The road network is a necessary component in transportation. It facilitiesspatial movements of people and goods, and it also influences the optimal locations of facilities that usually serve as destinations of the movements. To fulfill the transportation needs and to adapt to the facility development, the road network is often organized hierarchically and asymmetrically with various road levels and spatial structures. The complexity of the road network increases along with the increase of road levels and spatial structures. However, location models locate facilities on a given road network, usually the most complex one, and the influence from the complexity of road network in finding optimal locations is not well-studied. This paper aims to investigate how the complexity of a road network affects the optimal facility locations by applying the widely-applied p-median model. The main result indicates that an increase in road network complexity, up to a certain level, can obviously improve the solution, and the complexity beyond that level does not always lead to better solutions. Furthermore, the result is not sensitive to the choice of algorithms. In a specific case study, a detailed sensitivity analysis of algorithm and facility number further provides insight into computation complexity and location problems from intra-urban to inter-urban.
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Increasing car usage and travel demands between residential locations and destinations in order to fulfill the various needs of residents is a primary cause of CO2 emissions. To win the battle against climate change, a better understanding of the question relating to which urban residential form may most effectively mitigate the CO2 emissions is the key pathway.
This dissertation is concerned with the above problem and it mainly considers three objectives in providing insights on answering the question. The first objective is to comprehensively and microscopically understand intra-urban car travel behavior. The second objective is to estimate the induced CO2 emissions from daily intra-urban car travel and to ex-ante evaluate residential plans. The third objective is to assess whether the governmental sustainable residential development objective is aligned with the objectives of the estate market actors. To explore the research questions related to the objectives, a microdata analysis process (data collection, data assessment and transformation, data storage, data analysis and decision-making) is applied and is found essential in gaining access to key variables in exploring the answer of a preferable urban form. The dissertation offers many new solutions to various technical aspects through a microdata analysis process.
The primary contribution of this dissertation is that it outlines an operational model that comprehensively integrates the investors’ investment strategy, the residents’ choice behavior, and the governmental sustainability objective in the interest of making an ex-ante assessment of residential plans. This ex-ante assessment provides decision-support in sustainable residential development at foremost local level.
The first finding from the implementation of the model on the case study is that the market actors’ objectives are, in general, aligned with the government’s sustainable residential development objective. The second finding indicates that re-shaping the urban form into a compact city is preferable in mitigating CO2 emissions, in spite of the fact that the case city is of a polycentric urban form. These findings provide support for those advocating the compact city as the ideal for sustainable residential development, and also provide foresight on settling the answer to the preferred re-shaping of urban forms in climate change.