Human thermal comfort researches mainly focus on the relation between the environmental factors (e.g. ambient temperature, air humidity, and air velocity, etc.) and the thermal comfort sensation based on a large amount of subjective field investigations. Although some physiological factors, such as skin temperature and metabolism were used in many thermal comfort models,they are not enough to establish a perfect thermal comfort model. In this paper,another two physiological factors, i.e. heart rate variation (HRV) and electroencephalograph (EEG), are explored for the thermal comfort study. Experiments were performed to investigate how these physiological factors respond to the environmental temperatures, and what is the relationship between HRV and EEG and thermal comfort. The experimental results indicate that HRV and EEG may be related to thermal comfort, and they may be useful to understand the mechanism of thermal comfort.

Train dispatching (TD) is at the forefront of all rail operations that transport passengers or goods. Recent technological advances and the explosion of digital data have introduced data-driven methods (DDMs) in rail operations. In this study, DDMs on the TD problem are briefly explored, focusing on relevant studies on delay distribution, delay propagation, and timetable rescheduling. Data-driven TD methods, including statistical methods (SM), graphical models (GM), and machine learning (ML) methods are reviewed. Then, key issues in establishing different data-driven models for the TD problem are addressed. Subsequently, ML methods are considered to be among the most promising DDMs that lead to innovative TD methods, relying on rich data obtained from train operations. This study emphasizes the potentials for designing new alternatives in the three key fields of interest and provides directions for further research on TD. Future research, including the ML-driven TD and intelligent TD, were discussed in this study. INDEX TERMS Data-driven, delay distribution, delay propagation, timetable rescheduling, train dispatching, machine learning.

Salts, in both solid and liquid forms, are used in winter road maintenance because of their effectiveness in breaking and preventing the bonding of snow and ice to road surfaces. Because of the detrimental effects of salt on infrastructure and the environment, many alternative materials are being tested for snow and ice control during winter. This paper summarizes the results of field tests that compared two beet molasses–based materials with regular salt and salt brine when used as prewetting and anti-icing agents. The performance comparison was based on more than 100 h of friction readings along with high-resolution image data collected over nine snow events. Application rates, test route, and comparison methodology along with experimental results, recommendations, and potential for future research are also presented.

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