Roadrunners Proposal

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PROPOSAL

POSTER

APPLICATION

RESEARCH PAPER

Interim presentation slides: Roadrunners Interim.pdf

As a city-state that is scare of land, Singapore faces the challenge of a growing vehicle population. To address this issue, the Intelligent Transport System (ITS) aims to make the city-state’s transport system safer and more efficient. Some of these ITS solutions include the Expressway Monitoring Advisory System (EMAS) which monitors and alerts motorists of traffic incidents, and Electronic Regulatory Signs (ERS) which enhances the visibility of traffic signs. Under the Smart Mobility 2030 strategic plan, LTA hopes to pave the way for a more comprehensive and sustainable ITS ecosystem.

While ITS aims to provide a safer experience for motorists, there is currently no known system in place to analyze the occurrence of traffic accidents. Traffic accidents are inevitable in any country and Singapore is no exception. They pose a huge problem to the safety of road users as they often result in fatalities and injuries. Therefore, our group aims to analyse the patterns of traffic accidents in Singapore and provide recommendations to reduce the occurence of traffic accidents in Singapore.

Through our project, we aim to:

1. Detect patterns of traffic incidents in Singapore and identify potential accident hotspots
2. Analyse potential causes of traffic incidents in Singapore
3. Analyse traffic-related issues such as heavy congestions or roadworks
4. Evaluate the effectiveness of accident prevention measures implemented by LTA
5. Possible recommendations to LTA to further reduce traffic incidents

Data	Source	Data Type/Method
Road Network	OpenStreet Map	Example
LTA Road Camera	data.gov.sg	SHP
Singapore Police Force Digital Traffic Red Light Cameras	data.gov.sg	SHP
Singapore Police Force Digital Speed Enforcement Cameras	data.gov.sg	SHP
MCE KPE Speed Camera	data.gov.sg	SHP
Singapore Police Force Mobile Speed Cameras	data.gov.sg	SHP
Singapore Police Force Police Speed Laser Cameras	data.gov.sg	SHP
Singapore Police Force Fixed Speed Cameras	data.gov.sg	SHP
Bollard	data.gov.sg	SHP
Convex Mirror	mytransport.sg	SHP
ERP Gantry	mytransport.sg	SHP
Lamp Post	mytransport.sg	SHP
Road Crossing	mytransport.sg	SHP
Speed Regulating Strip	mytransport.sg	SHP
Guard Rail	mytransport.sg	SHP
Railing	mytransport.sg	SHP
Road Hump	mytransport.sg	SHP
Traffic Light	mytransport.sg	SHP
Traffic Sign	mytransport.sg	SHP
Word Marking	mytransport.sg	SHP
Accident	mytransport.sg	API → JSON → CSV Script to call API periodically to retrieve JSON file. Script converts JSON to CSV file. Attributes: Type::String Latitude::double Longitude::double Message::String
Heavy Traffic	mytransport.sg

	Key Challenges	Description	Solution
1.	Lack of readily available data	There is currently no known data source that provides historical traffic accidents data in Singapore. There is only a real time API of traffic accidents from LTA.	Learn to write a script that perform autonomous calling of the API Create a regular schedule for the calling of API
2.	Unfamiliarity with R Shiny	We are unfamiliar with R programming language due to the lack of prior experience	Independent learning starting from week 5 Learning from each other Consult Prof Kam
3.	Unfamiliarity with spatial analysis techniques	We are unsure what spatial analysis techniques to use and how to apply it as we lack prior experience in geospatial analysis	Conduct literature review on the commonly used spatial analysis techniques Research how we these techniques are executed Independent learning on the analysis techniques from week 5 Learning from each other Consult Prof Kam

To gain a better understanding of how we could proceed with our analysis, we decided to conduct a literature review. Here are the summaries of some research paper on spatial analysis of traffic accidents:

1. GIS-based spatial analysis of urban traffic accidents: Case study in Mashhad, Iran

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Aim of study: to use geographic information technology (GIS) and spatial-statistical analysis to gain insights of the traffic accident patterns in Mashhad, Iran.

Results of kernel density level for accidents leading to injury from March 21, 2011 to March 19, 2012

Methodology:
1. Kernel Density Estimation

• To determine static hotspots

2. Nearest Neighbour Distance Analysis

• Used to determine if the accidents are clustered based on the nearest distance between two neighbouring accident points

3. K-function output analysis

• Used to provide a more accurate analysis of points distribution

Learning Points:
1. Spatial Analysis Techniques

• This study is similar to our project. Hence, we can learn the analysis technique they have used and apply it to our study
• Similarly, we can use Kernel Density Estimation to detect traffic accident hotspots and Nearest Neighbour K function to determine if the accidents are randomly distributed or clustered

Areas for improvement:
1. Hard to follow up

• As this analysis is done on a proprietary software (Arcview), it is impossible to reproduce the same study done by the researchers. Thus, it is hard for other researchers to follow up on their study.

2. Brazilian Road Traffic Fatalities: A Spatial and Environmental Analysis

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Aim of study: to analyse road traffic accidents hotspots in BR 277 highway located in the state of Parana, southern Brazil and performed environmental analysis to identify patterns contributing to the traffic accidents.

Kernel density and wavelet analysis hotspots. 3A) All Fatal Crashes

Methodology:
1. Kernel Density Estimation

• To determine accident hotspots

2. Wavelet

• Complement Kernel exploratory analysis

3. K-function output analysis

• To reduce the variables into similar variance components
• Then developed regression models to evaluate the impact of built environmental components on fatal crashes

Learning Points:

1. Spatial Analysis Techniques

• Apart from using Kernel Density Estimation to develop hotspots as well as K function to determine complete spatial randomness like the previous study, this research also explores the impact of how the human built environment affects the occurrence of accidents.
• We could possibly learn from this project how the built environment analysis is being executed and then determine how various infrastructures on the road affects the occurrence of accidents.

Areas for improvement:
1. Hard to follow up

• Similar to the previous study, this analysis is done on a proprietary software (QGIS), it is impossible to reproduce the same study done by the researchers. Thus, it is hard for other researchers to follow up on their study.

3. IS415 2013-14 Assignment 2 – Heng U San 

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Aim of study: to analyse the distribution of GP Clinics, Preschools and Bus Stops in Bedok and provide recommendation on how amenities could be better planned.

Density function for buildings

Methodology:
1. Nearest Neighbour Index

• lpp function – to measure distance between points along a linear network

2. K-function

• To determine the clustering type

Learning Points:

1. Clear and easy to understand

• U San offered a very clear and easy to understand explanation of how Nearest Neighbour Index and K function works. This helped us significantly in understanding how these techniques are used in the other research papers.
• U San’s work was well documented. She clearly explained the step by step procedure of how he obtained her results as well as the R functions used for analysis. This makes it much easier for other researchers to reproduce a similar study.
• To analyse the spatial distribution of bus stops, U San included a road network constraint in the various analysis. This is done because bus stops can only occur on road networks. Similar to our study, accidents can only occur on road networks. Thus the road network constraint should be included in our analysis or else our result will not make sense.

Areas for improvement:
1. Sharing of codes

• U San did well in documenting her step by step procedure, teaching other researchers to know how to reproduce a similar study. However, it will be even better if U San could share a R notebook of her codes so that researchers could reproduce the exact same study and continue her research from where she stopped.

Feel free to leave any comments! :)

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