Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems

2020 ◽  
Vol 182 ◽  
pp. 107484 ◽  
Author(s):  
Azzedine Boukerche ◽  
Yanjie Tao ◽  
Peng Sun
Keyword(s):  
Artificial Intelligence ◽  
Traffic Flow ◽  
Intelligent Transportation Systems ◽  
Intelligent Transportation ◽  
Transportation Systems ◽  
Prediction Methods ◽  
Vehicular Traffic ◽  
Traffic Flow Prediction ◽  
Flow Prediction ◽  
Vehicular Traffic Flow

Pruned Fast Learning Fuzzy Approach for Data-Driven Traffic Flow Prediction

2016 ◽  
Vol 20 (7) ◽  
pp. 1181-1191 ◽  
Author(s):  
Chengdong Li ◽  
◽  
Yisheng Lv ◽  
Jianqiang Yi ◽  
Guiqing Zhang ◽  
...  
Keyword(s):  
Traffic Flow ◽  
Intelligent Transportation Systems ◽  
Fuzzy Rule ◽  
Transportation Systems ◽  
Fuzzy Rules ◽  
Data Driven ◽  
Fuzzy Approach ◽  
Traffic Flow Prediction ◽  
Fast Learning ◽  
Flow Prediction

Traffic flow prediction plays an important role in intelligent transportation systems. With the rapid growth of traffic flow data, fast and accurate traffic flow prediction methods are now required. In this paper, we propose a novel fast learning data-driven fuzzy approach for the traffic flow prediction problem. In the proposed approach, to achieve fast learning, an extreme learning machine is utilized to optimize the consequent parameters of the fuzzy rules. Further, a fuzzy rule pruning strategy that involves measuring the firing levels of the fuzzy rules is presented to obtain reduced fuzzy inference systems. To evaluate the performance of the proposed approach, it was experimentally applied to traffic flow prediction and its results compared with those of widely used methods. The experimental results verify that the proposed approach can achieve satisfactory performance. The comparisons show that the proposed approach can obtain better (sometimes similar) performances, but with a simpler structure, fewer parameters, and much faster learning speed than the other methods.

scholarly journals TrafficWave: Generative Deep Learning Architecture for Vehicular Traffic Flow Prediction

2019 ◽  
Vol 9 (24) ◽  
pp. 5504 ◽  
Author(s):  
Donato Impedovo ◽  
Vincenzo Dentamaro ◽  
Giuseppe Pirlo ◽  
Lucia Sarcinella
Keyword(s):  
Deep Learning ◽  
Traffic Flow ◽  
Short Term Memory ◽  
Vehicular Traffic ◽  
Traffic Flow Prediction ◽  
Critical Areas ◽  
Flow Prediction ◽  
Vehicular Traffic Flow ◽  
Day Of The Week ◽  
Gated Recurrent Unit

Vehicular traffic flow prediction for a specific day of the week in a specific time span is valuable information. Local police can use this information to preventively control the traffic in more critical areas and improve the viability by decreasing, also, the number of accidents. In this paper, a novel generative deep learning architecture for time series analysis, inspired by the Google DeepMind’ Wavenet network, called TrafficWave, is proposed and applied to traffic prediction problem. The technique is compared with the most performing state-of-the-art approaches: stacked auto encoders, long–short term memory and gated recurrent unit. Results show that the proposed system performs a valuable MAPE error rate reduction when compared with other state of art techniques.

scholarly journals Short-Term Traffic Flow Prediction of Highway Network Based on Network-Constrained Lasso and NN

2018 ◽  
Vol 160 ◽  
pp. 07003
Author(s):  
Cong Wu ◽  
Zhaozheng Chen ◽  
Xiaofei Li
Keyword(s):  
Neural Networks ◽  
Traffic Flow ◽  
Intelligent Transportation Systems ◽  
Back Propagation ◽  
Transportation Systems ◽  
Time Data ◽  
Short Term ◽  
Traffic Flow Prediction ◽  
Flow Prediction ◽  
Highway Network

Accurate and timely traffic flow prediction is important for the successful deployment of intelligent transportation systems. Most of existing methods have not made good use of information from adjacent sections to analyse the trends of the object section. A new method for traffic flow prediction of highway network, namely network-constrained Lasso (Least absolute shrinkage and selection operator) and Neural Networks, was proposed. Unlike existing methods, our approach incorporated all the spatial and temporal information available in a highway network to carry our short-term traffic flow prediction for the objective section. To capture the spatial correlation of traffic network, the Laplacian matrix was introduced to describe the highway network structure. Subsequently, a network-constrained Lasso method was applied for sparse variable selection. With the extracted historic and real-time data, the back propagation neural networks were implemented to predict traffic flow at different time intervals in future. The experimental results verified that the proposed method could achieve above 90% average accuracy in the 30-minutes speed predictions for 78 road sections.

scholarly journals An Improved Hybrid Highway Traffic Flow Prediction Model Based on Machine Learning

2020 ◽  
Vol 12 (20) ◽  
pp. 8298
Author(s):  
Zhanzhong Wang ◽  
Ruijuan Chu ◽  
Minghang Zhang ◽  
Xiaochao Wang ◽  
Siliang Luan
Keyword(s):  
Prediction Model ◽  
Traffic Flow ◽  
Intelligent Transportation Systems ◽  
Transportation Systems ◽  
Permutation Entropy ◽  
Support Vector ◽  
Gray Wolf ◽  
Highway Traffic ◽  
Traffic Flow Prediction ◽  
Flow Prediction

For intelligent transportation systems (ITSs), reliable and accurate real-time traffic flow prediction is an important step and a necessary prerequisite for alleviating traffic congestion and improving highway operation efficiency. In this paper, we propose an improved hybrid predicting model including two steps: decomposition and prediction to predict highway traffic flow. First, we adopted the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) method to adaptively decompose the original nonlinear, nonstationary, and complex highway traffic flow data. Then, we used the improved weighted permutation entropy (IWPE) to obtain new reconstructed components. In the prediction step, we used the gray wolf optimizer (GWO) algorithm to optimize the least-squares support vector machine (LSSVM) prediction model established for each reconstruction component and integrate the prediction results of each subsequence to obtain the final prediction result. We experimentally validated the effectiveness of the proposed approach. The research results reveal that the proposed model is useful for predicting traffic flow and its changing trends and also allowing transportation officials to make more effective traffic decisions.

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