Transportation Behavioral Data and Climate Change

In 2017, transportation became the largest single source of greenhouse gas emissions from the United States. Globally, the 2014 Intergovernmental Panel on Climate Change report found that, without far more aggressive policies, “transportation emissions could increase at a faster rate than emissions from other energy end use sectors” reaching 12 Gt CO2-eq/year by 2050 (Sims et al., 2014). The overwhelming challenge of combatting these emissions is made far more difficult by the fact that so little is known about transportation behavior. To use a cliché – if we can’t measure it, we can’t manage it. And transportation must be managed if we are to avoid the most catastrophic consequences of climate change. In this dissertation, I propose that better data collection is necessary to achieve reduction of transportation-related emissions. Happily, advances in technology make this more feasible today than at any time in the past. The costs of massive computing resources have gone down, the world is swarming with mobile devices like smartphones and connected cars collecting massive (if messy) amounts of data, and new techniques in data science and machine learning have emerged to help get clean answers out of all that data in a privacy-appropriate manner. In some cases, these new techniques will displace older ones. In other cases, the old ways have inherent advantages. In other cases yet, fusing new and old techniques will yield the most productive results.In Chapter One, I lay out a framework to organize the types of transportation behavioral data that must be collected regularly to adequately measure and manage transportation’s impact on climate. This builds on classic climate impact frameworks, adapting them to the particular measurement challenges presented by transportation. In Chapter Two, I provide a history of US transportation data collection since World War II as well as a review of traditional, modern, and emerging transportation data collection technologies. I then map each technology onto each behavioral data collection need identified in Chapter One, matching each behavior to the best respective data collection technique.Chapters Three and Four provides an example of analysis done using the traditional data collection techniques, notably Household and Commercial Travel Surveys, to explore changes in PMT related to shopping and retail freight since 1969, as well as freight for fuel transportation. They demonstrate and take advantage of the key benefits of traditional techniques: that they go back in history, that they collect clearly stated trip purposes, vehicle occupancies, demographics (including gender, an important demographic but particularly difficult to deduce from the new data collection sources), trip distances, chaining behavior, commodities logged, and more. As it turns out, these benefits are critical: the historical trends of the past 40 years allow behavioral insight that would not have been possible with a shorter term study, and gender dynamics are key to understanding the behaviors at hand. However, the analysis in Chapters Three and Four also highlights some of the key limitations of survey-based analysis. The fact that data was only collected every five to ten years severely limits the analysis, such as limiting the exploration that can be done on the impacts of the Great Recession. In addition, fallibilities in human memory are especially pronounced in short trips, trip chains, and non-work related trips, all of particular importance to this study. Chapters Five lays out theoretically, and then Chapter Six demonstrates via case study in India, how personal GPS diary devices can be used to log detailed data about individual trips. It demonstrates the key benefit of this data – highly individualized characteristics. Taking the example of vehicle electrification, this chapter demonstrates two ways such granular data is important: in one example, such data to give feedback to an individual to influence their car buying behavior. In the second, the granularity found with this new data collection techniques reveals the importance of highly localized policy making and emissions modeling based on driving patterns in different cities.Chapter Seven uses the emerging technology of mass amounts of locational data, collected passively via smart phones, to explore how urban density at home and work interacts with total, work-related, and non-work-related miles driven. This demonstrates the great strength of this type of data – massive sample size combined with high spatial granularity and longitudinal data collection. These strengths enable the analysis at statistically meaningful scale of patterns across many geographies, individuals, and times of year. Thus, this data can shed light on questions about the relationship of density and miles travelled which previously have not been answered conclusively due to data constraints

Non-Traditional Methods to Obtain Annual Average Daily Traffic (AADT)

693JJ319C000015The use of passive data from location-based smartphone applications (LBS) and Global Positioning Services (GPS) to collect Annual Average Daily Traffic (AADT) has the potential to greatly reduce costs to State Department of Transportations (DOTs) and Metropolitan Planning Organizations (MPOs) and expand the coverage of up-to-date counts. This report evaluates the technical and statistical validity of traffic data derived from these sources using machine learning methods. Validity was determined by comparison to 4255 permanent counters, and a survey of recent publications about accuracy expectations. The document covers the input data and the development of the machine learning models and model validation. The results include the error by road volume, roadway and regional characteristics compared to typical estimation. The effects of reduced trip sample, ping rate, spatial accuracy and reference counters were also tested. The applicability of Probe Data was tested for other factors including, day of week, month of year, directional and ramp AADT, work zones ADT, K and D factors, peak hour truck data, special events or unusual weather and AADT by vehicle type

Guidelines for Obtaining AADT Estimates from Non-Traditional Sources

693JJ319C000015The use of passive data from location-based smartphone applications (LBS) and Global Positioning Services (GPS) to collect Annual Average Daily Traffic (AADT) has the potential to greatly reduce costs to State Department of Transportations (DOTs) and Metropolitan Planning Organizations (MPOs) and expand the coverage of up-to-date counts. This report evaluates the technical and statistical validity of traffic data derived from these sources using machine learning methods. Validity was determined by comparison to 4255 permanent counters, and a survey of recent publications about accuracy expectations. The document covers the input data and the development of the machine learning models and model validation. The results include the error by road volume, roadway and regional characteristics compared to typical estimation. The effects of reduced trip sample, ping rate, spatial accuracy and reference counters were also tested. The applicability of Probe Data was tested for other factors including, day of week, month of year, directional and ramp AADT, work zones ADT, K and D factors, peak hour truck data, special events or unusual weather and AADT by vehicle type

