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Intelligent Road Traffic Control

Omkar Bahiwal

Omkar Bahiwal

Mumbai, Maharashtra

6 0
  • 0 Collaborators

Cities across the world are growing rapidly, thousands of vehicles are being added to the road everyday resulting in increased road traffic. This causes traffic congestions, Pollution, consumption of resources and Economic loss. The need of the hour is a technology for managing the road traffic. IRTC is an application that analyses, estimates the demands of road traffic users and calculate the timings of the traffic lights grid. The Traffic flow routes are predicted at peak hours (office hours, etc). This can help us to learn and channelize the traffic according to its regular flow. It also classifies the types of users to prioritise them, giving an insight of the mobility within the city and driving behaviour of the users. ...learn more

Project status: Under Development

Internet of Things, Artificial Intelligence

Intel Technologies
Intel Python

Code Samples [1]Links [1]

Overview / Usage

The application basically analyses and estimates the demands of road traffic users and calculates the timings of the traffic lights grid. The GPS location data of the users is sourced from available APIs and OpenData sources. The near-realtime data gives us the number of vehicles passing through the intersections, their location in road segments and the distance between them. That will help us to analyse the traffic demands and hence adjust the timings of traffic lights based on the demands. The Traffic flow routes can be analysed at peak hours (office hours, etc). This can help us to learn and channelize the traffic according to its regular flow.
The users, their features and functionalities -

  1. Emergency Vehicle : The app will take input for a path from the Emergency vehicle user, when in emergency, the Traffic lights in that path will be optimised for smooth movement of the emergency vehicle. The nearby users in the traffic will be notified about an emergency vehicle arriving near them and ask them to give way for the same.
  2. Public Transport : The app will analyse this class of vehicle with its daily route, using Amazon machine learning and optimise the traffic light timings for them. For instance, If a Bus is running late on its regular route, the bus will have less waiting time on the traffic signals.
  3. Regular User : The regular user will provide the traffic data. Which will help our system to analyse the traffic density and congestions in realtime. The user can also send images of congestions and defects in road (eg. potholes) at several locations. The media generated will be Stored in Amazon S3. The user can also notify about the accident spots on their nearby locations. The authorities will be informed with this data in order to take proper action. The waiting time of the user will decrease on the traffic signals. According to the traffic density.

Methodology / Approach

The user will be sending GPS location anonymously to a Low Latency DB in real- time. The near-realtime data will give us the number of vehicles in a street section and the distance between them, that will help us to analyse the traffic demands and calculate the timings of traffic lights. An ML model will predict the mobility and regular routes of the users during a particular time of day in the week. The Traffic flow routes can be analysed at peak hours (office discharge, etc). This can help us to channelize the traffic according to its regular flow. With the use of OpenStreetMap to obtain information about the road network and some of its infrastructure, including locations of junctions and traffic lights, and for visualisations.

Technologies Used

AWS Lambda, DynamoDB, OpenStreetMap, LeafletJS, CanvasJS.

Repository

https://github.com/ombahiwal/Irtc-aws-prototype

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