Real-Time Android Application for Traffic Density Estimation

Kerouh, Fatma; Ziou, Djemel

doi:10.1109/access.2018.2868610

Cited by 10 publications

(4 citation statements)

References 9 publications

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“…For traffic prediction and differentiation of traffic congestion levels, the authors employed the 14 on-road sensors named (D1–D14) at multiple spots. In [ 29 ], the authors discussed the use of surveillance cameras for traffic data collection, which uses more advanced sensors, such as surveillance cameras. For deriving the traffic information from video sources, these devices use computer vision techniques.…”

Section: Related Workmentioning

confidence: 99%

“…The authors of [ 20 , 21 , 23 , 25 ] estimated traffic by using computer vision algorithms. In [ 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 ], the authors counted automobiles by using traffic cameras and live video feeds.…”

Section: Related Workmentioning

confidence: 99%

“…In addition, research was conducted into the urban and highway-based estimating systems, as well as the relevant design strategies for TDE. The authors of [ 25 , 26 , 27 , 28 , 29 , 30 , 31 ] worked on urban scenarios, whereas the authors of [ 24 , 25 , 32 , 34 , 39 , 41 ] presented estimations based on the highway scenario. In reference, Live data was utilized by the authors in the sources in order to carry out the distributed state observers of TDE.…”

Section: Related Workmentioning

confidence: 99%

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A Novel Markov Model-Based Traffic Density Estimation Technique for Intelligent Transportation System

Beenish

Javid

Fahad

et al. 2023

Sensors

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An intelligent transportation system (ITS) aims to improve traffic efficiency by integrating innovative sensing, control, and communications technologies. The industrial Internet of things (IIoT) and Industrial Revolution 4.0 recently merged to design the industrial Internet of things–intelligent transportation system (IIoT-ITS). IIoT sensing technologies play a significant role in acquiring raw data. The application continuously performs the complex task of managing traffic flows effectively based on several parameters, including the number of vehicles in the system, their location, and time. Traffic density estimation (TDE) is another important derived parameter desirable to keep track of the dynamic state of traffic volume. The expanding number of vehicles based on wireless connectivity provides new potential to predict traffic density more accurately and in real time as previously used methodologies. We explore the topic of assessing traffic density by using only a few simple metrics, such as the number of surrounding vehicles and disseminating beacons to roadside units and vice versa. This research paper investigates TDE techniques and presents a novel Markov model-based TDE technique for ITS. Finally, an OMNET++-based approach with an implementation of a significant modification of a traffic model combined with mathematical modeling of the Markov model is presented. It is intended for the study of real-world traffic traces, the identification of model parameters, and the development of simulated traffic.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

A Novel Markov Model-Based Traffic Density Estimation Technique for Intelligent Transportation System

Beenish

Javid

Fahad

et al. 2023

Sensors

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“…Saat ini Android sudah banyak dimiliki oleh banyak masyarakat, terlebih lagi mahasiswa. Untuk mengoptimalkan fungsi dari Android selain untuk jejaring sosial, ada baiknya jika Android dilengkapi fasilitas video streaming untuk proses streaming [6] [10].…”

Section: Pendahuluanunclassified

Pengembangan Purwarupa Aplikasi Layanan Video Streaming Berbasis Mobile Melalui Jaringan WLAN

Ananta

2018

CBN

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Perkembangan teknologi video yang sangat signifikan memungkinkan untuk diimplementaskkan dalam salah satu metode untuk menunjang kegiatan perkuliahan terutama teknologi streaming yang mampu memberikan layanan kepada banyak pengguna untuk menyaksikan isi video terutama konten video pendidikan. Tujuan ini dapat dicapai dengan pengembangan purwarupa aplikasi layanan video streaming berbasis mobile melalui jaringan wireless secara lokal di internal kampus Fakultas Ilmu Komputer, Universitas Brawijaya. Metode pengembangan purwarupa aplikasi layanan video streaming ini menggunakan model Prototyping SDLC. Untuk mengetahui performa sitem layanan streaming yang dikembangkan, pada penelitian ini dilakukan pengujian non fungsional berupa pengukuran dan perbandingan performa sistem yang mengacu pada beberapa metrik QoS yaitu nilai delay, jitter, packet loss, dan throughput pada kondisi jaringan tanpa gangguan dan kondisi jaringan pada jam aktif perkuliahan. Pengujian non fungsional menunjukkan bahwa nilai jitter, delay, dan packet loss lebih kecil dan nilai throughput pada kondisi pertama lebih besar dibandingkan dengan kondisi kedua. Hal ini dikarenakan pada kondisi kedua terjadi fluktuasi traffic jaringan kampus FILKOM UB karena aktivitas dari user yang berbeda-beda pada kondisi jam aktif perkuliahan. Di sisi lain, hasil pengujian fungsional menunjukkan bahwa semua fitur yang ada pada aplikasi dapat berjalan dengan baik sesuai dengan kebutuhan fungsional.

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