Impacts of biodiesel blending on freight emissions in the Midwestern United States

We use a combination of petroleum–diesel models, datasets and tools along with biodiesel-specific corrections to create a roadway-level emissions inventory capable of evaluating spatial, temporal and scale aspects of fuel distribution options for the Midwestern US. Specifically, we compare the emissions of a year-round “low-blend” biodiesel implementation scenario, already under consideration in a variety of states, with a more strategic summer-only, interstate-only “high-blend” scenario. Our results indicate that spatial and seasonal distribution decisions do affect the overall emissions impacts of any biodiesel deployment, even those at low-blend levels. However, we also finds that changes in emissions due to biodiesel are considerably smaller than those anticipated from improvements to engine and control technologies.     

Influence of vehicle driving parameters on the noise caused by passenger cars in urban traffic

In this work, a sample of vehicles has been instrumented to measure of variables that influence vehicle noise emissions in Madrid. A circuit reproducing a normal travel pattern in large city is traveled by a fleet of vehicle models representing the fleets of cars in a European city. A sample of drivers covers the test track under different traffic conditions. Driving parameters and noise emitted have been recorded in each test and average values have been extracted. These data have been analyzed to define the noise emissions produced by a vehicle in real driving conditions and to identify the noisiest driving behaviors.     

A socioeconomic analysis of commuting professionals

Transportation - Everyday commuting as a mobility phenomenon is well-investigated and has been the topic of many contributions. Nevertheless, the distinct determinants of the commuting...     

Crew resource management training in the maritime industry: a literature review

The study is based on a literature review of recent empirical research on crew resource management (CRM) training in the maritime industry, organised around what non-technical skills to learn and how. The review indicates that existing work is dominated by individualistic theories of learning with less focus on learning as a social process. Five main categories of skills that need to be trained are identified: assertiveness, decision-making, communication, situation awareness and team coordination. We argue that it is necessary to operationalise these broad concepts further, emphasising the work context and crew-specific needs. The review also shows that a combination of classroom lectures and simulator-based exercises is commonly used in maritime education and training in these skills. The learning effect seems to be suffering from training programmes that are exported ‘as is’ from aviation and not adjusted to the maritime domain or to operation-specific needs. This paper examines maritime crew resource management training from a social learning perspective, involving the view that learning is a context bound, social process that might take place in communities of practice (CoP). A CoP is a group (e.g. a crew) wherein members share an activity and learn from each other. It is argued that CRM training programmes will benefit from including a social learning perspective. Factors that enable the assessment of teams are discussed, and it is argued that the training should be tailored to existing crews, emphasising a learning environment as close to reality as possible.     

Biogeography-based combinatorial strategy for efficient autonomous underwater vehicle motion planning and task-time management

Autonomous Underwater Vehicles (AUVs) are capable of spending long periods of time for carrying out various underwater missions and marine tasks. In this paper, a novel conflict-free motion planning framework is introduced to enhance underwater vehicle’s mission performance by completing maximum number of highest priority tasks in a limited time through a large scale waypoint cluttered operating field, and ensuring safe deployment during the mission. The proposed combinatorial route-path planner model takes the advantages of the Biogeography-Based Optimization (BBO) algorithm toward satisfying objectives of both higher-lower level motion planners and guarantees maximization of the mission productivity for a single vehicle operation. The performance of the model is investigated under different scenarios including the particular cost constraints in time-varying operating fields. To show the reliability of the proposed model, performance of each motion planner assessed separately and then statistical analysis is undertaken to evaluate the total performance of the entire model. The simulation results indicate the stability of the contributed model and its feasible application for real experiments.     

Enabling a viable technique for the optimization of LNG carrier cargo operations

In this study, we optimize the loading and discharging operations of the Liquefied Natural Gas (LNG) carrier. First, we identify the required precautions for LNG carrier cargo operations. Next, we prioritize these precautions using the analytic hierarchy process (AHP) and experts’ judgments, in order to optimize the operational loading and discharging exercises of the LNG carrier, prevent system failure and human error, and reduce the risk of marine accidents. Thus, the objective of our study is to increase the level of safety during cargo operations.     

Investigating the influence of assigning a higher priority to scheduling work and school activities in the activity-based models on the simulated travel/activity patterns

Transportation - Two dynamic, gap-based activity scheduling models are tested by applying a short-run microsimulation approach to replicate workers’ travel/activity patterns over a 1-week...     

Using multiple hybrid spatial design network analysis to predict longitudinal effect of a major city centre redevelopment on pedestrian flows

Transportation - Predicting how changes to the urban environment layout will affect the spatial distribution of pedestrian flows is important for environmental, social and economic sustainability....     

Key technologies of ship remote control system in inland waterways under ship-shore cooperation conditions北大核心CSCD

［Objective ］ To meet the requirements of remotely controlling ship in curved, narrow and crowded inland waterways, this paper proposes an approach that consists of CNN-based algorithms and knowledge based models under ship-shore cooperation conditions. ［Method］On the basis of analyzing the characteristics of ship-shore cooperation, the proposed approach realizes autonomous perception of the environment with visual simulation at the core and navigation decision-making control based on deep reinforcement learning, and finally constructs an artificial intelligence system composed of image deep learning processing, navigation situation cognition, route steady-state control and other functions. Remote control and short-time autonomous navigation of operating ships are realized under inland waterway conditions, and remote control of container ships and ferries is carried out. ［Results］The proposed approach is capable of replacing manual work by remote orders or independent decision-making, as well as realizing independent obstacle avoidance, with a consistent deviation of less than 20 meters. ［Conclusions］The developed prototype system carries out the remote control operation demonstration of the above ship types in such waterways as the Changhu Canal Shenzhou line and the Yangtze River, proving that a complete set of algorithms with a CNN and reinforcement learning at the core can independently extract key navigation information, construct obstacle avoidance and control awareness, and lay the foundation for inland river intelligent navigation systems. © 2022 Journal of Clinical Hepatology. All rights reserved.