Multi-modal Generative Adversarial Networks for Traffic Event Detection in Smart Cities

Coenen, Frans ORCID: 0000-0003-1026-6649, Wang, Wei, Chen, Qi, Huang, Kaizhu and De, Suparna (2021) Multi-modal Generative Adversarial Networks for Traffic Event Detection in Smart Cities. Expert Systems With Applications, 177. p. 114939.

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Official URL: http://dx.doi.org/10.1016/j.eswa.2021.114939

Abstract

Advances in the Internet of Things have enabled the development of many smart city applications and expert systems that help citizens and authorities better understand the dynamics of the cities, and make better planning and utilisation of city resources. Smart cities are composed of complex systems that usually process and analyse big data from the Cyber, Physical, and Social worlds. Traffic event detection is an important and complex task in smart transportation modelling and management. We address this problem using semi-supervised deep learning with data of different modalities, e.g., physical sensor observations and social media data. Unlike most existing studies focusing on data of single modality, the proposed method makes use of data of multiple modalities that appear to complement and reinforce each other. Meanwhile, as the amount of labelled data in big data applications is usually extremely limited, we extend the multi-modal Generative Adversarial Network model to a semi-supervised architecture to characterise traffic events. We evaluate the model with a large, real-world dataset consisting of traffic sensor observations and social media data collected from the San Francisco Bay Area over a period of four months. The evaluation results clearly demonstrate the advantages of the proposed model in extracting and classifying traffic events.

Item Type:	Article
Uncontrolled Keywords:	Multi-modal learning, Semi-supervised learning, Traffic event detection, Generative adversarial network, Smart transportation, Deep learning
Divisions:	Faculty of Science and Engineering > School of Electrical Engineering, Electronics and Computer Science
Depositing User:	Symplectic Admin
Date Deposited:	14 May 2021 07:34
Last Modified:	18 Jan 2023 22:47
DOI:	10.1016/j.eswa.2021.114939
Related URLs:	Author Publisher
URI:	https://livrepository.liverpool.ac.uk/id/eprint/3122658