This study focused on the adoption of Plug-in Electric Vehicles (PEVs) as a policy towards having a more sustainable transportation with lower Greenhouse Gas (GHG) emissions. The current paper aimed to explore potential factors that can be attributed to purchasing PEVs in order to estimate their penetration in 58 California counties. A Multiple Logistic Regression Analysis was applied to the 2012 California Household Travel Survey dataset, which includes both PEV and conventional car buyers’ information, as well as some other secondary data sources. The model developed a broad set of factors including demographic and travel-related characteristics, socioeconomic variables, and infrastructural and regional specifications. The results identified that a household’s income, maximum level of education in the household, the buyer’s car sharing status, charging stations density, and gas price in the region can significantly impact PEV adoption. The model was validated using data from the 2012 Household Travel Survey conducted in the Delaware Valley region. With sufficient data availability, the methodology can be applied to evaluate changes in vehicle fleet composition and the levels of emissions in response to transportation policies. The model is believed to have a wide range of applications in electricity utilizing, gasoline/diesel retailing, and battery and automotive manufacturing. Additionally, the model can assist policy makers and transportation planners to optimize their infrastructural investments by identifying counties where the response of drivers to added charging station would be maximized, implying that larger benefits can be achieved.
Carpooling is a common method of reducing traffic congestion and emissions of air pollutants and greenhouse gases. Which types of incentives are most effective at encouraging carpooling, and what magnitude of reductions could be achieved by applying such incentives? To answer these questions, we develop a statistical model relating High Occupancy Vehicle (HOV) lanes and other potential factors to carpooling propensity in all 50 U.S. states and the District of Columbia. At the state level, we find HOV lane-kilometers together with higher-than-average gasoline prices to be effective in promoting carpooling, particularly among people with lower Human Development Index (HDI) and/or larger households. An in-depth analysis of 58 counties in California finds that HOV lane-kilometers also positively impact carpooling rates for individual counties. However, average travel time to work replaces socio-demographic variables as a secondary motivator. For a hypothetical scenario where existing HOV lane-kilometers in each state are expanded by 0.5 meters for every hour of total daily travel time to work, we find this strategy has the greatest potential to reduce annual carbon dioxide equivalent (CO2e) in the District of Columbia, by 4.5%, followed by Hawaii, New York, and New Jersey. The smallest potential is found in states with the lowest population density, led by North Dakota. Nationally, 1.83 MMT of CO2e or 0.16% of light duty vehicle emissions would be reduced under this scenario while at the county-level, California could reduce 0.34 MMT or 0.31% of light-duty vehicles CO2e emissions. The results assist policy makers in optimizing infrastructural investments.
Online Estimation of Travel Time Variability Using the Integrated Traffic Incident and Weather Data Roxana J. Javid
The effects of traffic incidents on travel time and its variability have been researched extensively in the past, and the majority of these studies were conducted by using single source data. As transportation data becomes more available, there is a need to explore how data from different sources can be integrated in order to obtain better results. This study attempts to develop tools to estimate highway clearance time by using integrated weather, road geometry, and incident data. The methodology is configured to run with real-time data, thus providing operators with travel time variations under incident conditions. This study integrates comprehensive traffic data provided by the highway Performance Measurement System (PeMS) in California, U.S., and detailed weather data to develop a robust regression model. The validation results indicate that estimated highway clearance time results in fairly accurate estimations of travel time variability. Application of the model through a micro-simulation indicates that equipping travelers with estimated travel time variability can shorten the total travel time by almost 60 percent. The contribution of this research is the interweaving of several real datasets and the identification of interactions, which can be advantageous to Traffic Incident Management (TIM).
Selection of CO2 mitigation strategies for road transportation in the United States using a multi-criteria approach Roxana J. Javid Carbon dioxide emissions from human activities are the primary cause of recent climate change. In the United States, the road transportation sector is one of the largest sources of these emissions. Any policies to reduce emissions must therefore include mitigation strategies for on-road transportation. The aim of this paper is to propose a multi-criteria method, Analytical Hierarchy Process, to rank various on-road emissions mitigation strategies including reduce, avoid, and replace strategies. The method׳s results are obtained based on a survey of transportation and climate science professionals. The Analytical Hierarchy Process was applied to two regional scenarios of a midsize-small city (Lubbock, Texas) and a metropolitan area (Dallas, Texas). To evaluate the effectiveness of these strategies, the Motor Vehicle Emissions Simulator model was run for Dallas. The aim of the model was to estimate the potential carbon dioxide mitigation for a given strategy allocation. Our survey identified no difference between the rankings of reduce, avoid, and replace strategies for our metropolitan and midsize-small city areas. Reduce strategies had the highest preference score of 40% followed by avoid strategies with 36% and replace strategies with 24%. An optimum mixed mitigation scenario would achieve reductions in carbon dioxide emissions of 17% by 2030 from 2010 levels. The contributions of this study are two-fold. First, we evaluate generic scenarios in detail and apply them to a real-world case study. Second, the approach is both simple and generalizable. Applications of this type of platform include ranking transportation strategies on mitigating carbon dioxide emissions, evaluating the strategies, prioritizing budgets, and developing assessments of mitigation potential.