Modified Fuzzy-Based Smart Barricade Movement for Traffic Management System

Raskar, Charushila; Nema, Shikha

doi:10.1007/s11277-020-07856-4

Cited by 8 publications

(5 citation statements)

References 36 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…e system will automatically trigger the trigger at the platform after detecting anomalies, and the trigger will promptly notify the predefined emergency contact. e device is highly practical and can enable users to be detected in time after abnormal movement so that they can be rescued as soon as possible and reduce the rate of casualties caused by accidents [11]. In addition, the wide coverage, low power consumption, low cost, and high stability of IoT technology can maximize the data transmission efficiency of these wearable devices, and the environment is not limited to indoor but extends to the whole city and is more suitable for various outdoor environments and special people, so that the smart wearable sport intelligent monitoring devices with communication functions are easier to use, are more universal, and have a broader market application prospect and have certain scientific research significance.…”

Section: Related Workmentioning

confidence: 99%

[Retracted] Optimization of IoT‐Based Motion Intelligence Monitoring System

Qiao¹,

Zhang

Chen

2021

Complexity

View full text Add to dashboard Cite

We design and implement an intelligent IoT-based motion monitoring system to realize the monitoring of three important parameters, namely, the type of movement, the number of movements, and the period of movement in physical activities, and optimize the system to support the simultaneous use by multiple users. Considering the motion monitoring scenario for smart fit, the framework of an IoT-based motion monitoring system is proposed. The framework contains components such as active acquisition nodes, wireless access points, data processing servers, and terminals. In terms of algorithm optimization, research related to active pattern recognition and periodic calculation methods is conducted. For active pattern recognition, two types of classification algorithms with different complexity are proposed based on Support Vector Machine (SVM) and deep neural networks, respectively, to adapt to scenarios with different computational capabilities. For period calculation, a method based on over-zero detection and wavelet transform is proposed to count the number of actions and calculate the period of each action. In 100 times action cycle calculation experiments, the count statistic calculation method achieves 100% calculation accuracy and the active cycle calculation results are close to the real value, which proves the effectiveness of the cycle calculation method. The system provides a multiuser-oriented communication method and realizes accurate and reliable human movement monitoring, which has a wide application prospect in the fields of physical education and rehabilitation training.

show abstract

Section: Related Workmentioning

confidence: 99%

[Retracted] Optimization of IoT‐Based Motion Intelligence Monitoring System

Qiao¹,

Zhang

Chen

2021

Complexity

View full text Add to dashboard Cite

show abstract

“…Managing vehicular traffic was a major challenge for town planners in metropolitan areas. MGOA (modified grasshopper optimization algorithm) is a new position in [24] that uses a revolutionary way to measure vehicle transmission and distribution numbers. When deciding whether or not to leave a barricade partly open, the position of the barrier must take nature and traffic density into account.…”

Section: Related Workmentioning

confidence: 99%

Feasibility of Economic Forecasting Model Based on Intelligent Algorithm of Smart City

2022

Mobile Information Systems

View full text Add to dashboard Cite

Smart cities make better use of space and have less traffic, cleaner air, and more efficient municipal services, improving people’s quality of life. The vast number of vehicles continually seeking to reach crowded spots in smart cities complicates acquiring a public parking space. It presents challenges for both traffic and residents. With such vast populations, road congestion is a serious challenge. It wastes vital resources such as fuel, money, and, most importantly, time. Finding a good location to park is one of the reasons for traffic congestion on the highway. This paper proposes a deep learning-based economic forecasting model (DL-EFM) for long-term economic growth in smart cities. Traffic management is vital for cities to guarantee that people and products can move freely across the city. Many automobiles attempting to reach crowded areas in smart cities make getting a public parking place difficult. It is inconvenient for both drivers and residents. Different traffic management authorities have implemented an artificial neural network (ANN) to resolve the issue, and modern vehicle systems have been coupled with intelligent parking solutions. The experimental outcome of the deep learning-based economic forecasting model improves traffic estimation, accuracy prediction in traffic flow, traffic management, and smart parking when compared to existing methods.

show abstract

“…This article [43] introduces a new type of traffic management system that uses an intelligent roadblock system. The basic concept behind the intelligent system is to determine the location of roadblocks based on the nature and intensity of traffic.…”

Section: Comparison Of Tccms Using Thematic Taxonomymentioning

confidence: 99%

“…REACT is a traffic management system based on Vehicular communication. More motorist intervention required under this proposed system Gomides, T.S., et al [43] In this model, the location of the roadblock is determined by the fuzzy logic system, and the system also focuses on finding the optimal membership limit to obtain better results.…”

Section: Study Proposed Approach Efficiency Weaknessmentioning

confidence: 99%

Traffic Efficiency Models for Urban Traffic Management Using Mobile Crowd Sensing: A Survey

et al. 2021

View full text Add to dashboard Cite

The population is increasing rapidly, due to which the number of vehicles has increased, but the transportation system has not yet developed as development occurred in technologies. Currently, the lowest capacity and old infrastructure of roads do not support the amount of vehicles flow which cause traffic congestion. The purpose of this survey is to present the literature and propose such a realistic traffic efficiency model to collect vehicular traffic data without roadside sensor deployment and manage traffic dynamically. Today’s urban traffic congestion is one of the core problems to be solved by such a traffic management scheme. Due to traffic congestion, static control systems may stop emergency vehicles during congestion. In daily routine, there are two-time slots in which the traffic is at peak level, which causes traffic congestion to occur in an urban transportation environment. Traffic congestion mostly occurs in peak hours from 8 a.m. to 10 a.m. when people go to offices and students go to educational institutes and when they come back home from 4 p.m. to 8 p.m. The main purpose of this survey is to provide a taxonomy of different traffic management schemes for avoiding traffic congestion. The available literature categorized and classified traffic congestion in urban areas by devising a taxonomy based on the model type, sensor technology, data gathering techniques, selected road infrastructure, traffic flow model, and result verification approaches. Consider the existing urban traffic management schemes to avoid congestion and to provide an alternate path, and lay the foundation for further research based on the IoT using a Mobile crowd sensing-based traffic congestion control model. Mobile crowdsensing has attracted increasing attention in traffic prediction. In mobile crowdsensing, the vehicular traffic data are collected at a very low cost without any special sensor network infrastructure deployment. Mobile crowdsensing is very popular because it can transmit information faster, collect vehicle traffic data at a very low cost by using motorists’ smartphone or GPS vehicular embedded sensor, and it is easy to install, requires no special network deployment, has less maintenance, is compact, and is cheaper compared to other network options.

show abstract

Modified Fuzzy-Based Smart Barricade Movement for Traffic Management System

Cited by 8 publications

References 36 publications

[Retracted] Optimization of IoT‐Based Motion Intelligence Monitoring System

[Retracted] Optimization of IoT‐Based Motion Intelligence Monitoring System

Feasibility of Economic Forecasting Model Based on Intelligent Algorithm of Smart City

Traffic Efficiency Models for Urban Traffic Management Using Mobile Crowd Sensing: A Survey

Contact Info

Product

Resources

About