Multi-modal generative adversarial networks for traffic event detection in smart cities

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 semisupervised 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.

Automated summary

Advancements in the Internet of Things have enabled the development of smart city applications and expert systems, utilizing deep learning techniques to analyze big data from the Cyber, Physical, and Social worlds to enhance city resource planning and utilization, particularly in the field of traffic event detection.