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Co-modality transportation aims to integrate passenger and freight flows into a single system by sharing public transport between passengers and freight. This paper proposes a crowdsourced co-modality transportation system (CCTS) for enhancing the practical implementation of co-modality transportation in urban areas. Firstly, empirical investigations are conducted, including semi-structured interviews with industry practitioners and online surveys with passengers, to collect system design considerations. Secondly, an overview of the crowdsourced co-modality management platform (CCMP) is presented by highlighting three key components: co-modality services, information infrastructure, and decision support system. Thirdly, we formulate the parcel assignment problem in the CCTS and examine the factors that influence the performance of the CCTS. The empirical investigations suggest that crowdsourced passengers care about the potential risks and the degree of operation difficulty in crowdsourced activities but do not care about privacy issues. In addition, the experimental studies show that crowdsourced co-modality is more feasible and promising if more passengers are willing to provide crowdsourcing services. [ABSTRACT FROM AUTHOR]

There is a tendency for accidents and even fatalities to arise when people enter hazardous work areas during the construction of projects in urban areas. A limited amount of research has been devoted to developing vision-based proximity warning systems that can determine when people enter a hazardous area automatically. Such systems, however, are unable to identify specific hazards and the status of a piece of plant (e.g., excavator) in real-time. In this paper, we address this limitation and develop a real-time smart video surveillance system that can detect people and the status of plant (i.e. moving or stationary) in a hazardous area. The application of this approach is demonstrated during the construction of a mega-project, the Wuhan Rail Transit System in China. We reveal that our combination of computer vision and deep learning can accurately recognize people in a hazardous work area in real-time during the construction of transport projects. Our developed systems can provide instant feedback concerning unsafe behavior and thus enable appropriate actions to be put in place to prevent their re-occurrence. [ABSTRACT FROM AUTHOR]