فایل ورد کامل دریافت اطلاعات ازدحام دستگاه های موبایل برای بررسی تحرک های شهری به صورت هوشمند

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تعداد صفحات این فایل: ۲۲ صفحه

بخشی از مقاله انگلیسیعنوان انگلیسی:Mobile crowd sensing for smart urban mobility~~en~~

Abstract

In this paper, we present the application of the mobile crowd-sensing paradigm in supporting efficient, safe and green mobility in urban environments. We have developed the CitySensing framework demonstrating the viability of a common crowd-sourcing platform applied to various urban mobility domains. We argue that today’s mobile devices, with integrated or add-on sensors, can be efficiently used to crowd source diverse information in domains that are relevant to urban life and mobility (traffic, air quality and citizens’ everyday activities). This is illustrated by three distinct mobile applications, developed on top of the CitySensing framework, that contribute to a common goal of smarter urban mobility. Commonly integrated accelerometer and GPS are used to infer traffic events and conditions. Externally attached or integrated air quality sensors enable suggestions for city areas adequate for outdoor activities on a specific day of the week or hour of the day. Mobile phone usage statistics and analysis can present valuable information to urban planning services to better adapt to citizens’ habits and mobility. The analysis of this massive amount of crowd sensed data (so-called Big Data) within the cluster/cloud infrastructure enables detection of situations and events that influence human mobility, and dissemination of notifications and recommended actions.

۱ Introduction

With advancements and the proliferation of mobile devices with increasing computing, communication and sensing capabilities, mobile users have become important sources of sensing data in a globally spread wireless sensor network (Campbell et al. 2008). Mobile crowd sensing represents an approach for a new sensing and geo-crowdsourcing paradigm that leverages the power of various mobile devices, such as smartphones, tablets, wearable devices, smart sensors, etc. It is based on the human ability to acquire local geospatial information and knowledge through sensor-enhanced mobile devices, and the possibility to share this information/knowledge with other users and a wide community (Goodchild 2007; Kamel Boulos et al. 2011). As the world population becomes increasingly urban, cities worldwide need to leverage information and communications technologies to improve their functions, enhance efficiency, improve competitiveness and the economy and provide better environment for their citizens; to become ‘smarter’. Smart mobility is one of the key characteristics of a smart city, and also one of the biggest challenges that smart cities face (Batty et al. 2012). The world population makes more than 64% of all travel kilometers within urban environments, which is expected to triple by 2050. Thus, a high priority for cities around the world is to support citizens’ mobility within the urban environment based on safety, efficiency and environmental protection (Motta et al. 2015). Mobile crowd sensing paradigm will enable key methods, techniques and systems in that direction. The application of analysis, reasoning and data mining techniques on the mobile crowd sensed data provides useful insights in citizens’ mobility and supports better citizen involvement in monitoring urban spaces.

In this article, we describe main concepts, methods and technologies of mobile crowd sensing in smart cities and present the CitySensing framework developed to support smart urban mobility through participation and intelligence of the crowd (Section 2). Several mobile demo applications have been developed based on this framework that illustrate the use of mobile crowd sensing in different city’s scenarios and provide insights how such applications could improve citizens’ mobility (Section 3). Although there are still open challenges and real-life deployments of developed applications, the initial evaluations look promising in supporting smart citizens’ mobility, as concluded in Section 4.

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