Bengaluru Public Transport — YoY Ridership Dashboard (BMTC + BMRCL)

Compare year-over-year (YoY) ridership trends for Bengaluru’s two major public transport agencies: BMTC (buses) and BMRCL / Namma Metro (metro rail). The app standardizes units, derives core KPIs, and visualizes long-term shifts in public transport usage.

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Project Overview

• Goal: Provide a clean, defensible view of how BMTC and BMRCL ridership evolves year by year, with derived KPIs to support capacity planning, policy evaluation, and climate impact narratives.
• Stack: Python, pandas, plotly, Streamlit.
• Outputs: Interactive charts (trend, YoY growth, mode split), KPI cards, and a tidy table ready for export.

This Project was created as a contribution at the Bengaluru Climate Hackathon, hosted by WRI India, BBMP, OpenCity, BCAP and partners.

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Datasets (sources)

1. BMTC Ridership Data — data.gov.in (Open Government Data)
2. BMRCL Ridership Data — Wikipedia — Namma Metro (officially cited figures)

Notes

• BMTC is typically available as year-wise totals or average daily numbers.
• BMRCL often reports annual totals (in crore passengers). This dashboard converts to average daily (lakh) for an apples-to-apples comparison.

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KPIs & Definitions

• BMTC Daily (lakh) — Average daily BMTC ridership, in lakh.
• Metro Daily (lakh) — Average daily BMRCL ridership, in lakh.
• Total PT(Passenger Traffic) Daily (lakh) = BMTC Daily + Metro Daily
• Metro Share (%) = (Metro Daily / Total PT Daily) × 100
• YoY Growth (%) of Total PT = pct_change(Total PT Daily) × 100

Unit conventions: 1 crore = 100 lakh.
If BMRCL provides annual (crore):
Metro Daily (lakh) = annual_crore × 100 / 365.

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Expected Data Schema

The app expects a year-level table after ingestion/standardization. You can prepare it in two ways:

Option A — Single combined file (recommended)

ridership_yoy.csv (or .xlsx) with columns:

Year	BMTC_Daily_Lakh	Metro_Daily_Lakh
2018	22.4	4.8
2019	23.1	5.6
2020	12.0	2.3

Option B — Two raw files (merged in code)

• bmtc_yoy.csv with columns: Year, BMTC_Daily_Lakh
• bmrcl_yoy.csv with columns: Year, Metro_Annual_Crore or Year, Metro_Daily_Lakh
  (If annual is provided, the app converts to daily in lakh using the formula above.)

Keep raw downloads in data/raw/ and the clean/derived table in data/processed/.

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What the Dashboard Shows

• Trend Line: Total PT Daily (lakh) over years, with BMTC vs Metro stacked or overlaid.
• Mode Split (Donut/Bar): Metro Share (%) for a selected year.
• YoY Growth Line/Bar: Year-over-year growth of Total PT (%).
• KPI Cards: Selected year’s values + Δ vs previous year.
• Events Overlay (optional): Policy changes (e.g., fare schemes), new line openings, pandemic years.

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Development Phases

1. Requirements & KPI Scoping
   Define the question (PT shift). Choose metrics: BMTC Daily (lakh), Metro Daily (lakh), Total PT Daily, Metro Share (%), YoY Growth (%).

2. Data Sourcing
   Identify sources: BMTC year-wise (data.gov.in), BMRCL annual (Wikipedia/Namma Metro). Note access limits (no station-wise; some pages lack APIs).

3. Data Collection / Ingestion
   Download CSV/XLSX, read into pandas. Keep raw files. Log source URLs and “Updated On” dates.

4. Collation & Schema Design
   Standardize columns → Year, BMTC_Daily_Lakh, Metro_Daily_Lakh (or Metro_Annual_Crore to derive).

5. Cleaning & Normalization
   Strip commas/strings, coerce numeric, ensure consistent units. Convert Metro annual crore → daily lakh with daily_lakh = annual_crore × 100 / 365. Handle missing/duplicate years.

6. Feature Engineering

   • Total_PT_Daily_Lakh = BMTC_Daily_Lakh + Metro_Daily_Lakh
   • Metro_Share_Pct = Metro_Daily_Lakh / Total_PT_Daily_Lakh × 100
   • YoY_Growth_Pct = pct_change(Total_PT_Daily_Lakh) × 100
     Round for display; keep full precision internally.

7. Packaging & Deployment
   requirements.txt (streamlit, plotly, pandas, openpyxl), optional runtime.txt. Deploy to Streamlit Cloud. Add “Data Sources” links in the footer.

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Local Setup

# 1) Clone
git clone https://github.com/advik-holalu/Bengaluru-Public-Transport-Daily-Ridership-Dashboard.git
cd Bengaluru-Public-Transport-Daily-Ridership-Dashboard

# 2) (Optional) Virtual env
python -m venv .venv
# macOS/Linux:
source .venv/bin/activate
# Windows PowerShell:
# .\.venv\Scripts\Activate.ps1

# 3) Install
pip install -r requirements.txt

# 4) Run the app
streamlit run app.py

Team Behind the Work

• Advik R Holalu
• Akshath S Bhandiwad
• Joselyn Riana Manoj
• K Jaidev Shankar Reddy
• Sanjan Dileep