A hybrid integrated deep learning model for the prediction of citywide spatio-temporal flow volumes

Ren, Yibin; Chen, Huanfa; Han, Yong; Cheng, Tao; Zhang, Yang; Ge, Chunhua

doi:10.1080/13658816.2019.1652303

Cited by 58 publications

(36 citation statements)

References 33 publications

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“…Inspired by this initial work, many hybrid frameworks have been proposed. For instance, the combination of CNN, which captures spatial dependency and RNN/LSTM, which captures temporal dependency have recently become favoured approaches (Yu et al, 2017b;Zhao et al, 2017;Ren et al, 2019a). Similar works can also be found in the literature (Ke et al, 2017;Chen et al, 2018;Cheng et al, 2018;Liao et al, 2018;Liu et al, 2018a;Yao et al, 2018).…”

Section: Introductionmentioning

confidence: 71%

A novel residual graph convolution deep learning model for short-term network-based traffic forecasting

Zhang

Cheng

Ren

et al. 2019

International Journal of Geographical Information Science

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Short-term traffic forecasting on large street networks is significant in transportation and urban management, such as real-time route guidance and congestion alleviation. Nevertheless, it is very challenging to obtain high prediction accuracy with reasonable computational cost due to the complex spatial dependency on the traffic network and the time-varying traffic patterns. To address these issues, this paper develops a residual graph convolution long short-term memory (RGC-LSTM) model for spatial-temporal data forecasting considering the network topology. This model integrates a new graph convolution operator for spatial modelling on networks and a residual LSTM structure for temporal modelling considering multiple periodicities. The proposed model has few parameters, low computational complexity, and a fast convergence rate. The framework is evaluated on both the 10-min traffic speed data from Shanghai, China and the 5-min Caltrans Performance Measurement System (PeMS) traffic flow data. Experiments show the advantages of the proposed approach over various state-of-the-art baselines, as well as consistent performance across different datasets.

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Section: Introductionmentioning

confidence: 71%

A novel residual graph convolution deep learning model for short-term network-based traffic forecasting

Zhang

Cheng

Ren

et al. 2019

International Journal of Geographical Information Science

Self Cite

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“…We performed the pre-processing phase by using the max filter for noise removal. We also used Z-score normalization for avoiding attributes with big scales of data from outweighing attributes with smaller scales of data for the distance-based methods [32][33][34] and the Quartile filter for outlier filtering.…”

Section: Pre-processing Stepsmentioning

confidence: 99%

Crime Spatiotemporal Prediction With Fused Objective Function in Time Delay Neural Network

et al. 2020

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In the criminology area, to detain the serial criminal, the forthcoming serial crime time, distance, and criminal's biography are essential keys. The main concern of this study is on the upcoming serial crime distance, time, and suspect biographies such as age and nationality. In conjunction with having time delays, the dynamic classifier, like Time Delay Neural Network (TDNN) utilized to perform nonlinear techniques-based predictions. The TDNN classifier system, like Back Propagation Through Time (BPTT) and Nonlinear Autoregressive with Exogenous Input (NARX) are two prominent examples. However, BPTT and NARX techniques are unable to identify the dynamic system by using single-activation functions due to producing lower accuracy. Hence, during the training phase, the direct minimization of the TDNN error can further enhance the single activation function. Thus, this work introduces an enhanced NARX (eNARX) model based on the proposed activation functions of SiRBF via fusion of two functions of the hyperbolic tangent (Tansig) and Radial Basis Function (RBF), in the same hidden layer. If a fusion of activation functions can affect the TDNN error minimization, then fusing of the Tansig and RBF functions can produce a precise prediction for crime spatiotemporal. To evaluate the proposed technique and compared it with existing NARX and BPTT, we utilized five time-series datasets, namely, Dow Jones Index, Monthly River flow in cubic meters per second, Daily temperature, and UKM-PDRM datasets namely, "Suspect & Capture" and "Crime Plotting." The analysis of the results demonstrated that the proposed eNARX produce higher accuracy in comparison to other techniques of NARX and BPTT. Consequently, the proposed technique provides more effective results for the prediction of commercial serial crime.

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“…In particular, a Gaussian Markov random fields model that can cope with noisy and missing data, and a residual model which exploits the spatio-temporal dependence among different flows and regions of the city, as well as the effect of weather. A combination of predicting methods is also given in [112], where authors combine a spatio-temporal residual network (ST-ResNet) with a long short-term memory (LSTM) neural network.…”

Section: Combined Approachesmentioning

confidence: 99%

Sensing and Forecasting Crowd Distribution in Smart Cities: Potentials and Approaches

Cecaj

Lippi

Mamei

et al. 2021

IoT

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The possibility of sensing and predicting the movements of crowds in modern cities is of fundamental importance for improving urban planning, urban mobility, urban safety, and tourism activities. However, it also introduces several challenges at the level of sensing technologies and data analysis. The objective of this survey is to overview: (i) the many potential application areas of crowd sensing and prediction; (ii) the technologies that can be exploited to sense crowd along with their potentials and limitations; (iii) the data analysis techniques that can be effectively used to forecast crowd distribution. Finally, the article tries to identify open and promising research challenges.

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A hybrid integrated deep learning model for the prediction of citywide spatio-temporal flow volumes

Cited by 58 publications

References 33 publications

A novel residual graph convolution deep learning model for short-term network-based traffic forecasting

A novel residual graph convolution deep learning model for short-term network-based traffic forecasting

Crime Spatiotemporal Prediction With Fused Objective Function in Time Delay Neural Network

Sensing and Forecasting Crowd Distribution in Smart Cities: Potentials and Approaches

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