Leveraging Spatio-Temporal Patterns for Predicting Citywide Traffic Crowd Flows Using Deep Hybrid Neural Networks

Ahmad, Ali; Zhu, Yanmin; Chen, Qiuxia; Yu, Jiadi; Cai, Haibin

doi:10.1109/icpads47876.2019.00025

Cited by 77 publications

(18 citation statements)

References 11 publications

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“…Traffic flow analysis may be complicated, however, because it is impacted by a variety of complex elements, such as regional spatial and temporal interdependence and external causes. DHSTNet [2] is a deep hybrid spatiotemporal dynamic neural network that is proposed in this study for forecasting inflows and outflows in each region of a metropolis. It is possible to combine the result of core components of this model to generate a single result by assigning different weights to different trunks and branches.…”

Section: Related Workmentioning

confidence: 99%

Passenger flow prediction in bus transportation system using deep learning

Nagaraj¹,

Gururaj²,

Swathi³

et al. 2022

Multimed Tools Appl

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The forecasting of bus passenger flow is important to the bus transit system’s operation. Because of the complicated structure of the bus operation system, it’s difficult to explain how passengers travel along different routes. Due to the huge number of passengers at the bus stop, bus delays, and irregularity, people are experiencing difficulties of using buses nowadays. It is important to determine the passenger flow in each station, and the transportation department may utilize this information to schedule buses for each region. In Our proposed system we are using an approach called the deep learning method with long short-term memory, recurrent neural network, and greedy layer-wise algorithm are used to predict the Karnataka State Road Transport Corporation (KSRTC) passenger flow. In the dataset, some of the parameters are considered for prediction are bus id, bus type, source, destination, passenger count, slot number, and revenue These parameters are processed in a greedy layer-wise algorithm to make it has cluster data into regions after cluster data move to the long short-term memory model to remove redundant data in the obtained data and recurrent neural network it gives the prediction result based on the iteration factors of the data. These algorithms are more accurate in predicting bus passengers. This technique handles the problem of passenger flow forecasting in Karnataka State Road Transport Corporation Bus Rapid Transit (KSRTCBRT) transportation, and the framework provides resource planning and revenue estimation predictions for the KSRTCBRT.

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Section: Related Workmentioning

confidence: 99%

Passenger flow prediction in bus transportation system using deep learning

Nagaraj¹,

Gururaj²,

Swathi³

et al. 2022

Multimed Tools Appl

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“…Most of the research scholars applied CNN for different kinds of problems specifically in the computer vision area [9]. According to [20] presented residual learning that allows those networks which have a [13] explicit explicit explicit DHSTNet [5] explicit explicit explicit STD-Net explicit explicit explicit more deep structure. According to [21], they used a recurrent neural network (RNN) for sequence learning tasks as well as researchers used long short term memory (LSTM) that enables CNN to understand long term temporal dependency.…”

Section: Related Workmentioning

confidence: 99%

“…Mostly they forecast even billions of individual mobility traces instead of aggregated flows of the crowd in a region. Similarly [5], they proposed an approach namely DHSTNet to forecast citywide traffic crowd flows prediction using ConvLSTM network. In addition, some studies first depict the states of the traffic into images and then 2D CNNs applied for citywide crowd flows prediction [4].…”

Section: Related Workmentioning

confidence: 99%

Modeling Dynamic Spatio-Temporal Correlations for Urban Traffic Flows Prediction

Awan¹,

Ahmad

Khan

et al. 2021

IEEE Access

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Prediction of traffic crowd movement is one of the most important component in many applications' domains ranging from urban management to transportation schedule. The key challenge of citywide crowd flows prediction is how to model spatial and dynamic temporal correlation. However, in recent years several studies have been done, but they lack the ability to effectively and simultaneously model spatial and temporal dependencies among traffic crowd flows. To address this issue, in this paper a novel spatio-temporal deep hybrid neural network proposed termed STD-Net to forecast citywide crowd traffic flows. More specifically, STD-Net contains four major branches, i.e., closeness, period volume, weekly volume, and external branches, respectively. We design a residual neural network unit for each property to depict the spatio-temporal features of traffic flows. For various branches, STD-Net provides distinct weights and then combines the outputs of four branches together. Extensive experiments on two large-scale datasets from New York bike and Beijing taxi have demonstrated that STD-Net achieves competitive performances the existing state-of-the-art prediction baselines. INDEX TERMSDeep learning, Urban crowd flows, Neural networks, Long short-term memory, Convolutional neural network

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“…(1) The forward training propagation process of the training data from the input layer through the hidden layer to the output. (2) The error between the expected output and the actual output of the network is transmitted backward by the output layer through the hidden layer, and the weight and threshold are adjusted continuously. (3) Iterative network memory training process with forward training propagation and reverse error propagation.…”

Section: Bp Neural Network Modelmentioning

confidence: 99%

“…e.g. A data aggregation based approach to exploit dynamic spatio-temporal correlations for citywide crowd flows prediction in fog computing [3]; Leveraging Spatio-Temporal Patterns for Predicting Citywide Traffic Crowd Flows Using Deep Hybrid Neural Networks [2] and so on.…”

Section: Introductionmentioning

confidence: 99%

Fault diagnosis of rolling bearing based on back propagation neural network optimized by cuckoo search algorithm

Xiao

Liao

Bartoš

et al. 2021

Multimed Tools Appl

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In order to improve the accuracy of rolling bearing fault diagnosis in mechanical equipment, a new fault diagnosis method based on back propagation neural network optimized by cuckoo search algorithm is proposed. This method use the global search ability of the cuckoo search algorithm to constantly search for the best weights and thresholds, and then give it to the back propagation neural network. In this paper, wavelet packet decomposition is used for feature extraction of vibration signals. The energy values of different frequency bands are obtained through wavelet packet decomposition, and they are input as feature vectors into optimized back propagation neural network to identify different fault types of rolling bearings. Through the three sets of simulation comparison experiments of Matlab, the experimental results show that, Under the same conditions, compared with the other five models, the proposed back propagation neural network optimized by cuckoo search algorithm has the least number of training iterations and the highest diagnostic accuracy rate. And in the complex classification experiment with the same fault location but different bearing diameters, the fault recognition correct rate of the back propagation neural network optimized by cuckoo search algorithm is 96.25%.

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Leveraging Spatio-Temporal Patterns for Predicting Citywide Traffic Crowd Flows Using Deep Hybrid Neural Networks

Cited by 77 publications

References 11 publications

Passenger flow prediction in bus transportation system using deep learning

Passenger flow prediction in bus transportation system using deep learning

Modeling Dynamic Spatio-Temporal Correlations for Urban Traffic Flows Prediction

Fault diagnosis of rolling bearing based on back propagation neural network optimized by cuckoo search algorithm

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