BRT Passenger Flow Analysis

Project Overview

This capstone project analyzes TransJakarta passenger flow data (April 2023) to identify critical routes and time periods based on travel duration and passenger volume. The goal is to support efficient fleet allocation and scheduling decisions.

Tools used:

• Python (pandas, seaborn, folium)
• Tableau (interactive dashboard)

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Objective

Identify the most congested time slots and longest-duration routes to optimize fleet deployment and improve service quality.

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Repository Structure

├── cleaned_dataset/           # Cleaned dataset (.xlsx)
├── raw_dataset/               # Raw dataset
├── tableau_dashboard/         # .twbx file (Tableau)
├── capstone_m2_data_analysis.ipynb  # Main EDA notebook
└── README.md

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Key Analysis

• Rush Hour Impact

  Significant increase in travel time during 06:00–08:00 and 17:00–19:00 (Mann-Whitney test, p < 0.05)

• Top 10 Longest Routes

  Routes with the highest average travel duration

• Top 10 Most Crowded Routes

  Based on passenger transaction volume

• Critical Route Identification

  Combined top 10 from both metrics → risky & high-load corridors

• Demographic Pattern

  Age 19–45 dominates rush hour traffic

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Strategic Recommendations

1. Adjust headway intervals during rush hours on crowded routes
2. Trial express (skip-stop) service on long-duration routes
3. Reduce frequency on underutilized long routes, reallocate to fast high-volume ones

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Interactive Dashboard

Built with Tableau

Includes KPIs, critical route map, travel time trends, and age segmentation

Transjakarta Dashboard

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Tech Stack

• Python 3.13
• Jupyter / VS Code
• Tableau Public

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Author

Built by @bonifasiusx – Capstone M2 | Data Science