HackNITR 2020

For the "HackNITR" Hackathon 2020

Rush Hour - The Traffic Management Project

Youtube Video

Our project is from the track - "Smart Cities". It is a traffic management project, incorporating the fields of Machine Learning, Web Development, and App Development. At each traffic signal, for each side, it counts the number of cars, scooters, buses, etc, and then assigns a corresponding priority and time for that particular signal, instead of just assigning a fixed time to all irrespective of the number of vehicles present at that time. Thus, this will save the time of the commuters. Also, our project contains a special feature for ambulances, fire-fighters, and other emergency-response teams to mark in the location of their starting and destination points and according to it, algorithms of all the traffic signals along that way will be altered in a certain way, allowing the emergency vehicles to pass the signal without any stopping at the signal.

The different fields of our project include:

• Machine Learning: This would count the number of cars, buses, etc. on each side, for all the four directions, then would assign the different priorities and times for the green signal, according to the no. of vehicles present. If the number of vehicles is the same, more priority would be assigned to that direction, where more no. of buses or other larger vehicles are present.
• App: Every owner and/or driver of all emergency vehicles like ambulances, fire engines, etc., will have this app, where they could mark in the location of their starting and destination points and according to it, algorithms of all the traffic signals along that way will be altered in a certain way, allowing the emergency vehicles to pass the signal without any stopping at the signal.
• Website: This website would be for the traffic controlling head officer of each area of the cities, where he could log in as admin, and look ever any anomalies for his area, like, if a certain signal requires more or less time for each side, how many emergency vehicles passed the signals and if they stopped there, etc.

Advantages:

1. Very less human intervention at each signal.
2. Quick and free roads for all the emergency vehicles, thus, allowing better social amenities for the citizens.
3. Better surveillance of the breaking of traffic rules, like overspeeding, wrong sides, not following the traffic lights, etc., because the same computer vision to count the vehicles, can also be used to detect them.
4. Overall monitoring increases coordination between different signals and helps in the betterment of one of the core features of a Smart City - Traffic Management.

Getting Started

These instructions will get anyone a copy of the project up and running on his/her local machine for development and testing purposes. See deployment for notes on how to deploy the project on a live system:

1. Clone or download the repository in the local machine.
2. To understand the working algorithm, read the Algorithms Explained.md file.
3. Follow the below Prerequisites section to install some of the software's and frameworks required beforehand.

Prerequisites

This contains a list of things a person needs before-hand to install the software and how to install them.

1. For the Machine Learning part:

pip install -r requirements.txt

2. For the App development part:

1. Install the latest version of Flutter.
2. Get the latest version of an IDE like Android Studio or Visual Studio Code.
3. Have a physical device like a smartphone or an emulator with the latest version of Android installed.

Installing

This contains a step by step series of examples that tell anyone how to get a development environment running.

Steps:

1. Go to the 'Rush Hour UI' Folder.
2. Run the 'pip install -r requirements.txt' command.
3. Run './run.sh' command.

Deployment

The Deployment of this project requires some IoT and another Hardware stack. But we have provided a UI script file for the software simulation of the traffic lights at the signal, working according to our algorithm.

Built With (Tech Stack)

• HTML - The Website front-end development language used.

• CSS - The Website front-end development language used.

• Flutter - The app development framework used.

• Node.js - Website Backend development environment, also used to integrate the ML project on a website.

• Keras - The Machine Learning framework used for counting the number of vehicles at each signal(object detection).

• Matplotlib - The Library used for Visualization .

• OpenCV - The Library used for Image-Processing .

• CVlib - The Machine Learning framework used for counting the number of vehicles at each signal(object detection).

• Google Maps APIs - Used to enter the Source and Destination locations of emergency vehicles in the App.

• Firebase - Used in the Backend of the Flutter app for authentication.

Authors

• Raj Sahu - Machine Learning - ( https://github.com/raj-sahu)
• Harshit Raj - Website Backend & Deployment - (https://github.com/prodigioharshit)
• Rishav Raj - Website Frontend & Working Algorithm - ( https://github.com/rishav139 )
• Rohan Nishant - App Development and Integration - ( https://github.com/rondon1947 )

Acknowledgments

A very heartful thanks to the authors and owners of the following articles which helped understand and re-build the traffic management system.

• Article 1
• Article 2
• Article 3

And also lots of gratitude for the whole team of "HackNITR 2020" for providing us the perfect platform to showcase our idea.