Traffic Prediction using Machine Learning

Traffic Prediction using Machine Learning

IEEE BASE PAEPR ABSTRACT:

Traffexplainer: A Framework Toward GNN-Based Interpretable Traffic Prediction

IEEE BASE PAPER ABSTRACT:

With the increasing traffic congestion problems in metropolises, traffic prediction plays an essential role in intelligent traffic systems. Notably, various deep learning models, especially graph neural networks (GNNs), achieve state-of-the-art performance in traffic prediction tasks but still lack interpretability. To interpret the critical information abstracted by traffic prediction models, we proposed a flexible framework termed Traffexplainer toward GNN-based interpretable traffic prediction. Traffexplainer is applicable to a wide range of GNNs without making any modifications to the original model structure. The framework consists of the GNN-based traffic prediction model and the perturbation-based hierarchical interpretation generator. Specifically, the hierarchical spatial mask and temporal mask are introduced to perturb the prediction model by modulating the values of input data. Then the prediction losses are backward propagated to the masks, which can identify the most critical features for traffic prediction, and further improve the prediction performance. We deploy the framework with five representative GNN-based traffic prediction models and analyze their prediction and interpretation performance on three real-world traffic flow datasets. The experiment results demonstrate that our framework can generate effective and faithful interpretations for GNN-based traffic prediction models, and also improve the prediction performance.

PROJECT OUTPUT VIDEO:

ALGORITHM / MODEL USED:

Gradient Boosting Regressor, Random Forest Regressor.

OUR PROPOSED PROJECT ABSTRACT:

The rapid growth in urban vehicular movement has created a critical need for intelligent traffic management systems that can efficiently predict and control traffic flow. The project titled “Traffic Prediction using Machine Learning” aims to forecast traffic volume based on real-time environmental and temporal factors, thereby assisting in optimizing traffic operations and reducing congestion. The system is developed using Python as the primary programming language, with HTML, CSS, and JavaScript for the front end and Flask as the web framework to integrate model functionalities within a user-friendly interface.

The dataset employed in this research consists of 48,204 records, encompassing key features such as traffic volume, holiday, temperature (temp), rain, snow, clouds, weather, weather description, date & time. These features collectively represent environmental conditions, temporal attributes, and meteorological parameters influencing traffic flow.

The proposed system utilizes two regression-based machine learning algorithms: Gradient Boosting Regressor and Random Forest Regressor to analyze and predict hourly traffic volume. Gradient Boosting Regressor is an advanced ensemble technique that builds models sequentially, with each new model correcting errors from previous ones. Random Forest Regressor is a parallel ensemble method that creates multiple decision trees to improve prediction accuracy and reduce overfitting

The Gradient Boosting Regressor achieved a training mean absolute error (MAE) of 0.326 and a testing MAE of 0.413, demonstrating its ability to capture complex non-linear dependencies. In comparison, the Random Forest Regressor achieved a training MAE of 0.131 and a testing MAE of 0.3379, exhibiting superior performance and generalization capability with reduced prediction error.

The system’s web-based interface allows users to input environmental and temporal parameters to predict the expected traffic volume dynamically. Through this integration of advanced regression models and a responsive web interface, the project effectively demonstrates the potential of machine learning in facilitating data-driven traffic forecasting, enabling smarter urban mobility solutions and contributing to the foundation of intelligent transportation systems.

SYSTEM REQUIREMENTS:

HARDWARE REQUIREMENTS: 

• System : Pentium i3 Processor.
• Hard Disk : 20 GB.
• Monitor : 15’’ LED.
• Input Devices : Keyboard, Mouse.
• Ram : 8 GB.

SOFTWARE REQUIREMENTS: 

• Operating System : Windows 10 / 11.
• Coding Language : Python 3.12.0.
• Web Framework : Flask.
• Frontend : HTML, CSS, JavaScript.

