scholarly journals Research on Traffic Acoustic Event Detection Algorithm Based on Sparse Autoencoder

2020 ◽  
Vol 308 ◽  
pp. 05002
Author(s):  
Xiaodan Zhang ◽  
Yongsheng Chen ◽  
Guichen Tang
Keyword(s):  
Feature Vector ◽  
Road Traffic ◽  
Intelligent Transportation ◽  
Research Direction ◽  
Traffic Monitoring ◽  
Detection Algorithm ◽  
Acoustic Event ◽  
Traffic State ◽  
Sparse Autoencoder ◽  
New Research

Road traffic monitoring is very important for intelligent transportation. The detection of traffic state based on acoustic information is a new research direction. A vehicles acoustic event classification algorithm based on sparse autoencoder is proposed to analysis the traffic state. Firstly, the multidimensional Mel-cepstrum features and energy features are extracted to form a feature vector of 125 features; Secondly, based on the computed features, the five-layers autoencoder is trained. Finally, vehicle audio samples are collected and the trained autoencoder is tested. The experimental results show that detection rate of the traffic acoustic event reaches 94.9%, which is 12.3% higher than that of the traditional Convolutional Neural Networks (CNN) algorithm.

scholarly journals LPWAN-based hybrid backhaul communication for intelligent transportation systems: architecture and performance evaluation

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Taghi Shahgholi ◽  
Amir Sheikhahmadi ◽  
Keyhan Khamforoosh ◽  
Sadoon Azizi
Keyword(s):  
Low Power ◽  
Intelligent Transportation Systems ◽  
Intelligent Transportation System ◽  
Road Traffic ◽  
Intelligent Transportation ◽  
Traffic Monitoring ◽  
Transportation Systems ◽  
Area Network ◽  
Emergency Vehicle ◽  
Wide Area Network

AbstractIncreased number of the vehicles on the streets around the world has led to several problems including traffic congestion, emissions, and huge fuel consumption in many regions. With advances in wireless and traffic technologies, the Intelligent Transportation System (ITS) has been introduced as a viable solution for solving these problems by implementing more efficient use of the current infrastructures. In this paper, the possibility of using cellular-based Low-Power Wide-Area Network (LPWAN) communications, LTE-M and NB-IoT, for ITS applications has been investigated. LTE-M and NB-IoT are designed to provide long range, low power and low cost communication infrastructures and can be a promising option which has the potential to be employed immediately in real systems. In this paper, we have proposed an architecture to employ the LPWAN as a backhaul infrastructure for ITS and to understand the feasibility of the proposed model, two applications with low and high delay requirements have been examined: road traffic monitoring and emergency vehicle management. Then, the performance of using LTE-M and NB-IoT for providing backhaul communication infrastructure has been evaluated in a realistic simulation environment and compared for these two scenarios in terms of end-to-end latency per user. Simulation of Urban MObility has been used for realistic traffic generation and a Python-based program has been developed for evaluation of the communication system. The simulation results demonstrate the feasibility of using LPWAN for ITS backhaul infrastructure mostly in favor of the LTE-M over NB-IoT.

scholarly journals A New Distributed and Scalable Network Protocol Targeting Intelligent Transportation Systems

2011 ◽  
Vol 2011 ◽  
pp. 1-7
Author(s):  
M. Meribout
Keyword(s):  
Intelligent Transportation Systems ◽  
Road Traffic ◽  
Response Times ◽  
Cluster Head ◽  
Data Communication ◽  
Intelligent Transportation ◽  
Traffic Monitoring ◽  
Transportation Systems ◽  
Traffic Information ◽  
Scalable Network

Vehicular networks are the major ingredients of the envisioned Intelligent Transportation Systems (ITS) concept. An important component of ITS which is currently attracting wider research focus is road traffic monitoring. The actual approaches for traffic road monitoring are characterized by longer response times and are also subject to higher processing requirements and possess high deployment costs. In this paper, we propose a completely distributed and scalable mechanism for wireless sensor network-based road traffic monitoring. The approach relies on the distributed and bidirectional exchange of traffic information between the vehicles traversing the routes and a miniature cluster head and takes into consideration both the security and reliability of data communication. In addition, the communication between nodes is collision-free since the underlined data link layer protocol relies on a heuristic time multiplexed-based protocol. The performance analysis shows that the proposed mechanism usually outperforms other algorithms for different traffic densities.

