🚆 Thai Railway Reliability Intelligence

An advanced data scraping and analysis suite designed to quantify and visualize train delays for the Thai Railway system. This project specifically focuses on the route between Bangkok (Krung Thep Aphiwat) and Thung Song Junction.

📌 Project Overview

This tool allows users to:

1. Scrape: Extract historical tracking data from the official Thai Railway TTS API.
2. Analyze: Process lateness patterns across hundreds of historical trips.
3. Visualize: Interact with a modern dashboard to evaluate travel risks and identify common causes of delays.
4. Live Status: Pull real-time tracking for train 169 directly from ttsview.railway.co.th and watch its current delay update every 30 seconds.

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🚀 Getting Started

1. Prerequisites

Ensure you have Python 3.8+ installed. Install dependencies via requirements.txt:

pip install -r requirements.txt

2. Data Collection (Scraping)

The scraper uses a predefined mapping of dates and runhashes to fetch specific tracking information.

• Script: scrape_delays.py
• Input: date_runhash_map.csv
• Execution:

  python3 scrape_delays.py

• Output: Generates station_delays.csv containing arrival/departure lateness and reported delay causes.

3. Launching the Analytics Dashboard

The interactive dashboard is built with Streamlit and provides a premium light-themed interface.

• Script: analyze_delays.py
• Execution:

  streamlit run analyze_delays.py

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📊 Analyzer Features

🔍 Configuration Sidebar

• Station Selector: Compare performance across multiple stations simultaneously.
• Time Granularity: View data in Daily, Monthly, or Yearly averages.
• Risk Threshold (N): Set your custom lateness threshold (e.g., 30 mins) to calculate probability of failure.
• Analysis Period: Select a specific historical date range for focused research.

📈 Performance Metrics

• Worst/Best Lateness: Identify the extreme outliers in the dataset.
• Reliability Score: Percentage of trips arriving within a 10-minute window of the schedule.
• Major Delays Counter: Instant count of incidents exceeding 1 hour.

🧠 Intelligence Logs

• Root Cause Analysis: A breakdown of official reasons for delays (e.g., track clearance, construction).
• Major Incidents (>1h): A dedicated deep-dive table for critical timing failures, showing exactly what went wrong and when.

🛰️ Live Status (Train 169)

• Connects to the SRT WebSocket at wss://ttsview.railway.co.th:5000 and emits viewSubTrain with today's runhash to receive every station's live status.
• Renders the current station, latest delay, and per-station ETAs (actual → estimated → schedule + delay propagation).
• Auto-refreshes every 30 seconds while the tab is open.

Provide today's runhash

The WebSocket bypasses Cloudflare's Turnstile so no JWT or browser automation is required, but you do need to tell the app which runhash represents today's train 169:

1. Visit https://ttsview.railway.co.th/v3/ and open the train 169 row. The browser opens a /v3/search/?qType=21&qParam=... URL.
2. Paste either the full URL or just the qParam value into the runhash input on the Live Status tab.
3. The runhash is cached at ~/.cache/train_analysis/runhash_169.json for the rest of the day. The next morning you'll be asked to provide the new one.

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📁 File Structure

• scrape_delays.py: Python script for automated historical data extraction.
• analyze_delays.py: Streamlit-based intelligence dashboard.
• live_view.py: Live Status tab (WebSocket fetch + auto-refresh + ETA rendering).
• srt_socket.py: Socket.IO client for viewSubTrain live tracking.
• station_delays.csv: The primary dataset (generated after scraping).
• date_runhash_map.csv: Mapping file required for API queries.
• station_data.json: Metadata for railway stations.

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⚖️ Disclaimer

This tool is intended for personal analysis and journey planning. It relies on publicly available data from the Thai Railway API. Arrival times are subject to real-time changes and operational incidents.