Smart cities, urban sensing, and big data: mining geo-location in social networks

Sacco, Daniele; Motta, Gianmario; You, Linlin; Bertolazzo, N.; Carini, F.; Ma, Tianyi

doi:10.1016/b978-0-12-812013-2.00005-8

Cited by 9 publications

(7 citation statements)

References 43 publications

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“…Other Self-Organizing Maps (SOM) are unsupervised ANN that reduce the input dimensionality with the aim of representing distribution as a "map", where similar points are mapped carefully together [53].…”

Section: Artificial Neural Network (Ann)mentioning

confidence: 99%

A review of Machine Learning (ML) algorithms used for modeling travel mode choice

Pineda-Jaramillo

2019

DYNA

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In recent decades, transportation planning researchers have used diverse types of machine learning (ML) algorithms to research a wide range of topics. This review paper starts with a brief explanation of some ML algorithms commonly used for transportation research, specifically Artificial Neural Networks (ANN), Decision Trees (DT), Support Vector Machines (SVM) and Cluster Analysis (CA). Then, these different methodologies used by researchers for modeling travel mode choice are collected and compared with the Multinomial Logit Model (MNL) which is the most commonly-used discrete choice model. Finally, the characterization of ML algorithms is discussed and Random Forest (RF), a variant of Decision Tree algorithms, is presented as the best methodology for modeling travel mode choice.

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Section: Artificial Neural Network (Ann)mentioning

confidence: 99%

A review of Machine Learning (ML) algorithms used for modeling travel mode choice

Pineda-Jaramillo

2019

DYNA

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“…A recent trend in data collection is the adoption of humans as sensors through dedicated platforms or social media [20]. Aguilera et al [16] developed a platform that promotes the use of human sensors for implementing participatory data services, while Sacco et al [42] described an information service for mining Tweets with the intention of understanding human behavior. The influence of social media platforms and the ubiquity of smartphones contribute to the challenge of exploiting heterogeneous data sources.…”

Section: Related Workmentioning

confidence: 99%

Integration and Exploitation of Sensor Data in Smart Cities through Event-Driven Applications

Alvarez

Morales

Kraak

2019

Sensors

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Smart cities are urban environments where Internet of Things (IoT) devices provide a continuous source of data about urban phenomena such as traffic and air pollution. The exploitation of the spatial properties of data enables situation and context awareness. However, the integration and analysis of data from IoT sensing devices remain a crucial challenge for the development of IoT applications in smart cities. Existing approaches provide no or limited ability to perform spatial data analysis, even when spatial information plays a significant role in decision making across many disciplines. This work proposes a generic approach to enabling spatiotemporal capabilities in information services for smart cities. We adopted a multidisciplinary approach to achieving data integration and real-time processing, and developed a reference architecture for the development of event-driven applications. This type of applications seamlessly integrates IoT sensing devices, complex event processing, and spatiotemporal analytics through a processing workflow for the detection of geographic events. Through the implementation and testing of a system prototype, built upon an existing sensor network, we demonstrated the feasibility, performance, and scalability of event-driven applications to achieve real-time processing capabilities and detect geographic events.

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“…When compared to other visual technology, parallel coordinate graph is well ahead in term of time taken to analyze its data [17]. Parallel coordinate alsowork well with many of visual analytic technology such as filtering, sorting, zooming, slicing, dicing and brushing [18][19].…”

Section: Background Studymentioning

confidence: 99%

Visual analytics: designing flexible filtering in parallel coordinate graph

Idrus¹,

Zainuddin²,

Jaafar³

2018

J. Fundam and Appl Sci.

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Data visualization is a technique of creating visual image big data. The visualization reveals hiddenprocess is known as visual analytics.that is congested and hardly reveal the data patterns.flexibility to users to control over the others. However, most of the filtering is has designed a structured process for formulating graph since it is a widely used technique for visualizing m flexible visual filtering presentations for parallel coordinate graph have finding support a wide range of visual analytics needs in parallel coordinated graph.

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Smart cities, urban sensing, and big data: mining geo-location in social networks

Cited by 9 publications

References 43 publications

A review of Machine Learning (ML) algorithms used for modeling travel mode choice

A review of Machine Learning (ML) algorithms used for modeling travel mode choice

Integration and Exploitation of Sensor Data in Smart Cities through Event-Driven Applications

Visual analytics: designing flexible filtering in parallel coordinate graph

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