Lighting systems in intelligent transportation system (ITS) can play a crucial role in promoting sustainability through various measures, including enhanced driving safety, energy efficiency and environmental protection. This letter presents a framework for integrating sustainable development goals into ITS lighting systems. We first explore the necessity of headlights in future traffic systems, particularly in the context of autonomous vehicles (AVs). Moreover, we discuss active safety technologies, such as night perception enhancement and collaborative lighting in connected vehicles, which contribute to enhancing safety during nighttime driving. Next, the letter analyzes the impact of vehicle-to-infrastructure (V2I) communication on intelligent street light systems. We propose a novel street light perception enhancement framework based on V2I, along with an enhanced lighting scheme for faulty vehicles. Lastly, the letter addresses the importance of energy conservation in future networked street lighting system. Several energy-saving schemes, including demand-based lighting and time-of-day optimization, are discussed, emphasizing the need for efficient energy management and sustainable practices in lighting infrastructure. As a summary of a part of Distributed/Decentralized Hybrid Workshop on Sustainability for Transportation and Logistics (DHW-STL), the letter aims to contribute to the development of sustainable lighting systems.

The concept of green supply chain management (GSCM) describes how to reduce the negative impact of the supply chain on the environment while balancing the economic and social benefits of a company being in the supply chain. Selecting the optimal multi-dimensional GSCM scheme, a typical multi-criteria decision-making (MCDM) problem, is a crucial step in implementing the GSCM concept. Therefore, this paper constructs a multi-dimensional GSCM index system for the comprehensive analysis of the important influencing factors of GSCM. Then, cross-entropy combining the interval type-2 trapezoidal fuzzy set (IT2TFS) is adopted to determine the weight distribution of GSCM indices, and a hybrid MCDM method integrating the IT2TFS prospect–regret method is proposed to analyze the psychological behaviors of decision makers who are selecting the best GSCM scheme. Moreover, the case study, comparative analysis, and sensitivity analysis are presented to verify the effectiveness and reasonableness of the proposed MCDM method. The results affirm the validity of the proposed MCDM method, with A₄ identified as the optimal GSCM scheme, demonstrating its effectiveness and applicability in MCDM problems.

Lighting systems in intelligent transportation system (ITS) can play a crucial role in promoting sustainability through various measures, including enhanced driving safety, energy efficiency and environmental protection. This letter presents a framework for integrating sustainable development goals into ITS lighting systems. We first explore the necessity of headlights in future traffic systems, particularly in the context of autonomous vehicles (AVs). Moreover, we discuss active safety technologies, such as night perception enhancement and collaborative lighting in connected vehicles, which contribute to enhancing safety during nighttime driving. Next, the letter analyzes the impact of vehicle-to-infrastructure (V2I) communication on intelligent street light systems. We propose a novel street light perception enhancement framework based on V2I, along with an enhanced lighting scheme for faulty vehicles. Lastly, the letter addresses the importance of energy conservation in future networked street lighting system. Several energy-saving schemes, including demand-based lighting and time-of-day optimization, are discussed, emphasizing the need for efficient energy management and sustainable practices in lighting infrastructure. As a summary of a part of Distributed/Decentralized Hybrid Workshop on Sustainability for Transportation and Logistics (DHW-STL), the letter aims to contribute to the development of sustainable lighting systems.