ANLY482 AY2017-18T2 Group08 : Project Overview
| Description | Methodology |
Project Background
Beginning their operations in January 2017, oBike is Singapore’s first homegrown stationless smart bicycle-sharing company which uses technology to change how transportation is viewed locally. oBike has bicycles located all over the island, and these bikes have built-in Bluetooth locks to enable one-way first and last mile commuting. This provides a convenient and environmentally friendly commute option for all, especially given Singapore’s compact size and interconnected urban areas. Despite intense competition stemming from other bicycle-sharing companies such as Mobike and OFO, oBike has achieved over one million downloads since its inception. Data has revealed that the company has consistently been at the top in terms of total number of application downloads, earning them the title of “Southeast Asia’s bike-sharing leader.”
However, in recent months, Singapore’s Land and Transport Authority (LTA) has issued new rules and regulations, requiring bicycles to be parked in designated yellow boxes around the islands. LTA enforcers survey the island, and issue tickets to bike-sharing companies in the event where bicycles are found to be illegal parked. From the time a ticket is issued, oBike has a mere four hours to move their illegally-parked bicycles. Failure to do so will incur hefty fines. Consequently, oBike’s current challenge is to manage illegal parking of their bicycles.
Project Motivation
As Albert Einstein had once said, “Education is what remains after one has forgotten what one has learnt in school.” Keeping in mind this mantra, we understand the importance of applying what we have learnt in the world of business. Further, as final-year students with a second major in Analytics, this module serves as a platform for us to develop our analytical skills and to gain legitimate hands-on experience. It gives us a taste of what analytics has to offer, thereby preparing us for the real world.
In today’s world, environmental degradation, climate change and sustainability issues pose a great problem to businesses and individuals alike. oBike’s mission statement is to build a sustainable mode of transportation for public masses and to achieve energy savings and reduce carbon dioxide emissions globally. As we strongly resonate with their mission and values, we have decided to collaborate with oBike to value-add to their business via analytics. The scope of this project also challenges us to take the initiative to acquire greater knowledge and develop new skills above and beyond what we have learnt in school. As individuals with a zest to learn more, this project will definitely be an eye-opening experience.
Project Objective
Taking into consideration oBIke's business problem, this analytics practicum seeks to help oBike address their challenge via data analytics by achieving the following objectives:
- Identify hotspots and locations where illegal parking occurs
- Project the illegal parking patterns by analysing historical data
- Determine suitable areas for yellow boxes to be painted
This project scope is focused entirely on Singapore, due to limitations in the data available.
Scope of Work
The scope of the project includes the following:
- Project Discovery
We first developed an understanding of the business domain so as to discover business goals, requirements and problems the client is currently facing. This was done via a face to face meeting with a client where we had an initial understanding of the illegal bike parking problem that they faced, as well as the hefty fines issued by LTA. In addition, secondary research was done to better understand oBike's competitors, LTA regulations as well as the bike-sharing industry as a whole.
- Data Preparation
Data would be collected from oBike in the form of CSV files. We would then proceed to data cleaning and data preparation. Exploratory data analysis (EDA) will be used to develop a better understanding of the data set and dashboards would then be used to visualize it. This process is further elaborated in Section ‘5.2 Data Preparation & Cleaning’ and Section ‘5.3 Exploratory Data Analysis (EDA)’.
- Model Planning and Building
Results from the EDA phase would be used for model planning and development. The model would then be validated before being used for analysis and to generate insightful findings for the client. We plan to use a 3-step process to fulfil the client's objectives as follows:
- Descriptive analytics – Use of data mining techniques such as geospatial analysis and data visualisation to develop an in-depth understanding of current illegal bike parking patterns
- Predictive analytics – Use of statistical models and forecasting techniques such as time series forecasting to predict future illegal bike parking trends, for development of a preventive approach.
- Prescriptive analytics – Use of existing data and forecasted trends to suggest optimal locations for additional yellow boxes to be painted by bike-sharing companies to reduce illegal parking proble## s.
- Communication of Insights
Throughout the duration of this project, the project progress will be communicated to the client via weekly updates. Finalised insights obtained from the model would then be communicated to the client via a presentation. These findings will also be shared via an analytics practicum conference and submitted in the form of a final paper.
