Impacts of Attitudinal Factors and Prospective Roadway Designs on Autonomous Vehicle Adoption and Vehicle Fuel Choice
Yuntao Guo (email@example.com), University of Hawai'i, ManoaShow Abstract
Dustin Souders, Clemson University
Samuel Labi, Purdue University
Srinivas Peeta, Georgia Institute of Technology (Georgia Tech)
Irina Benedyk, University at Buffalo, SUNY
Yujie Li, Purdue University
The advent of autonomous vehicles (AVs) offers the promise of enhanced safety and mobility for travelers and increased efficiency for transportation system operations. AVs are likely to have significant impacts on the forms and functions of the built environment. In addition, the increasing urban air pollution, energy consumption, and climate change foster the need to promote adopting alternative fuel AVs such as all-electric AVs (EAVs) instead of gasoline-powered AVs (GAVs). This study aims to understand the impacts of attitudinal factors and roadway designs on people’s intention to use AVs and buy EAVs and GAVs. Fourteen latent attitudinal factors related to people’s perceptions and attitudes towards AV and EV technologies, driving, the environment, and personal innovativeness were considered. Urban roadway designs for accommodating AVs were created featuring dedicated AV lanes equipped with wireless charging for EAVs and replacing roadside parking for AV pick-up/drop-off zones. Structural equation models were estimated using stated preference survey data collected from over 1,300 people across the United States. The model estimation results show that the perceived advantages of AVs and EAVs, their potential to improve road safety, their compatibility with their users’ lifestyles and travel needs, and attitudes towards driving are key factors affecting their intention to use AVs and buy EAVs. The study results and insights can be used by transportation planners and policymakers to develop road network design guidelines and policies that target various attitudinal factors to influence AV adoption and promote EAVs over GAVs.
A Data Partitioning-based Artificial Neural Network Model to Estimate Real-driving Energy Consumption of Electric Buses
Yunteng Zhang, University of California, DavisShow Abstract
YUCHE CHEN (firstname.lastname@example.org), University of South Carolina
Ruixiao Sun, University of South Carolina
Abhishek Dubey, Vanderbilt University
Philip Pugliese, Chattanooga Area Regional Transportation Authority
Reliable and accurate estimation of electric bus energy consumption is critical for electric bus operation and planning. But energy prediction for electric buses is challenging because of diversified driving cycles of transit services. We propose to establish a data-partition based artificial neural network model to predict energy consumption of electric buses at microscopic level and link level. The purpose of data partitioning is to separate data into charging and discharging modes and then develop most efficient prediction for each mode. We utilize a long-term transit operation and energy consumption monitoring dataset from Chattanooga, SC to train and test our neural network models. The microscopic model estimates energy consumption of electric bus at 1Hz frequency based on instantaneous driving and road environment data. The prediction errors of micro model ranges between 8% and 15% on various instantaneous power demand, vehicle specific power, bins. The link-level model is to predict average energy consumption rate based on aggregated traffic pattern parameters derived from instantaneous driving data at second level. The prediction errors of link-level model are around 15% on various average speed, temperature and road grade conditions. The validation results demonstrate our models’ capability to capture impacts of driving, meteorology and road grade on electric bus energy consumption at different temporal and spatial resolution.
Trust in citizen acceptance of climate policy:Comparing perceptions of government competence, integrity and value similarity
Shelby Kitt, Simon Fraser UniversityShow Abstract
Jonn Axsen, Simon Fraser University
Zoe Long (email@example.com), Simon Fraser University
Ekaterina Rhodes, University of Victoria
This study examines the role of citizen trust in explaining climate policy support, using the case of clean transportation policies in Canada – namely a carbon tax, electric vehicle purchase incentives and three regulations. Through a representative survey of 1,552 Canadian citizens collected in 2019, we assess: 1) support and opposition of policies, 2) trust in several key actors, and 3) other factors associated with policy support. The majority of respondents support purchase incentives and most regulations, whereas support is considerably lower for a carbon tax. Factor analysis identifies three different types of trust in key actors: competence, integrity and value similarity. Fewer than 50% of respondents trust their national or provincial government regarding climate change issues in general, or according to each type of trust. Regression analysis explores the role of trust in policy support, while controlling for values and demographic characteristics. Perceptions of national government “competence” is the only trust variable that is a consistent, positive predictor of support for all five policies tested. Other forms of trust (integrity and value similarity), and trust in the provincial government, are not consistently associated with policy support.
