Abstract: Cellular automata (CA) models have been for several years, employed to describe urban phenomena like growth of human settlements, changes in land use and, more recently, dispersion of air pollutants. We propose to adapt CA to study the dispersion of anthropogenic heat emissions on the micro scale. Three dimensional cubic CA with a constant cell size of 0.15m have been implemented. Simulations suggest an improvement in processing speed compared to conventional computational fluid dynamics (CFD) models, which are limited in scale and yet incapable of solving simulations on local or larger scale. Instead of solving the Navier-Stokes equations, as in CFD, only temperature and heat differences for the CA are modeled. Radiation, convection and turbulence have been parameterized according to scale. This CA based approach can be combined with an agent-based traffic simulation to analyse the effect of driving behavior and other microscopic factors on urban heat.

Abstract: Available large amount of mobility data stimulates the work in discovering patterns and understanding regularities. Comparatively, less attention has been paid to the study of variability, which, however, has been argued as equally important as regularities in previous related work, since variability identifies diversity. In a transport network, variability exists from day to day, from person to person, and from place to place. In this paper, we present a set of measuring of variability at individual and aggregated levels using multi-day smart-card data. Statistical analysis, correlation matrix and network-based clustering are applied and the potential usage of measured results for urban applications are discussed. We take Singapore as a case study and use one-week smart-card data for analysis. An interesting finding is that though the number of trips and mobility patterns varies from day to day, the overall spatial structure of urban movement remains the same throughout the whole week. We consider this paper as a tentative work towards a generic framework for measuring regularity and variability, which contributes to the understanding of transit, social and urban dynamics.

Abstract: We analyse the structure and resilience of a massive encounter network generated from commuters who share the same bus ride on a single day. The network is created by using smartcard data that contains detailed travel information of all the commuters who utilised the public bus system during a typical weekday in the whole of Singapore. We show that the network structure is of random-exponential type with small world features rather than a scale-free network. Within one day, 99.97% of all commuters became connected approximately within 7 steps of each other. We report on how this network structure changes upon application of a threshold based on the encounter duration (TE). Among others, we demonstrate a 50% reduction on the size of the giant cluster when TE=15mins. We then assess the dynamics of infection spreading by comparing the effect of both random and targeted node removal strategies. By assuming that the network characteristic is invariant day after day, our simulation indicates that without node removal, 99% of the commuter network became infected within 7 days of the onset of infection. While a targeted removal strategy was shown to be able to delay the onset of the maximum number of infected individuals, it was not able to isolate nodes that remained within the giant component.

Abstract: The need for more sustainable, liveable and resilient cities demands improved methods for studying urban infrastructures as integrated wholes. Progress in this direction would be aided by the ability to effectively reuse and integrate existing computational models of urban systems. Building on the concept of multi-model ecologies, this paper describes ongoing efforts to facilitate model reuse and integration in the Holistic Urban Energy Simulation (HUES) platform - an extendable simulation environment for the study of urban multi-energy systems. We describe the design and development of a semantic wiki as part of the HUES platform. The purpose of this wiki is to enable the sharing and navigation of model metadata - essential information about the models and datasets of the platform. Each model and dataset in the platform is represented in the wiki in a structured way to facilitate the identification of opportunities for model reuse and integration. As the platform grows, this will help to ensure that it develops coherently and makes efficient use of existing formalized knowledge. We present the core concepts of multi-model ecologies and semantic wikis, the current state of the platform and associated wiki, and a case study demonstrating their use and benefit.