Traffic incident detection algorithm based on non-parameter regression

2003 ◽  
Author(s):  
Tang Shuming ◽  
Gong Xiaoyan ◽  
Wang Feiyue
Keyword(s):  
Detection Algorithm ◽  
Incident Detection ◽  
Traffic Incident

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 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.

scholarly journals Advanced Fuzzy-Logic-Based Traffic Incident Detection Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Changhong Zhu ◽  
Zhenjun Guo ◽  
Jie Ke
Keyword(s):  
Influencing Factor ◽  
Poor Performance ◽  
Weather Conditions ◽  
Detection Algorithm ◽  
Incident Detection ◽  
Traffic Incident ◽  
Factor Module ◽  
Standard Normal ◽  
Time To Detection ◽  
Mean Time

This study demonstrates an incident detection algorithm that uses the meteorological and traffic parameters for improving the poor performance of the automatic incident detection (AID) algorithms under extreme weather conditions and for efficiently using the meteorological devices on advanced freeways. This algorithm comprises an incident detection module that is based on learning vector quantization (LVQ) and a meteorological influencing factor module. Field data are obtained from the Yuwu freeway in Chongqing, China, to verify the algorithm. Further, the performance of this algorithm is evaluated using commonly used criteria such as mean time to detection (MTTD), false alarm rate (FAR), and detection rate (DR). Initially, an experiment is conducted for selecting the algorithm architecture that yields the optimal detection performance. Additionally, a comparative experiment is performed using the California algorithm, exponential smoothing algorithm, standard normal deviation algorithm, and McMaster algorithm. The experimental results demonstrate that the algorithm proposed in this study is characterized by high DR, low FAR, and considerable suitability for applications in AID.

Traffic incident detection system based on video image processing

2008 ◽  
Vol 28 (7) ◽  
pp. 1886-1889 ◽  
Author(s):  
Qin WANG ◽  
Shan HUANG ◽  
Hong-bin ZHANG ◽  
Quan YANG ◽  
Jian-jun ZHANG
Keyword(s):  
Image Processing ◽  
Detection System ◽  
Video Image ◽  
Incident Detection ◽  
Traffic Incident ◽  
Video Image Processing

A wavelet-based freeway incident detection algorithm with adapting threshold parameters

2011 ◽  
Vol 19 (1) ◽  
pp. 1-19 ◽  
Author(s):  
Young-Seon Jeong ◽  
Manoel Castro-Neto ◽  
Myong K. Jeong ◽  
Lee D. Han
Keyword(s):  
Detection Algorithm ◽  
Incident Detection
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