Density based Traffic Monitoring system in Haze and FOG Conditions

2021 ◽  
Vol 9 (3) ◽  
pp. 286-293
Keyword(s):  
Real Time ◽  
Traffic Congestion ◽  
Traffic Management ◽  
New Technologies ◽  
Weather Conditions ◽  
Extraction Process ◽  
Traffic Monitoring ◽  
Traffic Density ◽  
Morphological Operations ◽  
Conventional Methods

The problem of traffic congestion has increased now-a-day’s due to the rapid growth of population in major cities. Overwhelming number of vehicles and insufficient roads are the major causes of traffic congestion. This needs new technologies to be adopted, and a better approach for effective traffic management. In the literature, researchers use conventional methods such as IR sensor, wireless sensor, and Fuzzy logic to measure the traffic density. The main limitations of such conventional methods are that they require personal monitoring of the traffic and ineffective to work in foggy weather. The main aim of this work is to develop a real-time adaptive density-based traffic management system that can quantify number of vehicles on roads under foggy weather conditions. The proposed system involves video acquisition, frame extraction, fog removal and vehicle counting. At first, the video is captured by camera and split into number of frames using frame extraction process. The Dark channel prior (DCP) algorithm is used to remove the fog from each frame and the background subtraction method and certain morphological operations are used to count the number of vehicles in real-time. Based on the vehicle count, the system specifies the time required to clear the traffic. This could facilitate ease traffic flow, save time, and even operate in foggy weather conditions, which is an improvement from the conventional timer-based operations of traffic signals.

scholarly journals Adaptive Traffic Management System using CNN (YOLO)

2021 ◽  
Vol 9 (VI) ◽  
pp. 3726-3732
Author(s):  
B. Sowmya
Keyword(s):  
Real Time ◽  
Traffic Flow ◽  
Traffic Control ◽  
Traffic Congestion ◽  
Traffic Management ◽  
Object Identification ◽  
Traffic Signal ◽  
Traffic Density ◽  
Loop Control ◽  
Control Framework

The huge number of vehicles on the roadways is making congestion a significant problem. The line longitudinal vehicle waiting to be processed at the crossroads increases quickly, and the traditionally used traffic signals are not able to program it properly. Manual traffic monitoring may be an onerous job since a number of cameras are deployed over the network in traffic management centers. The proactive decision-making of human operators, which would decrease the effect of events and recurring road congestion, might contribute to the easing of the strain of automation.The traffic control frameworks in India are now needed as it is an open-loop control framework, without any input or detection mechanism. Inductive loops and sensors employed in existing technology used to detect the number of passing vehicles. The way traffic lights are adapted is highly inefficient and costly in this existing technology. The aim was to build a traffic control framework by introducing a system for detection ,which gives an input to the existing system (closed loop control system) in order to adapt to the changing traffic density patterns and to provide the controller with a crucial indication for ongoing activities. By this technique, the improvement of the signals on street is extended and thus saves time by preventing traffic congestion. This study proposes an algorithm for real-time traffic signal control, depending on the traffic flow. In reality, the features of competitive traffic flow at the signposted road crossing are used by computer vision and by machine learning. This is done by the latest, real-time object identification, based on convolutional Neural Networks network called You Look Once (YOLO). Traffic signal phases are then improved by data acquired in order to allow more vehicles to pass safely over minimal wait times, particularly the line long and the time of waiting per vehicle.This adjustable traffic signal timer is used to calculate traffic density utilizing YOLO object identification using live pictures of cameras in intervals and adjusts the signal timers appropriately, therefore decreasing the road traffic congestion, ensuring speedier transit for persons, and reducing fuel consumption. The traffic conditions will improve enormously at a relatively modest cost. Inductive loops are a viable but costly approach. This method thereby cuts expenses and outcomes quickly.

scholarly journals Design and Development of Traffic Control System using Image Processing

2021 ◽  
Vol 9 (VI) ◽  
pp. 3247-3252
Author(s):  
G. Kalyan
Keyword(s):  
Image Processing ◽  
Real Time ◽  
Traffic Control ◽  
Traffic Congestion ◽  
Traffic Management ◽  
Red Light ◽  
Traffic Density ◽  
The Road ◽  
Real Time Traffic ◽  
On The Road

