Data Analysis and Applied Machine Learning Techniques for Smart Parking
Faculty Advisor Name
Dr. Samy Eltawab; Dr. Prajacta Belsare
Department
Department of Computer Science
Description
Abstract:
The fourth industrial revolution has catalyzed the evolution of urban landscapes into smart cities, ushering in intelligent transportation systems that heavily rely on large-scale data-driven models. Among these models, smart parking stands out as a focal point, drawing significant attention from both researchers and industry professionals. However, the diversity of scenarios and environments demands tailored approaches due to the multitude of factors influencing people's schedules and behavior.
This research project focuses on investigating traffic assignments within the context of parking prediction in a mid-size university environment featuring five strategically located parking garages around the campus. The primary objective is to leverage various machine-learning techniques to accurately predict parking demand, thereby optimizing the utilization of available parking spaces. The methodology involves the systematic collection of data from different parking lots, with a subset of this data earmarked for training machine learning algorithms to make predictions.
The core of the investigation lies in evaluating the performance of the trained algorithms against data that was not used in the training process. This comparative analysis serves as a robust measure of the models' effectiveness and generalizability, providing insights into their real-world applicability. Recognizing potential challenges, the project addresses the possibility of missing data due to network or server issues. To mitigate this, fitting algorithms are employed to interpolate missing data before training the machine learning models, ensuring the reliability of the predictions.
The anticipated outcomes of this project extend beyond the academic realm, aiming to streamline traffic assignments in university settings and alleviate the continuous challenge of finding parking spaces. The practical implications of successfully implementing these predictive models are substantial, promising significant time and fuel consumption savings for students and drivers. The elimination of the need to circle parking lots for extended periods translates into a more efficient and seamless parking experience, contributing to a more sustainable and user-friendly transportation system within the university environment.
In tandem with technological advancements, the project underscores the paramount importance of privacy and security in handling the collected data and machine learning results. A robust framework will be established to safeguard against unauthorized access and manipulation, ensuring that the personal information of individuals and the integrity of the predictive models remain intact. This dual emphasis on efficiency and security positions the project as a holistic endeavor that not only enhances operational aspects but also prioritizes the ethical considerations inherent in handling sensitive data within smart city initiatives.
The ultimate goal of the project is to inform and recommend users about parking options before reaching their destination. This recommendation system will take into consideration various factors, including real-time traffic conditions and events such as basketball games, football games, or other university events that could impact parking spot availability. The project aims to optimizing the utilization of parking spaces and further contributing to the efficiency of the university's transportation system.
Data Analysis and Applied Machine Learning Techniques for Smart Parking
Abstract:
The fourth industrial revolution has catalyzed the evolution of urban landscapes into smart cities, ushering in intelligent transportation systems that heavily rely on large-scale data-driven models. Among these models, smart parking stands out as a focal point, drawing significant attention from both researchers and industry professionals. However, the diversity of scenarios and environments demands tailored approaches due to the multitude of factors influencing people's schedules and behavior.
This research project focuses on investigating traffic assignments within the context of parking prediction in a mid-size university environment featuring five strategically located parking garages around the campus. The primary objective is to leverage various machine-learning techniques to accurately predict parking demand, thereby optimizing the utilization of available parking spaces. The methodology involves the systematic collection of data from different parking lots, with a subset of this data earmarked for training machine learning algorithms to make predictions.
The core of the investigation lies in evaluating the performance of the trained algorithms against data that was not used in the training process. This comparative analysis serves as a robust measure of the models' effectiveness and generalizability, providing insights into their real-world applicability. Recognizing potential challenges, the project addresses the possibility of missing data due to network or server issues. To mitigate this, fitting algorithms are employed to interpolate missing data before training the machine learning models, ensuring the reliability of the predictions.
The anticipated outcomes of this project extend beyond the academic realm, aiming to streamline traffic assignments in university settings and alleviate the continuous challenge of finding parking spaces. The practical implications of successfully implementing these predictive models are substantial, promising significant time and fuel consumption savings for students and drivers. The elimination of the need to circle parking lots for extended periods translates into a more efficient and seamless parking experience, contributing to a more sustainable and user-friendly transportation system within the university environment.
In tandem with technological advancements, the project underscores the paramount importance of privacy and security in handling the collected data and machine learning results. A robust framework will be established to safeguard against unauthorized access and manipulation, ensuring that the personal information of individuals and the integrity of the predictive models remain intact. This dual emphasis on efficiency and security positions the project as a holistic endeavor that not only enhances operational aspects but also prioritizes the ethical considerations inherent in handling sensitive data within smart city initiatives.
The ultimate goal of the project is to inform and recommend users about parking options before reaching their destination. This recommendation system will take into consideration various factors, including real-time traffic conditions and events such as basketball games, football games, or other university events that could impact parking spot availability. The project aims to optimizing the utilization of parking spaces and further contributing to the efficiency of the university's transportation system.
