DUBUS - Dublin Bus Trips 🚌🚌🚌

📢📢📢 IMPORTANT NOTE 📢📢📢

The below provides a short overview of the project + an installation guide.

For the Youtube demo, links to the app, full descriptions and explanations please see the Main notebook or download it from Lab4_Main! 🧘

Table of contents

• Project Overview
• DUBUS - Dublin Bus Trips Congestion
• DUBUS - Dublin Bus Trips Analytics
• Usage
• Code Structure
• Installation

Project Overview

Welcome to DUBUS! Your Dublin bus trip app for congestion prediction and trip analytics!

The app includes two parts - DUBUS - Dublin Bus Trips Congestion and DUBUS - Dublin Bus Trips Analytics.

We gather raw bus sensor data, aggregate it into meaningful bus trips and present it to you for your city planning or other business uses.

DUBUS - Dublin Bus Trips Congestion

This part of the application includes an interactive map that displays the journeys with predicted congestion, from uploaded stream or batch bus data.

DUBUS - Dublin Bus Trips Analytics

This part of the application includes modern-day data exploration tools, bundled in a convenient interface.

Part of the app:

Usage

DUBUS - Dublin Bus Trips Congestion is run from Lab4_UI_part1.ipynb and DUBUS - Dublin Bus Trips Analytics is run from Lab4_UI_part2.ipynb.

Once the app is up and running you can use it easily, just follow the in-app instructions.

The in-app instructions describe in detail the different options that you have when using the application, and also thorough instructions on how to use the app.

Follow the installation steps to install the app on your machine.

Additionally, if this is your first time running the app, please refer to first time setup part in the installation guide.

Notes,

1. As we are displaying our app inside Databricks, you must have a valid Databricks account inorder to interact with the app.
2. The VM server must be on for the app to work.
3. There are security limitations. Please see the notes section in the installation guide on how to deal with them.
4. When using your own data make sure it follows the accepted schema. In general, records should include the fields - '_id', 'delay', 'congestion', 'lineId', 'vehicleId', 'timestamp', 'areaId', 'areaId1', 'areaId2', 'areaId3', 'gridID', 'actualDelay', 'longitude', 'latitude', 'currentHour', 'dateTypeEnum', 'angle', 'ellapsedTime', 'vehicleSpeed', 'distanceCovered', 'journeyPatternId', 'direction', 'busStop', 'poiId', 'poiId2', 'systemTimestamp', 'calendar', 'filteredActualDelay', 'atStop', 'dateType', 'justStopped', 'justLeftStop', 'probability', 'anomaly', 'loc'. Specific types examples and can be found in the notebooks.

Code Structure

The project consists of multiple notebooks that work and interact together.

Lab4_Preparations consists of the preprocessing part, in which we also train and save the ML model, and the relevant helper models (indexer and encoder, for categorical variables).

Lab4_UI_part1 - The front-end of the DUBUS - Dublin Bus Trips Congestion App (first part).

Lab4_UI_part2 - The front-end of the DUBUS - Dublin Bus Trips Analytics App (second part).

Lab4_functions includes all the functions we used through out the project. Includes back-end connection functions (such as connecting to Elastic) as well.

In addition, Lab4_Main includes explanations and descriptions about the reasoning behind Part 1 (Preprocessing & App) and Part 2 (EDA, Article and App).

Installation

We provide full explanations on how to set up and get the app up and running on your system as well.

For the full installation guide please see the installation guide

However, be aware! There are a lot of steps that need to be taken in order to allow everything (DataBricks, Kibana, ElasticSearch) to work and communicate with each other.