Traffic Forecasting Model Using Advanced Machine Learning Techniques for Efficient Transportation Planning and Enhanced Urban Mobility

Project Abstract:

Background: Accurate traffic forecasting is essential for efficient transportation planning, infrastructure development, and enhanced urban mobility. Traditional methods for traffic prediction often rely on historical data and static models, which may not account for the dynamic nature of traffic patterns. With the availability of real-time traffic data, machine learning (ML) models can be employed to improve the accuracy of traffic forecasting. This project aims to develop a robust and reliable traffic forecasting model using advanced machine learning techniques to facilitate informed decision-making in transportation planning and contribute to enhanced urban mobility.

Objectives:

1. To collect, preprocess, and analyze real-time traffic data from multiple sources, such as traffic sensors, GPS data, and open data repositories.
2. To identify the most relevant features for effective traffic forecasting using feature selection techniques.
3. To implement various machine learning algorithms, including time-series forecasting, regression, ensemble methods, and deep learning, to create a high-performance traffic forecasting model.
4. To evaluate the performance of the forecasting model using appropriate metrics and validate its effectiveness in predicting traffic patterns.
5. To provide actionable insights and recommendations for transportation planners, policymakers, and urban developers based on the traffic forecasting model’s output.

Methods:

1. Data collection and preprocessing: The project will involve the collection of real-time traffic data from various sources, including traffic sensors, GPS data, and open data repositories. Data preprocessing steps, such as data cleaning, normalization, and encoding, will be performed to ensure the data is suitable for ML model training.
2. Feature selection: Techniques such as Recursive Feature Elimination (RFE), Principal Component Analysis (PCA), and correlation analysis will be used to identify the most relevant features for traffic forecasting.
3. Model development: ML algorithms, including time-series forecasting methods like ARIMA and SARIMA, regression models, ensemble methods like Random Forest, and deep learning models like LSTM, will be applied to develop the traffic forecasting model. Hyperparameter tuning and model selection will be conducted through cross-validation and grid search techniques.
4. Model evaluation: The performance of the ML models will be assessed using metrics such as Mean Absolute Error (MAE), Mean Squared Error (MSE), and R-squared.
5. Insights and recommendations: The traffic forecasting model’s output will be analyzed to derive actionable insights and recommendations for transportation planners, policymakers, and urban developers, enabling data-driven decision-making and efficient transportation planning.

Expected Outcomes: The project will result in a comprehensive traffic forecasting model capable of accurately predicting traffic patterns based on real-time data. The implementation of this model in transportation planning and decision-making processes will enable more efficient infrastructure development, optimized traffic management, and ultimately contribute to enhanced urban mobility for residents.

Keywords: Traffic forecasting, machine learning, real-time data, feature selection, data preprocessing, model evaluation, transportation planning, urban mobility, data-driven decision-making.