Electrification of Road Freight Transport – Energy Consumption Analysis of Overhead Line Hybrid Trucks
Ferdinand Schöpp, Technische Universitat DarmstadtShow Abstract
Özgür Öztürk, Technische Universitat Darmstadt
Regina Linke, Technische Universitat Darmstadt
Jürgen Wilke, Technische Universitat Darmstadt
Manfred Boltze, Technische Universitat Darmstadt
Climate change requires a fundamental approach regarding diversified concepts and technologies for future generations. A sector, which is at the beginning of a major transformation, is the road freight transport sector. A promising approach to counteract climate change is the so-called “eHighway”, which is currently being tested in various field trials. In this context, trucks are equipped with a pantograph, allowing an electric power supply during their operation. Funded by the German Federal Ministry for the Environment, Nature Conservation and Nuclear Safety and led by the respective road authority Hessen Mobil, Technische Universität Darmstadt is responsible for the accompanying scientific research of the related German research project “ELISA”. Considering the experiences gained in a real world test operation, this research study provides a detailed description of different operating modes for “Overhead Line Hybrid (OH) trucks”. Moreover, the main goal of this study is to give insights into the related energy consumption information of the different electric and hybrid operating modes. For this purpose, the truck’s behaviour under monitored conditions and the operation in its designated future operational environment are considered. As a major result, 6+1 operating modes can be identified. Under certain circumstances, some operating modes allow a zero fuel consumption of an OH truck, even though the total fuel consumption mainly depends on the share an eHighway has related to the total distance travelled in a considered general context. Keywords: Road Freight Transport, eHighway, Overhead Line Hybrid Truck, Energy Consumption, Fuel Consumption, Electricity Consumption, Catenary, Climate Change, Greenhouse Gases.
Decomposition Analysis of Energy Consumption from Freight Transport in China
Feng Zhu, Beijing Jiaotong UniversityShow Abstract
Xu Wu, Beijing Jiaotong University
Freight transport as a critical economic industry imposes enormous challenges for energy consumption reduction. China's freight transport related energy consumption has experienced dramatic growth in recent decades, causing great concerns over its negative environmental impacts. Using the logarithmic mean Divisia index (LMDI) method, this study identifies the major driving factors and measures their corresponding contributions of freight transport associated energy consumption from 1998 to 2018 in China. The decomposition analysis focuses on five impacted factors that are responsible for freight transport related energy consumption: the population scale effect, the per capita economic activity effect, the freight transport intensity effect, the freight transport structure effect, and the freight energy intensity effect. The research results indicate that the main contributor to the increase of freight energy consumption is the per capita economic activity effect, followed by the freight transport structure effect, and finally the population scale effect. On the other hand, freight transport intensity and freight energy intensity both play significant roles in decreasing the freight transport related energy consumption over the entire study period. Notably, highway freight transport proves to be the significant factor of the freight energy consumption changes and contains enormous reduction potential. Based on the research findings, policy implications are provided to promote freight transport related energy consumption reductions and improve the sustainability of freight transport system.
A Life Cycle Assessment of a Shared, Autonomous and Electric Vehicle Fleet in a Regional Scale
Mariana Vilaca (firstname.lastname@example.org), University of AveiroShow Abstract
Gonçalo Santos, Universidade de Coimbra
Mónica Oliveira, Universidade de Aveiro
Gonçalo Homem de Almeida Correia, Technische Universiteit Delft
Margarida Coelho, Universidade de Aveiro
Shared mobility, automated vehicle technology, and vehicle electrification have the potential to dramatically reshape road transportation. Despite the large potential of shared automated electric vehicles (SAEVs) to significantly improve mobility systems, societal and economic factors, there are remaining concerns about their environmental and energy-related effect over their lifetime. This paper presents a life cycle assessment (LCA) of a shared autonomous and electric (SAE) regional mobility service to explore the environmental implications of a fleet deployment. A flow-based optimization approach was applied to obtain the future SAE fleet configuration for one passenger and four passenger’s vehicle capacity based on one-year travel demand of a medium-size region. Life cycle impact assessment of the SAEVs system was modeled considering different electricity generation scenarios and the impact categories: global warming potential, stratospheric ozone depletion, tropospheric ozone formation, fine particulate formation, and terrestrial acidification were evaluated. Results highlight that the implementation of SAEVs services should consider the maximum vehicle occupation to reduce the impacts of the system during the use phase as well as the incorporation of electricity generated by renewable sources (namely, wind and photovoltaic power systems). This analysis points for opportunities to improve environmental and energy performance of mobility systems with the usage of regional SAEVs. Keywords: Shared automated electric vehicles (SAEVs), Life Cycle Assessment, Impacts, Regional mobility.
