Traffic Density Estimation Protocol Using Vehicular Networks

2013 ◽  
pp. 1-12
Author(s):  
Adnan Noor Mian ◽  
Ishrat Fatima ◽  
Roberto Beraldi
Keyword(s):  
Density Estimation ◽  
Vehicular Networks ◽  
Traffic Density

scholarly journals HOG, LBP and SVM based Traffic Density Estimation at Intersection

2019 ◽  
Author(s):  
Devashish Prasad ◽  
Kshitij Kapadni ◽  
Ayan Gadpal ◽  
Manish Visave ◽  
Kavita Sultanpure
Keyword(s):  
Density Estimation ◽  
Traffic Density

Traffic density estimation via a multi-level feature fusion network

2022 ◽  
Author(s):  
Ying-Xiang Hu ◽  
Rui-Sheng Jia ◽  
Yong-Chao Li ◽  
Qi Zhang ◽  
Hong-Mei Sun
Keyword(s):  
Density Estimation ◽  
Feature Fusion ◽  
Traffic Density ◽  
Multi Level

An automatic traffic density estimation using Single Shot Detection (SSD) and MobileNet-SSD

2019 ◽  
Vol 110 ◽  
pp. 176-184 ◽  
Author(s):  
Debojit Biswas ◽  
Hongbo Su ◽  
Chengyi Wang ◽  
Aleksandar Stevanovic ◽  
Weimin Wang
Keyword(s):  
Density Estimation ◽  
Traffic Density ◽  
Single Shot ◽  
Shot Detection

scholarly journals A Novel Approach of Traffic Density Estimation Using CNNs and Computer Vision

2021 ◽  
Vol 5 (4) ◽  
pp. 80-84
Author(s):  
Luong Anh Tuan Nguyen ◽  
Thanh Xuan Ha
Keyword(s):  
Neural Networks ◽  
Computer Vision ◽  
Traffic Flow ◽  
Density Estimation ◽  
Traffic Density ◽  
Signal Control ◽  
Traffic Signal Control ◽  
Modern Life ◽  
Traffic Jam ◽  
Novel Approach

In modern life, we face many problems, one of which is the increasingly serious traffic jam. The cause is the large volume of vehicles, inadequate infrastructure and unreasonable distribution, and ineffective traffic signal control. This requires finding methods to optimize traffic flow, especially during peak hours. To optimize traffic flow, it is necessary to determine the traffic density at each time in the streets and intersections. This paper proposed a novel approach to traffic density estimation using Convolutional Neural Networks (CNNs) and computer vision. The experimental results with UCSD traffic dataset show that the proposed solution achieved the worst estimation rate of 98.48% and the best estimation rate of 99.01%.

Road traffic density estimation

2022 ◽  
pp. 65-98
Author(s):  
Fouzi Harrou ◽  
Abdelhafid Zeroual ◽  
Mohamad Mazen Hittawe ◽  
Ying Sun
Keyword(s):  
Density Estimation ◽  
Road Traffic ◽  
Traffic Density
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