ANN based short-term traffic flow forecasting in undivided two lane highway

Sharma, B.R.; Kumar, Sachin; Tiwari, Prayag; Yadav, Pranay; Nezhurina, Marina I.

doi:10.1186/s40537-018-0157-0

Cited by 67 publications

(34 citation statements)

References 29 publications

(31 reference statements)

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“…In particular, SVR was proposed for short-term traffic flow forecasting in [3], which is an online learning weighted algorithm. Meanwhile, the work of [12] shows that ANN has been applied in short-term traffic flow forecasting and shown a superior performance than other traditional ML approaches.…”

Section: A Traditional Machine Learningmentioning

confidence: 99%

“…Consequently, the robustness of model can be improved and the computational cost for traffic flow prediction can be reduced. We then combine results Y wide [11][12][13][14]. Finally, We select the root mean square prop (RMSProp) optimizer to minimize the square errors between the prediction value and the actual target value (line 15).…”

Section: Algorithm Analysismentioning

confidence: 99%

See 1 more Smart Citation

Wide-Attention and Deep-Composite Model for Traffic Flow Prediction in Transportation Cyber–Physical Systems

Zhou

Dai

Wang

2021

IEEE Trans. Ind. Inf.

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Recently, traffic flow prediction has drawn significant attention because it is a prerequisite in intelligent transportation management in urban informatics. The massively-available traffic data collected from various sensors in Transportation Cyber-Physical Systems brings the opportunities in accurately forecasting traffic trend. Recent advances in deep learning shows the effectiveness on traffic flow prediction though most of them only demonstrate the superior performance on traffic data from a single type of vehicular carriers (e.g., cars) and does not perform well in other types of vehicles. To fill this gap, we propose a wide-attention and deep-composite (WADC) model consisting of a wide-attention module and a deep-composite module in this paper. In particular, the wide-attention module can extract global key features from traffic flows via a linear model with selfattention mechanism. The deep-composite module can generalize local key features via Convolutional Neural Network component and Long Short-Term Memory Network component. We also perform extensive experiments on different types of traffic flow datasets to investigate the performance of WADC model. Our experimental results exhibit that WADC model outperforms other existing approaches.

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Section: A Traditional Machine Learningmentioning

confidence: 99%

Section: Algorithm Analysismentioning

confidence: 99%

Wide-Attention and Deep-Composite Model for Traffic Flow Prediction in Transportation Cyber–Physical Systems

Zhou

Dai

Wang

2021

IEEE Trans. Ind. Inf.

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show abstract

“…All the aforementioned work requires significant prior domain knowledge and feature engineering to achieve better performance. ANN model in [34] have the advantage over the previous algorithms due to its capability to work with multi-dimensional data without any feature engineering, and also due to its potential to perceive the non-linear relationship between input and output features to provide generalized solutions. However, due to its shallow depth in architecture, the accuracy of the model was not satisfactory, so the researchers shift their research directed toward the deep machine learning architecture.…”

Section: Related Workmentioning

confidence: 99%

City-Wide Traffic Congestion Prediction Based on CNN, LSTM and Transpose CNN

et al. 2020

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Traffic congestion is a significant problem faced by large and growing cities that hurt the economy, commuters, and the environment. Forecasting the congestion level of a road network timely can prevent its formation and increase the efficiency and capacity of the road network. However, despite its importance, traffic congestion prediction is not a hot topic among the researcher and traffic engineers. It is due to the lack of high-quality city-wide traffic data and computationally efficient algorithms for traffic prediction. In this paper, we propose (i) an efficient and inexpensive city-wide data acquisition scheme by taking a snapshot of traffic congestion map from an open-source online web service; Seoul Transportation Operation and Information Service (TOPIS), and (ii) a hybrid neural network architecture formed by combing Convolutional Neural Network, Long Short-Term Memory, and Transpose Convolutional Neural Network to extract the spatial and temporal information from the input image to predict the networkwide congestion level. Our experiment shows that the proposed model can efficiently and effectively learn both spatial and temporal relationships for traffic congestion prediction. Our model outperforms two other deep neural networks (Auto-encoder and ConvLSTM) in terms of computational efficiency and prediction performance. INDEX TERMS Convolutional neural network, long short-term memory, partially convolutional neural network, spatiotemporal feature, traffic congestion forecasting, transport network.

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“…The MLP model generally works well in the capture of complex and non-linear relations, but it usually requires a large volume of data and complex training. Many researchers, therefore, consider it as the most commonly implemented network topology [44][45][46]. Recently, in the study by Chen et al, they adapted a novel approach using dynamic graph hybrid automata for the modeling and estimation of density on an urban freeway in the city of Beijing, China [47].…”

Section: Related Workmentioning

confidence: 99%

Short Term Traffic State Prediction via Hyperparameter Optimization Based Classifiers

Zahid

Chen

Jamal

et al. 2020

Sensors

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Short-term traffic state prediction has become an integral component of an advanced traveler information system (ATIS) in intelligent transportation systems (ITS). Accurate modeling and short-term traffic prediction are quite challenging due to its intricate characteristics, stochastic, and dynamic traffic processes. Existing works in this area follow different modeling approaches that are focused to fit speed, density, or the volume data. However, the accuracy of such modeling approaches has been frequently questioned, thereby traffic state prediction over the short-term from such methods inflicts an overfitting issue. We address this issue to accurately model short-term future traffic state prediction using state-of-the-art models via hyperparameter optimization. To do so, we focused on different machine learning classifiers such as local deep support vector machine (LD-SVM), decision jungles, multi-layers perceptron (MLP), and CN2 rule induction. Moreover, traffic states are evaluated using traffic attributes such as level of service (LOS) horizons and simple if–then rules at different time intervals. Our findings show that hyperparameter optimization via random sweep yielded superior results. The overall prediction performances obtained an average improvement by over 95%, such that the decision jungle and LD-SVM achieved an accuracy of 0.982 and 0.975, respectively. The experimental results show the robustness and superior performances of decision jungles (DJ) over other methods.

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ANN based short-term traffic flow forecasting in undivided two lane highway

Cited by 67 publications

References 29 publications

Wide-Attention and Deep-Composite Model for Traffic Flow Prediction in Transportation Cyber–Physical Systems

Wide-Attention and Deep-Composite Model for Traffic Flow Prediction in Transportation Cyber–Physical Systems

City-Wide Traffic Congestion Prediction Based on CNN, LSTM and Transpose CNN

Short Term Traffic State Prediction via Hyperparameter Optimization Based Classifiers

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