Traffic congestion is now a big issue. Although it seems to penetrate throughout the world, urban towns are the ones which are most effected. And it is expanding in nature that it is necessary to understand the density of roads in real time to better regulate signals and efficient management of transport. Various traffic congestions, such as limited capacity, unrestricted demand, huge Red Light waits might occur. While insufficient capacity and unlimited demand are somehow interconnected, their delay in lighting is difficult to encode and not traffic dependant. The necessity to simulate and optimise traffic controls therefore arises in order to better meet this growing demand. The traffic management of information, ramp metering, and updates in real-time has been frequently used in recent years for image processing and monitoring systems. An image processing can also be used for the traffic density estimation. This research describes the approach for the computation of real-time traffic density by image processing for using live picture feed from cameras. It focuses also on the algorithm for the transmission of traffic signals on the road according to the density of vehicles and therefore aims to reduce road congestion, which reduces the number of accidents.

scholarly journals Road Traffic Congestion Management Based on a Search-Allocation Approach

2017 ◽  
Vol 18 (1) ◽  
pp. 25-33 ◽  
Author(s):  
Jamal Raiyn
Keyword(s):  
Travel Time ◽  
Real Time ◽  
Traffic Congestion ◽  
Traffic Management ◽  
Road Traffic ◽  
Smart Cities ◽  
Self Regulation ◽  
Congestion Management ◽  
Shortest Route ◽  
Road Traffic Congestion

Abstract This paper introduces a new scheme for road traffic management in smart cities, aimed at reducing road traffic congestion. The scheme is based on a combination of searching, updating, and allocation techniques (SUA). An SUA approach is proposed to reduce the processing time for forecasting the conditions of all road sections in real-time, which is typically considerable and complex. It searches for the shortest route based on historical observations, then computes travel time forecasts based on vehicular location in real-time. Using updated information, which includes travel time forecasts and accident forecasts, the vehicle is allocated the appropriate section. The novelty of the SUA scheme lies in its updating of vehicles in every time to reduce traffic congestion. Furthermore, the SUA approach supports autonomy and management by self-regulation, which recommends its use in smart cities that support internet of things (IoT) technologies.

scholarly journals Dynamic Traffic Light Control

2020 ◽  
Vol 8 (6) ◽  
pp. 3228-3231
Keyword(s):  
Traffic Congestion ◽  
Traffic Management ◽  
Red Light ◽  
Traffic Monitoring ◽  
Intelligent Transport System ◽  
Light Control ◽  
Traffic Light ◽  
Traffic Lights ◽  
Traffic Light Control ◽  
Traffic Management System

Intelligent Transport System (ITS) is blooming worldwide. The Traditional Traffic management system is a tedious process and it requires huge man power, to overcome this we have proposed an automatic Traffic monitoring system that has effective fleet management. The current transportation system at intersections and junctions has Traffic Lights with Fixed durations which increase the unnecessary staying time which intern harms the environment. An Adaptive traffic light control is implemented using SUMO simulator, that changes the duration of Green and Red light according to the traffic flow. This is an effective and efficient way to reduce the Traffic congestion. The traffic congestion is determined by taking the object count using deep learning approach (Convolutional Neural Network).

scholarly journals Artificial Intelligence-Enabled Traffic Monitoring System

2020 ◽  
Vol 12 (21) ◽  
pp. 9177
Author(s):  
Vishal Mandal ◽  
Abdul Rashid Mussah ◽  
Peng Jin ◽  
Yaw Adu-Gyamfi
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
Real Time ◽  
Monitoring System ◽  
Traffic Management ◽  
Traffic Monitoring ◽  
Deep Convolutional Neural Networks ◽  
Stage Of Development ◽  
Daunting Task ◽  
The Impact

Manual traffic surveillance can be a daunting task as Traffic Management Centers operate a myriad of cameras installed over a network. Injecting some level of automation could help lighten the workload of human operators performing manual surveillance and facilitate making proactive decisions which would reduce the impact of incidents and recurring congestion on roadways. This article presents a novel approach to automatically monitor real time traffic footage using deep convolutional neural networks and a stand-alone graphical user interface. The authors describe the results of research received in the process of developing models that serve as an integrated framework for an artificial intelligence enabled traffic monitoring system. The proposed system deploys several state-of-the-art deep learning algorithms to automate different traffic monitoring needs. Taking advantage of a large database of annotated video surveillance data, deep learning-based models are trained to detect queues, track stationary vehicles, and tabulate vehicle counts. A pixel-level segmentation approach is applied to detect traffic queues and predict severity. Real-time object detection algorithms coupled with different tracking systems are deployed to automatically detect stranded vehicles as well as perform vehicular counts. At each stage of development, interesting experimental results are presented to demonstrate the effectiveness of the proposed system. Overall, the results demonstrate that the proposed framework performs satisfactorily under varied conditions without being immensely impacted by environmental hazards such as blurry camera views, low illumination, rain, or snow.

