Signal Proposal
Contents
PROBLEM & MOTIVATION
Efforts by the Singapore Traffic Police in educating the public on road safety over the years have decreased the number of Fatal Accidents in Singapore by 15.7% in 2017 as compared to 2016 (Chua, 2018). Despite the improvement, Singapore's road fatalities per 100,000 motor vehicles of 20.2 in 2015 is still relatively high as compared to countries, such as Japan, which has achieved a low 6.5 (World Health Organisation, 2015). Accidents involving motorcyclists and elderly jaywalkers were highlighted as key concerns by the Singapore Traffic Police as motorcycle accidents accounts for more than half of the traffic accidents in 2017 and the number of elderly jaywalkers road fatalities are on the rise.
Leeds, Yorkshire town of close to 800,000 people, is home to Open Data Institute Leeds which was created to explore and deliver the potential of open innovation with data at city scale. In fact, despite Leeds being a small city in England, it is well-known for housing several data-heavy institutions, commercial enterprises and academia, all which contributed to the rich public data set that Leeds offers. Not surprising, Leeds is now a hub for data activity, with some businesses handling over 30 million data events daily to uncover consumer insights (Turner, 2018). Therefore, Leeds serves as an appropriate model for Singapore, a city-state aiming to derive people-centric solutions to address urban challenges through Smart Nation initiatives, to emulate from.
To better derive insights from traffic accidents, we will be using relevant datasets from Leeds to analyse potential factors that could correlate with road accidents. Variables such as location of shops and weather would be analysed and the analyses would be linked to Singapore. We would also be recommending appropriate preventive measures could be put into place by the respective authorities in Singapore so both preventive and corrective actions could be put into place.
OBJECTIVES
In our project, we would be creating a geovisualisation that is able to achieve the following objectives:
- Gain an overview of traffic accident hotspots
- Explore possible correlations with traffic accidents
- Identify zones which are more prone to accidents
- Recommend additional datasets that should be collected
SELECTED DATASETS
The following datasets will be used for analysis, as elaborated below:
| Dataset | Format | Data Attribute | Source |
|---|---|---|---|
| Leeds Road Traffic Accidents (2009 - 2017) | CSV |
|
UK Open Database |
| Local Authority Districts (Leeds) | SHP | UK Consumer Data Research Centre (CDRC) | |
| Leeds Road | SHP | UK Consumer Data Research Centre (CDRC) | |
| Leeds RoadTunnel Network | SHP | UK Consumer Data Research Centre (CDRC) | |
| Leeds Motorway Junction | SHP | UK Consumer Data Research Centre (CDRC) |
DATA PREPARATION
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LITERATURE REVIEW
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APPROACH
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STORYBOARD
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TOOLS & TECHNOLOGIES
Tools and technologies
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Data Architecture
TO BE FILLED!
KEY CHALLENGES
The following are some of the key technical challenges that we may face throughout the course of the project:
| Key Challenges | Mitigation Plan |
|---|---|
| Unfamiliarity with spatial analysis methods |
|
| Unfamiliarity with R and Rshiny Libraries |
|
| Unfamiliarity with Leeds geographical area |
|
TIMELINE
TO BE FILLED!
REFERENCES
ACKNOWLEDGEMENT
The team wishes to thank Professor Kam Tin Seong for his support and guidance throughout this project.
COMMENTS
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