Complex social systems are driven by human will and actions, with numerous typical complex systems directly or indirectly related to human interactions. With the increasing accumulation of electronic footprints and big data, exploring the complexity of social security emerging from human interactions and endogenous forces has become paramount. By investigating the endogenous drivers hidden behind many complex socio-economic phenomena, this research explores the causes of large-scale financial market disruptions that traditional economics cannot effectively explain. Through quantitative statistics of a vast number of individual and group behaviors, studying the intrinsic dynamic complexity manifested in time and space, this research aims to unveil the statistical regularities hidden in intelligent transportation systems. A deep concern for the intrinsic dynamics of human behavior within these complex social systems forms the core motivation behind this work. Leveraging the concepts of complex systems and metaphors to represent the inherent properties of social systems, this book attempts to characterize the evolution of social systems through the lens of human behavior endogenous forces using big data technology. It examines the hidden endogenous forces within complex social systems from the perspectives of public safety, intelligent transportation, and behavioral economics. Based on the researched issues, fundamental assumptions are proposed, theoretical models are established to explore the mechanisms underlying these regularities and their potential dynamical impacts.