Transitions to Sustainable Mobility from a Theory of Social Practice Perspective: Understanding Barriers to Modal Shift in Malta
Rosalie Camilleri (email@example.com), University of MaltaShow Abstract
Maria Attard, University of Malta
Robin Hickman, University College London
Transport has become locked into an unsustainable form. One major barrier to limiting emissions from the transport sector is the dependence of personal mobility on the car. Modal shift to low-carbon transport is essential to the decarbonisation of transport, yet it has not been fully understood. This paper uses the theories of social practices to provide new insights into the barriers to modal shift. It is based on the analysis of qualitative data on everyday mobility practices in the island state of Malta. The results show the constituent elements of low-carbon forms of mobility that are essential for these modes to be performed and endured. Shifting to these low-carbon forms of mobility means addressing the elements of these practices which are absent from the transport system. The analysis is also valuable to demonstrate how other social practices are complexed with mobility practices thus influencing recruitment of practitioners to one form of mobility and not the other. The coordination between these social practices and the complexities of everyday lives opens new sites of interventions aimed at decarbonising transport.
Externalities of Policy-Induced Scrappage: The Case of Automotive Regulations
Connor Forsythe, Carnegie Mellon UniversityShow Abstract
Akshaya Jha, Carnegie Mellon University
Jeremy Michalek, Carnegie Mellon University
Kate Whitefoot, Carnegie Mellon University
Social welfare implications of many transportation policies--including fuel economy standards, fuel taxes, and inspection programs--depend on how policy-induced changes to used vehicle scrappage (e.g.: the Gruenspecht effect) affect fleet-wide vehicle travel. While prior literature has estimated the vehicle price elasticity of scrappage, the effect of scrappage on fleet travel distance has not been empirically estimated. We exploit the staggered removal of state-wide safety inspection programs across the U.S. from 1970 to 2017 as an instrumental variable to estimate the fleet size elasticity of fleet travel distance for changes in fleet size resulting from policy-induced scrappage. Using a difference-in-difference model, we estimate that removal of safety inspections causes a 3% to 4% (depending on model specification) average increase in vehicle registrations. In a two-stage least-squares regression, we estimate the average annual fleet size elasticity of distance traveled. We reject the hypothesis, assumed or partially assumed in the literature and in regulatory impact assessments, that this elasticity is equal to 1 (at the 90% or 95% level, depending on model specification). These results imply that policy effects on travel-related externalities via the Gruenspecht effect or other vehicle scrappage effects may be smaller than assumed in prior analyses.
Who Supports Which Low-Carbon Transport Policies? Identifying Segments Among Canadian Citizens
Shelby Kitt, Simon Fraser UniversityShow Abstract
Zoe Long (firstname.lastname@example.org), Simon Fraser University
Jonn Axsen, Simon Fraser University
Citizen support is considered important for successful climate policy, though the literature overlooks how support varies by policy type and stringency and citizen characteristics. We focus on nine climate policies that apply to the transport sector (most at two stringency levels), including carbon taxes, financial and non-financial incentives for zero-emission vehicles (ZEVs), and three supply-focused regulations (for fuels, vehicle emissions, and ZEVs). Citizen response to each policy and stringency was collected via a representative sample survey of Canadian citizens in 2019 (n=1552). We find overall high levels of support (64% to 77%) for most policies, including vehicles emissions and low-carbon fuel standards, ZEV subsidies, investment in public charging infrastructure, and education campaigns. Support is lower for carbon taxes (27% to 42%), HOV-lane access for ZEVs (49%), and ZEV sales mandates (48% to 57%). Exploratory factor analyses indicates that the nine policies can be grouped into five distinct policy types: “supply-focused regulations” (including vehicle emissions and low-carbon fuel standards), “demand-focused initiatives” (ZEV subsidies, charger deployment and education), carbon tax, HOV-lane access for ZEVs, and ZEV mandate. Finally, using cluster analysis, we find evidence of three relatively homogenous clusters: 1) those who are “Universally Supportive” of the policies (78% to 98% support), 2) those “Supportive Except Carbon Tax” (63% to 91% support, but 71% oppose carbon tax), and 3) those who are “Mostly Opposing” (18% to 76% oppose, 5% to 44% support). We find some characteristics to differ significantly between these groups, including values, environmental concern, age, education, and region in Canada.
