Introduction
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About the Project
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Packages Used
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Library packages extend R capabilities for analysis, visualization, and customization. Hence, following packages are used for the project:
1. flexdashboard: Provides a framework for creating interactive dashboards using R Markdown. It allows you to combine narrative text, data visualizations, and interactive components in a single document.
2. tidyverse: A collection of R packages designed for data science, including data manipulation (dplyr), data visualization (ggplot2), and data importing/exporting (readr, readxl).
3. ggplot2: A powerful and flexible package for creating static data visualizations in R. It follows the grammar of graphics paradigm, allowing you to build complex plots layer by layer.
4. htmltools: Provides functions for creating and manipulating HTML content in R. It’s commonly used in conjunction with other packages for generating HTML output in R Markdown documents or Shiny applications.
5. viridis: Offers color palettes designed to be perceptually uniform and accessible to individuals with color vision deficiencies. It’s particularly useful for creating visually appealing and inclusive data visualizations.
6. shiny: Enables the creation of interactive web applications directly from R. It allows you to build dynamic user interfaces that respond to user input and update in real-time.
8. shinyWidgets: Provides additional user interface components and widgets for Shiny applications. It includes elements like sliders, date pickers, file uploaders, and more.
9. readxl: Offers functions for reading Excel files into R data frames. It provides a convenient way to import data from Excel spreadsheets for analysis.
10. plotly: Allows you to create interactive plots and visualizations in R using the Plotly JavaScript library. It supports a wide range of chart types and provides features like zooming, panning, and hover tooltips.
11. leaflet: Provides tools for creating interactive maps in R using the Leaflet JavaScript library. It allows you to add layers, markers, polygons, and other elements to your maps, and supports interactions like zooming and panning.
12. highcharter: Offers an interface to the Highcharts JavaScript library, allowing you to create interactive and responsive charts in R. It supports a variety of chart types and customization options.
13. rsconnect: Provides functions for deploying Shiny applications, R Markdown documents, and other content to the RStudio Connect platform. It simplifies the process of sharing and publishing your R projects online.
[1] 731 9[1] 64 6# A tibble: 711 × 9
Type `Stop Code` `Stop Name` Y_2018 Y_2019 Y_2021 Y_2022 Latitude Longitude
<chr> <dbl> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 CITY 10 RAILWAY STA… 5.32e6 6.20e6 2.20e6 3.18e6 21.2 72.8
2 BRTS 10 RAILWAY STA… 1.02e6 1.22e6 1.04e6 1.12e6 21.2 72.8
3 BRTS 6009 KAMREJ TERM… 1.20e6 1.28e6 7.60e5 9.67e5 21.3 73.0
4 BRTS 2028 D.G.V.C.L U… 7.38e5 8.79e5 6.69e5 8.75e5 21.2 72.9
5 BRTS 2074 MAHARANA PR… 3.47e5 5.39e5 5.47e5 7.93e5 21.2 72.9
6 BRTS 2069 DINDOLI KHA… 2.48e5 4.47e5 5.15e5 7.65e5 21.2 72.9
7 BRTS 4054 SARTHANA NA… 7.76e5 8.16e5 4.84e5 7.18e5 21.2 72.9
8 BRTS 2040 UDHANA DARW… 6.83e5 7.53e5 5.75e5 7.08e5 21.2 72.8
9 BRTS 2041 KHARWARNAGA… 4.56e5 5.96e5 4.41e5 6.84e5 21.2 72.8
10 BRTS 2032 SITANAGAR B… 5.22e5 6.36e5 4.52e5 6.42e5 21.2 72.9
# ℹ 701 more rows# A tibble: 36 × 6
`Route No.` Type `Number of Diesel / CNG Bus` `Number of Electric Bus`
<dbl> <chr> <dbl> <dbl>
1 1 CITY 5 0
2 2 CITY 5 0
3 11 BRT 16 0
4 12 BRT 30 0
5 13 BRT 12 14
6 14 BRT 13 18
7 16 BRT 12 16
8 18 BRT 6 0
9 19 BRT 12 0
10 20 BRT 12 0
# ℹ 26 more rows
# ℹ 2 more variables: `Total Bus Count` <dbl>, `Total Dead KM` <dbl>Row
Original Data Dictionary
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About the Data - Data from the Sitilink service in Surat was procured, comprising stop-wise ridership data of the city bus service and BRTS spanning the last five years from 2018 to 2022 and it also includes Geo Locations of stops. The dataset also contained information regarding the total bus counts categorized by various fuel types such as CNG and Electric, and segmentation based on whether the buses operated on BRTS or city bus routes. Additionally, insights into dead kilometer routes were included. Notably, the dataset necessitated cleaning due to numerous unfilled data cells.
Dimension of sheet 1: 731 9Dimension of sheet 2: 64 6Sheet 1 - Ridership Data
| Column.Name | Variable.Type | Variable.Description |
|---|---|---|
| Type | Character | Whether it is a BRTS or City Bus |
| Stop Code | Double | Bus Station Code |
| Stop Name | Character | Bus Station Name |
| Y_2018 | Character | BRTS & City bus stopwise ridership for the year 2018 |
| Y_2019 | Character | BRTS & City bus stopwise ridership for the year 2019 |
| Y_2021 | Double | BRTS & City bus stopwise ridership for the year 2021 |
| Y_2022 | Character | BRTS & City bus stopwise ridership for the year 2022 |
| Latitude | Character | Latitude of the Bus Station |
| Longitude | Character | Longitude of the Bus Station |
Sheet 2 - Bus Count Data
| Column.Name | Variable.Type | Variable.Description |
|---|---|---|
| Route No. | Character | Describes different number of Bus Routes in the system |
| Type | Character | Whether it is a BRTS or City Bus |
| Number of Diesel / CNG Bus | Double | No. of Diesel or CNG Buses |
| Number of Electric Bus | Double | No. of Electric Buses |
| Total Bus Count | Double | Total no. of buses on that specific routes |
| Total Dead KM | Character | Dead Kilometer travelled by the bus during the end of the journey |
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Cleaned Data Dictionary
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Data Cleaning - First step, the category of each variable column was checked, and subsequently, the respective variable columns were adjusted to their correct coding variable type. For example, the “Year 2018 Ridership (Y_2018)” column was initially in character variable type. Therefore, it was transformed into a Double variable type as it contains numerical values. Second step, due to the presence of a few missing values in the data, a data cleaning process was initiated. This involved removing rows containing ‘NA’ fields to ensure data integrity and consistency.
