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11.
Prasad Kumar Bhaskaran Ravindran Rajesh Kumar Rahul Barman Ravichandran Muthalagu 《Journal of Marine Science and Technology》2010,15(2):160-175
This work reports a new methodology for deriving monthly averages of temperature (T) and salinity (S) fields for the Indian Ocean based on the use of an artificial neural network (ANN). Investigation and analysis were performed
for this region with two distinct datasets: (1) monthly climatological data for T and S fields (in 1° × 1° grid boxes) at standard depth levels of the World Ocean Atlas 1994 (WOA94), and; (2) heterogeneous randomly
distributed in situ ARGO, ocean station data (OSD) and profiling (PFL) floats. A further numerical experiment was conducted
with these two distinct datasets to train the neural network model. Nonlinear regression mapping utilizing a multilayer perceptron
(MLP) is employed to tackle nonlinearity in the data. This study reveals that a feed-forward type of network with a resilient
backpropagation algorithm is best suited for deriving T and S fields; this is demonstrated by independently using WOA94 and in situ data, which thus tests the robustness of the ANN model.
The suppleness of the T and S fields derived from the ANN model provides the freedom to generate a new grid at any desired level with a high degree of
accuracy. Comprehensive training, testing and validation exercises were performed to demonstrate the robustness of the model
and the consistency of the derived fields. The study points out that the parameters derived from the ANN model using scattered,
inhomogeneous in situ data show very good agreement with state-of-the-art WOA climatological data. Using this approach, improvements
in ocean climatology can be expected to occur in a synergistic manner with in situ observations. Our investigation of the
Indian Ocean reveals that this approach can be extended to model global oceans. 相似文献
12.
The current study contributes to the already substantial scholarly literature on telecommuting by estimating a joint model of three dimensions—option, choice and frequency of telecommuting. In doing so, we focus on workers who are not self-employed workers and who have a primary work place that is outside their homes. The unique methodological features of this study include the use of a general and flexible generalized hurdle count model to analyze the precise count of telecommuting days per month, and the formulation and estimation of a model system that embeds the count model within a larger multivariate choice framework. The unique substantive aspects of this study include the consideration of the “option to telecommute” dimension and the consideration of a host of residential neighborhood built environment variables. The 2009 NHTS data is used for the analysis, and allows us to develop a current perspective of the process driving telecommuting decisions. This data set is supplemented with a built environment data base to capture the effects of demographic, work-related, and built environment measures on the telecommuting-related dimensions. In addition to providing important insights for policy analysis, the results in this paper indicate that ignoring the “option” dimension of telecommuting can, and generally will, lead to incorrect conclusions regarding the behavioral processes governing telecommuting decisions. The empirical results have implications for transportation planning analysis as well as for the worker recruitment/retention and productivity literature. 相似文献
13.
Chandra R. Bhat Konstadinos G. Goulias Ram M. Pendyala Rajesh Paleti Raghuprasad Sidharthan Laura Schmitt Hsi-Hwa Hu 《Transportation》2013,40(5):1063-1086
This paper develops and estimates a multiple discrete continuous extreme value model of household activity generation that jointly predicts the activity participation decisions of all individuals in a household by activity purpose and the precise combination of individuals participating. The model is estimated on a sample obtained from the post census regional household travel survey conducted by the South California Association of Governments in the year 2000. A host of household, individual, and residential neighborhood accessibility measures are used as explanatory variables. The results reveal that, in addition to household and individual demographics, the built environment of the home zone also impacts the activity participation levels and durations of households. A validation exercise is undertaken to evaluate the ability of the proposed model to predict participation levels and durations. In addition to providing richness in behavioral detail, the model can be easily embedded in an activity-based microsimulation framework and is computationally efficient as it obviates the need for several hierarchical sub-models typically used in extant activity-based systems to generate activity patterns. 相似文献
14.
This article investigates the impact of alternative data smoothing and traffic prediction methods on the accuracy of the performance of a two-stage short-term urban travel time prediction framework. Using this framework, we test the influence of the combination of two different data smoothing and four different prediction methods using travel time data from two substantially different urban traffic environments and under both normal and abnormal conditions. This constitutes the most comprehensive empirical evaluation of the joint influence of smoothing and predictor choice to date. The results indicate that the use of data smoothing improves prediction accuracy regardless of the prediction method used and that this is true in different traffic environments and during both normal and abnormal (incident) conditions. Moreover, the use of data smoothing in general has a much greater influence on prediction performance than the choice of specific prediction method, and this is independent of the specific smoothing method used. In normal traffic conditions, the different prediction methods produce broadly similar results but under abnormal conditions, lazy learning methods emerge as superior. 相似文献
15.
Children are an often overlooked and understudied population group, whose travel needs are responsible for a significant number
of trips made by a household. In addition, children’s travel and activity participation during the post-school period have
direct implication for adults’ activity-travel patterns. A better understanding of children’s after school activity-travel
patterns and the linkages between parents and children’s activity-travel needs is necessary for accurate prediction and forecasting
of activity-based travel demand modeling systems. In this paper, data from the 2002 Child Development Supplement of the Panel
Study of Income Dynamics is used to undertake a comprehensive assessment of the post-school out-of-home activity-location
engagement patterns of children aged 5–17 years. Specifically, this research effort utilizes a multinomial logit model to
analyze children’s post-school location patterns, and employs a multiple discrete–continuous extreme value model to study
the propensity of children to participate in, and allocate time to, multiple activity episode purpose-location types during
the after-school period. The results show that a wide variety of demographic, attitudinal, environmental, and others’ activity-travel
pattern characteristics impact children’s after school activity engagement patterns. 相似文献