REFERENCE:

Lingbai Kong, Hanchen Yang, Wengen Li, Yichao Zhang, Jihong Guan, and Shuigeng Zhou, “Traffexplainer: A Framework Toward GNN-Based Interpretable Traffic Prediction”, IEEE TRANSACTIONS ON ARTIFICIAL INTELLIGENCE, VOL. 6, NO. 3, MARCH 2025.

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Frequently Asked Questions (FAQ’s) and Answers

1. What is the main objective of this project?

The primary objective of this project is to predict hourly traffic volume using environmental and temporal factors such as weather conditions, temperature, precipitation, and time. The system leverages machine learning algorithms to assist in traffic management, congestion control, and decision-making for smart city applications.

2. Which technologies are used in this project?

• Programming Language: Python • Frontend: HTML, CSS, JavaScript • Backend Framework: Flask • Machine Learning Libraries: Scikit-learn, Pandas, NumPy, Matplotlib These technologies work together to build, train, and deploy predictive models accessible through a web-based interface.

3. What algorithms or models are implemented in the project?

The project uses two regression-based machine learning algorithms: 1. Gradient Boosting Regressor – focuses on sequential learning and minimizes prediction errors iteratively. 2. Random Forest Regressor – uses multiple decision trees to improve prediction stability and accuracy.

4. What dataset is used for this project?

The project uses the Metro Interstate Traffic Volume Dataset, which contains 48,204 records of traffic and environmental data. It includes attributes like: holiday, temperature (temp), rain_1h, snow_1h, clouds_all, weather_main, weather_description, and date_time, from which year, month, and day were extracted.

5. What are the key features of the dataset?

• holiday: Categorical variable indicating U.S. national or regional holidays. • temp: Average temperature in Kelvin. • rain_1h: Amount of rainfall in millimeters. • snow_1h: Amount of snowfall in millimeters. • clouds_all: Cloud coverage percentage. • weather_main / weather_description: Textual weather descriptions. • date_time: Timestamp of data collection in local CST. • traffic_volume: The target variable indicating hourly I-94 westbound traffic volume.

6. What performance results were achieved by the models?

• Gradient Boosting Regressor: o Train MAE: 0.326 o Test MAE: 0.413 • Random Forest Regressor: o Train MAE: 0.131 o Test MAE: 0.3379 These results show that the Random Forest Regressor provided higher prediction accuracy and better generalization performance.

7. What is Mean Absolute Error (MAE) and why is it used here?

MAE measures the average difference between the predicted and actual values. It is used to evaluate regression model performance: lower MAE values indicate more accurate predictions. In this project, MAE effectively quantifies how close the predicted traffic volume is to the actual recorded traffic.

8. How does the system work?

1. The user enters parameters like temperature, rainfall, weather type, date, and time through a web interface. 2. The input is sent to the Flask backend, where it is pre-processed and passed to the selected model. 3. The trained machine learning model predicts the expected traffic volume. 4. The result is displayed on the web page instantly. This enables real-time traffic prediction in an interactive and user-friendly way.

9. Why were Gradient Boosting and Random Forest chosen for this project?

Both algorithms are powerful ensemble-based regressors capable of handling large datasets and capturing non-linear relationships between variables. • Gradient Boosting Regressor is known for fine-tuning prediction errors. • Random Forest Regressor provides high accuracy and reduces overfitting through multiple decision trees.

10. How is this project different from the existing systems?

Unlike earlier GNN-based frameworks like Traffexplainer, which are complex and computationally expensive, this system provides a lightweight, interpretable, and web-deployable solution. It focuses on real-time usability and simplicity while maintaining reliable accuracy.

11. Who can use this system?

• Traffic authorities for congestion management. • Urban planners for analyzing traffic trends. • Commuters for anticipating traffic density. • Researchers and students for studying machine learning-based traffic prediction.

12. What are the practical applications of this system?

• Real-time traffic volume forecasting • Congestion management and route optimization • Scheduling of public transport and logistics • Data analysis for smart city infrastructure planning