Road Accident Analysis for Community Awareness

6th Semester Project - 2022

Department of CE, Faculty of Engineering, UoP

Project Page: https://cepdnaclk.github.io/e17-6sp-road-accident-analyzer

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Product Owners

• Dr. Upul Jayasinghe - email Senior Lecturer - Department of CE
• Dr. Namal Karunarathna - email Senior Lecturer - Department of CE
• Dr. Samath Dharmaratne Faculty of Medicine, UoP

Scrum Marster

• Mrs. Anjalee Wanigarathne - email

Development Team

• Pubudu Bandara (E/17/027) - email, GitHub
• Dhananjaya Morais (E/17/212) - email, GitHub
• Ishara Nawarathna (E/17/219) - email, GitHub

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Introduction

Road traffic injuries are a leading cause of death and disability. In low and middle income countries, vulnerable road users are commonly involved in crashes with severe injuries. Road traffic injuries are a major public health problem globally. About 1.2 million people are killed and more than 50 million are injured due to road traffic crashes annually.More than 90% of these deaths and injuries occur in low and middle income countries (LMIC) due to rapid motorization, lack of road safety culture, poor road conditions, and lack of education on road safety. Our aim is to reduce road accidents using machine learning techniques.

Problem

The prevailing strategies are not sufficient enough to reduce the frequency and severity of road accidents.

Motivations

• The Daily death toll from road accidents
• Inefficiency of police traffic management
• Difficulty in handling the casualties in hospitals.

Objectives

• Reduce Road Accidents
• To identify the main factors associated with a road accident (accident data analysis).

Requirements

Functional Requirements

• Data Visualization
• Obtain Main Factors of the Road Accidents
• Ability to upload new Datasets
• Predict the future Accidents

Non-Functional Requirements

• Performance of the Prediction Model
• Improved UI / UX
• Security
• Scalability

Solution

An online system to provide more accurate information on road accidents (both analysis and predictions). We are going to build a real time web application which can be used by the public without logging to the system.

Solution Architecture

High Level System Organization

Data Flow of the System

Technologies used

• Motor: Asynchronous Python driver for MongoDB
• FatsAPI: Web framework for developing RESTful APIs in Python
• MongoDB Atlas: Cloud database service
• Asyncio: Library to write concurrent code, often a perfect fit for IO-bound and high-level structured network code
• React: JS library for building UIs
• Pandas: Data analysis and manipulation tool
• TensorFlow: Software library for ML and AI
• Keras: Software library that provides a Python interface for ANNs

Use Case diagram

Machine Learning Model

Workflow of the Machine Learning Process

• Data Collection - US Accidents Dataset(2016 - 2021) from Kaggle
• Data Preprocessing
• Develop the machine learning model
• Model Fitting
• Model Evaluation
• Model Deployment

Process

Workplan

Future work

Robust Data Handling

• Datasets with different attributes
• Resulting in a more accurate and up-to-date mode

Realtime Modeling

• Users can analyze and predict on their own datasets

Current Progress

• Analyzing the Dataset
• Create API endpoint

• Develop Homepage