Traffic Accident Prediction Model using Deep Learning

[alo7lika]

🔴 Project Title: Traffic Accident Prediction Model using Deep Learning

🔴 Aim: To develop a deep learning model that predicts the likelihood of traffic accidents based on historical data and various contributing factors, ultimately enhancing road safety.

🔴 Dataset: Historical traffic accident data, including features like weather conditions, traffic volume, time of day, and geographical location.

🔴 Approach: Implement a Recurrent Neural Network (RNN) model, specifically using Long Short-Term Memory (LSTM) networks. The model will focus on time-based patterns in the traffic accident data to predict future events based on historical data. The project will include exploratory data analysis (EDA) to understand the dataset's characteristics and distributions, ensuring that EDA includes visualizations of the relationships between features and the target variable.

📍 Follow the Guidelines to Contribute to the Project:

1. Create a separate folder named "Traffic Accident Prediction Model."

2. Inside that folder, include the following components:

   • Images: To store the required images.
   • Dataset: To store the dataset or information/source about the dataset.
   • Model: To store the deep learning model you've created using the dataset.
   • requirements.txt: This file will contain the required packages/libraries to run the project on other machines.

3. Inside the Model folder, fill out the README.md file properly, including visualizations and conclusions.

🔴🟡 Points to Note:

• Issues will be assigned on a first-come, first-served basis; 1 Issue == 1 PR.
• The "Issue Title" and "PR Title" should be the same. Include the issue number along with it.
• Follow Contributing Guidelines & Code of Conduct before you start contributing.

✅ To be Mentioned while taking the issue:

• Full Name: Alolika Bhowmik
• GitHub Profile Link: Alolika Bhowmik
• Email ID: alolikabhowmik72@gmail.com
• Participant ID (if applicable): [your_participant_id]
• Approach for this Project: Implement a deep learning model using LSTM networks to predict traffic accidents based on historical data. The project will include exploratory data analysis and visualizations to support findings.
• What is your participant role? (Mention the Open Source program): Participant in GSSOC EXT 24.

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Happy Contributing! 🚀

All the best. Enjoy your open-source journey ahead. 😎

[github-actions[bot]]

Thank you for creating this issue! We'll look into it as soon as possible. Your contributions are highly appreciated! 😊

[alo7lika]

ADD LABELS GSSOC EXT 24 AND HACKTOBERFEST. ASSIGN ME THIS WORK @abhisheks008 .THANK YOU!

[SiyaaNegi]

I'd like to work on this issue, can you assign me?

[abhisheks008]

Hi @alo7lika as this repository mainly focuses on deep learning methods over machine learning methods, you need to focus on the deep learning techniques. You can update your approach and share here. Also please share the dataset URL for this problem statement.

[alo7lika]

Hi @alo7lika as this repository mainly focuses on deep learning methods over machine learning methods, you need to focus on the deep learning techniques. Please feel free to update your approach and share it with me here. Also please share the dataset URL for this problem statement.

Hi @abhisheks008 ,

Thank you for the clarification! I will focus on implementing deep learning techniques for the Traffic Accident Prediction Model. Specifically, I plan to utilize Recurrent Neural Networks (RNNs), particularly Long Short-Term Memory (LSTM) networks, as they are well-suited for capturing time-based patterns in traffic accident data.

I utilized a synthetic dataset in this project to develop the Traffic Accident Prediction Model. This dataset was created to simulate realistic traffic accident scenarios and includes various features such as weather conditions, traffic volume, time of day, and geographical location. The synthetic nature of the dataset allows for flexibility in testing different modeling approaches without being constrained by the limitations of real-world data. This enables comprehensive exploration of the relationships between the features and the target variable, ultimately contributing to developing a robust model for predicting traffic accidents.

[alo7lika]

Hi @alo7lika as this repository mainly focuses on deep learning methods over machine learning methods, you need to focus on the deep learning techniques. Please feel free to update your approach and share it with me here. Also please share the dataset URL for this problem statement.

Hi @abhisheks008 ,

Thank you for the clarification! I will focus on implementing deep learning techniques for the Traffic Accident Prediction Model. Specifically, I plan to utilize Recurrent Neural Networks (RNNs), particularly Long Short-Term Memory (LSTM) networks, as they are well-suited for capturing time-based patterns in traffic accident data.

I utilized a synthetic dataset in this project to develop the Traffic Accident Prediction Model. This dataset was created to simulate realistic traffic accident scenarios and includes various features such as weather conditions, traffic volume, time of day, and geographical location. The synthetic nature of the dataset allows for flexibility in testing different modeling approaches without being constrained by the limitations of real-world data. This enables comprehensive exploration of the relationships between the features and the target variable, ultimately contributing to developing a robust model for predicting traffic accidents.

@abhisheks008 assign me the work. I have mentioned the details above , and also updated it

[abhisheks008]

Pretty good approach. Assigning this issue to you @alo7lika

[github-actions[bot]]

Hello @alo7lika! Your issue #905 has been closed. Thank you for your contribution!