A type of flexible polyurethane (FPU) based on renewable-sourced polyol was prepared and then modified with halogen-free flame retardants, namely, alumina trihydrate (ATH) and triphenyl phosphate (TPhP), to further increase its flameresistant properties. The optimum loading for additives was determined based on analysing the changes in physicomechanical properties, thermal properties, and flame-retardant behaviours of modified FPU materials. An FPU-coated textile was then prepared; its smoke-generating behaviours and flammability were investigated in comparison with pristine fabric, pristine FPU, and respective modified FPU. The results confirmed good synergistic effect between ATH and TPhP, which helped increasing flame-resistant properties of applied materials, while also maintained reasonable flexibility for fabric-coating applications. However, the usage of modified FPU as coating material also proved to cause more toxic smoke emissions during the short burning duration of coated-fabric, an issue that needed to be investigated more thoroughly in order to guarantee the safety of people during catastrophic events.

Composite phase change materials were prepared from bentonite and a novel eutectic mixture of organic carbonates. The bentonite was firstly modified by ion exchange reaction with cetrimonium chloride, then used to adsorb the eutectic mixture by solution intercalation method. Initial investigation showed that at the maximum adsorption ratio of 60%, the prepared material showed good shape stability while retaining suitable melting temperature and good latent heat capability of 30.21 °C and 84.03 J g−1, respectively.

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The intersection management system can increase traffic capacity, vehicle safety, and the smoothness of all vehicle movement. Platoons of connected vehicles (CVs) use communication technologies to share information with each other and with infrastructures. In this paper, we proposed a deep reinforcement learning (DRL) model that applies to vehicle platooning at an isolated signalized intersection with partial detection. Moreover, we identified hyperparameters and tested the system with different numbers of vehicles (1, 2, and 3) in the platoon. To compare the effectiveness of the proposed model, we implemented two benchmark options, actuated traffic signal control (ATSC) and max pressure (MP). The experimental results demonstrated that the DRL model has many outstanding advantages compared to other models. Through the learning process, the average waiting time of vehicles in the DRL method was improved by 20% and 28% compared with the ATSC and MP options. The results also suggested that the DRL model is effective when the CV penetration rate is over 20%.