Bike Sharing Demand Analysis Using Advanced Machine Learning Techniques for Enhanced Transportation Planning and Bike Sharing Service Optimization

Project Abstract:

Background: Bike sharing systems are becoming increasingly popular in urban areas as an eco-friendly and convenient mode of transportation. Accurate demand prediction models for bike sharing systems can provide valuable insights for transportation planning, resource allocation, and service optimization. The Bike Sharing dataset contains information on bike sharing usage patterns along with various environmental and temporal factors, offering an opportunity to apply machine learning (ML) techniques for demand prediction analysis. This project aims to develop a robust and reliable demand prediction model using advanced machine learning techniques, leveraging the Bike Sharing dataset to enhance transportation planning and bike sharing service optimization.

Objectives:

1. To preprocess and analyze the Bike Sharing dataset, which includes data on bike sharing usage patterns and various environmental and temporal factors.
2. To identify the most relevant features for effective demand prediction using feature selection techniques.
3. To implement various machine learning algorithms, including regression, ensemble methods, and deep learning, to create a high-performance demand prediction model.
4. To evaluate the performance of the prediction model using appropriate metrics and validate its effectiveness in predicting bike sharing demand.
5. To provide insights and recommendations for transportation planning, resource allocation, and bike sharing service optimization based on the Bike Sharing demand analysis.

Methods:

1. Data preprocessing: Data preprocessing steps, such as data cleaning, normalization, encoding, and handling missing values, will be performed to ensure the Bike Sharing dataset 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 demand prediction.
3. Model development: ML algorithms, including Linear Regression, Ridge Regression, Lasso Regression, Decision Trees, Random Forest, Gradient Boosting, and deep learning models like Neural Networks and LSTM, will be applied to develop the demand prediction 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), Root Mean Squared Error (RMSE), and R-squared.
5. Insights and recommendations: The Bike Sharing demand analysis will be used to derive insights and recommendations for transportation planning, resource allocation, and bike sharing service optimization.

Expected Outcomes: The project will result in a comprehensive demand prediction model capable of accurately predicting bike sharing demand. The implementation of this model in transportation planning and bike sharing service optimization will enable more informed decisions, leading to enhanced resource allocation, improved customer satisfaction, and more effective urban planning.

Keywords: Bike Sharing dataset, demand prediction, machine learning, feature selection, data preprocessing, regression, ensemble methods, deep learning, transportation planning, resource allocation, service optimization.