A Freeway Traffic Incident Detection Algorithm Based on Neural Networks

2004 ◽  
pp. 912-919
Author(s):  
Xuhua Yang ◽  
Zonghai Sun ◽  
Youxian Sun
Keyword(s):  
Neural Networks ◽  
Detection Algorithm ◽  
Incident Detection ◽  
Freeway Traffic ◽  
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.

GAID: Genetic Adaptive Incident Detection for Freeways

2003 ◽  
Vol 1856 (1) ◽  
pp. 96-105 ◽  
Author(s):  
Prasenjit Roy ◽  
Baher Abdulhai
Keyword(s):  
Neural Networks ◽  
Feature Space ◽  
Detection Algorithm ◽  
Incident Detection ◽  
Time Step ◽  
Detection Algorithms ◽  
Bayesian Update ◽  
Average Condition ◽  
The Difference ◽  
Input Variables

Extensive research on point-detector-based automatic traffic-impeding incident detection indicates the potential superiority of neural networks over conventional approaches. All approaches, however, including neural networks, produce detection algorithms that are location specific—that is, neither transferable nor adaptive. A recently designed and ready-to-implement freeway incident detection algorithm based on genetically optimized probabilistic neural networks (PNN) is presented. The combined use of genetic algorithms and neural networks produces GAID, a genetic adaptive incident detection logic that uses flow and occupancy values from the upstream and downstream loop detector stations to automatically detect an incident between the said stations. As input, GAID uses modified input feature space based on the difference of the present volume and occupancy condition from the average condition for time and location. On the output side, it uses a Bayesian update process and converts isolated binary outputs into a continuous probabilistic measure—that is, updated every time step. GAID implements genetically optimized separate smoothing parameters for its input variables, which in turn increase the overall generalization accuracy of the detector algorithm. The detector was subjected to off-line tests with real incident data from a number of freeways in California. Results and further comparison with the McMaster algorithm indicate that GAID with a PNN core has a better detection rate and a lower false alarm rate than the PNN alone and the well-established McMaster algorithm. Results also indicate that the algorithm is the least location specific, and the automated genetic optimization process makes it adapt to new site conditions.

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.

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