Abstract
A framework for traffic congestion prediction and visualization based on machine learning and Fuzzy Comprehensive Evaluation named MF-TCPV is proposed in this paper. The framework uses DataX and DataV to implement the integration of multi-source heterogeneous traffic data and the visualization of congestion prediction results. A deep prediction model named LSTM-SPRVM based on deep learning algorithms, machine learning algorithms, and Spark parallelization technology for the prediction of traffic congestion features in the future is proposed. In MF-TCPV, traffic congestion is divided into six levels based on Fuzzy Comprehensive Evaluation and traffic congestion features such as average speed, road occupancy rate, and traffic flow density. MF-TCPV is validated based on the real data of Whitemud Drive in Canada. The experimental results demonstrate that MF-TCPV is capable of predicting the traffic congestion accurately and displaying prediction results visually. LSTM-SPRVM is better than other existing deep learning models in terms of prediction accuracy, and MF-TCPV can intuitively visualize the prediction results of traffic congestion.
Highlights
Intelligent Traffic Systems (ITS) is an integrated system that combines advanced science and technology such as electronic information technology, data communication technology, sensor technology, control theory, operational research, and artificial intelligence to improve the transportation industry
The main contributions of this paper are summarized as follows: ⚫ A novel framework named MF-TCPV is proposed for the prediction and the visualization of traffic congestion, which can be divided into three layers— — raw data layer, data processing layer, and data presentation layer. ⚫ In the data processing layer, a deep prediction model based on deep learning and machine learning is proposed for congestion evaluation parameter prediction, called long short-term memory networks combined relevance vector machine based on spark parallelization
Based on the above literature review, we propose LSTM-SPRVM as a part of MF-TCPV to predict traffic congestion features
Summary
Intelligent Traffic Systems (ITS) is an integrated system that combines advanced science and technology such as electronic information technology, data communication technology, sensor technology, control theory, operational research, and artificial intelligence to improve the transportation industry. We consider the prediction of traffic congestion features and the determination of the current congestion to build a framework for traffic congestion prediction and visualization, called Machine Learning and Fuzzy Comprehensive Evaluation based Framework for VOLUME XX, 2017. Prediction and Visualization of Traffic Congestion (or MF-TCPV), which can evaluate short-term traffic congestion in the future by using the prediction results of traffic congestion features. The main contributions of this paper are summarized as follows: ⚫ A novel framework named MF-TCPV is proposed for the prediction and the visualization of traffic congestion, which can be divided into three layers— — raw data layer, data processing layer, and data presentation layer. ⚫ In the data processing layer, a deep prediction model based on deep learning and machine learning is proposed for congestion evaluation parameter prediction, called long short-term memory networks combined relevance vector machine based on spark parallelization (or LSTM-SPRVM).
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
