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To analyze the behavioural strategies of the manufacturers reducing carbon emissions and the conditions of carbon tax for encouraging manufacturers to reduce carbon emissions under consumers are more willing to buy low-carbon products, the evolutionary game model is developed. Analyzing the impact of the consumers low carbon preference, carbon and tax carbon emissions reduction to manufacturers reducing carbon emissions. It found that the evolutionary equilibrium results of the system affected by the cost of unit reducing carbon emissions and the consumer sensitivity of carbon emissions. There is boundary condition of carbon tax rate, and only when the carbon tax rate is greater than boundary condition, the carbon tax policy can effectively encourage manufacturers to reduce carbon emissions. Government should reduce the costs of reducing carbon emissions and increase consumer sensitivity of carbon emissions, and the carbon tax rate must be greater than boundary conditions for promote the reduction of carbon emissions.

This study aims to provide a better understanding of human factors and human performance mechanism in aviation accident analysis by visualisation and taxonomy analysis using mapping knowledge domain (MKD). An overview of aviation accident analysis involving human factors is firstly conducted, followed by an introduction of several conceptual models and human reliability analysis methods. Finally, a specific framework for risk assessment of human factors in aviation is proposed. The human factors analysis and classification system structure combined with the quantitative method is the most frequently used mode in aviation accident analysis. From a macro ergonomic perspective, systematic consideration has been emphasised frequently in existing research. Pilots or individuals are thought to be associated with the organisations and environment they located in. On the other hand, the micro ergonomic aspect prefers a quantitative and probabilistic way to reveal the individual's failure mode or error rate. Based on previous studies, the proposed framework provides a systematic view to help with the evaluation of human performance in aviation, taking into account human issues as well as management and environmental factors.

Liquefied Natural Gas (LNG) is a clean, efficient, and economic energy, which is mainly transported by LNG carriers in the marine industry. Massive storage of LNG has led to public concern about the possible hazards of LNG carriers for people and facilities. This paper aims to determine the appropriate level of a safety zone and to develop an optimized traffic organization scheme. An innovative ECSO framework is proposed to deal with these two tasks. The results of the comparative analysis show that the influence degree of the optimized scheme is 21%, which is distinctly lower than the previous scheme (33%). To some content, the proposed framework can provide meaningful enlightenments for stakeholders to improve port service level and ensure the safe operation of LNG carriers.

The aviation industry is criticized as one of the fastest growing industries in emitting greenhouse gases, which accelerates the process of global warming. The current trend of increasing demand for air travel has made it difficult to have a significant effect in the short term with existing policies and regulations as well as technological improvement; therefore, the emissions reduction now is heavily relying on both air travelers’ and airline industries’ behavioral change. Here, an overview of aviation energy-related behavioral research is given, from air travelers, airlines, and aviation logistic perspective, respectively. The air traveler issue involves their attitudes toward flying and climate change, and willingness to pay for carbon offset, which is from individual view, while airlines’ fuel usage and carbon emissions, choice of aircraft size, environmental friendless, and energy efficiency are discussed from macroscopic view. Furthermore, the behavioral models for both air travelers and airlines are summarized. Overall, this chapter introduces a comprehensive review for behavioral research on transport and energy in the context of aviation. Future research direction is provided from practical and methodological aspects.

Unreliable transit services can negatively impact transit ridership and discourage passengers from regularly choosing public transport. As the most important content of bus service reliability, accurate bus arrival prediction can improve travel efficiency for enabling a reliable and convenient transportation system. Accordingly, this paper proposes a novel deep learning method, i.e. variational mode decomposition long short-term memory (VMD-LSTM), for bus travel speed prediction in urban traffic networks using a forecast of bus arrival information on variable time horizons. The method uses the temporal and spatial patterns of the average bus speed series. The results show that the VMD-LSTM model outperforms other models on forecasting bus link speed series in future time intervals, whereas the artificial neural network model achieves the worst prediction. In conclusion, the VMD-LSTM method can detect irregular peaks of transit samples from a series of temporal or spatial variations and performs better on major and auxiliary corridors.

