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• Passenger's non-compliance behavior is defined and observed. • Critical time indicators are defined to measure the alighting and boarding efficiency. • A simulation model is proposed to simulate passengers' alighting and boarding process at metro platform. • Effects of Passenger's non-compliance behaviors under different passenger volume conditions are analyzed. • Different alighting and boarding rules could be applied to increase alighting and boarding efficiency. Passengers' alighting and boarding process in metro stations has attracted increasing research attention since it has significant influence on the platform passenger distribution as well as the train dwell time. In this study, a field survey was firstly performed in a densely populated metro station at downtown area in Hong Kong to observe passengers' alighting and boarding characteristics and collect sample data. One commonly existing phenomenon is found that boarding passengers start to get aboard even when there are alighting passengers still inside the metro carriage. This is defined as passengers' non-compliance behaviors in this paper. In addition, time indicators are defined to measure the alighting and boarding efficiency. Then a microscopic pedestrian simulation model based on the Social Force Model is proposed to simulate the passengers' alighting and boarding patterns at metro platform. The verification result shows the good applicability of the proposed model to simulate the actual situation. Finally, several simulation tests are conducted to explore the impacts that passengers' non-compliance behaviors have on the alighting and boarding efficiency in different passenger volume conditions. The simulation result shows that higher level of passengers' non-compliance behaviors leads to longer passenger's alighting duration and boarding duration, but the influence on the overall transaction time is related to different passenger volume conditions. Thus, metro station facility could apply different alighting and boarding rules in different passenger volume conditions to increase alighting and boarding efficiency. [ABSTRACT FROM AUTHOR]

Abstract: Building energy computer simulation software is a useful tool for achieving sophisticated design and evaluation of the thermal performance of buildings. For successful thermal and energy simulation of buildings, it requires hourly weather data such as dry bulb air temperature, relative humidity, solar radiation, wind speed, etc. Nowadays, an urban city faces a problem of an urban heat island which causes the urban area to have a higher air temperature than the rural region. Since the currently available weather dataset used in building simulation software mainly comes from weather stations located in remote and rural areas, the impact of the urban heat island on thermal and energy performance of buildings may not be effectively reflected. This paper reports an approach to construct a modified typical meteorological weather file, taking into account the urban heat island effect in the summer season. Field measurements have been carried out in the summer months and the corresponding urban heat island intensities were then determined. With a morphing algorithm, an existing typical meteorological year weather file was modified. An office building and a typical residential flat were modeled with a renowned building energy simulation program EnergyPlus. Computer simulations were conducted using the existing and modified typical meteorological year weather files. It was found that there was around a 10% increase in air-conditioning demand caused by the urban heat island effect in both cases. The implications of this and further work will also be discussed in this paper. [Copyright &y& Elsevier]

Abstract: Building cooling load prediction is one of the key factors in the success of energy-saving measures. Many computational models available in the industry have been developed from either forward or inverse modeling approaches. However, these models usually require extensive computer resources and lengthy computation. This paper discusses the use of the multi-layer perceptron (MLP) model, one of the artificial neural network (ANN) models widely adopted in engineering applications, to estimate the cooling load of a building. The training samples used include weather data obtained from the Hong Kong Observatory and building-related data acquired from an existing prestigious commercial building in Hong Kong that houses a mega complex and operates 24h a day. The paper also discusses the practical difficulties encountered in acquiring building-related data. In contrast to other studies that use ANN models to predict building cooling load, this paper includes the building occupancy rate as one of the input parameters used to determine building cooling load. The results demonstrate that the building occupancy rate plays a critical role in building cooling load prediction and significantly improves predictive accuracy. [Copyright &y& Elsevier]

