Information extraction for traffic congestion in social network: Case study: Bekasi city

Alifi, Muhammad Riza; Supangkat, Suhono Harso

doi:10.1109/ictss.2016.7792848

Cited by 13 publications

(7 citation statements)

References 7 publications

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“…Bi-LSTM-CNN mengkombinasikan Bidirectional Recurrent Neural Network dengan LSTM untuk mentransformasikan kata sesuai karakteristiknya menjadi sebuah entitas nama secara dua arah menggunakan jaringan LSTM maju dan jaringan LSTM mundur, serta CNN yang mampu memodelkan informasi hingga level karakter suatu kata (Chiu & Nichols, 2016). Atribut-atribut berita yang diperlukan diambil sebagai informasi menggunakan pendekatan rule-based (Alifi & Supangkat, 2016). Selain itu, digunakan fitur tambahan berupa Part-of-speech (POS) Tagging untuk meningkatkan performa model NER (Rifani et al, 2019).…”

Section: Data Dan Sumber Dataunclassified

Analisis Pola Kecelakaan Lalu Lintas Menggunakan Algoritma Decision Tree Berdasarkan Ekstraksi Informasi dari Berita Online Menggunakan Named Entity Recognition (NER)

Susanto,

Yuniarto

2023

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Jalan tol sebagai jalan bebas hambatan tidak membuat jalan tol terbebas dari kecelakaan lalu lintas. Di Provinsi Jawa Barat, terdapat total 20 ruas tol dengan panjang ruas tol mencapai 521,15 km pada 2020. Tol Cipali adalah salah satu ruas dengan fatalitas tertinggi di dunia. Penanggulangan kecelakaan lalu lintas penting dilakukan sebagai upaya untuk mengurangi kejadian kecelakaan lalu lintas. Namun, data official mengenai kecelakaan lalu lintas di jalan tol oleh lembaga resmi tidak tersedia secara rinci, sehingga digunakan sumber data alternatif seperti berita online. NER dengan Bi-LSTM-CNN dimanfaatkan untuk melakukan ekstraksi data kecelakaan. Hasil dari ekstraksi berita dianalisis dengan membuat aturan keputusan untuk mengetahui pola kecelakaan yang terjadi. Aturan keputusan ini berupa decision tree dengan dataset yang menggunakan data tiga tol dengan fatalitas tertinggi dengan fitur imputasi mode by concept sebagai metode penanganan missing value dan jalan tol sebagai atribut, menghasilkan nilai f1-score sebesar 67,76% dan nilai akurasi sebesar 75,49%.

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Section: Data Dan Sumber Dataunclassified

Analisis Pola Kecelakaan Lalu Lintas Menggunakan Algoritma Decision Tree Berdasarkan Ekstraksi Informasi dari Berita Online Menggunakan Named Entity Recognition (NER)

Susanto,

Yuniarto

2023

semnasoffstat

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“…As the global urban population is projected to surge to 66 % or 70 % by 2050 (O'Dwyer et al, 2019), there are escalating concerns about the environmental, managerial, and security impacts. In response to this challenge, smart cities which heavily rely on information and communication technologies (ICTs) have been proposed and realised in various nations (Aguilera et al, 2017;Alifi & Supangkat, 2016;Al_Turjman & Baali, 2022;Galindo, 2014;Yamakami, 2017;Szpilko et al, 2020). Smart cities integrate different technologies, such as the Internet of Things, blockchain, artificial intelligence, machine learning (ML), and deep reinforcement learning (DRL), to provide comprehensive solutions (Alam et al, 2017;Ali et al, 2020;Allam & Dhunny, 2019;Liu et al, 2019;Bilan et al, 2022).…”

