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A Generic Data-Driven Recommendation Systems for Smart Transportation Applications.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	A Generic Data-Driven Recommendation Systems for Smart Transportation Applications./
作者:	Wan, Xiangpeng.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2021,
面頁冊數:	148 p.
附註:	Source: Dissertations Abstracts International, Volume: 83-02, Section: B.
Contained By:	Dissertations Abstracts International83-02B.
標題:	Engineering. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28491434
ISBN:	9798534672664

A Generic Data-Driven Recommendation Systems for Smart Transportation Applications.
Wan, Xiangpeng.

A Generic Data-Driven Recommendation Systems for Smart Transportation Applications. - Ann Arbor : ProQuest Dissertations & Theses, 2021 - 148 p.

Source: Dissertations Abstracts International, Volume: 83-02, Section: B.

Thesis (Ph.D.)--Stevens Institute of Technology, 2021.

This item must not be sold to any third party vendors.

Current urbanization trends are leading to heightened demand of smarter technologies to facilitate a variety of applications in smart cities and, particularly, Intelligent Transportation Systems (ITS). Automated crowdsensing constitutes a strong base for ITS applications by providing novel and rich data streams regarding congestion tracking, public transportation monitoring, and real-time navigation. This high amount of data can be collected from diverse heterogenous sources such as infrastructure-based sensors, vehicle social network, human input, official agents, and social media. This abundant and diverse data can be exploited to help drivers, passengers, and ITS users in general assess the situation of ITS systems, evaluate traffic network statuses, improve the navigation of vehicles in urban areas, and enhance the quality of services of several transportation means. Thanks to the spread of on-board and infrastructure-based sensors, collecting and sharing data become very common, especially in urban areas, where several novel data-driven applications exist including Google Navigation, Waze, and parking localization service. Research related to Vehicular Social Networks (VSN) has emerged in parallel with the implementation and exploration of Internet of Things (IoT) technology in ITS. VSNs represent a solution that will improve the information sharing among drivers through vehicular communication technologies. Data collection and propagation techniques have improved in recent years. In the past, most relied exclusively on a single entity, while most of them now involve the collaboration from multiple participants, including road users, roadside units, and web-based information to combat traffic congestion. For example, Waze built a navigation application that collects vehicle speed, while offering user input functionality to report traffic conditions for other drivers. One drawback to this application is that incident reporting requires manual input from the user, which is dependent on the participation and veracity of the user. The manual input of data by a driver into a mobile device during operation of a vehicle is also inherently unsafe. Input synchronization with local authorities would also improve the performance of the application. In our framework, the area of interest is managed by a central data warehouse server with multiple functionalities including acceptance, processing, and preparation of data from multiple sources in order to monitor urban traffic conditions in real-time and we propose to employ optimization and artificial intelligence techniques to further enhance several ITS applications.

ISBN: 9798534672664Subjects--Topical Terms:

586835
Engineering.
Subjects--Index Terms:

IntelligentTransportation Systems

A Generic Data-Driven Recommendation Systems for Smart Transportation Applications.
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