Transportation Behavioral Data and Climate Change

In 2017, transportation became the largest single source of greenhouse gas emissions from the United States. Globally, the 2014 Intergovernmental Panel on Climate Change report found that, without far more aggressive policies, “transportation emissions could increase at a faster rate than emissions from other energy end use sectors” reaching 12 Gt CO2-eq/year by 2050 (Sims et al., 2014). The overwhelming challenge of combatting these emissions is made far more difficult by the fact that so little is known about transportation behavior. To use a cliché – if we can’t measure it, we can’t manage it. And transportation must be managed if we are to avoid the most catastrophic consequences of climate change. In this dissertation, I propose that better data collection is necessary to achieve reduction of transportation-related emissions. Happily, advances in technology make this more feasible today than at any time in the past. The costs of massive computing resources have gone down, the world is swarming with mobile devices like smartphones and connected cars collecting massive (if messy) amounts of data, and new techniques in data science and machine learning have emerged to help get clean answers out of all that data in a privacy-appropriate manner. In some cases, these new techniques will displace older ones. In other cases, the old ways have inherent advantages. In other cases yet, fusing new and old techniques will yield the most productive results.In Chapter One, I lay out a framework to organize the types of transportation behavioral data that must be collected regularly to adequately measure and manage transportation’s impact on climate. This builds on classic climate impact frameworks, adapting them to the particular measurement challenges presented by transportation. In Chapter Two, I provide a history of US transportation data collection since World War II as well as a review of traditional, modern, and emerging transportation data collection technologies. I then map each technology onto each behavioral data collection need identified in Chapter One, matching each behavior to the best respective data collection technique.Chapters Three and Four provides an example of analysis done using the traditional data collection techniques, notably Household and Commercial Travel Surveys, to explore changes in PMT related to shopping and retail freight since 1969, as well as freight for fuel transportation. They demonstrate and take advantage of the key benefits of traditional techniques: that they go back in history, that they collect clearly stated trip purposes, vehicle occupancies, demographics (including gender, an important demographic but particularly difficult to deduce from the new data collection sources), trip distances, chaining behavior, commodities logged, and more. As it turns out, these benefits are critical: the historical trends of the past 40 years allow behavioral insight that would not have been possible with a shorter term study, and gender dynamics are key to understanding the behaviors at hand. However, the analysis in Chapters Three and Four also highlights some of the key limitations of survey-based analysis. The fact that data was only collected every five to ten years severely limits the analysis, such as limiting the exploration that can be done on the impacts of the Great Recession. In addition, fallibilities in human memory are especially pronounced in short trips, trip chains, and non-work related trips, all of particular importance to this study. Chapters Five lays out theoretically, and then Chapter Six demonstrates via case study in India, how personal GPS diary devices can be used to log detailed data about individual trips. It demonstrates the key benefit of this data – highly individualized characteristics. Taking the example of vehicle electrification, this chapter demonstrates two ways such granular data is important: in one example, such data to give feedback to an individual to influence their car buying behavior. In the second, the granularity found with this new data collection techniques reveals the importance of highly localized policy making and emissions modeling based on driving patterns in different cities.Chapter Seven uses the emerging technology of mass amounts of locational data, collected passively via smart phones, to explore how urban density at home and work interacts with total, work-related, and non-work-related miles driven. This demonstrates the great strength of this type of data – massive sample size combined with high spatial granularity and longitudinal data collection. These strengths enable the analysis at statistically meaningful scale of patterns across many geographies, individuals, and times of year. Thus, this data can shed light on questions about the relationship of density and miles travelled which previously have not been answered conclusively due to data constraints

Shop 'Till We Drop: A Historical and Policy Analysis of Retail Goods Movement in the United States

The movement of retail goods is central to modern economies and is a significantbut understudiedfraction of our overall energy footprint. Thus, we propose a new category for energy analysis called Retail Goods Movement (RGM) that draws its boundaries around the portion of freight dedicated to retail goods and the portion of driving dedicated to shopping. Historically, the components of RGM have not enjoyed policy priority. However, the net payoff from energy research and policy directed at RGM may now be high enough relative to other options to deserve increased investment. We combine a quantitative decomposition of the dynamics of RGM energy use with a qualitative discussion of what trends could have contributed to them. The RGM sector’s energy use grew from 1.3 EJ (2.8% U.S.) in 1969 to 7.0 EJ (6.6% U.S.) in 2009. The major drivers were increases in population, freight tonnage (before 1990), distance freighted per tonne and driven per shopping trip (after 1990), and weekly shopping trips per household (before 1995). RGM energy intensity increased per capita (180%), per constant dollar GDP (60%), and per retail expenditure (140%). Finally, we describe policy recommendations that could become the basis of a sound RGM resource plan

The Palgrave Handbook of South–South Migration and Inequality

The Palgrave Handbook of South–South Migration and Inequalit