In the context of the decarbonization of the shipping industry, the application of clean energy technologies is a catalyst for decarbonization. With the number of potential clean energy technologies expanding, the uncertainties in terms of technology maturity, policy regulation, and economics make clean energy technologies decision much more difficult. Therefore, it is urgent to establish a clean energy technology selection scenario for the green ship industry to assist shipowners in decision-making. Based on this, a technology selection model based on rough set (RS) and approximate ideal solution ranking (TOPSIS) is constructed. Using RS to reduce the evaluation index and calculate the weight can avoid the one-sidedness of subjective weighting. Using the TOPSIS method to rank alternatives. This paper selects seven clean energy technology alternatives, namely LNG power, LPG power, methanol power, HVO power, pure battery power, hydrogen fuel cell, and ammonia fuel cell, respectively, as the evaluation objects. Taking two types of vessels as examples, it is concluded that LNG power technology is suitable for large coastal ro-ro passenger vessels, and pure battery power technology is suitable for small inland river short-distances vessels. The results are in line with reality, which verifies the scientificity and validity of the proposed model.

Seafarers are prone to reduce behavioral reliability under high workloads, resulting in human errors and accidents. To explore the changes in seafarers’ workload and physiological activities under complex task conditions, a bridge simulator experiment was conducted to collect the EEG and ECG data of 23 seafarers. The power in different EEG sub-bands was extracted from a one-channel EEG acquisition headset employed by Welch’s method and ratio processing. The features such as root mean square of RR interval difference (RMSSD) were extracted from ECG. Then, an improved seafarer workload recognition method based on EEG combined with ECG and complex task scenarios was proposed, and the performance of the machine learning algorithm was evaluated by cross-validation. Compared with the recognition model that only uses the task scenarios as the workload calibration, the EEG recognition model based on the workload level calibrated by the ECG and the task scenarios is more effective, with an accuracy rate of 92.5%, an increase of 25.9%. The results show that the improved workload recognition model can effectively identify seafarers’ workload, and the model trained with the bagging algorithm has the best performance. Furthermore, time domain features of EEG and ECG fluctuate regularly with the task scenarios’ complexity. The research results can develop online intelligent monitoring, and human–computer interaction active early warning technology and equipment.

To improve first/last mile travel services between metro stations and communities, this study modeled and analyzed four kinds of feeder bus operation strategies in terms of travel time and accessibility. The analytical modeling was used to compare the travel times and the simulation experiments were used to compare the accessibilities of different operation strategies. The results showed that when the ratio between length and width of study area increases, the number of stops for the fixed route transit with fixed stops will increase. When the travel demand is low, the demand responsive transit with separate routes has the highest accessibility. When the travel demand is high, the fixed route transit with fixed stops provides the highest accessibility. In addition, the ratio of flows in two passenger directions has different influences on the four operation strategies. This study can provide guidance for feeder bus operation to improve public transportation attraction.