An Algorithm based on VANET Technology to Count Vehicles Stopped at a Traffic Light

Contreras, Manuel; Gamess, Eric

doi:10.1007/s13177-019-00184-3

Cited by 11 publications

(3 citation statements)

References 16 publications

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“…Two important databases (WoS and Scopus) were searched using appropriate keywords and started narrowing down to find the most relevant studies based on a clear process of screening the papers. The results of this process culminated in the selection of 39 articles that were within the smart cities area and Papers RQ1 RQ2 RQ3 [16] Particle Swarm optimization, SUMO, VANET…”

Section: Discussionmentioning

confidence: 99%

A Bibliometric Review and Analysis of Traffic Lights Optimization

Witeck

Rocha

Silva

et al. 2022

Computational Science and Its Applications – ICCSA 2022 Workshops

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The significant increase in the number of vehicles in urban areas emerges the challenge of urban mobility. Researchers in this area suggest that most daily delays in urban travel times are caused by intersections, which could be reduced if the traffic lights at these intersections were more efficient. The use of simulation for real intersections can be effective in optimizing the cycle times and improving the traffic light timing to coordinate vehicles passing through intersections. From these themes emerge the research questions: How are the existing approaches (optimization techniques and simulation) to managing traffic lights smartly? What kind of data (offline and online) are used for traffic lights optimization? How beneficial is it to propose an optimization approach to the traffic system? This paper aims to answer these questions, carried out through a bibliometric literature review. In total, 93 articles were analyzed. The main findings revealed that the United States and China are the countries with the most studies published in the last ten years. Moreover, Particle Swarm Optimization is a frequently used approach, and there is a tendency for studies to perform optimization of real cases by real-time data, showing that the praxis of smart cities has resorted to smart traffic lights.

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Section: Discussionmentioning

confidence: 99%

A Bibliometric Review and Analysis of Traffic Lights Optimization

Witeck

Rocha

Silva

et al. 2022

Computational Science and Its Applications – ICCSA 2022 Workshops

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show abstract

“…Penelitian terkait dengan perhitungan jumlah kendaraan yang dilakukan oleh Shirantika, Premaratne, Xheng dan Halloran (2019) menghasilkan kesimpulan bahwa penggunaan kamera yang terpasang pada ruas jalan dapat memberikan informasi yang akurat terkait dengan kecepatan kendaraan dan jumlah kendaraan [3]. Penelitian lain yang dilakukan oleh Contreras dan Gamess menghasilkan kesimpulan bahwa teknologi VANET yang digunakan mampu menghitung jumlah kendaaraan yang berhenti di lampu lalu lintas dengan akurat [4]. Penelitian lain yang dilakukan oleh Song, Wang, Fang, Yun, Zhang dan Li (2019) menghasilkan kesimpulan perhitungan jumlah kendaraan dari video rekaman kamera yang terpasang pada ruas jalan dapat menghasilkan tingkat akurasi hingga 90% [5].…”

Section: Pendahuluanunclassified

Aplikasi Perhitungan Jumlah Kendaraan Dengan Menggunakan Google Maps API

Sudarsono,

Kusrini,

Arief

2023

FAHMA

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Proses penghitungan kendaraan memberikan informasi yang tepat tentang arus lalu lintas, dan waktu puncak lalu lintas di jalan raya. Makalah ini menyajikan penghitung kendaraan dengan menggunakan data dari Google Maps API. Penghitungan kendaraan akan menggunakan rumus rasio densitas dari metode Greenshield, Greenberg dan Underwood. Implementasi dari teknik yang diusulkan telah dilakukan dengan menggunakan bahasa pemrograman PHP dan framework codeigniter. Hasil dari penelitian ini adalah sebuah aplikasi penghitung kendaraan berbasis web yang diharapkan dapat membantu dinas terkait dalam meningkatkan pelayanan kepada masyarakat sebagai pengguna jalan

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“…The authors of [28] introduce a novel algorithm for counting vehicles stopped at a traffic light using VANET technology. The algorithm is based on the idea of the propagation of a count request message from the RSU (originating node) toward the vehicles that are at the end of the waiting line, and the propagation of a response message (with the number of vehicles counted) in the opposite direction, that is, from the vehicles at the end of the line toward the RSU.…”

Section: Related Workmentioning

confidence: 99%

Real-Time Counting of Vehicles Stopped at a Traffic Light Using Vehicular Network Technology

Contreras

Gamess

2020

IEEE Access

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Vehicular Adhoc NETworks (VANETs) is a new and emerging technology for wireless communications that has attracted considerable attention in the last years from the academic, scientific, industrial and governmental communities, due to the improvements and the new features that it brings to the Intelligent Transportation Systems (ITSs). In this paper, we present an algorithm that uses VANET technology to determine the total number of vehicles that are stopped at a traffic light at an intersection.To facilitate an efficient counting, we divide the road into fixed-size road regions and through a leader election mechanism, we designate one vehicle in each region as the ''Region Leader'' that is in charge of computing and propagating the total number of nodes in its region. Additionally, the region leader will act as a router to retransmit counting information from other region leaders that are further away, so it can eventually reach the central processing point where it is processed. We have carried out extensive experiments in various scenarios to validate and analyze the behavior and precision of our new proposed algorithm. Also, we compare our algorithm with another strategy proposed by a research team led by Alok Rajan. Our simulations were done using Veins (Vehicle in Network Simulation), an open-source simulation framework that ties together two simulators: OMNeT++ for the network simulation and SUMO for the microscopic road traffic simulation. Both simulators run in parallel and communicate with each other through a TCP connection using a protocol called TraCI (Traffic Control Interface). Our simulation results show that the algorithm performs an effective counting of vehicles, with a reduced response time, and a small total number of control messages sent by the vehicles to accomplish the counting task, under different conditions of vehicular traffic loads.

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An Algorithm based on VANET Technology to Count Vehicles Stopped at a Traffic Light

Cited by 11 publications

References 16 publications

A Bibliometric Review and Analysis of Traffic Lights Optimization

A Bibliometric Review and Analysis of Traffic Lights Optimization

Aplikasi Perhitungan Jumlah Kendaraan Dengan Menggunakan Google Maps API

Real-Time Counting of Vehicles Stopped at a Traffic Light Using Vehicular Network Technology

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