Background: Many youth today are physically inactive. Recent attention linking the physical or built environment to physical activity in adults suggests an investigation into the relationship between the built environment and physical activity in children could guide appropriate intervention strategies.

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Several findings call into question current practices. The chief conclusion is that the accuracy of freight generation (FG) and freight trip generation (FTG) models depends on the consistency between the model's structure and actual FG-FTG patterns, the degree of internal heterogeneity of the economic and land use aggregation used to estimate the model, and the appropriateness of the spatial aggregation procedure used to obtain the desired FG-FTG estimates. Relative to model structure, the paper establishes strong reasons to treat FG and FTG as separate concepts, because the latter is the output of logistic decisions, whereas the former is determined by the economics of production and consumption. The connection between business size variables–for example, employment–and FG is relatively strong because they are economic input factors, whereas the one with FTG is weaker because inventory and transportation costs come into play. Thus it is generally not correct to assume proportionality between FTG and business size or to assume that using constant FTG rates could be problematic. For instance, only 18% of the industry sectors in New York City exhibit constant FTG rates per employee. For economic and land use aggregation, the finer the level of detail the better, as independent variables have a better chance to explain FG-FTG. In the case of spatial aggregation, the correct aggregation procedure depends on the underlying disaggregate model. For a FG-FTG model to work well, both economic and land use and spatial aggregations must be appropriate.

The massive volumes of trajectory data generated by inexpensive GPS devices have led to difficulties in processing, querying, transmitting and storing such data. To overcome these difficulties, a number of algorithms for compressing trajectory data have been proposed. These algorithms try to reduce the size of trajectory data, while preserving the quality of the information. We present results from a comprehensive empirical evaluation of many compression algorithms including Douglas-Peucker Algorithm, Bellman's Algorithm, STTrace Algorithm and Opening Window Algorithms. Our empirical study uses different types of real-world data such as pedestrian, vehicle and multimodal trajectories. The algorithms are compared using several criteria including execution times and the errors caused by compressing spatio-temporal information, across numerous real-world datasets and various error metrics.

The effects of land use and business size (quantified as number of employees) on freight trip generation were analyzed. Standard trip generation rates, ordinary least squares, and multiple classification analysis were applied to a New York City data set. Three land use classification codes—the City of New York zoning resolution (NYCZR), the Land-Based Classification Standards (LBCS), and the Institute of Transportation Engineers (ITE) manual—were used. The authors developed models for NYCZR and function and activity of LBCS and used the ITE manual's trip rates. Root mean square error analysis was used to compare the performance of these models. It was found that models for NYCZR and LBCS land use classification codes provide better alternatives to ITE trip rates because they give more accurate estimates of freight trip attraction, cover a wider range of land use classifications, and are exclusively for freight trip attraction.

The main objectives of this paper are to assess and define ways to enhance the transferability of freight trip generation (FTG) models. After the key premises that should guide the development of FTG models have been presented, the paper assesses transferability in two ways. The first is through analyses of how well representative FTG models are able to estimate the actual FTG for a number of external validation cases. The second is through FTG econometric models that assess the statistical significance of binary variables that represent specific geographic locations. In addition, the paper introduces and assesses the accuracy of a synthetic correction procedure that is intended to improve the transfer-ability and quality of the estimates provided by the FTG rates available in the literature. The results show that the models developed as part of the National Cooperative Freight Research Program's Project 25, Freight Trip Generation and Land Use, have better prediction capabilities than the models included in other compilations. In addition, the synthetic correction procedures improve transferability, and no locational effects are present in the test data.