A novel fuzzy deep-learning approach to traffic flow prediction with uncertain spatial–temporal data features

Chen, Weihong; An, Jiyao; Li, Renfa; Fu, Li; Xie, Guoqi; Bhuiyan, Zakirul Alam; Li, Keqin

doi:10.1016/j.future.2018.06.021

Cited by 122 publications

(54 citation statements)

References 23 publications

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“…Chang et al [17] presented a novel Fuzzy Deep Learning approach, called Fuzzy Deep Convolutional Network (FDCN), which was proposed for predicting the traffic flow of a city. They combined Fuzzy theory and Deep Residual Network to address the uncertainty.…”

Section: Related Workmentioning

confidence: 99%

“…Recently, Deep Learning is becoming an effective method for solving different pattern recognition and regression problems due to its ability to process raw data directly [16]. However, Deep Learning lacks the capability of addressing different types of uncertainty, since it is based on neural networks, inherently limited in addressing uncertainty [17]. On the other hand, BRBES is capable of addressing various types of uncertainty such as ignorance, incompleteness, ambiguity, vagueness, and imprecision in an integrated framework.…”

Section: Introductionmentioning

confidence: 99%

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A Deep Learning Inspired Belief Rule-Based Expert System

2020

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Recent technological advancements in the area of the Internet of Things (IoT) and cloud services, enable the generation of large amounts of raw data. However, the accurate prediction by using this data is considered as challenging for machine learning methods. Deep Learning (DL) methods are widely used to process large amounts of data because they need less preprocessing than traditional machine learning methods. Various types of uncertainty associated with large amounts of raw data hinder the prediction accuracy. Belief Rule-Based Expert Systems (BRBES) are widely used to handle uncertain data. However, due to their incapability of integrating associative memory within the inference procedures, they demonstrate poor accuracy of prediction when large amounts of data is considered. Therefore, we propose the integration of an associative memory based DL method within the BRBES inference procedures, allowing to discover accurate data patterns and hence, the improvement of prediction under uncertainty. To demonstrate the applicability of the proposed method, which is named BRB-DL, it has been fine tuned against two datasets, one in the area of air pollution and the other in the area of power generation. The reliability of the proposed BRB-DL method, has also been compared with other DL methods such as Long-Short Term Memory and Deep Neural Network, and BRBES by taking into account of the air quality dataset from Beijing city and the power generation dataset of a combined cycle power plant. BRB-DL outperforms the above-mentioned methods in terms of prediction accuracy. For example, the Mean Square Error value of BRB-DL is 4.12 whereas for Long

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Deep Learning Inspired Belief Rule-Based Expert System

2020

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“…Zhao et al proposed a novel traffic forecast model based on an LSTM network, which considered temporal-spatial correlation in a traffic system via a two-dimensional network composed of many memory units [13]. Chen et al established a fuzzy deep convolutional network to improve traffic flow prediction [14]. 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.…”

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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“…The allocation strategy significantly reduces the operating costs of the system. How to predict the traffic flow of the vehicle CPS correctly, Chen et al (2018) proposed a method based on fuzzy deep-learning methods (FDCN), by establishing a fuzzy deep convolutional neural network and analysing the temporal and spatial correlation of traffic flow, the impact of uncertain data ultimately reduced.…”

Section: Cps In Transportationmentioning

confidence: 99%

Research on modelling and scheduling strategy for mine transportation control system based on CPS

Yang

Zhang

2019

IJES

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Cyber-physical systems (CPS) have made great strides in industrial control, intelligent transportation, remote medical and other fields. Coal mine transportation control system is a representative multi-subsystem of CPS. In this study, for the sake of optimising the scheduling mechanism for event response and precise control of tramcar behaviour in the system, we propose an event-orient scheduling algorithm (EOSA) to achieve a rapid response based on building a no-memory continuous time model. Therefore, the control system can match tasks in the queue according to the real-time load situation, and each physical entity can accurately execute the instruction under discrete event environment. Simulation results have shown that the proposed algorithm has a higher execution speed compared with the hybrid genetic algorithm and fuzzy clustering scheduling algorithm. Our approach realises the load balancing of global task scheduling and is more suitable for mine transportation scenario.

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A novel fuzzy deep-learning approach to traffic flow prediction with uncertain spatial–temporal data features

Cited by 122 publications

References 23 publications

A Deep Learning Inspired Belief Rule-Based Expert System

A Deep Learning Inspired Belief Rule-Based Expert System

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

Research on modelling and scheduling strategy for mine transportation control system based on CPS

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