Dimension of sheet 1: 711 9Dimension of sheet 2: 36 6Sheet 1 - Ridership Data
| Column.Name | Variable.Type | Reason.for.changing |
|---|---|---|
| Type | Character | Same as earlier |
| Stop Code | Double | Same as earlier |
| Stop Name | Character | Same as earlier |
| Y_2018 | Double | This column was changed from character to double data type as it contains numbers |
| Y_2019 | Double | This column was changed from character to double data type as it contains numbers |
| Y_2021 | Double | This column was changed from character to double data type as it contains numbers |
| Y_2022 | Double | This column was changed from character to double data type as it contains numbers |
| Latitude | Double | This column was changed from character to double data type as it contains numbers |
| Longitude | Double | This column was changed from character to double data type as it contains numbers |
Sheet 2 - Bus Count Data
| Column.Name | Variable.Type | Variable.Description |
|---|---|---|
| Route No. | Character | Describes different number of Bus Routes in the system |
| Type | Character | Whether it is a BRTS or City Bus |
| Number of Diesel / CNG Bus | Double | No. of Diesel or CNG Buses |
| Number of Electric Bus | Double | No. of Electric Buses |
| Total Bus Count | Double | Total no. of buses on that specific routes |
| Total Dead KM | Character | Dead Kilometer travelled by the bus during the end of the journey |
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Cleaned Data Snapshot
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Sheet 1 - Ridership Data
# A tibble: 6 × 9
Type `Stop Code` `Stop Name` Y_2018 Y_2019 Y_2021 Y_2022 Latitude Longitude
<chr> <dbl> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 CITY 10 RAILWAY STAT… 5.32e6 6.20e6 2.20e6 3.18e6 21.2 72.8
2 BRTS 10 RAILWAY STAT… 1.02e6 1.22e6 1.04e6 1.12e6 21.2 72.8
3 BRTS 6009 KAMREJ TERMI… 1.20e6 1.28e6 7.60e5 9.67e5 21.3 73.0
4 BRTS 2028 D.G.V.C.L UR… 7.38e5 8.79e5 6.69e5 8.75e5 21.2 72.9
5 BRTS 2074 MAHARANA PRA… 3.47e5 5.39e5 5.47e5 7.93e5 21.2 72.9
6 BRTS 2069 DINDOLI KHAR… 2.48e5 4.47e5 5.15e5 7.65e5 21.2 72.9Sheet 2 - Bus Count Data
# A tibble: 6 × 6
`Route No.` Type `Number of Diesel / CNG Bus` `Number of Electric Bus`
<dbl> <chr> <dbl> <dbl>
1 1 CITY 5 0
2 2 CITY 5 0
3 11 BRT 16 0
4 12 BRT 30 0
5 13 BRT 12 14
6 14 BRT 13 18
# ℹ 2 more variables: `Total Bus Count` <dbl>, `Total Dead KM` <dbl>Data Analysis
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Introduction to Surat Sitilink
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Sitilink is a special-purpose vehicle company owned by the Surat Municipal Corporation (SMC). Sitilink is responsible for managing all buses operating within the City Bus System and Bus Rapid Transit system. Presently, the buses under Sitilink’s purview are owned under a Gross Cost Contract arrangement. Under this contract, Sitilink pays the operator based on the Cost per Kilometer, while revenue generated is collected by Sitilink on a per kilometer earning basis.
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Overview of BRTS & City Bus System of Surat
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Overall Ridership 2022
Bus Stop Count
Bus Stop Location of City bus service and BRTS
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Total ridership on bus based public transport is around 57.19 Lakhs (in the year 2022). 65% of total bus ridership comprises of BRTS ridership, where as City Bus System (CBS) only attracts 35% of the total bus-based public-transport ridership
Total bus stops of bus based public transport are 711 (in the year 2022). 21% of total bus stops comprises of BRTS, where as City Bus System (CBS) has 79% of the total bus stops.
It can be deduced that Bus Rapid Transit System (BRTS) stops exhibit a higher concentration within the city, primarily linking demand centers such as Kamrej (Residential), Railway Station (Core), Adajan GSRTC (Commercial), Kadodara (OG), Pandesara GIDC (Industrial), Ved Road (Residential), Udhna Darwaja (Core), and Sarthana Nature Park (Residential). Conversely, city bus stops are spread within in the city and extend towards the periphery of the city. Additionally, it can be inferred that there are instances where BRTS and City Bus Routes overlap in certain areas.
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Comparative analysis of BRTS & City Bus System
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Number of Buses in BRTS and City Bus System
Number of Diesel Buses in BRTS and City Bus System
Number of Electric Buses in BRTS and City Bus
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Total Dead Km Travelled by Buses
Ridership Trend Over the Years (2018-22)