Examining the Sensitivities and Electrical Loads Associated with Vehicle Automation for Battery Electric and ICE vehicles
Eric Rask (email@example.com), Argonne National LaboratoryShow Abstract
Simeon Iliev, Argonne National Laboratory
Kevin Stutenberg, Argonne National Laboratory
David LaRue, FEV North America, Inc.
Vehicle automation, especially higher-level capabilities that require minimal input from the driver and allow for more efficient operation and coordination, show significant potential for decreased fuel/energy consumption. While these automation systems offer many potential strategies for efficient operation, the sensors, processing and actuation components required by these systems represent a new additional power load, which can reduce or cancel out the benefits provided by these new eco-capabilities. Given the importance of understanding the impact of automation system loads on overall vehicle operational efficiency, this work refines and expands the current literature related to automation loads and their impacts using a mix of experimental vehicle testing, analysis, and on-road commercial and prototype system testing. The sensitivity analysis portion of this work showed impacts of electrical loads vary significantly depending on cycle-average power consumption and thus should be represented as an additional load applied to a cycle, not a percentage increase in consumption. Field testing of a Cadillac CT6 with Super Cruise, a L2+ automation system, found automation system loads slightly above 100W when actuation, processing, and sensing loads are included. Field testing of an automated vehicle prototype, provided by an industrial project partner, found automation loads ranging between 300W and 400W for functionalities including hands-free highway operation (L3) and fully self-driving operation and navigation at lower speeds (L4). These electrical load levels suggest that many automation functions may be implementable at loads lower than the 2-4 kW seen in recent driverless capable pilot fleets.
The Effects of Climate and Climate Change on Passenger Vehicle Energy Consumption
Daniel Henrique (firstname.lastname@example.org), University of TorontoShow Abstract
Paul Kushner, University of Toronto
I. Daniel Posen, University of Toronto
Many aspects of passenger vehicle performance, including energy consumption and emissions, can be affected by external operating conditions such as weather. To date, these effects have been studied often in isolation by researchers from different engineering disciplines. This paper presents an inclusive review of known relationships between climate variables and passenger vehicle energy consumption, and estimates changes in energy consumption due to climate change. The scope includes both internal combustion engine vehicles (ICEVs) as well as battery electric vehicles (BEVs). Climate variables including ambient temperature, precipitation, and surface winds are examined in this paper, with a quantitative focus on temperature. The sensitivity of these relationships to rising temperatures from climate change were analyzed for four North American cities of differing climates. By 2035-2050, under a business as usual scenario (RCP 8.5), annual average energy consumption increased up to 2.2% for ICEVs, with changes ranging from -1.9 to +3.3% for BEVs. On a seasonal level, the changes were much more pronounced in the summers and winters, but were in opposite directions and cancelled out to produce the modest net changes reported. For BEVs specifically, the findings may have implications for long-term forecasts of electricity demand and electric grid planning.
