Improving Green Transportation Management by Intelligent Control and Legal Reasoning
Abstract
There have been ongoing debates about green transportation in which rapid urbanization has created challenges for policy makers and individuals to manage, regulate, and adapt in recent decades. Hence, a comprehensive framework of the intelligent control system that can help determine the right legal reasoning that can provide sustainability and accountability is the need of the hour. This study investigates practical experiences of transport authorities and the extent to which these experiences help them optimize and transform. We developed a decision support model, named Green Trans, by using the time series and SEM-regression approach, where we analyzed the dynamics, and further compared the effects of intelligent control with the legal reasoning on the performance of transportation management. For the proposed framework, the traffic data reported during the observation period ranging from 2015 to 2024 (a dataset of 120 monthly observations is included) is analyzed and a total of 42 variables were depicted more relevant to the selected model. Standard regression analyses were performed to detect causal relationships, mediating effects, and moderating influences. By identifying three categories of (dis)agreement with green regulations: those demonstrated by commuters, regulators, and operators, this study expands the understanding of transportation governance beyond its focus on compliance and enforcement and highlights the environmental and economic implications of sustainable mobility. Findings argue that inconsistent policy norms, coupled with environmental concerns and the regulatory power of the state and the judiciary, continue to shape the institutional behavior of stakeholders and push some actors to shift from resistance into adaptation and innovation from obligation. Estimation of the predictive value in advance of time series analyses for policy measures could improve the policy acceptance rate. Our improved forecasting rate for these indicators, particularly those with a strong legal basis, may present opportunities for appropriate policy interventions.