scholarly journals A Deep Cycle Limit Learning Machine Method for Urban Expressway Traffic Incident Detection

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
YunFeng Fang ◽  
Qingfang Yang ◽  
Lili Zheng ◽  
Xiangyu Zhou ◽  
Bo Peng
Keyword(s):  
Extreme Learning Machine ◽  
Traffic Congestion ◽  
Regression Tree ◽  
Detection Algorithm ◽  
Incident Detection ◽  
Layer By Layer ◽  
Traffic Incident ◽  
Urban Expressway ◽  
Learning Machine ◽  
Automatic Event Detection

In Beijing, Shanghai, Hangzhou, and other cities in China, traffic congestion caused by traffic incidents also accounts for 50% to 75% of the total traffic congestion on expressways. Therefore, it is of great significance to study an accurate and timely automatic traffic incident detection algorithm for ensuring the operation efficiency of expressways and improving the level of road safety. At present, many effective automatic event detection algorithms have been proposed, but the existing algorithms usually take the original traffic flow parameters as input variables, ignoring the construction of feature variable sets and the screening of important feature variables. This paper presents an automatic event detection algorithm based on deep cycle limit learning machine. The traffic flow, speed, and occupancy of downstream urban expressway are extracted as input values of the deep-loop neural network. The initial connection weights and output thresholds of the deep-loop neural network are optimized by using the improved particle swarm optimization (PSO) algorithm for global search. The higher classification accuracy of the extreme learning machine is trained, and the generalization performance of the extreme learning machine is improved. In addition, the extreme learning machine is used as a learning unit for unsupervised learning layer by layer. Finally, the microwave detector data of Tangqiao viaduct in Hangzhou are used to verify the experiment and compared with LSTM, CNN, gradient-enhanced regression tree, SVM, BPNN, and other methods. The results show that the algorithm can transfer low-level features layer by layer to form a more complete feature representation, retaining more original input information. It can save expensive computing resources and reduce the complexity of the model. Moreover, the detection accuracy of the algorithm is high, the detection rate is higher than 98%, and the false alarm rate is lower than 3%. It is better than LSTM, CNN, gradient-enhanced regression tree, and other algorithms. It is suitable for urban expressway traffic incident detection.

Highway Traffic Incident Detection and Optimal Sensor Placement Layout

2013 ◽  
Vol 650 ◽  
pp. 460-464 ◽  
Author(s):  
Lan Bai ◽  
Qi Sheng Wu ◽  
Mei Yang ◽  
Lan Xin Wei ◽  
Bo Li ◽  
...  
Keyword(s):  
Road Traffic ◽  
Detection Algorithm ◽  
Simulation Software ◽  
Incident Management ◽  
Incident Detection ◽  
Highway Traffic ◽  
Traffic Incidents ◽  
Traffic Incident ◽  
Before And After ◽  
Traffic Incident Management

Traffic incident detection is critical to the core of the traffic incident management process. In order to study the highway traffic incident detection algorithm and the layout spacing of the fixed detector, under the assumptions of the linear traffic flow, to detect traffic incidents as the goal, using TransModeler traffic simulation software to simulate the highway traffic conditions from Xian to Hanzhong, getting the changes in the macroscopic traffic parameters before and after the traffic incident, and analysis of the data, finally puts forward the optimal layout of spacing of basic road traffic incident detection.

scholarly journals An Improved LDA-Based ELM Classification for Intrusion Detection Algorithm in IoT Application

Sensors ◽  
2020 ◽  
Vol 20 (6) ◽  
pp. 1706 ◽  
Author(s):  
Dehua Zheng ◽  
Zhen Hong ◽  
Ning Wang ◽  
Ping Chen
Keyword(s):  
Discriminant Analysis ◽  
Intrusion Detection ◽  
Linear Discriminant Analysis ◽  
Extreme Learning Machine ◽  
Detection Efficiency ◽  
Detection Algorithm ◽  
Malicious Attacks ◽  
Linear Discriminant ◽  
Iot Devices ◽  
Learning Machine

The Internet of Things (IoT) is widely applied in modern human life, e.g., smart home and intelligent transportation. However, it is vulnerable to malicious attacks, and the current existing security mechanisms cannot completely protect the IoT. As a security technology, intrusion detection can defend IoT devices from most malicious attacks. However, unfortunately the traditional intrusion detection models have defects in terms of time efficiency and detection efficiency. Therefore, in this paper, we propose an improved linear discriminant analysis (LDA)-based extreme learning machine (ELM) classification for the intrusion detection algorithm (ILECA). First, we improve the linear discriminant analysis (LDA) and then use it to reduce the feature dimensions. Moreover, we use a single hidden layer neural network extreme learning machine (ELM) algorithm to classify the dimensionality-reduced data. Considering the high requirement of IoT devices for detection efficiency, our scheme not only ensures the accuracy of intrusion detection, but also improves the execution efficiency, which can quickly identify the intrusion. Finally, we conduct experiments on the NSL-KDD dataset. The evaluation results show that the proposed ILECA has good generalization and real-time characteristics, and the detection accuracy is up to 92.35%, which is better than other typical algorithms.

scholarly journals Large-Scale Traffic Congestion Prediction Based on the Symmetric Extreme Learning Machine Cluster Fast Learning Method

Symmetry ◽  
2019 ◽  
Vol 11 (6) ◽  
pp. 730 ◽  
Author(s):  
Yiming Xing ◽  
Xiaojuan Ban ◽  
Xu Liu ◽  
Qing Shen
Keyword(s):  
Extreme Learning Machine ◽  
Traffic Congestion ◽  
Large Scale ◽  
High Efficiency ◽  
Transportation Systems ◽  
Urban Traffic ◽  
Fast Learning ◽  
Congestion Prediction ◽  
Learning Machine ◽  
Scale Data

The prediction of urban traffic congestion has emerged as one of the most pivotal research topics of intelligent transportation systems (ITSs). Currently, different neural networks have been put forward in the field of traffic congestion prediction and have been put to extensive use. Traditional neural network training takes a long time in addition to easily falling into the local optimal and overfitting. Accordingly, this inhibits the large-scale application of traffic prediction. On the basis of the theory of the extreme learning machine (ELM), the current paper puts forward a symmetric-ELM-cluster (S-ELM-Cluster) fast learning methodology. In this suggested methodology, the complex learning issue of large-scale data is transformed into different issues on small- and medium-scale data sets. Additionally, this methodology makes use of the extreme learning machine algorithm for the purpose of training the subprediction model on each different section of road, followed by establishing a congestion prediction model cluster for all the roads in the city. Together, this methodology fully exploits the benefits associated with the ELM algorithm in terms of accuracy over smaller subsets, high training speed, fewer parameters, and easy parallel acceleration for the realization of high-accuracy and high-efficiency large-scale traffic congestion data learning.

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