scholarly journals Image Processing and IoT Based Dynamic Traffic Management System

2020 ◽  
pp. 180-188
Author(s):  
Nitin N. Sakhare ◽  
Subhash B. Tatale ◽  
S.R. Sakhare ◽  
Hemant Dusaane ◽  
Mamta Puri ◽  
...  
Keyword(s):  
Image Processing ◽  
Internet Of Things ◽  
Waiting Time ◽  
Traffic Management ◽  
Management System ◽  
Processing Unit ◽  
Time Data ◽  
Central Processing ◽  
Emergency Situations ◽  
Traffic Management System

Due to rise in number of vehicles the traffic management has become a major problem. Manual traffic system is not efficient. This paper presents adaptive traffic management system using Internet of Things (IoT) and Image processing. The proposed system has capability to analyze real time data using image processing. Using cameras, different lanes are monitored constantly. The data obtained from different lanes are examined. Detection and counting of number of vehicles in each lane is done by using image processing. The count from each lane is sent to the central processing unit. According to the count of vehicles algorithm calculates waiting time for each lane, then the signal lights will be decided. This system reduces the average waiting time and increases the efficiency of traffic clearance. The system also reduces the pollution due CO2 emission and useful in emergency situations, thus being adaptive traffic management using Internet of Things (IoT).

Evaluation of Operational Effects of I-66 Active Traffic Management System

2017 ◽  
Vol 2616 (1) ◽  
pp. 91-103 ◽  
Author(s):  
PilJin Chun ◽  
Michael D. Fontaine
Keyword(s):  
Travel Time ◽  
Traffic Management ◽  
Management System ◽  
Travel Time Reliability ◽  
Variable Speed ◽  
Time Data ◽  
Speed Limits ◽  
Variable Speed Limits ◽  
Traffic Management System ◽  
Virginia Department

In September 2015, the Virginia Department of Transportation instituted an active traffic management system on I-66 in Northern Virginia. I-66 is a major commuter route into Washington, D.C., that experiences significant recurring and nonrecurring congestion. The active traffic management system sought to manage existing capacity dynamically and more effectively with hard shoulder running, advisory variable speed limits, lane use control signs, and queue warning systems. An initial before-and-after analysis of the system’s operational effectiveness was performed with probe-based travel time data from the provider, INRIX, and used records from the active traffic management’s traffic operations center. On weekdays, statistically significant improvements were often observed during off-peak periods, but conditions did not improve during peak periods. Weekends showed the greatest improvements, with travel times and travel time reliability measures improving by 10% to 14%. Segment-level analysis revealed that most of the benefits were attained because of the use of hard shoulder running outside of the peak periods, which created additional capacity on I-66. Benefits due to advisory variable speed limits were inconclusive because of limited data.

scholarly journals Algorhythmic governance: Regulating the ‘heartbeat’ of a city using the Internet of Things

2016 ◽  
Author(s):  
Rob Kitchin
Keyword(s):  
Internet Of Things ◽  
Urban Space ◽  
Smart City ◽  
Traffic Management ◽  
Urban Life ◽  
Spatial Effects ◽  
Time Data ◽  
Capture Process ◽  
Number Of Factors ◽  
Traffic Management System

To date, research examining the socio-spatial effects of smart city technologies have charted how they are reconfiguring the production of space, spatiality and mobility, and how urban space is governed, but have paid little attention to how the temporality of cities is being reshaped by systems and infrastructure that capture, process and act on real-time data. In this paper, we map out the ways in which city-scale Internet of Things infrastructures, and their associated networks of sensors, meters, transponders, actuators and algorithms, are used to measure, monitor and regulate the polymorphic temporal rhythms of urban life. Drawing on Lefebvre (1992[2004]), and subsequent research, we employ rhythmanalysis in conjunction with Miyazaki’s (2012, 2013a/b) notion of ‘algorhythm’ and nascent work on algorithmic governance, to develop a concept of ‘algorhythmic governance’. We then use this framing to make sense of two empirical case studies: a traffic management system and sound monitoring and modelling. Our analysis reveals: (1) how smart city technologies computationally perform rhythmanalysis and undertake rhythm-work that intervenes in space-time processes; (2) three distinct forms of algorhythmic governance, varying on the basis of adaptiveness, immediacy of action, and whether humans are in, on-, of-, off-the-loop; (3) and a number of factors that shape how algorhythmic governance works in practice.

Intelligent Traffic Management Using Big Data Analytics and IOT

2021 ◽  
Vol 9 (10) ◽  
pp. 1495-1503
Author(s):  
Tharun Palla
Keyword(s):  
Machine Learning ◽  
Image Analysis ◽  
Internet Of Things ◽  
Traffic Management ◽  
Management System ◽  
Data Analytics ◽  
Heavy Traffic ◽  
Machine Learning Algorithms ◽  
Short Period ◽  
Traffic Management System

Abstract: With rapid growth in personal luxury and increasing jobs, People are comfortable using their personal vehicles rather than public transport to fulfill their transportation needs. This is because of ease of access and feasibility to use the vehicles at their own will at any point of time. It is leading to heavy traffic congestions and long waiting periods at traffic signals which is becoming a heavy burden in all major cities and will be affecting environment because of pollution caused by so many vehicles and also will disturb the individual’s time schedule. This paper proposes a system using data analytics, machine learning algorithms, Internet of things to predict the traffic flow, generate precise data about real time traffic congestions at that instant and rerouting the vehicles using navigation through a less congested path ultimately developing an Intelligent Traffic Management system. The architecture of the system is based on image analysis of vehicles using cameras at signals, using GPS in mobiles to monitor traffic in particular route. The combination of these two can be used to generate useful data about traffic congestions. Next part is calculating the efficient path to reach the destination with the generated data to minimize traffic and reach destination short period of time. The generated efficient route and traffic intensity is updated to the user with the help of maps application. Keywords: data analytics, machine learning, GPS, image analysis, intelligent traffic management system, Internet of things

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