Traffic Prediction for Intelligent Transportation System using Machine Learning

Project Code :TCMAPY378

Objective

This project aims to develop a tool for predicting accurate and timely traffic flow Information. In this work we use machine learning, genetic, soft computing and deep learning algorithms to analyze the big-data for the transportation system with much reduced complexity.

Abstract

The most important challenge to sustainable mobility is persistent congestions of differing strength and duration in the dense transport networks. The standard Adaptive Traffic Signal Control cannot properly address this kind of congestion. Deep learning-based mechanisms have proved their significance to anticipate in adjective outcomes to improve the decision making on the predictions of traffic length. The deep learning models have long been used in many application domains which needed the identification and prioritization of adverse factors for a simplifying human life. Several methods are being popularly used to handle real time problems occurring from traffic congestion. This study demonstrates the capability of DL models to overcome the traffic congestion by simply allowing the vehicles through a signal depending on the length of vehicles. Our proposed method integrates a numeral of approach, intended to advance the cooperativeness of the explore operation. In this work, we develop the application to regulate the traffic by releasing better signal at desired time intervals.

KEYWORDS: Traffic, YOLO, Deep Learning.

NOTE: Without the concern of our team, please don't submit to the college. This Abstract varies based on student requirements.

Block Diagram

Specifications

HARDWARE & SOFTWARE REQUIREMENTS

HARDWARE CONFIGURATION:

· Processor - I3/Intel Processor

· RAM - 4GB (min)

· Hard Disk - 128 GB

SOFTWARE CONFIGURATION:

• Operating System : Windows 7+

• Server-side Script : Python 3.6+

• IDE : PyCharm

• Libraries Used : Pandas, Numpy, Yolo

• Framework : Flask

Learning Outcomes

LEARNING OUTCOMES:

·         Scope of Real Time Application Scenarios.

·         Objective of the project .

·         How Internet Works.

·         What is a search engine and how browser can work.

·         What type of technology versions are used .

·         Use of HTML , and CSS on UI Designs .

·         Data Parsing Front-End to Back-End.

·         Working Procedure.

·         Introduction to basic technologies used for.

·         How project works.

·         Input and Output modules .

·         Frame work use.

·         About python.

·         About CNN

·         About YOLO

·         What is Deep learning.

·         What are Deep learning algorithms.

·         How can we identify and detect the vehicles.

·         How can we collect dataset.

·         Practical exposure to

·         Hardware and software tools.

·         Solution providing for real time problems.

·         Working with team/ individual.

·         Work on Creative ideas.

Demo Video

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