Daily long-term traffic flow forecasting based on a deep neural network

Qu, Licheng; Li, Wei; Li, Wenjing; Ma, Dongfang; Wang, Yinhai

doi:10.1016/j.eswa.2018.12.031

Cited by 146 publications

(53 citation statements)

References 38 publications

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“…This approach was built on fuzzy theory and the deep residual network model, and the key idea was to introduce the fuzzy representation into the DL model to lessen the impact of data uncertainty. Qu et al presented a traffic prediction method using a deep neural network based on historical traffic flow data and contextual factor data [15]. The main idea was that traffic flow within a short time period was strongly correlated with the starting and ending time points of the period together with a number of other contextual factors, such as days of week, weather, and season.…”

Section: Literature Reviewmentioning

confidence: 99%

Modelling the Publishing Process of Big Location Data Using Deep Learning Prediction Methods

et al. 2020

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Centralized publishing of big location data can provide accurate and timely information to assist in traffic management and for facilitating people to decide travel time and route, mitigate traffic congestion, and reduce unnecessary waste. However, the spatio-temporal correlation, non-linearity, randomness, and uncertainty of big location data make it impossible to decide an optimal data publishing instance through traditional methods. This paper, accordingly, proposes a publishing interval predicting method for centralized publication of big location data based on the promising paradigm of deep learning. First, the adaptive adjusted sampling method is designed to address the challenge of finding a reasonable release time via a prediction mechanism. Second, the Maximal Overlap Discrete Wavelet Transform (MODWT) is introduced for the decomposition of time series in order to separate different features of big location data. Finally, different deep learning models are selected to construct the entire framework according to various time-domain features. Experimental analysis suggests that the proposed prediction scheme is not only feasible, but also improves the prediction accuracy in contrast to the traditional deep learning mechanisms.

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Section: Literature Reviewmentioning

confidence: 99%

Modelling the Publishing Process of Big Location Data Using Deep Learning Prediction Methods

et al. 2020

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“…Neural networks can model complex nonlinear relationships and have an excellent performance in processing multidimensional data [22]. In recent years, deep neural networks have been involved in traffic flow prediction, such as deep belief networks (DBN) [23] and deep neural networks [24]. Although these neural network-based methods are suitable for small traffic networks or networks with a small number of roads, they cannot take advantage of spatial correlations among different roads and temporal dependencies of traffic flow variables.…”

Section: Introductionmentioning

confidence: 99%

Representation of Traffic Congestion Data for Urban Road Traffic Networks Based on Pooling Operations

Zhang

et al. 2020

Algorithms

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In order to improve the efficiency of transportation networks, it is critical to forecast traffic congestion. Large-scale traffic congestion data have become available and accessible, yet they need to be properly represented in order to avoid overfitting, reduce the requirements of computational resources, and be utilized effectively by various methodologies and models. Inspired by pooling operations in deep learning, we propose a representation framework for traffic congestion data in urban road traffic networks. This framework consists of grid-based partition of urban road traffic networks and a pooling operation to reduce multiple values into an aggregated one. We also propose using a pooling operation to calculate the maximum value in each grid (MAV). Raw snapshots of traffic congestion maps are transformed and represented as a series of matrices which are used as inputs to a spatiotemporal congestion prediction network (STCN) to evaluate the effectiveness of representation when predicting traffic congestion. STCN combines convolutional neural networks (CNNs) and long short-term memory neural network (LSTMs) for their spatiotemporal capability. CNNs can extract spatial features and dependencies of traffic congestion between roads, and LSTMs can learn their temporal evolution patterns and correlations. An empirical experiment on an urban road traffic network shows that when incorporated into our proposed representation framework, MAV outperforms other pooling operations in the effectiveness of the representation of traffic congestion data for traffic congestion prediction, and that the framework is cost-efficient in terms of computational resources.

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“…To date, many forecasting methods have been used to predict resource problems. These methods are divided into three categories: short-term traffic flow prediction techniques based on vector data flow (such as the wavelet analysis [12], support vector machine [13], the chaotic prediction model [14,15], and neural network [16,17]), short-term traffic flow prediction techniques based on matrix data flow [18][19][20][21] (such as the multivariate time series prediction model [18] and the Kalman filtering method [20]), and short-term traffic flow prediction techniques based on tensor data flow (such as the seasonal autoregressive integrated moving average + generalized autoregressive conditional heteroscedasticity (SARIMA + GARCH) model [22] and seasonal selfvector regression (Seasonal-SVR) prediction model [23]). The abovementioned prediction models are usually based on a large sample size and thus cannot be used to solve smallscale problems.…”

Section: Introductionmentioning

confidence: 99%

A Multimode Dynamic Short‐Term Traffic Flow Grey Prediction Model of High‐Dimension Tensors

Duan

Xiao

2019

Complexity

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Short-term traffic flow prediction is an important theoretical basis for intelligent transportation systems, and traffic flow data contain abundant multimode features and exhibit characteristic spatiotemporal correlations and dynamics. To predict the traffic flow state, it is necessary to design a model that can adapt to changing traffic flow characteristics. Thus, a dynamic tensor rolling nonhomogeneous discrete grey model (DTRNDGM) is proposed. This model achieves rolling prediction by introducing a cycle truncation accumulated generating operation; furthermore, the proposed model is unbiased, and it can perfectly fit nonhomogeneous exponential sequences. In addition, based on the multimode characteristics of traffic flow data tensors and the relationship between the cycle truncation accumulated generating operation and matrix perturbation to determine the cycle of dynamic prediction, the proposed model compensates for the periodic verification of the RSDGM and SGM grey prediction models. Finally, traffic flow data from the main route of Shaoshan Road, Changsha, Hunan, China, are used as an example. The experimental results show that the simulation and prediction results of DTRNDGM are good.

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Daily long-term traffic flow forecasting based on a deep neural network

Cited by 146 publications

References 38 publications

Modelling the Publishing Process of Big Location Data Using Deep Learning Prediction Methods

Modelling the Publishing Process of Big Location Data Using Deep Learning Prediction Methods

Representation of Traffic Congestion Data for Urban Road Traffic Networks Based on Pooling Operations

A Multimode Dynamic Short‐Term Traffic Flow Grey Prediction Model of High‐Dimension Tensors

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