AY1516 T2 Team CommuteThere Methodology

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Analyse Commuter Patterns

This analysis aims to identify commuter patterns of each demographic groups - students, adults and elderly - as each group has differing interests and preferences in the places to frequent at. Data used for this methodology involves the ez-link and points of interests (POI) data. Given that the places that each demographic groups frequent at varies due to differing interests and preferences, to include points of interests (POI) in this analysis will be helpful to understand which places attract various groups of people at various periods of the week. With that, our team conclude that POI should be places that serve the primary needs of the people.

Analysing commuter patterns is further segregated to two sub-methods:

1. Identifying common destination points

An initial analysis will be conducted to find out the common destinations that commuters travel to given that each demographic groups will have different needs and hence different places they frequent to. A heatmap of the common points will be visualized using QGIS. Areas with a darker intensity of colour would show the areas where many commuters alight at.

2. Identifying travel patterns

Travel patterns are categorized into four different segments: Island wide, inter town, intra town and most frequently travelled trips, where commuters may travel just within Tampines planning area, or within the east region, or island wide. To do so, we will use QGIS to map out.

Analysis of commuter patterns is split into 4 segments:

Segments	Description
Island wide	Overall commuting activity for each demographic groups as a whole, regardless of place of origin. This will provide an overview of the commuters’ travelling pattern in Singapore.
Inter town	Travelling patterns of the commuters whose trips originate from Tampines planning area and end in the East region i.e Bedok,Paya Lebar, Changi, Pasir Ris.
Intra town	Travelling patterns of the commuters whose trips originate and end in Tampines planning area i.e Tampines and Simei.
Most frequent travelled trips	Commuters who made the same trip for at least four times in a week can be categorised as such. The data for each demographic groups are analysed based on weekdays which has most of the activities reflected on.

Analyse Multimodal Transportation Patterns

1. Distribution Analysis on Multimode Commuters

In order to analyse multimode commuters, we will join the MRT and Bus dataset together using card number attribute, time attribute and date attribute.

1.1 Analyse Transfer Interval

According to Transit Link, a transfer can be from:

• the MRT/LRT to a bus service,
• a bus service to another bus service, or
• a bus service to the MRT/LRT

Transfer interval refers to the amount of time taken for the students to transfer from one mode of transportation to another mode of transportation.This is calculated using the difference between Bus entry time and MRT exit time (for MRT→Bus) and MRT entry time and Bus exit time(for Bus →MRT)

2. Analyse Relationship Between Walking and Bus Commuting

2.1 Least Cost Walk Path Analysis

Due to time constraint, our group will use the Student group as a proxy. In order to analyse the relationship between walking and bus commuting, we will compare the time taken to walk with the bus travelling time. Unlike bus travelling time, the time taken to walk is not provided in the dataset. This will be calculated using the walking distance, which will be derived from least cost walk path analysis, and the average walking speed of students derived from prominent research papers.

Our group has derived two methods to construct the least cost walk path namely the Traditional method and the Euclidean Distance method. Traditional method involves the use of QGIS extension plugins such as GRASS and SAGA whereas the Euclidean Distance involves the use of Hub Lines in MMGIS Plugins.

Traditional Method

Firstly, “landtype”, “road” and “tampines planning area” shapefiles are assigned with impedance value to denote the amount effort that the pedestrian has to make. Higher impedance denotes greater amount of effort made by the pedestrian. For “landtype” shapefile, land that can be trespassed will have a value of 1 whereas those that cannot be trespassed will have a value of 100. For “road” shapefile, expressways will have a value of 100 and 1 otherwise. For “tampines planning area” shapefile, it will have an impedance value of 0 to indicate a flat land.

Secondly, all the above mentioned shapefiles are rasterized with pixel of 50m x 50m. “landtype” and “road” rasters are then merged using GRASS r.patch. Next, cumulative cost of moving from an origin of a particular route is calculated using GRASS r.walk.

With an output of cumulative cost raster layer generated by GRASS r.walk, we will be able to construct the least cost walk path using SAGA least cost paths function. As the resulting line layer did not have distance information, the distance will be calculated using the $length formula in field calculator.

Euclidean Distance Method

Walking distance derived from the Euclidean Distance Method is a straight-line distance from an origin to a destination. The walk paths are constructed using Hub lines function in MMQGIS.

2.2 Comparing Time Taken to Walk and Bus Transportation

For the purpose of our analysis, we will be using the Euclidean Distance method as it is a more straightforward and less tedious method as compared to the Traditional method. We will construct 10 least cost paths using both Traditional and Euclidean Distance method, and compare the difference in the distance by subtracting the distance derived from the Euclidean Distance method from the Traditional method. The average and standard deviation of these differences will be used to calculate the error bound i.e Mean of Differences + 2 x(Standard Deviation of Differences).

As the Traditional method is more representative of the actual path used by the pedestrian as compared to the Euclidean Distance Method, the upper error bound will be added to the distance derived from the Euclidean Distance method instead of taking into account both lower and upper error bound. After which, we will compare the bus travelling time and the time taken to walk. If the bus travelling time is shorter than the time taken to walk, we are able to deduce that bus commuting and walking has a negative correlation.