Road Traffic Accident Research in India: A Scientometric Study from 1977 to 2020

Hugar, Jayaprakash G; Naseer, Mirza Muhammad; Waris, Abu; Khan, Muhammad Ajmal

doi:10.2139/ssrn.3893062

Cited by 4 publications

(2 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The used data were collected from Web of Science (WOS) literature database, which contains three major literature index databases, that is, SCI, SSCI and A&HCI [24]. There are also other data sources which could be used for bibliometric analysis of traffic management.…”

Section: Datamentioning

confidence: 99%

Trend analysis of traffic management based on literature data mining and graph analysis tools

Ding

Liu

Wang

et al. 2023

IET Intelligent Trans Sys

View full text Add to dashboard Cite

Studites on traffic management is crucial for the development of intelligent transportation systems and smart cities. However, identifying the development stages of traffic management field based on bibliometric analysis is still lacking. In this study, CiteSpace and VOSviewer software are used to explore “traffic management” field by summarizing development process and predicting future research trend. A total of 3,028 relevant documents over the past 40 years were collected from Web of Science. Results show that (1) studies on traffic management were mainly published by researchers from USA (30.55%), China (20.90%), and some European countries; (2) the key traffic management research contents can be classified into four categories, that is, background requirements, traffic problems, method models, and control strategies; (3) the evolution process can be divided into four stages, that is, budding stage (1990–1994), development stage (1995–2003), calm stage (2004–2010), and maturation stage (2011–); (4) machine learning, deep learning and other intelligent algorithms have played more important roles in recent years, and connected vehicle management is also a potential development trend. Results suggest that cooperative vehicle‐infrastructure systems or machine learning‐based studies might be the hotspots on traffic management studies.

show abstract

Section: Datamentioning

confidence: 99%

Trend analysis of traffic management based on literature data mining and graph analysis tools

Ding

Liu

Wang

et al. 2023

IET Intelligent Trans Sys

View full text Add to dashboard Cite

show abstract

“…According to National Highway Traffic Safety Administration report, USA, 24.5% accidents and Ministry of Road Transport and Highways report, India, 27% accidents are caused by driver fatigues. Risk of accident increases by four to five times in almost all countries [11]. Regular accidents especially in densely populated country like India heavily affects people safety.…”

Section: Introductionmentioning

confidence: 99%

Face Positioned Driver Drowsiness Detection Using Multistage Adaptive 3D Convolutional Neural Network

Adhithyaa,

Tamilarasi,

Sivabalaselvamani

et al. 2023

ITC

View full text Add to dashboard Cite

Accidents due to driver drowsiness are observed to be increasing at an alarming rate across all countries and it becomes necessary to identify driver drowsiness to reduce accident rates. Researchers handled many machine learning and deep learning techniques especially many CNN variants created for drowsiness detection, but it is dangerous to use in real time, as the design fails due to high computational complexity, low evaluation accuracies and low reliability. In this article, we introduce a multistage adaptive 3D-CNN model with multi-expressive features for Driver Drowsiness Detection (DDD) with special attention to system complexity and performance. The proposed architecture is divided into five cascaded stages: (1) A three level Convolutional Neural Network (CNN) for driver face positioning (2) 3D-CNN based Spatio-Temporal (ST) Learning to extract 3D features from face positioned stacked samples. (3) State Understanding (SU) to train 3D-CNN based drowsiness models (4) Feature fusion using ST and SU stages (5) Drowsiness Detection stage. The Proposed system extract ST values from the face positioned images and then merges it with SU results from each state understanding sub models to create conditional driver facial features for final Drowsiness Detection (DD) model. Final DD Model is trained offline and implemented in online, results show the developed model performs well when compared to others and additionally capable of handling Indian conditions. This method is applied (Trained and Evaluated) using two different datasets, Kongu Engineering College Driver Drowsiness Detection (KEC-DDD) own dataset and National Tsing Hua University Driver Drowsiness Detection (NTHU-DDD) Benchmark Dataset. The proposed system trained with KEC-DDD dataset produces accuracy of 77.45% and 75.91% using evaluation set of KEC-DDD and NTHU-DDD dataset and capable to detect driver drowsiness from 256×256 resolution images at 39.6 fps at an average of 400 execution seconds.

show abstract

Smart License Approval Using AIOT

Jagati

Raja

2022

Computer, Communication, and Signal Processing

View full text Add to dashboard Cite

Road Traffic Accident Research in India: A Scientometric Study from 1977 to 2020

Cited by 4 publications

References 4 publications

Trend analysis of traffic management based on literature data mining and graph analysis tools

Trend analysis of traffic management based on literature data mining and graph analysis tools

Face Positioned Driver Drowsiness Detection Using Multistage Adaptive 3D Convolutional Neural Network

Smart License Approval Using AIOT

Contact Info

Product

Resources

About