About: Research on Short-Term Urban Traffic Congestion Based on Fuzzy Comprehensive Evaluation and Machine Learning

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An Entity of Type : schema:ScholarlyArticle, within Data Space : wasabi.inria.fr associated with source document(s)

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
has title	Research on Short-Term Urban Traffic Congestion Based on Fuzzy Comprehensive Evaluation and Machine Learning
Creator	Yang, Chun Hu, Ting Mei, Yuan
Source	PMC
abstract	There are many factors that affect urban traffic flow. In the case of severe traffic congestion, the vehicle speed is very slow, which results in the GPS positioning system’s estimation of the vehicle speed being very inaccurate, which in turn leads to poor reliability of the estimated congestion time of the road segment. The main contents of this study are: in the case of urban traffic congestion, the prediction and analysis of the degree of traffic congestion and the length of congestion. Taking the dynamic traffic data of Shenzhen on June 9, 2014 as an example, the road section of Binhe Avenue is selected, and the data of traffic flow, average speed of traffic volume and traffic volume density in the current time period are calculated after data preprocessing, as a measure of traffic. The main impact indicators of congestion status. Then we use the fuzzy comprehensive evaluation method to divide TSI as a traffic congestion evaluation index and divide the road congestion into four levels. In this way, we can get the congestion of the road in each time period of the day and the time required to pass. Then we use the random forest, adaboost, GBDT, Lasso CV and BP neural networks in the machine learning algorithm to build models to measure traffic congestion for training and testing. Finally, the BP neural network has the best effect on this problem, and mean square error is 0.0190. Finally, we used BP neural network to predict and congest the road in the next three hours. From the experimental simulation results, this method can effectively analyze and predict the real-time traffic congestion.
has issue date	2020-07-11(xsd:dateTime)
bibo:doi	10.1007/978-981-15-7205-0_9
has license	no-cc
sha1sum (hex)	e981e9c156e06e0f10d497fdbe3f21f3bc3f0b2a
schema:url	https://doi.org/10.1007/978-981-15-7205-0_9
resource representing a document's title	Research on Short-Term Urban Traffic Congestion Based on Fuzzy Comprehensive Evaluation and Machine Learning
has PubMed Central identifier	PMC7351684
schema:publication	Data Mining and Big Data
resource representing a document's body	covid:e981e9c156e06e0f10d497fdbe3f21f3bc3f0b2a#body_text
is schema:about of	named entity 'speed' named entity 'degree' named entity 'USE' named entity 'STATUS' named entity 'BEING' named entity 'ERROR' named entity 'Lasso' named entity 'machine learning' named entity 'traffic congestion' named entity 'speed' named entity 'predict' named entity 'data preprocessing' named entity 'segment' named entity 'estimated' named entity 'time' named entity 'traffic' named entity 'traffic congestion' named entity 'flow' named entity 'evaluation' named entity 'positioning system' named entity 'neural networks' named entity 'selected' named entity 'traffic data' named entity 'Shenzhen' named entity 'time period' named entity 'traffic congestion' named entity 'traffic congestion' named entity 'time period' named entity 'traffic flow' named entity 'impact' named entity 'models' named entity 'nearest neighbor algorithm' named entity 'neural network' named entity 'variance' named entity 'neuron' named entity 'CART' named entity 'support vector machine' named entity 'CART' named entity 'algorithm' named entity 'random forest' named entity 'regression trees' named entity 'Regressor' named entity 'sklearn' named entity 'neuron' named entity 'June 19, 2014' named entity 'neural network' named entity 'neural network' named entity 'decision tree' named entity 'Pearson correlation coefficient' named entity 'algorithm' named entity 'Gradient Boosting' named entity 'traffic congestion' named entity 'nonlinear optimization' named entity 'algorithm' named entity 'error estimation' named entity 'variance' named entity 'neural network' named entity 'normalized' named entity 'traffic congestion' named entity 'traffic congestion' named entity 'algorithm' named entity 'decision tree' named entity 'classifiers' named entity 'decision trees' named entity 'correlation' named entity 'random forest' named entity 'error propagation' named entity 'deep learning' named entity 'classification trees' named entity 'GBDT' named entity 'decision trees' named entity 'neuron' named entity 'cross-validation' named entity 'traffic congestion' named entity 'Lasso' named entity 'neurons' named entity 'MSE' named entity 'tensorflow' named entity 'neuron' named entity 'linear model' named entity 'June 10'

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