scholarly journals LPWAN-Based Hybrid Backhaul Communication for Intelligent Transportation Systems: Architecture and Performance Evaluation

2020 ◽  
Author(s):  
Taghi Shahgholi ◽  
Amir Sheikhahmadi ◽  
Keyhan Khamforoosh ◽  
Sadoon Azizi
Keyword(s):  
Low Power ◽  
Intelligent Transportation Systems ◽  
Data Exchange ◽  
Road Traffic ◽  
Intelligent Transportation ◽  
Traffic Monitoring ◽  
Transportation Systems ◽  
Area Network ◽  
Emergency Vehicle ◽  
Communication Infrastructure

Abstract There are more than 1.3 billion vehicles around the world and rapidly growing which causing worldwide challenges such as congestion, huge fuel consumption, and emissions. The solution to these issues could be expansion of infrastructure or making efficient use of the current infrastructure using current technological advances by implementing Intelligent Transportation Systems (ITSs). In this paper, we proposed and explored the possibility of using cellular-based Low-Power Wide-Area Network (LPWAN) communications, LTE-M and Narrowband Internet of Things (NB-IoT), for ITS applications. LTE-M and NB-IoT are designed to provide long-range, low power, and lowcost communication infrastructure and can be a viable promising option for immediate implementation in the real world. In order to understand the feasibility of using LPWAN for ITS, we investigated two applications with low and high delay requirements: road traffic monitoring and emergency vehicle management and preemption. Then, the performance of using LTE-M and NB-IoT for providing backhaul communication infrastructure has been evaluated in a realistic simulation environment and compared for these two scenarios in terms of end to end latency per user. SUMO traffic simulator has been used for realistic traffic generation and a Python-based program with the ability to live data exchange with SUMO has been developed for communication performance evaluations. The simulation results demonstrate the feasibility of using LPWAN for ITS backhaul infrastructure where it was in favor of the LTE-M over NB-IoT.

Implementation of Vehicle Detection System Using WSN

2013 ◽  
Vol 411-414 ◽  
pp. 1459-1464
Author(s):  
Yun Long Li ◽  
Chun Xin Wang ◽  
Xiao Li Zhou ◽  
Huan Juan Wang ◽  
Ya Kun Liu
Keyword(s):  
Road Traffic ◽  
Detection System ◽  
Vehicle Detection ◽  
Intelligent Transportation ◽  
Detection Algorithm ◽  
State Machine ◽  
Sensor Nodes ◽  
Guidance System ◽  
The Road ◽  
On The Road

Vehicle Detection System plays a basic role in the field of intelligent transportation, and is the cornerstone of constructing modern intelligent transportation system. This paper presents a new vehicle detection algorithm using WSN that called the adaptive state machine. The algorithm can adaptively update the threshold and baseline; use the state machine to achieve the aim of the accurate and efficient vehicle detection. It can be used for the detection of road traffic flow, and can be used in large parking vehicle guidance system. On the road, we have deployed 76 Sensor Nodes to evaluate the performance. We observe the accurate of the road vehicle detection rate of vehicle detection system is nearly 98%.

scholarly journals Support Vector Machine Applied to Road Traffic Event Classification

2018 ◽  
Vol 231 ◽  
pp. 04001
Author(s):  
Maciej Blaszke ◽  
Bozena Kostek
Keyword(s):  
Support Vector Machine ◽  
Feature Vector ◽  
Road Traffic ◽  
Traffic Monitoring ◽  
Support Vector ◽  
Intelligent Transport System ◽  
Signal Recognition ◽  
Event Classification ◽  
Intelligent Transport ◽  
Single Vehicle

The aim of this paper is to present results of road traffic event signal recognition. First, several types of systems for road traffic monitoring, including Intelligent Transport System (ITS) are shortly described. Then, assumptions of creating a database of vehicle signals recorded in different weather and road conditions are outlined. Registered signals were edited as single vehicle pass by. Using the Matlab-based application a feature vector containing 48 parameters was extracted and analyzed in the context of parameter separability and classification effectiveness employing SVM (Support Vector Machine) algorithm. In conclusion, the classifier developed and its effectiveness were discussed.

scholarly journals LPWAN-Based Hybrid Backhaul Communication for Intelligent Transportation Systems: Architecture and Performance Evaluation

