An Efficient Traffic Congestion Monitoring System on Internet of Vehicles

Nguyen, Duc Binh; Dow, Chyi‐Ren; Hwang, Seung Jae

doi:10.1155/2018/9136813

Cited by 41 publications

(36 citation statements)

References 21 publications

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“…To tackle these challenges, a low-carbon and efficient communication networks are needed. Fortunately, these necessities has led to the appearance of the green IoT (G-IoT) [162,163]. The key component of G-IoT is its energy-efficient characteristics throughout the life cycle, i.e., design, production, deployment, and ultimately disposal [129].…”

Section: Green Iotmentioning

confidence: 99%

Internet of Things (IoT) and the Energy Sector

et al. 2020

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Integration of renewable energy and optimization of energy use are key enablers of sustainable energy transitions and mitigating climate change. Modern technologies such the Internet of Things (IoT) offer a wide number of applications in the energy sector, i.e, in energy supply, transmission and distribution, and demand. IoT can be employed for improving energy efficiency, increasing the share of renewable energy, and reducing environmental impacts of the energy use. This paper reviews the existing literature on the application of IoT in in energy systems, in general, and in the context of smart grids particularly. Furthermore, we discuss enabling technologies of IoT, including cloud computing and different platforms for data analysis. Furthermore, we review challenges of deploying IoT in the energy sector, including privacy and security, with some solutions to these challenges such as blockchain technology. This survey provides energy policy-makers, energy economists, and managers with an overview of the role of IoT in optimization of energy systems.

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Section: Green Iotmentioning

confidence: 99%

Internet of Things (IoT) and the Energy Sector

et al. 2020

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“…Traffic flow using MQTT broker architecture was also discussed and studied. He developed two interfaces one for client side and the other for monitoring unit (8) . Yucheng Huang proposed novel approach for analyzing traffic congestion using IoT based wireless sensor system.…”

Section: Related Workmentioning

confidence: 99%

Intelligent signalling system to control traffic in vehicular ad hoc networks R

Lalitha¹,

Srinivas²,

Kumar³

et al. 2020

IJST

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Traffic congestion problem persists more at junctions and causes inconvenience to public. Owing to this, people may not reach their destinations in time. Although there are alerts in mobile phones regarding arrival times of flight services and other journey related information due to sudden traffic jams at junctions these advanced technology innovations are being becoming fragile. Since long, many researchers have been putting their efforts to find remedies for reducing traffic congestion. Objectives: The key focus is on balancing forwarding time and waiting time at junctions based on the number of vehicles arrived at that junction. Methods: The proposed system uses Internet of Things (IoT) based monitoring to control signaling system. IR sensors are used to count number of vehicles passing over the lane by triggering clock for object detection. The signaling time can be changed dynamically based on the vehicle count, so that more time is allocated to the lanes which have more traffic and the remaining time is adjusted among other lanes. This reduces congestion at dense traffic lanes. Findings: There will be time variant setting of signal lights based on the density of the traffic. The dense traffic lanes will be allotted more time and will be cleared first. Expanding on this point, the time adjustment is done based on the vehicle count not on periodical fixation of intervals. Novelty: The ecosystem developed provides an ultimate solution to vehicle users for comfortable movement on the roads without being delayed.

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“…Mandal et al characterize the stoppage pattern of public buses from historical GPS logs [9] [10]. Nguyen et al [12] propose a traffic congestion monitoring system using historical and real-time vehicular data. By modelling traffic flow from historical logs, they have predicted and verified traffic congestion.…”

Section: Study Ofmentioning

confidence: 99%

“…Hence, the overall challenge is to derive a speed profile from ambient road features without directly using speed signatures to eliminate anomalies due to specific driving maneuvers, especially when the sample set is sparse. Existing Works: Prior research has been carried out on intelligent road traffic and transport management systems [3,23] road traffic congestion measurement [12], travel time estimation [20], Modelling urban mobility pattern, Characterization of city traffic and road, etc. Researchers have worked on off-line or online GPS trace of vehicles, online social media data [22], sensor data from accelerometer and microphone of highend mobile phone or image or video captured by roadside cameras [13].…”

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confidence: 99%

“…11 https://www.ncbi.nlm.nih.gov/pmc/articles/ 12 https://developers.google.com/maps/documentati on/distance-matrix/start 13 https://stackoverflow.com/questions/432773 42/can-we-get-speed-limit-information-fromwaze-apis 14 http://opentraffic.io/#partes 15 Space-mean speed is the harmonic mean of the spot speed over a specified road segment of unit length. Space mean speed is always lower than the time mean speed as it weights slower vehicles more heavily as they occupy the road stretch for longer duration of time Contribution: Firstly, through an extensive study, we identified features that significantly influence the aggregate speed profile of a road segment and also can be easily derived.…”

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confidence: 99%

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RoadSpeedSense: Context-Aware Speed Profiling from Smart-phone Sensors

Mandal¹,

Sonowal²,

Kumar³

et al. 2018

EAI Endorsed Transactions on Energy Web

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INTRODUCTION: There are several online mapping systems like Google Maps, Waze, Here, Apple Maps, Bing Maps, etc. which are developed to visualize real-time traffic conditions which rely on crowdsourced GPS trails; obtained from worldwide smartphone users. Such systems still suffer from some limitations like a) inadequate traffic information in suburban cities and rural zones, b) system failure to infer the proper reasons for slow traffic state, c) difficulties in the extraction of raw traffic data for further development of any customized application. Significant spatio-temporal similarity patterns are observed in city traffic behavior unless there are some exceptional events like any disaster, VIP visit, international cricket match or bad weather condition, etc. OBJECTIVES: Designing a framework and developing a system which enables collection of raw sensor information from users and to identify a model to generate a speed profile of city roads using historical logs as well as to infer the context of slow traffic based on ambient subjective road features and to provide map visualization. METHODS: We have used road surface quality, density of vehicles, type of neighborhood and road geometry for developing speed profile for a particular road segment. We have carried out the experiments on different classification algorithms like, K-nearest Neighbor(KNN), Decision Tree(DT), Random Forest(RF) and Gradient Boost(GB) with necessary tuning of parameters. RESULTS: GB outperforms other classification algorithms in estimating the speed class of road segments among all classifier algorithms with highest F1-Score of 0.8345. A fair driver rating system which can be derived from our results. CONCLUSION: The results obtained from the proposed novel framework provide a proof of concept that speed profiles may be successfully derived from ambient road features even when sample space is sparse.

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An Efficient Traffic Congestion Monitoring System on Internet of Vehicles

Cited by 41 publications

References 21 publications

Internet of Things (IoT) and the Energy Sector

Internet of Things (IoT) and the Energy Sector

Intelligent signalling system to control traffic in vehicular ad hoc networks R

RoadSpeedSense: Context-Aware Speed Profiling from Smart-phone Sensors

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