The construction industry in Hong Kong has been exposed to serious housing production issues such as insufficient manpower, safety, environmental concerns and inefficient housing supply. Prefabrication housing production has shown as efficient construction model which enables to overcome these issues and it is applied in construction industry in Hong Kong. In this paper, hybrid modeling techniques that combine system dynamics and discrete event simulation are used to analyze the interrelationships of schedule risks within the six-day cycle assembly of prefabrication housing production. Moreover, a hybrid dynamic model is developed to simulate and evaluate the impact of schedule risks on the schedule performance of the six-day cycle assembly of PHP via the Anylogic software package. The resulting model is validated by data which is collected from a PHP project in Hong Kong. Based on the simulation results, the first most influential level contains five schedule risks, namely, inefficient verification of precast components because of ambiguous labels, misplacement on the storage site because of carelessness, owner crane breakdown and maintenance, slow quality inspection procedures, and inefficient design data transition that cause average schedule delays of more than 300 min. The second most influential level includes delay of the delivery of precast element to site, design information gap between designer and manufacturer, installation error of precast elements, and logistics information inconsistency because of human errors, with average schedule delays less than 300 min and greater than 200 min. Design change, low information interoperability between different enterprise resource planning systems, and inefficiency of design approval belong to the third level that imposes the least influence on the schedule delay of the assembly. This research contributes to the current knowledge of the management of prefabrication construction by developing an effective model that offers an in-depth understanding of how schedule performance of PHP is dynamically influenced by interrelationships of various risk variables. Also, it provides an experimental platform for simulating and analyzing schedule risks that significantly affect the schedule performance of the six-day cycle assembly of prefabrication housing production. Such critical schedule risks could be identified and managed prior to the implement of prefabrication housing production projects. Compared with traditional techniques, the hybrid dynamic model simultaneously considers macro and micro levels, thereby enabling project managers to gain a deeper insight into schedule management and acquire a multidimensional understanding of schedule delay. [ABSTRACT FROM AUTHOR]

Hong Kong suffers from an intense urban heat island (UHI) effect of up to 4 °C as a result of compact urban form and highly urbanized land cover. Enhancing the cooling efficiency of urban greenery is essential for improving the microclimate in high-density cities. This paper aims to delineate design strategies for urban greenery to maximize thermal benefits and mitigate the daytime UHI effect. Two site-specific design strategies for tree planting in the urban environment are proposed. The sky view factor (SVF)-based design approach and the wind-path design approach are evaluated in the neighbourhood scale in two climate-sensitive areas with different urban morphologies. Observed data and simulation results indicated that the cooling effect of urban trees is highly associated with SVF. Air temperature reduction (a 1.5 °C reduction) is the most profound for the high SVF scenario, whereas substantial radiation shading ( T mrt reduced to 34 °C) is detected in areas with medium-low SVFs. The modelling study also showed that the cooling of air temperature and sensible heat were twice as high for vegetation arranged in wind corridors than those for leeward areas. The study demonstrated that tree planting in conjunction with proper planning is an effective measure to mitigate daytime UHI. [ABSTRACT FROM AUTHOR]

Abstract: In subtropical Hong Kong, buildings consume the most of the electricity use. Computer simulation technique is a useful tool for predicting building thermal and energy performance. Simulation computer programs require weather data input to drive the mathematical models within the simulation tools for comparative studies and annual energy estimation. Typical weather year data representing the long-term climatic conditions are often used. Recently, there has been an increasing interest in incorporating daylight in the architectural and building designs to reduce building energy consumption. However, daylight parameters are not always available. In 2003, the International Commission on Illumination (CIE) adopted a range of 15 standard skies covering probably the whole spectrum of the usual skies in the world. Once the skies have been identified, the basic solar irradiance and daylight illuminance at various inclined planes can be obtained. This paper presents the work on the development and evaluation of two typical weather datasets, namely Typical Meteorological Year (TMY) and Test Reference Year (TRY). Measured hourly weather data for the 30-year period from 1979 to 2008 were analysed. Building energy simulations were conducted to evaluate the two weather datasets. Daylight parameters were derived using luminous efficacy approach and the sky conditions were categorized based on common climatic variables. [Copyright &y& Elsevier]

