The rapid advancement of Artificial Intelligence (AI) has led to a profound transformation in the transportation industry, particularly in driving the shift toward carbon neutrality and electrification. AI has proven to be a key enabler in formulating innovative strategies for optimizing electric vehicle (EV) fleets, thus advancing transportation services. While extensive research has been conducted on AI’s role in transportation innovation, there remains a significant gap in empirical studies focusing on optimizing the charging behavior of operational EV fleets, particularly within ride-hailing services. The rise of ride-hailing services has revolutionized the transportation landscape, and their transition to EV fleets presents a major opportunity. The integration of AI to optimize the operations of these EV ride-hailing fleets could substantially help achieve the dual objectives of reducing charging costs and simultaneously lowering carbon emissions. Therefore, this research develops a Neural Network (NN) trained with the Adaptive Moment Estimation (Adam) algorithm, based on 2.14 million charging events. The goal is to analyze current charging behaviors and evaluate the impact of key variables on costs and emissions, providing data-driven insights for potential improvements, thus addressing a critical research gap. The novelty of this study lies in its novel combination of deep learning algorithms with large-scale real-world charging data, proposing a new method for optimizing EV ride-hailing charging behavior, and providing practical solutions for promoting electric vehicle adoption and achieving low-carbon transportation.

From the perspective of the complex system, university ranking is a complex system that involves multiagent actors, which evolve over time. Yet, current major university rankings fail to reflect the system dynamics of the university innovation system. In this paper, we apply the complex system model in the field of the university innovation system in the context of university ranking in the countries along the Belt and Road, which is a long‐term overlooked field. We introduce a new method of university ranking based on the “winning and losing” relationship to measure the relative competitiveness between universities. This paper contributes to complex system research, the Belt and Road research, and the university ranking arena.

There is a great controversy between academic talent mobility and academic output. This paper obtains the list of mobile academic talents (experimental group) from NSF. It matches them into the control group list according to the indicators of an academic institution, department, research direction, entry time, age, and NSF fund obtained in the near year. It carries out a seven-year follow-up survey on the quantity and quality of papers published by the experimental and control groups, using the “quasi-experimental research” method for the first time. The results show that, on the one hand, the academic output of Chinese academic talents is highly stable. Mobility is not the critical variable affecting academic output, indicating that academic output is more related to talents' research habits and research ability, which shows the highly stable academic quality of scholars; on the other hand, the study finds that scholars returning from overseas manifest the stability of academic output whose number and quality of academic papers remain highly stable even if the mobility happens again. This study reveals the objective relationship between academic talent mobility and academic output. It has significant reference value for China in the next stage of attracting talent from the international academic labor market.