{"id":24847650,"url":"https://github.com/shhiivvaam/traffic_prediction","last_synced_at":"2026-04-12T00:41:46.656Z","repository":{"id":222960524,"uuid":"758849803","full_name":"shhiivvaam/Traffic_Prediction","owner":"shhiivvaam","description":"This project explores the application of advanced neural network architectures, including Multi-Layer Perceptron (MLP), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU), to forecast traffic volume. 👍👍✅ ","archived":false,"fork":false,"pushed_at":"2024-02-17T09:28:56.000Z","size":568,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-01-31T11:35:47.276Z","etag":null,"topics":["conv1d","conv2d","keras","lstm","matplotlib","numpy","pandas","seaborn","sklearn","statsmodels","tensorflow"],"latest_commit_sha":null,"homepage":"https://github.com/shhiivvaam/SME_Traffic_Prediction","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/shhiivvaam.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null}},"created_at":"2024-02-17T09:04:56.000Z","updated_at":"2024-03-03T05:04:04.000Z","dependencies_parsed_at":"2024-02-17T10:22:17.312Z","dependency_job_id":"239ca5df-09f2-4241-af42-c4c0a8f53b39","html_url":"https://github.com/shhiivvaam/Traffic_Prediction","commit_stats":null,"previous_names":["shhiivvaam/sme_traffic_prediction","shhiivvaam/traffic_prediction"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/shhiivvaam%2FTraffic_Prediction","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/shhiivvaam%2FTraffic_Prediction/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/shhiivvaam%2FTraffic_Prediction/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/shhiivvaam%2FTraffic_Prediction/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/shhiivvaam","download_url":"https://codeload.github.com/shhiivvaam/Traffic_Prediction/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":245632351,"owners_count":20647194,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["conv1d","conv2d","keras","lstm","matplotlib","numpy","pandas","seaborn","sklearn","statsmodels","tensorflow"],"created_at":"2025-01-31T11:30:47.902Z","updated_at":"2026-04-12T00:41:46.605Z","avatar_url":"https://github.com/shhiivvaam.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"## || Enhanced Traffic Volume Prediction Using Neural Networks || ✅\n\nWelcome to our repository dedicated to advancing traffic prediction through state-of-the-art neural network architectures, including the Multi-Layer Perceptron (MLP), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU). Our models are meticulously trained and evaluated on extensive historical traffic data sourced from diverse junctions.\n\n\n### Dataset Details\n\nOur dataset, meticulously curated from [mention source or collection methodology], comprises a wealth of historical traffic data spanning various junctions over specific time frames. Each record encapsulates critical attributes like DateTime, Vehicle Count, and Junction ID. Before model training commences, we conduct essential preprocessing steps, including:\n\n* **Feature Engineering:** Unveiling pertinent features like year, month, day, hour, and day of the week from DateTime.\n* **Normalization:** Standardizing numerical features to ensure uniform model training.\n* **Data Segmentation:** Partitioning the dataset into training and testing subsets to facilitate rigorous model evaluation.\n\n\n### Getting Started\n\nTo kickstart the project effortlessly:\n\n1. **Clone the Repository:**\n   \u003cpre\u003e\u003cdiv class=\"dark bg-gray-950 rounded-md\"\u003e\u003cdiv class=\"flex items-center relative text-token-text-secondary bg-token-main-surface-secondary px-4 py-2 text-xs font-sans justify-between rounded-t-md\"\u003e\u003cspan\u003e\u003c/span\u003e\u003cspan class=\"\" data-state=\"closed\"\u003e\u003cbutton class=\"flex gap-1 items-center\"\u003e\u003csvg width=\"24\" height=\"24\" viewBox=\"0 0 24 24\" fill=\"none\" xmlns=\"http://www.w3.org/2000/svg\" class=\"icon-sm\"\u003e\u003cpath fill-rule=\"evenodd\" clip-rule=\"evenodd\" d=\"M12 4C10.8954 4 10 4.89543 10 6H14C14 4.89543 13.1046 4 12 4ZM8.53513 4C9.22675 2.8044 10.5194 2 12 2C13.4806 2 14.7733 2.8044 15.4649 4H17C18.6569 4 20 5.34315 20 7V19C20 20.6569 18.6569 22 17 22H7C5.34315 22 4 20.6569 4 19V7C4 5.34315 5.34315 4 7 4H8.53513ZM8 6H7C6.44772 6 6 6.44772 6 7V19C6 19.5523 6.44772 20 7 20H17C17.5523 20 18 19.5523 18 19V7C18 6.44772 17.5523 6 17 6H16C16 7.10457 15.1046 8 14 8H10C8.89543 8 8 7.10457 8 6Z\" fill=\"currentColor\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/button\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv class=\"p-4 overflow-y-auto\"\u003e\u003ccode class=\"!whitespace-pre hljs language-bash\"\u003egit clone https://github.com/yourusername/traffic-prediction.git\n   \u003c/code\u003e\u003c/div\u003e\u003c/div\u003e\u003c/pre\u003e\n2. **Install Dependencies:**\n   \u003cpre\u003e\u003cdiv class=\"dark bg-gray-950 rounded-md\"\u003e\u003cdiv class=\"flex items-center relative text-token-text-secondary bg-token-main-surface-secondary px-4 py-2 text-xs font-sans justify-between rounded-t-md\"\u003e\u003cspan\u003e\u003c/span\u003e\u003cspan class=\"\" data-state=\"closed\"\u003e\u003cbutton class=\"flex gap-1 items-center\"\u003e\u003csvg width=\"24\" height=\"24\" viewBox=\"0 0 24 24\" fill=\"none\" xmlns=\"http://www.w3.org/2000/svg\" class=\"icon-sm\"\u003e\u003cpath fill-rule=\"evenodd\" clip-rule=\"evenodd\" d=\"M12 4C10.8954 4 10 4.89543 10 6H14C14 4.89543 13.1046 4 12 4ZM8.53513 4C9.22675 2.8044 10.5194 2 12 2C13.4806 2 14.7733 2.8044 15.4649 4H17C18.6569 4 20 5.34315 20 7V19C20 20.6569 18.6569 22 17 22H7C5.34315 22 4 20.6569 4 19V7C4 5.34315 5.34315 4 7 4H8.53513ZM8 6H7C6.44772 6 6 6.44772 6 7V19C6 19.5523 6.44772 20 7 20H17C17.5523 20 18 19.5523 18 19V7C18 6.44772 17.5523 6 17 6H16C16 7.10457 15.1046 8 14 8H10C8.89543 8 8 7.10457 8 6Z\" fill=\"currentColor\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/button\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv class=\"p-4 overflow-y-auto\"\u003e\u003ccode class=\"!whitespace-pre hljs language-bash\"\u003epip install -r requirements.txt\n   \u003c/code\u003e\u003c/div\u003e\u003c/div\u003e\u003c/pre\u003e\n3. **Execute the Main Script:**\n   \u003cpre\u003e\u003cdiv class=\"dark bg-gray-950 rounded-md\"\u003e\u003cdiv class=\"flex items-center relative text-token-text-secondary bg-token-main-surface-secondary px-4 py-2 text-xs font-sans justify-between rounded-t-md\"\u003e\u003cspan\u003e\u003c/span\u003e\u003cspan class=\"\" data-state=\"closed\"\u003e\u003cbutton class=\"flex gap-1 items-center\"\u003e\u003csvg width=\"24\" height=\"24\" viewBox=\"0 0 24 24\" fill=\"none\" xmlns=\"http://www.w3.org/2000/svg\" class=\"icon-sm\"\u003e\u003cpath fill-rule=\"evenodd\" clip-rule=\"evenodd\" d=\"M12 4C10.8954 4 10 4.89543 10 6H14C14 4.89543 13.1046 4 12 4ZM8.53513 4C9.22675 2.8044 10.5194 2 12 2C13.4806 2 14.7733 2.8044 15.4649 4H17C18.6569 4 20 5.34315 20 7V19C20 20.6569 18.6569 22 17 22H7C5.34315 22 4 20.6569 4 19V7C4 5.34315 5.34315 4 7 4H8.53513ZM8 6H7C6.44772 6 6 6.44772 6 7V19C6 19.5523 6.44772 20 7 20H17C17.5523 20 18 19.5523 18 19V7C18 6.44772 17.5523 6 17 6H16C16 7.10457 15.1046 8 14 8H10C8.89543 8 8 7.10457 8 6Z\" fill=\"currentColor\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/button\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv class=\"p-4 overflow-y-auto\"\u003e\u003ccode class=\"!whitespace-pre hljs language-bash\"\u003epython traffic_prediction.py\n   \u003c/code\u003e\u003c/div\u003e\u003c/div\u003e\u003c/pre\u003e\n\n### Model Architectures\n\n#### MLP (Multi-Layer Perceptron)\n\nThe MLP model is a formidable feedforward neural network, meticulously engineered to discern intricate relationships between input features and traffic volume. Key features include:\n\n* Dense layers empowered by ReLU activation functions.\n* Dropout layers for effective regularization and prevention of overfitting.\n* Stochastic Gradient Descent (SGD) optimizer with a dynamic learning rate schedule.\n\n#### GRU (Gated Recurrent Unit)\n\nGRU, a sophisticated variant of recurrent neural networks, excels in capturing temporal dependencies within sequential data. Our GRU model boasts:\n\n* Multiple GRU layers adept at uncovering temporal patterns in traffic data.\n* Strategically placed dropout layers to bolster generalization.\n* SGD optimizer with a meticulously crafted learning rate schedule.\n\n#### LSTM (Long Short-Term Memory)\n\nRenowned for its prowess in capturing long-term dependencies, LSTM stands tall among recurrent neural networks. Our LSTM model boasts:\n\n* LSTM layers furnished with memory cells to retain and update information over time.\n* Dropout layers for preemptive overfitting mitigation.\n* SGD optimizer fine-tuned with a dynamic learning rate schedule for efficient convergence.\n\n### Model Evaluation\n\nWe meticulously evaluate model performance using industry-standard metrics such as Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE). These metrics provide invaluable insights into the accuracy and robustness of our models. Additionally, we generate comparative plots to visually juxtapose predicted traffic volumes against actual values, facilitating qualitative assessment.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fshhiivvaam%2Ftraffic_prediction","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fshhiivvaam%2Ftraffic_prediction","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fshhiivvaam%2Ftraffic_prediction/lists"}