Daylighting has long been recognized as an important issue in architecture. In Hong Kong many buildings are constructed close to each other and hence the external environment plays a significant role in building designs. Recently, vertical daylight factor (VDF) has been used as a criterion to justify the provision of natural lighting in buildings. This paper studies the calculation approach of VDF in a heavily obstructed environment. The techniques for determining the sky component and the reflected daylight from surrounding buildings and ground surfaces are described. Calculation tools in the form of simple equations and diagrams through computer-simulation analysis were established. The performance of the proposed method was evaluated against the daylight illuminance obtained by other independent computer simulations. It was found that predictions from the proposed approach were in good agreements with those produced by simulated results. The findings provide architects and building designers with a simple method for estimating the daylight illuminance at early design and planning stage. [Copyright &y& Elsevier]

Abstract: This paper develops an overall methodology for investigating the thermal and power behaviors of semi-transparent single-glazed photovoltaic window for office buildings in Hong Kong. In order to estimate its overall energy performance, this study is conducted in terms of total heat gain, output power and daylight illuminance. Three simulation models are established, including one-dimensional transient heat transfer model, power generation model and indoor daylight illuminance model. A typical office room reference is chosen as case study, and the weather data from 2003 to 2007 from the Hong Kong Observatory are used as the simulation inputs. By incorporating the simulation results, the overall energy performance can be evaluated in terms of electricity benefits corresponding to five orientations of the studied typical office. The priority of office orientation considering overall energy performance is: south-east, south, east, south-west and west. The findings show that thermal performance is the primary consideration of energy saving in the entire system whereas electricity consumption of artificial lighting is the secondary one. The overall annual electricity benefits are about 900 kWh and 1300 kWh for water-cooled and air-cooled air-conditioning systems respectively. The application of semi-transparent PV glazed window can not only produce clean energy, but also reduce building energy use by reducing the cooling load and electrical lighting requirements, which definitely benefits our environmental and economic aspects. [Copyright &y& Elsevier]

Abstract: It becomes popular to use computer simulation technique to evaluate the performance of energy utilizations in buildings. The hourly weather data as simulation input is a crucial factor for the successful energy system simulation, and obtaining an accurate set of weather data is necessary to represent the long-term typical weather conditions throughout a year. This paper introduces a stochastic approach, which is called the first order multivariate Markov chain model, to generate the annual weather data for better evaluating and sizing different energy systems. The process for generating the weather data time series from the multivariate Markov transition probability matrices is described using 15-years actual hourly weather data of Hong Kong. The ability of this new model for retaining the statistical properties of the generated weather data series is examined and compared with the current existing TMY and TRY data. The main statistical properties used for this purpose are mean value, standard deviation, maximum value, minimum value, frequency distribution probability and persistency probability of the weather data sequence. The comparison between the observed weather data and the synthetically generated ones shows that the statistical characteristics of the developed set of weather data are satisfactorily preserved and the developed set of weather data can predict and evaluate different energy systems more accurately. [Copyright &y& Elsevier]

Abstract: Research into partnering performance measures for building and construction projects becomes crucial because a growing trend of client organizations has been observed to adopt partnering approach to procure their projects worldwide over the past decade. Although there are some related research studies and papers documented on this research area, few, if any, comprehensive and systematic research studies focus on developing a comprehensive, objective, reliable and practical performance evaluation model for partnering projects in construction. A Partnering Performance Index (PPI), which is composed of seven weighted Key Performance Indicators (KPIs), has been developed to measure, monitor, improve, and benchmark the partnering performance of construction projects in Hong Kong. A set of Quantitative Indicators (QIs) and well-defined ranges of Quantitative Requirements (QRs) for each QI have been further established using the Delphi survey technique and Fuzzy Set Theory. Evaluation of partnering success can now be based on quantitative evidences, thus tackling the subjectivity of performance evaluation. By making use of the Internet and database technology, PPI can be monitored on-line, thus saving much time, cost and efforts on data collection and retrieval than if they are done manually. As such, an Internet-based computerized partnering monitoring and benchmarking tool, namely the Computerized Partnering Performance Index System (CPPIS) has been developed. The Internet-based CPPIS enables project participants to input data at any time and location and the project administrators could perform data analysis via the Internet. The CPPIS also enables project managers to measure, monitor, improve and benchmark their partnering performances against those already stored in the database. Graphical presentations of data and various performance measures are also built in to assist various end-users to identify problematic areas and critical success factors for achieving partnering excellence. [Copyright &y& Elsevier]