Optimizing Urban Planning for Pedestrian Congestion
Over the years, with the increase in the number of people living in Sydney, the city itself becomes more congested and less comfortable for walking. One of the main concern in the city nowadays is the level of pedestrian congestion in the Sydney CBD during certain times. Therefore, improving the city to best accommodate the pedestrian flow is important.
In the Sydney CBD, it is often observed that there are certain areas of the urban pavement which is very congested. Pedestrians also have to walk around buildings causing people to have to walk in a certain route which leads to pedestrian congestion. With this problem, the research idea that I wanted to propose is a framework which implements the use of computational tools to optimize the structure of the city so that the quality of pedestrian qualities can be improved and thus ease pedestrian congestion.
The aim of the research is to create a computational workflow which could generate an urban pattern that could ease pedestrian congestion. To improve the city for a better pedestrian flow, the thought of restructuring the whole city is inefficient since it will take quite an amount of time and cost to build up a new city. Therefore, this research propose to create a short alternative route that exist within the building layout which does not require the restructuring of the whole city but rather on the building scale.
The objectives for this research idea besides to ease the pedestrian congestion is also to create a computational workflow which could generate the optimization. It also aims to improve the walkability of the city.
Creating a computational workflow which generates such pattern in the building scale provides urban planners and architects an easier way to design. To achieve the computational workflow that is aimed, generative algorithm could be used in the process. The research will most probably implements the use of python and grasshopper to produce the patterns, analyze them and optimize them using certain selected criterias.
With the rise of pedestrian congestion in the Sydney CBD, the case study to be use for this research will be a small area from the CBD. Using the existing city structure, the city will be analyzed using grasshopper before the computational workflow is developed. After the computational workflow is created, to test the accuracy of the tool, another area from Sydney that has the most pedestrian congestion will then be used.
As there has been no existing research regarding optimizing cities for pedestrian congestion, many early testings has to be done. Research on how the generative algorithms and what selected criterias for the generation also need to be further thought out for the experiment to work. Therefore, more detailed research on pedestrian flow in city planning needs to be explored.