Forecasting the Impact of Connected and Automated Vehicles on Energy Use: A Microeconomic Study of Induced Travel and Energy Rebound

The effects of public transit supply on the demand for automobile travel

Many urban indicators and functional citywide properties have been shown to scale with population due to agglomeration effects. We hypothesize that scaling relations may also exist for water-related urban indicators such as the water footprint. The water footprint is an indicator of water use that measures humans' appropriation of freshwater resources. We analyze the scaling of the water footprint for 65 mid- to large-sized US cities using both empirical estimates and a social interaction network model of city functioning. The network model is used to explain the presence of any scaling exponent in the empirical estimates of the urban water footprint by linking to previous theories of urban scaling. We find that the urban water footprint tends to approximately show sublinear scaling behavior with both population and gross domestic product. Thus, large cities tend to be more water footprint efficient and productive than mid-sized cities, where efficiency and productivity are quantified, in a broad sense, as deviations from a linear scaling exponent. We find the sublinear scaling may be linked to changes in urban economic structure with city size, which lead to large cities shifting water intensive economic activities to less populated regions. In addition, we find that green water contributes to the scaling both positively by transferring the dependence of food consumption on population into the water footprint and negatively by increasing heterogeneity. Overall, the proposed scaling relations allow for the comparison of water footprint efficiency and productivity of cities. Comparing these properties and identifying deviations from the expected behavior has implications for water resources and urban sustainability.

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Large cities get more for less: Water footprint efficiency across the US.

This article presents the results of a comprehensive disaggregated commodity flow model for South Africa. The wealth of data available enables a segmented analysis of future freight transportation demand in order to assist with the prioritisation of transportation investments, the development of transport policy and the growth of the logistics service provider industry. In 2011, economic demand for commodities in South Africa’s competitive surface-freight transport market amounted to 622 million tons and is predicted to increase to 1834m tons by 2041, which is a compound annual growth rate of 3.67%. Fifty percent of corridor freight constitutes break bulk; intermodal solutions are therefore critical in South Africa. Scenario analysis indicates that 80%of corridor break-bulk tons can by serviced by four intermodal facilities – in Gauteng, Durban, Cape Town and Port Elizabeth. This would allow for the development of an investment planning hierarchy, enable industry targeting (through commodity visibility), ensure capacity development ahead of demand and lower the cost of logistics in South Africa.

/pdf/the-importance-of-disaggregated-freight-flow-forecasts-to-1xsmrtcqz9.pdf

The importance of disaggregated freight flow forecasts to inform transport infrastructure investments

Conventional indicators of water use for urban areas account primarily for direct water use. In contrast, our objective here is to employ the water footprint (WF) concept and methodology to include the virtual or indirect water use to assess the production‐side and consumption‐side WF of 65 United States (U.S.) cities. The 65 cities include the largest metropolitan areas and some of the major mid‐sized cities in the U.S. We use metropolitan areas to define our city boundaries as this is the native spatial resolution of the main datasets used. To estimate the urban WFs, we integrated large and disparate datasets, including commodity flow (agricultural, livestock, and industrial commodities), water use, and socioeconomic data. By analyzing the estimated WF values, we found indirect water use accounts on average for 66% of the WF of consumption. We found some cities are net virtual water exporters (11 of 65) because they rely heavily on direct water uses or are heavy producers of industrial commodities. Also, WF patterns vary widely across the U.S. but regional patterns seem to emerge. For example, the dense cities of the U.S. northeast megaregion have a significantly low per capita WF relative to the other cities, while cities in the Gulf Coast megaregion have a significantly higher industrial WF of production and consumption. Furthermore, there is inequality in the WF of consumption where a few cities account for a disproportionate share of the total U.S. urban water uses.

Water Footprint of 65 Mid- to Large-Sized U.S. Cities and Their Metropolitan Areas

The Freight Analysis Framework (FAF) integrates data from a variety of sources to create a comprehensive national picture of freight movements among states and major metropolitan areas, by all modes of transportation. It provides a national picture of current freight flows to, from, and within the United States, assigns selected flows to the transportation network, and projects such freight flow patterns into the future. FAF3 is the third database of its kind, FAF1 provided estimates for truck, rail, and water tonnage for calendar year 1998, and FAF2 provided a more complete picture based on the Commodity Flow Survey for calendar year 2002. Since the first FAF effort a number of changes in both data products and in the sources of the data used to produce them have taken place. The FAF3 data products include both a regional database and network database with highway flow assignment. This document provides a detailed explanation of how the regional FAF3 data products were constructed from diverse data sources, using a variety of data modeling tools.

