No abstract

Abstract:The accurate estimation of the dynamic changes in population is a key component in effective urban planning and emergency management. We developed a model to estimate hourly dynamic changes in population at the community level based on subway smart card data. The hourly population of each community in six central districts of Beijing was calculated, followed by a study of the spatiotemporal patterns and diurnal dynamic changes of population and an exploration of the main sources and sinks of the observed human mobility. The maximum daytime population of the six central districts of Beijing was approximately 0.7 million larger than the night-time population. The administrative and commercial districts of Dongcheng and Xicheng had high values of population ratio of day to night of 1.35 and 1.22, respectively, whereas Shijingshan, a residential district, had the lowest value of 0.84. Areas with a high population ratio were mainly concentrated in Dongcheng, Xicheng, West Chaoyang, and Southeast Haidian. The daytime population distribution showed a hierarchical spatial pattern of planar centers and second scattered centers as opposed to multiple scattered centers during the night-time. This was because most people moved inward from the areas with a low-high to high-low population ratio of day to night from night-time to daytime, which can be explained by the process of commuting between residential areas and workplaces. Several distinctive phenomena (e.g., the distribution of new industrial parks, the so-called old residential areas, and colleges and universities) in the development of China are reflected by the spatiotemporal pattern of the distribution of population. The general consistency of the population ratios of day to night, population distribution, population variation of typical communities, and population mobility pattern with previous research suggests that the subway smart card data has potential in analyzing dynamic diurnal variations of urban population. This method can be easily duplicated to calculate hourly dynamic changes in population at the community level. These results can be used to estimate the potential hourly number of evacuees under different temporal scenarios of disasters and to support future urban planning in Beijing.

The correct location of earthquake emergency shelters and their allocation to residents can effectively reduce the number of casualties by providing safe havens and efficient evacuation routes during the chaotic period of the unfolding disaster. However, diverse and strict constraints and the discrete feasible domain of the required models make the problem of shelter location and allocation more difficult. A number of models have been developed to solve this problem, but there are still large differences between the models and the actual situation because the characteristics of the evacuees and the construction costs of the shelters have been excessively simplified. We report here the development of a multi-objective model for the allocation of residents to earthquake shelters by considering these factors using the Chaoyang district, Beijing, China as a case study. The two objectives of this model were to minimize the total weighted evacuation time from residential areas to a specified shelter and to minimize the total area of all the shelters. The two constraints were the shelter capacity and the service radius. Three scenarios were considered to estimate the number of people who would need to be evacuated. The particle swarm optimization algorithm was first modified by applying the von Neumann structure in former loops and global structure in later loops, and then used to solve this problem. The results show that increasing the shelter area can result in a large decrease in the total weighted evacuation time from scheme 1 to scheme 9 in scenario A, from scheme 1 to scheme 9 in scenario B, from scheme 1 to scheme 19 in scenario C. If the funding were not a limitation, then the final schemes of each scenario are the best solutions, otherwise the earlier schemes are more reasonable. The modified model proved to be useful for the optimization of shelter allocation, and the result can be used as a scientific reference for planning shelters in the Chaoyang district, Beijing.