Time Gap Modeling under Mixed Traffic Condition: A Statistical Analysis

This paper attempts to model vehicular time gap, which is defined as the time interval between any two successive arrivals of vehicles at a reference point of measurement on a road segment. Such an approach is justified under the non-lane-based heterogeneous traffic conditions prevailing in developing countries such as India, characterized by many “zero” time gaps due to simultaneous arrivals within a given road width. In addition, time gap data are characterized by a significant amount of data in the tail region due to long headways. Nevertheless, many researchers of time gap modeling have used light-tailed distributions that modeled time gaps satisfactorily due to two reasons: (a) The tail data was merged into a single bin; and (b) goodness-of-fit tests such as the Chi-square test, which has many limitations, were used. Further, some researchers have suggested different distributions for the same range of traffic flows, leading to ambiguity in distribution selection. In addition, bin size, which dictates the degree of fit of any distribution, has been ascribed very less importance in time gap modeling. Hence, this paper tries to consolidate and standardize the existing research in time gap modeling research by addressing all these issues. Two new distributions, namely Generalized Pareto (GP) and Generalized Extreme Value (GEV) with better tail modeling properties, have been proposed along with other conventional distribution to model vehicular time gaps over a wide range of flow from 550 vph to 4,100 vph. Two types of goodness-of-fit tests, namely Area-based and Distance-based tests, have been used. It has been found from the study that GP distribution fits the time gap data well (overall and tails) up to a flow range of 1,500 vph based on both kinds of tests, and GEV fits the data well for the flow levels above 1,500 vph based on the area test only.     

Redefining activity types: Who participates in which leisure activity?

This paper analyzes the activity choices of individuals and the links between socio-demographics, daily schedules and activity attributes using a new activity choice framework. Activities are first clustered into groups based on their salient attributes, such as duration, frequency, flexibility, planning times, and number of involved persons, rather than their functional types (work, leisure and household obligations), using a K-means cluster technique. This led to the creation of several new activity groups such as “long, temporally fixed, personally flexible activities”, “short and flexible activities”. These activity groups form the choice set for the mixed logit activity choice modeling structure developed for the leisure activities in the second part of the paper. The model results reveal the significant relationships between socio-demographics, temporal characteristics, and characteristics of the schedules on leisure activity choice. The results demonstrate how changing demographics and other activities in individuals’ schedules may affect the nature of the leisure activities and present the substitution and complimentary effects that these new activity groups have on one another.     

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...     

非收放式减摇鳍鳍轴校核算法研究

非收放式减摇鳍作为减摇鳍产品中非常重要的一个组成部分,而鳍轴也是其减摇力矩传力链中非常重要的一环,对于鳍轴综合应力的校核也就成了非收放式减摇鳍设计中的关键。通过比较考虑剪力的新算法以及仅做弯扭综合应力校核的2种算法,及它们在不同强度理论下的状态,综合讨论得出能使鳍轴应力值更加精确的算法。     

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.     

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.     

The identification and empirical characterization of vehicular (Lagrangian) fundamental diagrams in multilane traffic flow

Traditional macroscopic traffic flow modeling framework adopts the spatial–temporal coordinate system to analyze traffic flow dynamics. With such modeling and analysis paradigm, complications arise for traffic flow data collected from mobile sensors such as probe vehicles equipped with mobile phones, Bluetooth, and Global Positioning System devices. The vehicle‐based measurement technologies call for new modeling thoughts that address the unique features of moving measurements and explore their full potential. In this paper, we look into the concept of vehicular fundamental diagram (VFD) and discuss its engineering implications. VFD corresponds to a conventional fundamental diagram (FD) in the kinematic wave (KW) theory that adopts space–time coordinates. Similar to the regular FD in the KW theory, VFD encapsulates all traffic flow dynamics. In this paper, to demonstrate the full potential of VFD in interpreting multilane traffic flow dynamics, we generalize the classical Edie's formula and propose a direct approach of reconstructing VFD from traffic measurements in the vehicular coordinates. A smoothing algorithm is proposed to effectively reduce the nonphysical fluctuation of traffic states calculated from multilane vehicle trajectories. As an example, we apply the proposed methodology to explore the next‐generation simulation datasets and identify the existence and forms of shock waves in different coordinate systems. Our findings provide empirical justifications and further insight for the Lagrangian traffic flow theory and models when applied in practice. Copyright © 2015 John Wiley & Sons, Ltd.     

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.