Liquefied Natural Gas (LNG) is a clean, efficient and economic energy, which is mainly transported by LNG carriers in the marine industry. Possible spilling of cryogenic LNG puts safety of crew in danger due to fire and explosion hazards. Taking into account various uncertainties, a modified fault tree model for LNG spill accident during LNG carriers’ handling operations is constructed in this study. Human factor analysis is introduced with a goal to predict human errors in LNG carriers’ handling operation. Finally, the results of the Fault Tree Analysis (FTA) and Human Reliability Analysis (HRA) are combined, so that risks can be assessed using Monte Carlo Simulation (MCS). Comparing the results of risk assessment with the traditional FTA and corresponding criterion set, an important reference for LNG carrier management is provided for administration.

Safety is crucial to ensure the sustainability of aviation growth. To better clarify the influences of human factors on aviation accident risks, this study developed a hybrid HFACS-BN model (HFACS: Human Factors Analysis and Classification System, BN: Bayesian Network). The authors designed and implemented a questionnaire survey based on the four-level HFACS framework and collected valid data from 180 out of 649 aviation professionals working in the Ulaanbaatar International Airport, Mongolian in 2017. The model identified 35 major human factors out of 129 factors. The model validation was performed in terms of content validity and predictive validity. The results showed that even though a majority of respondents perceived that many human factors had a middle- or high-effect on aviation accident risks, the probability of the risks caused by human factors was estimated to be just 1.37%. The Unsafe Acts level is most influential to the risks among the four levels, while the Unsafe Supervision level contributes least. It is revealed that enhancing aviation professionals&rsquo, awareness of human factors should make full use of causal chaining effects among human factors. Finally, this study contributes to the literature from the perspectives of both methodological development and important empirical analysis.

This study analyzed the potentially dangerous driving behaviors of commercial truck drivers from both macro and micro perspectives. The analysis was based on digital tachograph data collected over an 11-month period and comprising 4373 trips made by 70 truck drivers. First, different types of truck drivers were identified using principal component analysis (PCA) and a density-based spatial clustering of applications with noise (DBSCAN) at the macro level. Then, a multilevel model was built to extract the variation properties of speeding behavior at the micro level. Results showed that 40% of the truck drivers tended to drive in a substantially dangerous way and the explained variance proportion of potentially extremely dangerous truck drivers (79.76%) was distinctly higher than that of other types of truck drivers (14.70%˜34.17%). This paper presents a systematic approach to extracting and examining information from a big data source of digital tachograph data. The derived findings make valuable contributions to the development of safety education programs, regulations, and proactive road safety countermeasures and management.

The consumption rate of LNG has increased rapidly in recent years, which draws the demand for LNG carriers. Over the past ten years, the number of ongoing LNG carriers has increased dramatically while more carriers are expected to be built in the years ahead. This has led to public concern about the safety of the LNG carriers. In this paper, an overall risk identification for LNG carriers is conducted considering the hazards of LNG as a marine fuel and specific hazards to LNG carriers. Based on this, Fault Tree Analysis has been utilized for risk assessment of LNG carriers. By doing so, this paper provides important reference for shipping management authorities and policymakers on developing effective measures to control the identified hazards, so as to improve the safety level of LNG carriers.

In order to improve road traffic safety and tackle traffic congestion problems, an integrated platform is designed for cooperative vehicle infrastructure systems (CVIS) with consideration of real traffic conditions of China and typical traffic scenes of intersections and segments. It designs schemes for integrated demonstration platform of cooperative vehicle infrastructure systems in China. Moreover, the integrated demonstration tests of 10 vehicles show that cooperative vehicle infrastructure technology can solve some traffic problems, and provides new solution for the current traffic congestion and crashes. A practical testbed has been laid for the subsequent studies on cooperative vehicle infrastructure systems in China. It has important guiding significance for the relevant study in the future.