Detecting and Mitigating Traffic Accidents using Machine Learning and Traffic Data

Local Chapter Gaborone, Botswana Chapter

Coordinated byBotswana ,

Ditiro Wilson Ramapte

Status: Completed

Project Duration: 07 May 2023 - 09 Jul 2023

Open Source resources available from this project

Github Repository

Project background.

Traffic accidents are a global concern, causing significant loss of lives and financial damages. According to the World Health Organization (WHO), traffic accidents lead to almost 1.3 million preventable deaths and an estimated 50 million injuries annually, making it the leading cause of death for children and young people worldwide. Furthermore, it is expected that these numbers will continue to rise over the next decade, hampering sustainable development, particularly in low and middle-income countries.

In Botswana, traffic accidents are one of the leading causes of death and injury, with many accidents attributed to human error, speeding, and poor road infrastructure. According to WHO, Botswana’s fatality rate of 20.1 per 100,000 people is higher than the global average of 17.1. Furthermore, with a population of only 2 to 2.5 million, Botswana has recorded 184,548 road accidents between 2011 and 2020.

The problem.

Traffic accidents are a major concern in Botswana, leading to the loss of many lives and significant financial losses. Here are some of the problems

– Economic losses: Road accidents in Botswana lead to significant economic losses, both for individuals and the government. Many individuals incur high medical bills and lose their sources of income due to injuries sustained in accidents. Motor Vehicle Accident Fund (MVA) spending over P40 million (US$ 3.8 million) annually on medical bills and claims due to accidents.
– Impact on mental health: Road accidents can have long-lasting psychological effects on individuals and families. Survivors of accidents may experience anxiety, depression, and post-traumatic stress disorder (PTSD), while families of victims may suffer from grief and emotional distress. The impact on mental health can have far-reaching consequences, affecting not only individuals but also their families and communities.

Project goals.

AI has proven to be a powerful tool for solving complex problems in a faster and more accurate way than ever before. The field of transportation is no exception, and machine learning algorithms have been shown to be effective in predicting and detecting potential traffic accidents.In this project, we aim to leverage this technological advancement to help mitigate the problem of traffic accidents in Botswana.That is why for 4 weeks our goal will be to develop a machine-learning model that can predict the likelihood of accidents occurring in different regions and areas in Botswana. This will include working on the following:- Collect and analyze traffic data to identify patterns and trends related to traffic accidents. - Analyze Botswana Police Service traffic data to identify primary causes of traffic accidents - Carry out data preprocessing and extensive data analysis into the cause of accidents. - Develop a traditional machine learning or deep learning model to predict traffic accidents - Carry out inference with the trained model using test data - Develop some suggestions on how traffic accidents could be mitigated based on data from the provided datasets - Build a dashboard to visualize our results

Project plan.

  • Week 1

    Task-0: Literature Review,

  • Week 2

    Task1:Data collection,
    Task2:Data pre-processing,

    Task 4: The models research phase

  • Week 3

    Data pre-processing,

  • Week 4

    Task 3/4: Implementing the models
    Task 5: preparation for data visualization, Implementing the models

  • Week 5

    Data visualization and Deployment

Learning outcomes.

The project aims to provide participants with practical experience in:
– Enhancing problem-solving, critical thinking, and analytical skills.
– Developing technical skills in data collection, analysis, and machine learning. – Improving communication, teamwork, and project management skills.
– Gaining practical experience in developing and implementing a real-world solution.
– Understanding the ethical and social implications of using technology to address societal issues.

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