scholarly journals Mixed Vehicular Traffic Flow Model on an Inclined Multilane Road

2020 ◽  
Vol 5 (7) ◽  
pp. 331-342
Author(s):  
Delina Mshai Mwalimo ◽  
Mary Wainaina ◽  
Winnie Kaluki
Keyword(s):  
Traffic Flow ◽  
Traffic Congestion ◽  
Traffic Management ◽  
Flow Model ◽  
Free Flow ◽  
Traffic Density ◽  
Fundamental Diagram ◽  
Traffic Flow Model ◽  
The Road ◽  
Slow Moving

This study outlines the Kerner’s 3 phase traffic flow theory, which states that traffic flow occurs in three phases and these are free flow, synchronized flow and wide moving jam phase. A macroscopic traffic model that is factoring road inclination is developed and its features discussed. By construction of the solution to the Rienmann problem, the model is written in conservative form and solved numerically. Using the Lax-Friedrichs method and going ahead to simulate traffic flow on an inclined multi lane road. The dynamics of traffic flow involving cars(fast moving) and trucks(slow moving) on a multi-lane inclined road is studied. Generally, trucks move slower than cars and their speed is significantly reduced when they are moving uphill on an in- clined road, which leads to emergence of a moving bottleneck. If the inclined road is multi-lane then the cars will tend to change lanes with the aim of overtaking the slow moving bottleneck to achieve free flow. The moving bottleneck and lanechange ma- noeuvres affect the dynamics of flow of traffic on the multi-lane road, leading to traffic phase transitions between free flow (F) and synchronised flow(S). Therefore, in order to adequately describe this kind of traffic flow, a model should incorporate the effect of road inclination. This study proposes to account for the road inclination through the fundamental diagram, which relates traffic flow rate to traffic density and ultimately through the anticipation term in the velocity dynamics equation of macroscopic traffic flow model. The features of this model shows how the moving bottleneck and an incline multilane road affects traffic transistions from Free flow(F) to Synchronised flow(S). For a better traffic management and control, proper understanding of traffic congestion is needed. This will help road designers and traffic engineers to verify whether traffic properties and characteristics such as speed(velocity), density and flow among others determines the effectiveness of traffic flow.

scholarly journals DEVELOPMENT OF WEBGIS BASED REAL TIME ROAD TRAFFIC INFORMATION SYSTEM

2018 ◽  
Vol IV-5 ◽  
pp. 37-45
Author(s):  
H. Shankar ◽  
M. Sharma ◽  
K. Oberai ◽  
S. Saran
Keyword(s):  
Information System ◽  
Real Time ◽  
Traffic Congestion ◽  
Road Traffic ◽  
Transportation Cost ◽  
Traffic Information ◽  
Traffic Density ◽  
Vehicle To Vehicle ◽  
Decision Making Processes ◽  
Inductive Loop Detector

<p><strong>Abstract.</strong> Rapid increase in road traffic density results into a serious problem of Traffic Congestion (TC) in cities. During peaks hours TC is very high and hence public search least congested path for their journeys in order to minimize ravel time and hence transportation cost. In this study, a new empirical model was developed to estimate congestion levels using real time road Traffic Parameters (TPs) such as vehicle density, speed, class and vehicle-to-vehicle (V2V) gap. These real time road TPs were collected using latest generation Inductive Loop Detector (ILD) technology. Further, a WebGIS based Road Traffic Information System (RTIS) for Dehradun city was developed for real time TD analyses and visualisation. This RTIS is very useful for public and user departments for planning and decision making processes. No other such system is available in India, which handles multiple traffic parameters simultaneously to provide solution of day-to-day problems.</p>

scholarly journals Combining operational models and data into a dynamic vessel risk assessment tool for coastal regions

2015 ◽  
Vol 12 (4) ◽  
pp. 1327-1388 ◽  
Author(s):  
R. Fernandes ◽  
F. Braunschweig ◽  
F. Lourenço ◽  
R. Neves
Keyword(s):  
Risk Assessment ◽  
Real Time ◽  
Oil Spill ◽  
Situational Awareness ◽  
Assessment Tool ◽  
Weather Conditions ◽  
Traffic Monitoring ◽  
Automatic Identification ◽  
Decision Support Model ◽  
Risk Levels

