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In 2013, the first electric taxis were launched in Hong Kong. In 2021, the HKSAR government announced that no new registration of fuel-propelled private cars, including hybrid vehicles, will be granted by the year 2035. Local public policies have shown a breakthrough milestone in the passenger transport sector and fostered the development of a smart city in Hong Kong as an international metropolis. This paper aims to study the feasibility of gradually introducing electric vehicles (EVs) in the Hong Kong taxi industry in the next decade. In particular, taxi license owners' intentions of purchasing electric vehicles for operational use and the driving behavior of taxi drivers in Hong Kong are investigated. Multiagent modelling with an integrated behavioral model is then adopted to analyze the survey data collected from 250 taxi drivers in Hong Kong, followed by interviews with industrial experts. The implications for future public policies are then discussed. To achieve emission reduction, the paper suggests a gradual, step-by-step promotion and transfer from conventional taxis to electric taxis in Hong Kong, with consideration of various factors and the interests of different stakeholders in the community. [ABSTRACT FROM AUTHOR]

Along with the increase of big data and the advancement of technologies, comprehensive data-driven knowledge of urban systems is becoming more attainable, yet the connection between big-data research and its application e.g., in smart city development, is not clearly articulated. Focusing on Human Mobility, one of the most frequently investigated applications of big data analytics, a framework for linking international academic research and city-level management policy was established and applied to the case of Hong Kong. Literature regarding human mobility research using big data are reviewed. These studies contribute to (1) discovering the spatial-temporal phenomenon, (2) identifying the difference in human behaviour or spatial attributes, (3) explaining the dynamic of mobility, and (4) applying to city management. Then, the application of the research to smart city development are scrutinised based on email queries to various governmental departments in Hong Kong. The identified challenges include data isolation, data unavailability, gaming between costs and quality of data, limited knowledge derived from rich data, as well as estrangement between public and private sectors. With further improvement in the practical value of data analytics and the utilization of data sourced from multiple sectors, paths to achieve smarter cities from policymaking perspectives are highlighted. [ABSTRACT FROM AUTHOR]

The complexity and variety of buildings and the defects of point cloud data are the main challenges faced by 3D urban reconstruction from point clouds, especially in metropolitan areas. In this paper, we developed a method that embeds multiple relations into a procedural modelling process for the automatic 3D reconstruction of buildings from photogrammetric point clouds. First, a hybrid tree of constructive solid geometry and boundary representation (CSG-BRep) was built to decompose the building bounding space into multiple polyhedral cells based on geometric-relation constraints. The cells that approximate the shapes of buildings were then selected based on topological-relation constraints and geometric building models were generated using a reconstructing CSG-BRep tree. Finally, different parts of buildings were retrieved from the CSG-BRep trees, and specific surface types were recognized to convert the building models into the City Geography Markup Language (CityGML) format. The point clouds of 105 buildings in a metropolitan area in Hong Kong were used to evaluate the performance of the proposed method. Compared with two existing methods, the proposed method performed the best in terms of robustness, regularity, and topological correctness. The CityGML building models enriched with semantic information were also compared with the manually digitized ground truth, and the high level of consistency between the results suggested that the produced models will be useful in smart city applications. [ABSTRACT FROM AUTHOR]