Techno Economic Evaluation of Low Carbon Scenarios with High Fuel Cell Vehicle Penetration in California
vishnu vijayakumar, University of California, DavisShow Abstract
Lewis Fulton, University of California, Davis
Zero-emission vehicles will play a central role in California’s efforts to achieve carbon neutrality by 2045. Even if hydrogen fuel cell vehicles play only a supporting role to electric vehicles, their numbers may grow dramatically. This study considers a scenario where FCEVs begin to achieve significant sales share by 2025, rising rapidly through 2035, for 10 vehicle categories. The study employed a transition model to track vehicle sales, stocks, and their resulting hydrogen demand within the state. The study also estimates number of hydrogen production and refueling facilities required for next 15 years. Cost of hydrogen for different production and delivery scenarios is estimated using DOE’s models and aggregated with the scenario tool. FCEV stocks reach 1.8 million by 2035. Long-haul trucks are the biggest consumers of hydrogen. Fuel-economy will improve for all vehicle categories and the annual travel-per vehicle will remain constant or decrease after 2025. By 2035, the annual hydrogen demand in California crosses 1 million tons. Nearly 80 central production plants, each with 30 tonne/day capacity, would be needed by 2035, requiring capital investments of $4.1 billion. Rapid increases in the number of refueling stations must occur between 2025 (113stations) and 2035 (1144 stations). High volumes trigger greater scale and learning effects for electrolytic hydrogen over steam methane reformed hydrogen in the long-term. Net electricity demand from electrolysis will be 32.6 TWh/year by 2035, roughly 10% of the total grid demand. Hydrogen transport using pipelines with higher refueling station capacities are cost-effective pathways, at high utilization rates.
Household Transportation Energy Affordability by Region and Socioeconomic Factors
Yan Zhou (email@example.com), Argonne National LaboratoryShow Abstract
Spencer Aeschliman, Argonne National Laboratory
David Gohlke, Argonne National Laboratory
Transportation fuel is an important component of household budgets, as 3.3% of total household expenditures are for vehicle fuel nationwide and over 50% of annual household expenditures on energy are for transportation. These average values vary geographically, and higher energy cost burdens are faced by households with lower incomes. Defining transportation energy affordability as the percentage of annual household income spent on vehicle fuel, this study aims to quantify affordability as a function of household characteristics and geography. Through analysis at the census tract level, this study (1) projects annual household VMT based on demographic factors using machine learning techniques, (2) estimates local differences in vehicle fuel economy and fuel price by zip code, and (3) quantifies resulting transportation energy affordability by census tract. This study found that the average affordability by census tract varies from 0.15% to 8%. The variation in affordability can be largely explained by income level and vehicle fuel efficiency. Suburban and rural households spend more on transportation energy compared to the urban households due to the usage of less fuel-efficient vehicle technologies and higher annual VMT. Lower-income groups have a wide distribution of the percentage of income spent on transportation energy, 1.2% to 8%, while the range for the highest income group ($125k+) is from 0.15% to 3.9%. This detailed transportation energy affordability analysis provides a better understanding of regional variations in household travel behavior, helps to determine where fuel-efficient vehicle technologies are more likely to be used, and improves estimates of vehicle ownership costs.
Understanding the Energy Consumption Distribution of Electric buses: A Case Study in Nanjing, China
Sirui Nan, Southeast UniversityShow Abstract
Tiezhu Li (firstname.lastname@example.org), Southeast University
Jiankun Peng, Southeast University
Zhaozhi Dong, Central research institute
Jian Sun, Central research institute
With increasing mass-adoption of electric buses, the energy consumption as a key performance index has attracted the attention of electric bus drivers, automakers and even policy-makers. Understanding and estimating the characteristics of energy usage for electric buses are critical in achieving a green transportation system. The main objective of this paper is to quantify the vehicle-related influencing factors and space-time distribution of energy consumption based on the real-word driving conditions (DCs) collected by four electric buses in Nanjing, China. The first step of this research is to process actual electric buses operation data by a space-time-based driving fragments division method. With the extracted driving fragments, the influencing factors such as driving mode, velocity and acceleration are studied using statistical method. Based on the analysis results, this paper further investigates the driving parameters and energy consumption distribution during peak and off-peak hours under different road types. By proposing the energy consumption factor as an evaluation index, the results show that the average energy consumption factors in the arterial road of off-peak hour and peak hour are 0.932 kWh/km and 1.185 kWh/km, which are higher than expressway by 17.826% and 11.477% and lower than secondary road by 24.778% and 14. 006%. Besides, the electric bus travelling on the expressway and the arterial road are the most energy-efficient situation when it travels at the speed of 45-50 km/h and 25- 30 km/h, respectively. However, for the secondary road, the electricity usage is less when the average travel velocity is less than 15 km/h.