Abstract. The technological evolution in terms of computational capacity, data acquisition systems, numerical modelling and operational oceanography is supplying opportunities for designing and building holistic approaches and complex tools for newer and more efficient management (planning, prevention and response) of coastal water pollution risk events. A combined methodology to dynamically estimate time and space variable shoreline risk levels from ships has been developed, integrating numerical metocean forecasts and oil spill simulations with vessel tracking automatic identification systems (AIS). The risk rating combines the likelihood of an oil spill occurring from a vessel navigating in a study area – Portuguese Continental shelf – with the assessed consequences to the shoreline. The spill likelihood is based on dynamic marine weather conditions and statistical information from previous accidents. The shoreline consequences reflect the virtual spilled oil amount reaching shoreline and its environmental and socio-economic vulnerabilities. The oil reaching shoreline is quantified with an oil spill fate and behaviour model running multiple virtual spills from vessels along time. Shoreline risks can be computed in real-time or from previously obtained data. Results show the ability of the proposed methodology to estimate the risk properly sensitive to dynamic metocean conditions and to oil transport behaviour. The integration of meteo-oceanic + oil spill models with coastal vulnerability and AIS data in the quantification of risk enhances the maritime situational awareness and the decision support model, providing a more realistic approach in the assessment of shoreline impacts. The risk assessment from historical data can help finding typical risk patterns, "hot spots" or developing sensitivity analysis to specific conditions, whereas real time risk levels can be used in the prioritization of individual ships, geographical areas, strategic tug positioning and implementation of dynamic risk-based vessel traffic monitoring.

scholarly journals A Smart Decision System for Digital Farming

Agronomy ◽  
2019 ◽  
Vol 9 (5) ◽  
pp. 216 ◽  
Author(s):  
Carlos Cambra Baseca ◽  
Sandra Sendra ◽  
Jaime Lloret ◽  
Jesus Tomas
Keyword(s):  
Decision Making ◽  
Real Time ◽  
Information Exchange ◽  
New Technologies ◽  
Open Data ◽  
Weather Conditions ◽  
Added Value ◽  
Aerial Images ◽  
Sampled Data ◽  
Application Development

New technologies have the potential to transform agriculture and to reduce environmental impact through a green revolution. Internet of Things (IoT)-based application development platforms have the potential to run farm management tools capable of monitoring real-time events when integrated into interactive innovation models for fertirrigation. Their capabilities must extend to flexible reconfiguration of programmed actions. IoT platforms require complex smart decision-making systems based on data-analysis and data mining of big data sets. In this paper, the advantages are demonstrated of a powerful tool that applies real-time decisions from data such as variable rate irrigation, and selected parameters from field and weather conditions. The field parameters, the index vegetation (estimated using aerial images), and the irrigation events, such as flow level, pressure level, and wind speed, are periodically sampled. Data is processed in a decision-making system based on learning prediction rules in conjunction with the Drools rule engine. The multimedia platform can be remotely controlled, and offers a smart farming open data network with shared restriction levels for information exchange oriented to farmers, the fertilizer provider, and agricultural technicians that should provide the farmer with added value in the form of better decision making or more efficient exploitation operations and management.

A MACHINE LEARNING FRAMEWORK FOR REAL-TIME TRAFFIC DENSITY DETECTION

2009 ◽  
Vol 23 (07) ◽  
pp. 1265-1284 ◽  
Author(s):  
JING CHEN ◽  
EVAN TAN ◽  
ZHIDONG LI
Keyword(s):  
Real Time ◽  
Traffic Flow ◽  
Density Estimation ◽  
Heavy Traffic ◽  
Weather Conditions ◽  
Traffic Density ◽  
Model Parameters ◽  
Loop Detectors ◽  
Real Time Traffic ◽  
Flow Information

Traffic flow information can be employed in an intelligent transportation system to detect and manage traffic congestion. One of the key elements in determining the traffic flow information is traffic density estimation. The goal of traffic density estimation is to determine the density of vehicles on a given road from loop detectors, traffic radars, or surveillance cameras. However, due to the inflexibility of deploying loop detectors and traffic radars, there is a growing trend of using video-content-understanding technique to determine the traffic flow from a surveillance camera. But difficulties arise when attempting to do this in real-time under changing illumination and weather conditions as well as heavy traffic congestions. In this paper, we attempt to address the problem of real-time traffic density estimation by using a stochastic model called Hidden Markov Models (HMM) to probabilistically determine the traffic density state. Choosing a good set of model parameters for HMMs has a significant impact on the accuracy of traffic density estimation. Thus, we propose a novel feature extraction scheme to represent traffic density, and a novel approach to initialize and construct the HMMs by using an unsupervised clustering technique called AutoClass. We show through extensive experiments that our proposed real-time algorithm achieves an average traffic density estimation accuracy of 96.6% over various different illumination and weather conditions.

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