A deep-learning model for urban traffic flow prediction with traffic events mined from twitter

Essien, Aniekan; Petrounias, Ilias; Sampaio, Pedro; Sampaio, Sandra

doi:10.1007/s11280-020-00800-3

Cited by 105 publications

(49 citation statements)

References 34 publications

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“…Thus, an intelligent transportation system through predicting future traffic is important, which is an indispensable part of a smart city. Accurate traffic prediction based on machine and deep learning modeling can help to minimize the issues [17,30,31]. For example, based on the travel history and trend of traveling through various routes, machine learning can assist transportation companies in predicting possible issues that may occur on specific routes and recommending their customers to take a different path.…”

Section: Applications Of Machine Learningmentioning

confidence: 99%

Machine Learning: Algorithms, Real-World Applications and Research Directions

2021

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In the current age of the Fourth Industrial Revolution (4IR or Industry 4.0), the digital world has a wealth of data, such as Internet of Things (IoT) data, cybersecurity data, mobile data, business data, social media data, health data, etc. To intelligently analyze these data and develop the corresponding smart and automated applications, the knowledge of artificial intelligence (AI), particularly, machine learning (ML) is the key. Various types of machine learning algorithms such as supervised, unsupervised, semi-supervised, and reinforcement learning exist in the area. Besides, the deep learning, which is part of a broader family of machine learning methods, can intelligently analyze the data on a large scale. In this paper, we present a comprehensive view on these machine learning algorithms that can be applied to enhance the intelligence and the capabilities of an application. Thus, this study's key contribution is explaining the principles of different machine learning techniques and their applicability in various real-world application domains, such as cybersecurity systems, smart cities, healthcare, e-commerce, agriculture, and many more. We also highlight the challenges and potential research directions based on our study. Overall, this paper aims to serve as a reference point for both academia and industry professionals as well as for decision-makers in various real-world situations and application areas, particularly from the technical point of view.

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Section: Applications Of Machine Learningmentioning

confidence: 99%

Machine Learning: Algorithms, Real-World Applications and Research Directions

2021

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“…System variables like state estimation for freeways, highways, and speed are valuable for traffic light operation improvement, surveillance, management and to improve human decision-making [21], [146]. Forecasting and traffic incident are other outputs of DF in TFA related applications, while incident detection could help in emergency response improvement [1], [21], [147]. Detection procedures for vehicles and pedestrians can improve DF methods manipulation to achieve better detection accuracy [53], [99], [110], [148].…”

Section: Analysis and Discussionmentioning

confidence: 99%

“…Extensive exploration of hybrid models brings a positive impact to the TFA study. They provide more dynamic models to deal with different kinds of system environments, including prediction [146], [147], fixing missing values from heterogeneous data [128], and post-impact of an incident [148]. SA improves DF model performance at a higher level and considers all required data and specifications [120].…”

Section: A Analysismentioning

confidence: 99%

“…Flexible system architecture with suitable technology can provide the means to overcome the challenges of modeling DF for TFA, like reducing the complexity of such systems. The source of data is not limited only to traffic sensors as discussed in the previous section, but recently, researchers also exploit more unstructured and dynamic data from social media networks, such as Twitter [9], [64], [147], and Instagram [155].…”

Section: ) Processing Complexity Issuesmentioning

confidence: 99%

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Review of Data Fusion Methods for Real-Time and Multi-Sensor Traffic Flow Analysis

et al. 2021

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Recently, development in intelligent transportation systems (ITS) requires the input of various kinds of data in real-time and from multiple sources, which imposes additional research and application challenges. Ongoing studies on Data Fusion (DF) have produced significant improvement in ITS and manifested an enormous impact on its growth. This paper reviews the implementation of DF methods in ITS to facilitate traffic flow analysis (TFA) and solutions that entail the prediction of various traffic variables such as driving behavior, travel time, speed, density, incident, and traffic flow. It attempts to identify and discuss real-time and multi-sensor data sources that are used for various traffic domains, including road/highway management, traffic states estimation, and traffic controller optimization. Moreover, it attempts to associate abstractions of data level fusion, feature level fusion, and decision level fusion on DF methods to better understand the role of DF in TFA and ITS. Consequently, the main objective of this paper is to review DF methods used for real-time and multi-sensor (heterogeneous) TFA studies. The review outcomes are (i) a guideline of constructing DF methods which involve preprocessing, filtering, decision, and evaluation as core steps, (ii) a description of the recent DF algorithms or methods that adopt real-time and multi-sensor sources data and the impact of these data sources on the improvement of TFA, (iii) an examination of the testing and evaluation methodologies and the popular datasets and (iv) an identification of several research gaps, some current challenges, and new research trends.INDEX TERMS Intelligent transportation systems, traffic flow analysis, data fusion; real-time processing, multi-sensor, heterogeneous data, machine learning.

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“…Although the superiority of DL-based methods for traffic forecasting has been substantially documented [ 14 , 18 , 20 , 25 ], parametric methods can still provide good results for specific cases. In this context, this paper aims to investigate and compare the prediction performances of both types of methods and to discuss their applicability in a real urban traffic scenario based on data collected from the city of Bucharest.…”

Section: Introductionmentioning

confidence: 99%

An Hybrid Approach for Urban Traffic Prediction and Control in Smart Cities

Culita

Caramihai

Dumitrache

et al. 2020

Sensors

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Smart cities are complex, socio-technological systems built as a strongly connected System of Systems, whose functioning is driven by human–machine interactions and whose ultimate goals are the well-being of their inhabitants. Consequently, controlling a smart city is an objective that may be achieved by using a specific framework that integrates algorithmic control, intelligent control, cognitive control and especially human reasoning and communication. Among the many functions of a smart city, intelligent transportation is one of the most important, with specific restrictions and a high level of dynamics. This paper focuses on the application of a neuro-inspired control framework for urban traffic as a component of a complex system. It is a proof of concept for a systemic integrative approach to the global problem of smart city management and integrates a previously designed urban traffic control architecture (for the city of Bucharest) with the actual purpose of ensuring its proactivity by means of traffic flow prediction. Analyses of requirements and methods for prediction are performed in order to determine the best way for fulfilling the perception function of the architecture with respect to the traffic control problem definition. A parametric method and an AI-based method are discussed in order to predict the traffic flow, both in the short and long term, based on real data. A brief comparative analysis of the prediction performances is also presented.

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A deep-learning model for urban traffic flow prediction with traffic events mined from twitter

Cited by 105 publications

References 34 publications

Machine Learning: Algorithms, Real-World Applications and Research Directions

Machine Learning: Algorithms, Real-World Applications and Research Directions

Review of Data Fusion Methods for Real-Time and Multi-Sensor Traffic Flow Analysis

An Hybrid Approach for Urban Traffic Prediction and Control in Smart Cities

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