The Freight Analysis Framework Version 3 (FAF3): A Description of the FAF3 Regional Database and How It Is Constructed

This report summarizes the auto travel demand elasticities with respect to different cost variables. The dependent variable of interest is vehicle miles of travel (VMT) of personally owned vehicles by U.S. households. Trip frequency, in terms of the number of trips made per day, is also considered as a dependent variable, though it requires a conversion (i.e., a trip length assumption) to put trip frequency into VMT terms. A relationship between household-level VMT and a set of explanatory variables is established using the direct demand approach. In particular, a log-log regression model is formulated and calibrated using the 2009 National Household Travel Survey (NHTS) dataset, supplemented with the national transit database and other data sources. Household-level travel demand elasticities with regards to fuel cost, maintenance cost, transit services, income and other socioeconomic characteristics are derived from the VMT regression model. A Poisson regression model is used to describe the relationship between trip frequency and various travel cost variables. In addition, to examine the effect of travel time and cost on the mode choice between flying and driving a personal vehicle, the long distance passenger travel mode choice is represented by a discrete choice model. Using a subset of 2001 NHTS long trip samples, a binary logit choice model is developed and calibrated, which includes access distance, travel time, cost, income, and travel party size as explanatory variables. Aggregate elasticities of long distance trips by personal vehicles are derived from the discrete choice model.

Analysis of Automobile Travel Demand Elasticities With Respect To Travel Cost

The Freight Analysis Framework (FAF) integrates data from a variety of sources to create a comprehensive picture of nationwide freight movements among states and major metropolitan areas for all modes of transportation. It provides a national picture of current freight flows to, from, and within the United States, assigns selected flows to the transportation network, and projects freight flow patterns into the future. The latest release of FAF is known as FAF4 with a base year of 2012. The FAF4 origin-destination-commodity-mode (ODCM) matrix is provided at national, state, major metropolitan areas, and major gateways with significant freight activities (e.g., El Paso, Texas). The U.S. Department of Energy (DOE) is interested in using FAF4 database for its strategic planning and policy analysis, particularly in association with the transportation of energy commodities. However, the geographic specification that DOE requires is a county-level ODCM matrix. Unfortunately, the geographic regions in the FAF4 database were not available at the DOE desired detail. Due to this limitation, DOE tasked Oak Ridge National Laboratory (ORNL) to assist in generating estimates of county-level flows for selected energy commodities by mode of transportation.

/pdf/shared-freight-transportation-and-energy-commodities-phase-lbtkg7e7mq.pdf

Shared Freight Transportation and Energy Commodities Phase One: Coal, Crude Petroleum, & Natural Gas Flows

The Freight Analysis Framework 3 (FAF3) is a Federal Highway Administration freight data product which provides a national origin-destination (O-D) matrix of commodity flows to, from, and within the United States. FAF3 freight flows are reported in terms of both annual tons and annual dollars of freight moved by mode of transportation. Based largely on the 2007 Commodity Flow Survey (CFS), FAF3 utilizes domestic freight flow characteristics, geographic regions, and the Standard Classification of Transported Goods (SCTG) commodity coding system from CFS. However, many freight flows were not captured by the 2007 CFS due to scope and sample size limitations. Approximately 100,000 establishments were sampled out of some 754,000 freight moving establishments in 2007 and imports are out of scope entirely. To estimate missing data values, the approach taken in FAF3 was to use a combination of a novel Log-linear modeling approach (LLM) with an iterative proportional fitting (IPF) routine that also uses additional data inputs to fill in the missing pieces. The complete FAF3 O-D–Commodity–Mode database is made up of 131 Origins x 131 Destinations x 43 Commodity Classes x 8 Modal categories, for annual tons and dollars. This study illustrates how the 2007 CFS data were integrated with several additional data sources using LLM and IPF to create a comprehensive FAF3 national freight flow matrix.

/pdf/overview-of-the-faf3-freight-flow-matrix-construction-uds4ah6zyy.pdf

Overview of the FAF3 Freight Flow Matrix Construction Process

The efficient and reliable flow of urban goods and services is essential for the economic well-being of the United States (U.S.), particularly the majority of Americans who live in urbanized areas. According to recently published estimates (FAF3, 2007), the U.S. freight system moves about 19 billion tons of goods valued at $17 trillion in 2007. These shipments result in a total of 5.7 trillion ton-miles of movements during the same year. The performance of the freight system has economic impacts on national productivity, the nation, the costs of goods and services, and the global competiveness of American industries. While demand for freight transportation has been rising steadily and shows little sign of abating, the capacity of the freight system has only modestly increased. Without an efficient freight system, other critical systems, such as energy supply, can be seriously impacted.

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A Primer on Recent Advancement on Freight Transportation

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