Section: Literature Reviewmentioning

confidence: 99%

Artificial Intelligence in the Smart City — A Literature Review

Szpilko,

Naharro,

Lăzăroiu

et al. 2023

Engineering Management in Production and Services

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The influence of artificial intelligence (AI) in smart cities has resulted in enhanced efficiency, accessibility, and improved quality of life. However, this integration has brought forth new challenges, particularly concerning data security and privacy due to the widespread use of Internet of Things (IoT) technologies. The article aims to provide a classification of scientific research relating to artificial intelligence in smart city issues and to identify emerging directions of future research. A systematic literature review based on bibliometric analysis of Scopus and Web of Science databases was conducted for the study. Research query included TITLE-ABS-KEY (“smart city” AND “artificial intelligence”) in the case of Scopus and TS = (“smart city” AND “artificial intelligence”) in the case of the Web of Sciences database. For the purpose of the analysis, 3101 publication records were qualified. Based on bibliometric analysis, seven research areas were identified: safety, living, energy, mobility, health, pollution, and industry. Urban mobility has seen significant innovations through AI applications, such as autonomous vehicles (AVs), electric vehicles (EVs), and unmanned aerial vehicles (UAVs), yet security concerns persist, necessitating further research in this area. AI’s impact extends to energy management and sustainability practices, demanding standardised regulations to guide future research in renewable energy adoption and developing integrated local energy systems. Additionally, AI’s applications in health, environmental management, and the industrial sector require further investigation to address data handling, privacy, security, and societal implications, ensuring responsible and sustainable digitisation in smart cities.

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“…2) Machine learning (ML) a) Supervised and Unsupervised ML: Researchers in [12] and [13] used the SVM algorithm to classify tweets as relevant and irrelevant to traffic. But they don't detect event types.…”

Section: A Event Detection Techniquesmentioning

confidence: 99%

“…By Keywords [8], [16], [18], [19], [41], [22], [30], By accounts [9], [10], [14], [15], [17] By Geo-coordinates [2], [25], [42] Using Machine Learning [46], [32], [33], [40], [29], [20], [21], [34], [12], [13] TABLE III.…”

Section: During Data Collectionmentioning

confidence: 99%

Smart Cities, Smarter Roads: A Review of Leveraging Cutting-Edge Technologies for Intelligent Event Detection from Social Media

Alomari,

Mehmood

2023

IJACSA

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The rapidly evolving landscape of smart cities and intelligent transportation systems makes the timely detection of traffic events a critical element for optimizing urban mobility. Furthermore, social media emerges as a valuable source of realtime information, with users acting as active sensors who spontaneously share observations and experiences related to traffic incidents. This review paper offers a comprehensive understanding of the state-of-the-art in traffic event detection from social media. The paper explores leveraging cutting-edge technologies including machine learning, and deep learning with big data technologies and high-performance computing. The discussion unfolds with an in-depth examination of the recent approaches for event detection followed by an exploration of the techniques of spatio-temporal information extraction and sentiment analysis, which are both considered fundamental aspects in enhancing the contextual understanding of traffic events. Further, the review explores the pivotal role of big data technologies in addressing scalability challenges inherent in the vast expanse of social data. The examination encompasses how big data frameworks facilitate efficient storage, processing, and analysis of large-scale social media datasets, thereby empowering machine learning and deep learning models for robust and realtime traffic event detection. Subsequently, the challenges and future directions have been highlighted. Addressing these challenges and leveraging advanced technologies, facilitates the proactive detection and management of these events, paving the way for smart mobility systems.

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Information extraction for traffic congestion in social network: Case study: Bekasi city

Cited by 13 publications

References 7 publications

Analisis Pola Kecelakaan Lalu Lintas Menggunakan Algoritma Decision Tree Berdasarkan Ekstraksi Informasi dari Berita Online Menggunakan Named Entity Recognition (NER)

Analisis Pola Kecelakaan Lalu Lintas Menggunakan Algoritma Decision Tree Berdasarkan Ekstraksi Informasi dari Berita Online Menggunakan Named Entity Recognition (NER)

Artificial Intelligence in the Smart City — A Literature Review

Smart Cities, Smarter Roads: A Review of Leveraging Cutting-Edge Technologies for Intelligent Event Detection from Social Media

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