An New Approach of Real-Time Traffic Flow Prediction Based on Intelligent Transportation Technology

2014 ◽  
Vol 988 ◽  
pp. 715-718
Author(s):  
Jia Yang Li ◽  
Qin Xue ◽  
Jin De Liu
Keyword(s):  
Real Time ◽  
Traffic Flow ◽  
Intelligent Transportation System ◽  
Intelligent Transportation ◽  
Short Term ◽  
New Approach ◽  
Traffic Flow Prediction ◽  
Real Time Traffic ◽  
Flow Prediction ◽  
Combination Approach

Short-term traffic flow forecasting is a core problem in Intelligent Transportation System .Considering linear and nonlinear, this paper proposes a short-term traffic flow intelligent combination approach. The weight of four forecasting model is given by the correlation coefficient and standard deviation method. The experimental results show that the new approach of real-time traffic flow prediction is higher precision than single method.

scholarly journals Short-term Traffic Flow Prediction Method in Bayesian Networks Based on Quantile Regression

2020 ◽  
Vol 32 (6) ◽  
pp. 821-835
Author(s):  
Jing Luo
Keyword(s):  
Quantile Regression ◽  
Traffic Flow ◽  
Intelligent Transportation System ◽  
Prediction Method ◽  
Time Data ◽  
Short Term ◽  
Internet Of Vehicles ◽  
Traffic Flow Prediction ◽  
Spatiotemporal Correlation ◽  
Flow Prediction

With the popularization of intelligent transportation system and Internet of vehicles, the traffic flow data on the urban road network can be more easily obtained in large quantities. This provides data support for shortterm traffic flow prediction based on real-time data. Of all the challenges and difficulties faced in the research of short-term traffic flow prediction, this paper intends to address two: one is the difficulty of short-term traffic flow prediction caused by spatiotemporal correlation of traffic flow changes between upstream and downstream intersections; the other is the influence of deviation of traffic flow caused by abnormal conditions on short-term traffic flow prediction. This paper proposes a Bayesian network short-term traffic flow prediction method based on quantile regression. By this method the trouble caused by spatiotemporal correlation of traffic flow prediction could be effectively and efficiently solved. At the same time, the prediction of traffic flow change under abnormal conditions has higher accuracy.

scholarly journals A Short-Term Traffic Flow Reliability Prediction Method considering Traffic Safety

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Shaoqian Li ◽  
Zhenyuan Zhang ◽  
Yang Liu ◽  
Zixia Qin
Keyword(s):  
Traffic Flow ◽  
Traffic Safety ◽  
Rapid Development ◽  
Prediction Method ◽  
Short Term ◽  
Traffic Flow Prediction ◽  
Real Time Traffic ◽  
De Algorithm ◽  
Flow Prediction ◽  
Bpnn Model

With the rapid development and application of intelligent traffic systems, traffic flow prediction has attracted an increasing amount of attention. Accurate and timely traffic flow information is of great significance to improve the safety of transportation. To improve the prediction accuracy of the backward-propagation neural network (BPNN) prediction model, which easily falls into local optimal solutions, this paper proposes an adaptive differential evolution (DE) algorithm-optimized BPNN (DE-BPNN) model for a short-term traffic flow prediction. First, by the mutation, crossover, and selection operations of the DE algorithm, the initial weights and biases of the BPNN are optimized. Then, the initial weights and biases obtained by the aforementioned preoptimization are used to train the BPNN, thereby obtaining the optimal weights and biases. Finally, the trained BPNN is utilized to predict the real-time traffic flow. The experimental results show that the accuracy of the DE-BPNN model is improved about 7.36% as compared with that of the BPNN model. The DE-BPNN is superior to the performance of three classical models for short-term traffic flow prediction.

Simplified Monte Carlo Model of Real-Time Traffic Flow Prediction

2011 ◽  
Vol 97-98 ◽  
pp. 867-871
Author(s):  
Jian Ping Xing ◽  
Ling Guo Meng ◽  
Can Sun ◽  
Jian Wen Li
Keyword(s):  
Monte Carlo ◽  
Real Time ◽  
Traffic Flow ◽  
Time Evolution ◽  
Monte Carlo Model ◽  
Traffic Flow Prediction ◽  
Real Time Traffic ◽  
Flow Prediction ◽  
Speed Up ◽  
Visual Interface

Simplified Monte Carlo collision model of real-time traffic flow prediction is proposed. In this model, two different road cells, roads and crosses, are configured. Vehicle distribution is generated by real-time traffic flow randomly. Based on two-dimensional topology, Monte Carlo collision between road and vehicles promote time evolution of the system. Monte Carlo collision is the core of the model and traffic flow is study target. The solution of relationship equations of road and vehicles is very simple in this model to speed up the computing. In addition, parameters can be corrected and configured at any time in the process of time evolution. Experimental results show that the model has the advantages of real-time, visual interface, easy configuration, and can be corrected by real-time feedback. The model can not only simulate and predict macroscopic data, such as flow, velocity distribution, but also follow the track of each vehicle in detail. So, the model can be used in researching both macroscopic and microscopic characteristics of vehicle movement.

scholarly journals The Traffic Flow Prediction Method Using the Incremental Learning-Based CNN-LTSM Model: The Solution of Mobile Application

2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
Yanli Shao ◽  
Yiming Zhao ◽  
Feng Yu ◽  
Huawei Zhu ◽  
Jinglong Fang
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Traffic Flow ◽  
Incremental Learning ◽  
Mobile Application ◽  
Learning Algorithm ◽  
Learning Algorithms ◽  
Short Term ◽  
Traffic Flow Prediction ◽  
Flow Prediction

With the acceleration of urbanization and the increase in the number of motor vehicles, more and more social problems such as traffic congestion have emerged. Accordingly, efficient and accurate traffic flow prediction has become a research hot spot in the field of intelligent transportation. However, traditional machine learning algorithms cannot further optimize the model with the increase of the data scale, and the deep learning algorithms perform poorly in mobile application or real-time application; how to train and update deep learning models efficiently and accurately is still an urgent problem since they require huge computation resources and time costs. Therefore, an incremental learning-based CNN-LTSM model, IL-TFNet, is proposed for traffic flow prediction in this study. The lightweight convolution neural network-based model architecture is designed to process spatiotemporal and external environment features simultaneously to improve the prediction performance and prediction efficiency of the model. Especially, the K-means clustering algorithm is applied as an uncertainty feature to extract unknown traffic accident information. During the model training, instead of the traditional batch learning algorithm, the incremental learning algorithm is applied to reduce the cost of updating the model and satisfy the requirements of high real-time performance and low computational overhead in short-term traffic prediction. Furthermore, the idea of combining incremental learning with active learning is proposed to fine-tune the prediction model to improve prediction accuracy in special situations. Experiments have proved that compared with other traffic flow prediction models, the IL-TFNet model performs well in short-term traffic flow prediction.

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