Since the turn of this century, cities are homes to more than half of the world's population even though they cover only three per cent of the Earth's land surface. Cities are often the fast-growing innovation and socio economic hub of a region; however, they also face the challenge of finding new space to accommodate the increasing number of urban habitants. While there has been a long tradition of urban modelling in geography and transportation research to understand urban growth dynamics and identify new growth directions, with its roots in both social physics and urban and regional economics, considerable globally efforts are being expended in developing models to understand how cities grow and evolve. However, urban modelling is still challenged by a diversity of methods, metrics, indicators and data (OECD 2011). After all, cities are places where people come together to interact with one another and therefore our understanding on the evolution of cities must be enriched by studies of networks, interactions, connections and transactions (Batty 2013). Thus, this special issue focuses on modelling the various aspects of urban dynamics ranging from urban evacuation modelling, agent-based simulation for on-line meetings to the modelling of truck driving and residents' outdoor recreation behaviours, all of which contributes to enhance our understanding on the interaction of urban individuals which shape the form and outcome of our cities.
This paper describes the design and implementation of an evacuation simulation model developed based on the Traffic Grid Model in NetLogo. In this model, different scenarios were tested in order to find out the best strategy within specific environments. The model is flexible and includes many parameters to adjust to environment conditions and agent rules. These parameters can be modified to study which driving factors contribute most to drivers' evacuation performance. This research also focuses on the method of results analysis and traffic performance evaluation for different combinations of two model parameters. In each experiment, we analysed metrics such as evacuation time and average car speed for each strategy under different population distribution patterns. The results showed that this model could reveal aneffective evacuation strategy for realistic scenarios.
We will introduce an integrated model of Online Decision Making Meetings (ODMM) for sustainable management of water resources, which combines online meetings with an agent-based simulation function. This model is able to supply the decision makers with visualized simulation results of different policy scenarios and then support them to have online discussions, communication and make final decisions. The model consists of a remote server, a simulation model for background processing and clients. We assume that several departments related to water resource management will join this meeting and make proposals based on their data and policy. These departments are clients in our model and they are granted with special authority according to their departments. During an online meeting, the clients can choose global parameters for a simulation model that relates to their department based on their roles. Afterwards, simulations for different scenarios can be conducted by the model and the simulation results will be displayed for and visualized by the clients through the server on time. Thus, the government planners can easily view the simulation results regarding different policy scenarios using this tool, thereby making informed decisions and policy implementations.
In 2011, the authors published an analytical paper in order to understand the driving behaviors of garbage trucks on the basis of the data collected by drive recorders. As further study, this paper focuses on finding significant correlations between driver consciousness and their unsafe driving experiences. The methodology includes many statistical tools such as statistical tests, analyses of variance, factor analysis, the principle component analysis, and so on. The results show that truck drivers have a lower probability of looking aside and of becoming irritated when driving the garbage trucks than private car drivers. They feel it is more difficult to drive on community roads in residential areas than on trunk roads. The statistical analyses show there were significant correlations between the drivers' personalities, their consciousness, and the experienced traffic accidents. In particular, the event “near accident with oncoming cars when passing each other is statistically significant with respect to the negation overtaking of the other cars, the awareness of driving on community roads and the convenience of driving the garbage trucks.
Outdoor recreation is one of the most important leisure activities of urban residents, with urban greenspace accruing the highest value of benefits among all greenspaces in the UK. However, access and trip-making to outdoor greenspaces by urban residents remain poorly understood. Existing trip making prediction models that have been established for assessing the recreation benefits of outdoor greenspaces have dealt separately with visits to urban and rural greenspaces. This makes it difficult to assess greenspace strategies when considering them as a whole infrastructure. Meanwhile there is a risk of misjudging the value (e.g. double counting) when they are summed mechanically. This research aims to investigate the strengths and weaknesses of predictive models of outdoor recreation travel. An output of the research is a new model with two components: (a) predominantly local trips and (b) predominantly non-local trips. The resultant model is able to make an assessment that seamlessly combines urban and rural greenspaces. It also links the spatial distribution of visits to key spatial factors, such as distribution of population, location of recreational sites, transport accessibility and travel time. The resulting quantification of the impacts of policy interventions provide a robust basis for decision making
Globally, there has been a substantial increase in the number of people who are not able to buy their own dwellings due to the phenomenal appreciation of real estate prices in China. With the growing worldwide demand for low-rent housing and the importance of supporting and stimulating sustainable development, the need for sustainable solutions in the low-rent housing sector is at a peak. For achieving new design methods for low-rent housing, we attempted to employ a practical project to explore the residential environment from the viewpoint of the Chinese national green building standards and municipal low-rent housing policy. Firstly we investigate the low-rent housing residential area in Tianjin, China. After a questionnaire and interview of local residents has been conducted, the characteristics of the residential environment are analysed. We consider that the outdoor space of low rent housing could be diversified to partly fulfil interior functions, by which way the cost of the low-rent housing could be reduced to a certain extent.