Improving Traffic Flow Prediction With Weather Information in Connected Cars: A Deep Learning Approach

Koesdwiady, Arief; Soua, Ridha; Karray, Fakhri

doi:10.1109/tvt.2016.2585575

Cited by 303 publications

(130 citation statements)

References 28 publications

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“…VNets provide vehicle drivers and other road users (e.g., road operators and pedestrians) with a wide range of information, which can be used to enhance the road safety, the public security, the traveling comfort of passengers and the efficiency of optimizating traffic flows [27]. In particular, we categorize the data sources generated from VNets into the following types.…”

Section: Data Sources and Necessities Of Bdamentioning

confidence: 99%

Big Data Analytics for Large-scale Wireless Networks

et al. 2019

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The wide proliferation of various wireless communication systems and wireless devices has led to the arrival of big data era in large scale wireless networks. Big data of large scale wireless networks has the key features of wide variety, high volume, real-time velocity and huge value leading to the unique research challenges that are different from existing computing systems. In this paper, we present a survey of the state-of-art big data analytics (BDA) approaches for large scale wireless networks. In particular, we categorize the life cycle of BDA into four consecutive stages: Data Acquisition, Data Preprocessing, Data Storage and Data Analytics. We then present a detailed survey of the technical solutions to the challenges in BDA for large scale wireless networks according to each stage in the life cycle of BDA. Moreover, we discuss the open research issues and outline the future directions in this promising area.

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Section: Data Sources and Necessities Of Bdamentioning

confidence: 99%

Big Data Analytics for Large-scale Wireless Networks

et al. 2019

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“…35,36 Moreover, training the RNN model requires that the delay window length be predetermined, but it is difficult to obtain the optimal value of this parameter automatically in practice. Firstly, as time intervals increase, RNN loses the ability to connect to far feature information.…”

Section: Nonparameter Modelsmentioning

confidence: 99%

Big data‐driven machine learning‐enabled traffic flow prediction

Kong

Jiang

et al. 2018

Trans Emerging Tel Tech

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Real‐time effective traffic flow big data prediction network has important application significance. Over the past few years, traffic flow data have been exploding and we have entered the big data era. The key challenge of traffic flow prediction network is how to construct an adaptive model relying on historical data. Existing big data‐driven traffic flow prediction networking approaches mainly use shallow learning, and there are unsatisfying for many realistic applications, which inspire us to rethink the traffic flow big data prediction problem with deep learning. In this paper, we propose a novel prediction approach based on machine learning. In addition to the minimum prediction error as the goal, we present the long short‐term memory model, which is a typical machine learning algorithm with deep learning network. This method is applied into the real‐world traffic big data from performance measurement system. Experimental results show that the proposed machine learning algorithm has more applicability and higher performance, compared with shallow machine learning prediction network.

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“…From a data-centric perspective, the key concept in smart cities lies in the sophisticated data analytics for understanding, monitoring, regulating and planning the city [16]. It is widely accepted that the process of smart city data analysis can be abstracted as four layers although different work may have some minor variations [5], [6], [12], [17]: data acquisition, data preprocessing, data analysis, and service provision, as shown in Figure 1: (1) the data acquisition layer is concerned about collecting and storing smart city data from various domains and sources; (2) the data preprocessing layer is responsible for preprocessing (e.g., data cleaning, selection and interpolation) to obtain data of higher quality before analytics, as smart city data of different modalities often contains noise, uncertainty and missing values; (3) the data analytics layer is to perform intelligent analysis using various machine learning techniques to discover previously unknown knowledge and insights specific to different applications, e.g., classification models can be used to recognise human activities and regression models to predict traffic flows; and (4) the service provision layer is to develop intelligent services and applications based on the outcome of data analytics, for example, with the patterns and events detected from traffic, pollution and weather sensory data analysis to provide better public services.…”

Section: Introductionmentioning

confidence: 99%

A Survey on an Emerging Area: Deep Learning for Smart City Data

Chen

Wang

et al. 2019

IEEE Trans. Emerg. Top. Comput. Intell.

121

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Rapid urbanisation has brought about great challenges to our daily lives, such as traffic congestion, environmental pollution, energy consumption, public safety and so on. Research on smart cities aims to address these issues with various technologies developed for the Internet of Things. Very recently, the research focus has shifted towards processing of massive amount of data continuously generated within a city environment, e.g., physical and participatory sensing data on traffic flow, air quality, and healthcare. Techniques from computational intelligence have been applied to process and analyse such data, and to extract useful knowledge that helps citizens better understand their surroundings and informs city authorities to provide better and more efficient public services. Deep learning, as a relatively new paradigm in computational intelligence, has attracted substantial attention of the research community and demonstrated greater potential over traditional techniques. This paper provides a survey of the latest research on the convergence of deep learning and smart city from two perspectives: while the technique-oriented review pays attention to the popular and extended deep learning models, the application-oriented review emphasises the representative application domains in smart cities. Our study showed that there are still many challenges ahead for this emerging area owing to the complex nature of deep learning and wide coverage of smart city applications. We pointed out a number of future directions related to deep learning efficiency, emergent deep learning paradigms, knowledge fusion and privacy preservation, and hope these would move the relevant research one step further in creating truly distributed intelligence for smart cities.

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Improving Traffic Flow Prediction With Weather Information in Connected Cars: A Deep Learning Approach

Cited by 303 publications

References 28 publications

Big Data Analytics for Large-scale Wireless Networks

Big Data Analytics for Large-scale Wireless Networks

Big data‐driven machine learning‐enabled traffic flow prediction

A Survey on an Emerging Area: Deep Learning for Smart City Data

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