Image Processing Technique for Traffic Density Estimation

Kurniawan, Freddy; Sajati, Haruno; Dinaryanto, Okto

doi:10.21817/ijet/2017/v9i2/170902117

Cited by 4 publications

(5 citation statements)

References 15 publications

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“…The performance of the proposed traffic density estimation is compared against Image ROI Analysis [8] and Adaptive threshold [12]. The performance is measured in terms of Mean Square Error (MSE) between the estimated and actual density.…”

Section: Resultsmentioning

confidence: 99%

“…The MSE results are given in Table V. The MSE in the proposed solution is 61.6% lower than ROI analysis [8] and 60.5% lower than adaptive threshold [12]. The performance of proposed flow estimation is measured against R-CNN tracker [1].…”

Section: Resultsmentioning

confidence: 99%

“…A background construction algorithm classifies the image into foreground and background parts. An edge detection algorithm is then run on foreground parts to calculate the occupied area in the road and based on it, traffic density is estimated [8]. A novel traffic density method.…”

Section: Related Workmentioning

confidence: 99%

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Flow Analysis of Vehicles on a Lane Using Deep Learning Techniques

Joshi,

Kulkarni

2023

JAIT

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Increased volume of vehicles on the road everywhere in the world. It becomes difficult to regulate the traffic manually across all the traffic signals throughout the cities for the entire day. It is costly and tedious for manual traffic control at traffic signals. An Intelligent Transportation System (ITS) is proposed as a solution for traffic management and to address various challenges caused by increased vehicular density. Traffic analysis from real time camera images is the most adopted method for traffic state estimations which is a most important component in smart traffic management. The current traffic density estimators from videos estimate the volume of vehicles or percentage of area occupied in the lane. But for intelligent traffic management, more accurate traffic state estimation in terms of classification, density, speed, and flow rate for different categories of vehicles in different segments of the lane is needed. This work introduces a deep learning-based fine-grained vehicle flow analysis from traffic videos. A finegrained traffic density distribution for different categories of vehicles over the entire coverage area of the lane with flow information is referred to as a dynamic traffic state map. A continuous traffic state map integrating vehicle categorization, lane density estimation, vehicle flow estimation, and orientation flow analysis. Vehicle categorization based on high and low-level features is proposed instead of area-based thresholding. A novel deep learning-based vehicle density estimation integrating coherence-based region segmentation with convolutional neural network and density estimate from the segment is proposed. The solution is able to provide an estimate of the traffic at a fine-grained level.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

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Flow Analysis of Vehicles on a Lane Using Deep Learning Techniques

Joshi,

Kulkarni

2023

JAIT

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“…Previous research has relied on a wide variety of methods and data sets, including sensor readings [24], probe vehicles [25], camera videos and images [26], CVs [2], and simulation environments [2][23]. Regarding approach, statistical modeling [23], artificial neural networks [24,25], Kalman filters (KF) [25], image processing [27], and machine learning (ML) [21,26] have all seen extensive use. Table 1 presents the most relevant studies that have proposed alternative methods for LOS assessment.…”

Section: -Related Workmentioning

confidence: 99%

Using a new algorithm in Machine learning Approaches to estimate level-of-service in hourly traffic flow data in vehicular ad hoc networks

Turki¹,

Hasson²

2023

Tikrit J. Pure Sci.

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The primary goals of transportation agencies and researchers studying traffic operations are to ease traffic and increase road safety through the use of vehicular ad hoc networks. Agencies can't achieve their goals without reliable and consistent data on the current traffic situation. The Level-of-Service (LOS) index is a helpful measure of freeway traffic operations. Conventional fixed-location cameras and sensors are impractical and expensive for gathering reliable traffic density data on every road in large networks. Flow data is a new, low-cost option that has the potential to boost safety and operations. This study proposes an algorithm for hourly LOS assessment by incorporating flow data provided by the MIDAS (Motorway Incident Detection and Automatic Signaling) system. The proposed algorithm uses machine learning techniques to classify LOS data based on the flow of traffic. The input features that are subject to prediction are a group of technical indicators. The real-world LOS was determined by analyzing data from stationary sensors. The outcomes demonstrate that technical indicators can be utilized to enhance the accuracy of LOS estimation (Random Forest= 93.1, k-nearest neighbors = 92.5, and Support Vector Machine = 91.4). The current work introduces a novel approach to the selection of technical indicators and their use as features, which allows for highly accurate short-term prediction of LOS estimation.

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“…If the value of n is greater than 60 but less than or equal to 100 then there is high congestion. But if a value of n is greater than 100 then there is very high congestion (Chiariotti, Condoluci, Mahmoodi, & Zanella, 2016;Kurniawan, Sajati, & Dinaryanto, 2017). TRAZER is an offline image processing system, developed to gather mixed traffic congestion data and it is able to capture lateral movements of vehicles this system become highly accurate by aligned video camera on central lane at certain altitude accuracy will be decreased if camera deviate from central lane, this approach allows to calculate both microscopic and macroscopic traffic properties over a certain length of road (Mallikarjuna, Phanindra, & Rao, 2009).…”

Section: Related Workmentioning

confidence: 99%

A Framework to Reduce Road Congestion by means of Data Analytics

Yousaf

Saher

Shahzad

et al. 2021

REAL

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The density of vehicles on the road especially in urban areas keeps on increasing to large amount day by day. Especially during the peak hours of the day, large amount of people wastes much of their time in traffic signals. Not only they waste energy by burning excess fuel and releasing CO2 emissions in the environment as well as their time and money. An idea has been proposed to monitor the traffic congestion by means of data analytics on image data and solve the critical traffic congestion issue. The CCTV or surveillance cameras installed at the top points on the roads acts as a medium to provide image data as an input to analyze road traffic congestion by counting the number of vehicles under specified interval of time. Monitoring of traffic congestion using image processing techniques is very useful for the future urban road planning such as: 1) if there is a need to make the road wider, 2) if there is a need to add more lanes on the road, 3) if there is need to make flyover or a bridge to control the traffic on the roads. It will help municipalities to structure and expansion of the roads.

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Image Processing Technique for Traffic Density Estimation

Cited by 4 publications

References 15 publications

Flow Analysis of Vehicles on a Lane Using Deep Learning Techniques

Flow Analysis of Vehicles on a Lane Using Deep Learning Techniques

Using a new algorithm in Machine learning Approaches to estimate level-of-service in hourly traffic flow data in vehicular ad hoc networks

A Framework to Reduce Road Congestion by means of Data Analytics

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