ANLY482 AY2017-18T2 Group08
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Abstract
Dockless bike-sharing is an increasingly common phenomenon in today’s transportation industry. Not only does it provide a cost-efficient and convenient mode of transportation in urban cities, it also helps to ease the carbon footprint by reducing reliance on traditional modes of transport such as buses, trains and cars. Unfortunately, this business model has hit a major snag – parking. Since the introduction of bike-sharing, illegal parking has been on the rise in many countries such as China, Japan and Singapore. Despite the growing prevalence of illegal bike-parking, existing research on the bike-sharing industry focuses mainly on examining business profitability and understanding bicycle route data. To fill this research gap, a practice research study has been conducted to demonstrate the use of L-function, bw.diggle and Kernel Density Estimation in analysing spatial point patterns of illegal bike-parking in the real world.
To begin, an overview of the bike-sharing industry and research motivations will be shared. Next, a review of relevant literatures of L-function and Kernel Density Estimation will be presented. Following which, the application of these tools to a case study with a bike-sharing company in Singapore will be illustrated and last but not least, relevant insights will be documented and explained. The case study focuses on two main regions in Singapore that have a high rate of illegal parking cases, namely Bedok and Jurong-West. It was observed that indiscriminate bike-parking shows signs of significant clustering in these regions, with “hotspots” concentrated specifically at landmarks such as HDBs and MRT stations. In addition, upon further analysis, it was noticed that generally, areas with yellow boxes (i.e. designated parking areas) present have a lower intensity of illegal bike-parking. Further, time period was said to have an effect on the intensity of clusters in various landmarks across these two regions. |
Project Status Overall Project Completion Status: 100% completed (estimate)
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