ◽  
Future Directions ◽  
Government Services ◽  
Blockchain Technology ◽  
Crypto Currency ◽  
Comprehensive Survey ◽  
Traditional Technologies

Blockchain (BC) is a technology whose value today is estimated by the success of Bitcoin. However, the spectrum of Blockchain applications goes beyond the financial sector. It has displayed enormous potential for revamping the customary industry with its key merits like decentralization, persistency, anonymity, and auditability. In this paper we conduct a comprehensive survey on the blockchain technology, explaining its structure and functioning. This work has analyzed the potential of BC in seven crucial sectors vis. voting systems, supply chain management, the security of Internet of Things (IoT), healthcare, intelligent transportation systems, government services, and tourism. Moreover, this paper has critically evaluated the traditional technologies used in various sectors, the problems in them, and the benefits that will be provided by the employment of BC. With its future directions, this paper will help researchers to create and realize new value for various sectors that is beyond anything we can imagine with existing technologies.

Autonomous data driven surveillance and rectification system using in-vehicle sensors for intelligent transportation systems (ITS)

2018 ◽  
Vol 139 ◽  
pp. 109-118 ◽  
Author(s):  
Aaqib Khalid ◽  
Tariq Umer ◽  
Muhammad Khalil Afzal ◽  
Sheraz Anjum ◽  
Adnan Sohail ◽  
...  
Keyword(s):  
Intelligent Transportation Systems ◽  
Intelligent Transportation ◽  
Transportation Systems ◽  
Data Driven ◽  
Vehicle Sensors
Sign in / Sign up

Export Citation Format

Share Document