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

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 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.

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.

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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