2020 ◽  
Author(s):  
Taghi Shahgholi ◽  
Amir Sheikhahmadi ◽  
Keyhan Khamforoosh ◽  
Sadoon Azizi
Keyword(s):  
Performance Evaluation ◽  
Low Power ◽  
Intelligent Transportation Systems ◽  
Road Traffic ◽  
Intelligent Transportation ◽  
Traffic Monitoring ◽  
Transportation Systems ◽  
Area Network ◽  
Emergency Vehicle ◽  
Wide Range

Abstract Increased number of the vehicles on the streets around the world has led to several problems including traffic congestion in many regions. Intelligent Transportation Systems (ITSs) are a viable solution for this problem by implementing efficient use of the current infrastructures. In this paper, the possibility of using cellular-based Low-Power Wide-Area Network (LPWAN) communications, LTE-M and NB-IoT, for ITS applications has been investigated. LTE-M and NB-IoT are designed to provide wide-range, low power and low cost communication infrastructures and can be a promising option which has the potential to be employed immediately in real systems. In order to to understand the feasibility of using LPWAN for ITS, two applications with low and high delay requirements have been examined: road traffic monitoring and emergency vehicle management. Then, the performance of using LTE-M and NB-IoT for providing backhaul communication infrastructure has been evaluated in a realistic simulation environment and compared for these two scenarios in terms of end to end delay per user. Simulation of Urban MObility (SUMO) has been used for realistic traffic generation and a Python-based program has been developed. This program has the ability to exchange live data with SUMO for communication performance evaluation. The simulation results demonstrate the feasibility of using LPWAN for ITS backhaul infrastructure mostly in favor of the LTE-M over NB-IoT.

Road Traffic Monitoring

IEE Review ◽  
1989 ◽  
Vol 35 (5) ◽  
pp. 188
Author(s):  
P.L. Belcher
Keyword(s):  
Road Traffic ◽  
Traffic Monitoring

A Robust Blind Image Forensic-Detection Method based on Fuzzy Technique

2019 ◽  
Vol 8 (3) ◽  
pp. 5926-5929
Keyword(s):  
Research Direction ◽  
Geometric Transformation ◽  
Detection Accuracy ◽  
Forensic Investigation ◽  
Image Forensic ◽  
Fuzzy Logic Approach ◽  
Logic Approach ◽  
Tampered Image ◽  
New Research ◽  
Blind Image

Blind forensic-investigation in a digital image is a new research direction in image security. It aims to discover the altered image content without any embedded security scheme. Block and key point based methods are the two dispensation options in blind image forensic investigation. Both the techniques exhibit the best performance to reveal the tampered image. The success of these methods is limited due to computational complexity and detection accuracy against various image distortions and geometric transformation operations. This article introduces different blind image tampering methods and introduces a robust image forensic investigation method to determine the copy-move tampered image by means of fuzzy logic approach. Empirical outcomes facilitate that the projected scheme effectively classifies copy-move type of forensic images as well as blurred tampered image. Overall detection accuracy of this method is high over the existing methods.

scholarly journals A Multi-Loop Vehicle-Counting Method under Gray Mode and RGB Mode

2021 ◽  
Vol 11 (15) ◽  
pp. 6831
Author(s):  
Yue Chen ◽  
Jian Lu
Keyword(s):  
Intelligent Transportation Systems ◽  
High Efficiency ◽  
Road Traffic ◽  
Low Cost ◽  
Rapid Development ◽  
Intelligent Transportation ◽  
Transportation Systems ◽  
Counting Method ◽  
Moving Vehicle ◽  
Available Information

With the rapid development of road traffic, real-time vehicle counting is very important in the construction of intelligent transportation systems (ITSs). Compared with traditional technologies, the video-based method for vehicle counting shows great importance and huge advantages in its low cost, high efficiency, and flexibility. However, many methods find difficulty in balancing the accuracy and complexity of the algorithm. For example, compared with traditional and simple methods, deep learning methods may achieve higher precision, but they also greatly increase the complexity of the algorithm. In addition to that, most of the methods only work under one mode of color, which is a waste of available information. Considering the above, a multi-loop vehicle-counting method under gray mode and RGB mode was proposed in this paper. Under gray and RGB modes, the moving vehicle can be detected more completely; with the help of multiple loops, vehicle counting could better deal with different influencing factors, such as driving behavior, traffic environment, shooting angle, etc. The experimental results show that the proposed method is able to count vehicles with more than 98.5% accuracy while dealing with different road scenes.

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