Biotechnological Insights into Travel Mode Selection Behavior: A Machine Learning Analysis in the Context of Big Data

Abstract

In the context of biotechnology and its synergy with emerging technologies such as the Internet of Things (IoT), big data, and artificial intelligence, the analysis of data signals, information content, individual interests, hobbies, and preferred data-driven methods has long been a focal point in the realm of transportation. Leveraging advancements in big data processing technology and machine learning algorithms, this study delves into the vast repository of travel mode data collected by various traffic travel detectors. The convergence of biotechnology with data science presents a unique opportunity to decode the intricacies of human behavior concerning travel mode selection. By harnessing the power of big data analytics and machine learning, this research endeavors to uncover patterns and insights related to individuals' travel mode preferences. It seeks to nurture independent and innovative thinking capabilities within the transportation sector. This study represents a pioneering exploration at the intersection of biotechnology, transportation, and data science. It signifies the potential for biotechnological applications to revolutionize our understanding of travel behavior and inform more sustainable, efficient, and personalized transportation solutions. Ultimately, the amalgamation of biotechnology, big data, and machine learning stands to shape the future of transportation in profound ways, enhancing our ability to make data-driven decisions that benefit individuals and society at large.