Group08 proposal
Re-imagining Bus Transport Network in Singapore
Contents
Overview
Bus rides in Singapore are just really slow, isn’t it? Do you ever have experiences where a bus ride that is supposed to be short and quick took way longer than expected? Are you frustrated that the bus stops at every stop even though there’s nobody boarding or alighting? And why do we have so many bus stops that almost nobody uses?
What if we can reimagine the public bus network in Singapore through data?
In this project, we will use data visualization techniques to map out all transportation nodes in Singapore, and re-propose a different way of organizing our bus services, which include bus stops, bus routes, and connectivity within subregion and from subregions to another subregion.
Scope
The scope of the project is limited to public buses in Singapore.
In order to map out the pattern of transportation in Singapore, we will mainly be using datasets from LTA datamall (https://www.mytransport.sg/content/mytransport/home/dataMall.html). In addition, we may supplement the data with other relevant datasets, such as geographical socioeconomic data, land use data (industrial area, commercial area, residential area), population density, or weather data.
Approach
The app aims to provide policy makers with the following information:
Transportation Flow
- Visualise the flow of people across the bus stops/planning subzones and the criticality of the bus stops/planning subzones to Singapore’s public bus transport network.
Travel Demand
Estimate the travel demand: Volume of people expected to travel between a particular origin and destination via a particular route and mode of travel (switch bus/direct bus)
The app should also be able to:
- Show the Impediment Value of subregion of bus stops and planning subzones
- Show the Degree of Centrality - The number of regions a region is connected via bus services
- Show the Closeness Centrality of every bus stops to identify how easily each bus stop can reach another bus stop
- Show the Betweenness Centrality of a bus stop as a connector or bridge between locations to locations
- Show the Connectivity of a bus stop based on the region the area is connected to. (i.e. higher frequency of buses per hour, higher connectivity)
Outcome
Where the visualisation could have useful practical implications to inform decision makers on policy making decisions in order to:
- Optimise bus routes to improve route utilisation. Reduce number of bus stops a service stops at to reduce congestion
- Optimise bus routes which could help to reduce congestion along certain bus stops
- Planning of bus stops, where should we place bus stops in order to maximize overall utility
- Plan the frequency of buses at certain times to minimize bus wait time and maximize throughput
- Advice city planners with regards to transportation flow in congested areas
Data Source
We will primarily be using data from LTA Data Mall. Data is not publically available but available upon a written request. For this project, we will need to write a script in order to make an API call to extract the data we need. Data includes Live data as well as Historical data.
Bus Arrival
Live data. Returns real-time Bus Arrival information for Bus Services at a queried Bus Stop, including: Estimated Time of Arrival (ETC), Estimated Location, Load information (how crowded the bus is).
Bus Services
Returns detailed service information for all buses currently in operation, including: first stop, last stop, peak / off peak frequency of dispatch.
Bus Route
Returns detailed route information for all services currently in operation, including: all bus stops along each route, first/last bus timings for each stop.
Bus Stops
Returns detailed information for all bus stops currently being services by buses, including: Bus Stop Code, location coordinates.
Passenger Volume by Bus Stops
Returns tap in and tap out passenger volume by weekdays and weekends for individual bus stop.
Passenger Volume by Origin Destination Bus Stops
Returns number of trips by weekdays and weekends from the origin to destination bus stops.
Visualization Feature
Methodology
We started this project with a broad question in mind, ‘How can we improve the bus transportation system in Singapore’. The methodology of the project is iterative in nature, we will build broad visualization, identify areas to deep dive and propose solutions for these issues.
Data Extraction - Requesting for access to data from LTA and building API interface to extract data Preliminary study - Reading up on existing work done on Singapore bus transportation network and understand transportation engineering models First phase analysis and visualization - Building of high level visualizations to clearly show status quo and areas for improvement Second phase analysis and visualization - Deep dive into issues and identify solutions Implementation - Build R-Shiny app and report
Solution may include graphical analysis, geospatial analysis in the realm of transport engineering such as Gravity Model, Network analysis, modelling Centrality.
Team Members
Tools and Packages
Reference
- Land Transport Datamall Documentation - published by LTA
- Spatial Network Analysis of Public Transport Systems - published by Data2X
- Weighted complex network analysis of travel route in Singapore public transport system- published by NUS
- Graphical visualisation of flows - published by Flows Mag
- Rerouting Buses using Data Science - Part I- published by Govtech Singapore
- Modelling the public transport network - Part II- published by Govtech Singapore
- How Govtech simulates four million bus rides a day- published by Govtech Singapore
- Journey to the end of the line - SMU MITB Project, Group 2 T17/18- published by SMU MITB
- Interactive Web Maps with R- published by R Studio