This paper looks at spatial patterns of freight and logistics activities and the planning and policy issues associated with them. Two important characteristics of the geography of the logistics industry are analyzed: (1) "Logistics sprawl," i.e. the spatial deconcentration of logistics facilities and distribution centers in metropolitan areas, and (2) the polarization of logistics activities, i.e. the concentration of logistics activities in very large metropolitan areas. The paper focuses on Atlanta, one of the largest metropolitan areas in the United States (U.S.). Like other very large cities in the U.S., in recent years logistics activities have increased considerably in Atlanta. The paper also examines the Piedmont Atlantic Megaregion (PAM), which has a total population of 15 million and includes Birmingham, Atlanta, Raleigh-Durham and Charlotte. PAM contains many distribution centers with a national and international market area, and is one of the country's fastest growing locations for logistics hubs. The megaregion concept is particularly well-suited to the analysis of freight transport systems, because freight transport's market areas, driven by global supply chains, are largely disconnected from a single city and spatially organized on a regional and multicity basis. Another focus of the paper is the question of planning for a more efficient locational pattern of freight facilities across metropolitan areas and within megaregions. Local governments compete for jobs and activities that generate tax revenues, and logistics has become a significant activity for many U.S. metropolitan areas. The megaregion concept can contribute to a more collaborative regional planning approach. Highlights We show: 1) the patterns of spatial deconcentration of logistics facilities in metro Atlanta, 2) the polarization of warehouses in the Piedmont Atlantic Megaregion, 3) local government perspectives on logistics activities and the lack of a regional approach.

The multinomial logit (MNL) model and its variations have been dominating the travel mode choice modeling field for decades. Advantages of the MNL model include its elegant closed-form mathematical structure and its interpretable model estimation results based on random utility theory, while its main limitation is the strict statistical assumptions. Recent computational advancement has allowed easier application of machine learning models to travel behavior analysis, though research in this field is not thorough or conclusive. In this paper, we explore the application of the extreme gradient boosting (XGB) model to travel mode choice modeling and compare the result with an MNL model, using the Delaware Valley 2012 regional household travel survey data. The XGB model is an ensemble method based on the decision-tree algorithm and it has recently received a great deal of attention and use because of its high machine learning performance. The modeling and predicting results of the XGB model and the MNL model are compared by examining their multi-class predictive errors. We found that the XGB model has overall higher prediction accuracy than the MNL model especially when the dataset is not extremely unbalanced. The MNL model has great explanatory power and it also displays strong consistency between training and testing errors. Multiple trip characteristics, socio-demographic traits, and built-environment variables are found to be significantly associated with people’s mode choices in the region, but mode-specific travel time is found to be the most determinant factor for mode choice.

For phonemic awareness, over the course of a school year, with concomitant classroom instruction, the gains made from short, intense treatment were similar to those made from continuous weekly treatment. At-risk kindergartners with moderate deficits benefited more than those with mild deficits. Children, particularly those with mild deficits, may improve substantially with only classroom instruction and incidental self-regulatory gains from treatment for another area.

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Health impact assessment (HIA) methods are used to evaluate the impact on health of policies and projects in community design, transportation planning, and other areas outside traditional public health concerns. At an October 2004 workshop, domestic and international experts explored issues associated with advancing the use of HIA methods by local health departments, planning commissions, and other decisionmakers in the United States. Workshop participants recommended conducting pilot tests of existing HIA tools, developing a database of health impacts of common projects and policies, developing resources for HIA use, building workforce capacity to conduct HIAs, and evaluating HIAs. HIA methods can influence decisionmakers to adjust policies and projects to maximize benefits and minimize harm to the public's health.