Beyond Adoption: Examining Zero-Emissions Miles Traveled (zVMT) in Households with Zero-emission Vehicles
Wenjian Jia, University of VirginiaShow Abstract
T. Donna Chen (email@example.com), University of Virginia
Both consumer adoption and usage impact zero-emission vehicles’ (ZEVs’) environmental benefits. While many studies examine consumers’ stated or revealed preferences for ZEV adoption, ZEV usage patterns are less studied. Based on the 2019 California vehicle survey data, this paper analyzes the usage of three ZEV types: battery electric vehicle (BEV), plug-in hybrid electric vehicle (PHEV), and fuel cell electric vehicle (FCEV). Results show that the mileage of the three ZEV types, on average, are higher than their household internal combustion engine vehicle (ICEV) counterparts. Furthermore, the zero-emissions mileage (zVMT) shows great variation across households. Determinants that contribute to such variation in zVMT are explored using multiple linear regression models. Greater battery range, home charging capability (regardless of charger type) and provision of special electricity rates for home charging are found to be positively correlated with the zVMT of PHEV households. The zVMT of BEV households are positively associated with Level 2 charging and solar panel installation at home, and workplace DC fast charging. Public DC fast charging station provision impacts the zVMT of both BEV and PHEVs, with a stronger effect for PHEVs. Similarly, the number of routinely used hydrogen refueling stations increases the zVMT of FCEVs. Lastly, when HOV lane access is rated as extremely important in the ZEV purchase decision, greater zVMT are found for both BEV and FCEV households, but not for PHEV households. Results of this study provide insights on policy implications to encourage household zVMT over ICEV VMT, achieving greater environmental benefits from ZEV adoption.
Investigating the Impacts of Travel Patterns on Vehicle Fuel USE in the U.S. and China
Shiqi Ou, Oak Ridge National LaboratoryShow Abstract
Chieh (Ross) Wang, Oak Ridge National Laboratory
Stacy Davis, Oak Ridge National Laboratory
Shasha Jiang, NA
Zhenhong Lin (firstname.lastname@example.org), Oak Ridge National Laboratory
Michael Hilliard, Oak Ridge National Laboratory
Ho-Ling Hwang, Oak Ridge National Laboratory
Xin He, Aramco Services Company
Steven Przesmitzki, Aramco Services Company
Jessey Bouchard, Aramco Services Company
The U.S. and China are the two largest vehicle markets in the world with a combined market share of nearly 50% of the world total in 2018. Such a tremendous vehicle market and on-road driving demand potentially bring a huge growth of oil demand to the globe. Therefore, the understanding of vehicle driving patterns and where the gasoline demand comes from in these two largest markets can be beneficial to planners and policy decision-makers in addressing transportation energy and environmental issues. This study estimates the correlations of vehicle mileages/fuel use with vehicle class/size and household characteristics; and these features could reflect the differences in vehicle purchase and driving behaviors. In addition, it also introduces a method to estimate probable annual fuel use in the vehicle markets. Specifically, this research provides the fuel use results by considering the possible heterogeneity of annual vehicle miles of travel (AVMT) by vehicle class/type. Some key findings are: 1) A larger-size vehicle tends to have a higher per vehicle AVMT than a smaller-size vehicle in both the U.S. and China, and it is more prominent in China; 2) The heterogeneity of annual fuel consumption is highly correlated to the household characteristics, such as income and household size; and 3) Explicit consideration of per-vehicle AVMT heterogeneity by vehicle class leads to higher estimates of total fuel use.
Car Dependence, Norms or Perceptions? Exploring Engagement with “Automobility” as a Predictor of Interest in Shared, Automated and Electric Mobility
Viviane Gauer (email@example.com), Simon Fraser UniversityShow Abstract
Jonn Axsen, Simon Fraser University
Elisabeth Dütschke, Fraunhofer-Institut fur System und Innovationsforschung
Zoe Long, Simon Fraser University
Sociologists have developed the concept of “automobility” to explain the dominance of the fossil fuel-powered car as the main travel mode, including technology, infrastructure, and cultural elements. In a novel application of this theory, we quantitatively explore automobility from a consumer perspective, identifying seven potential constructs of consumer engagement with automobility that might help to categorize individuals and explain their interest in different transportation modes and technologies – namely shared, automated, and electric mobility. We construct a total of 40 questionnaire items and analyze survey responses from a representative sample of 3,658 Canadian respondents implemented in Spring 2020. First, we conduct exploratory factor analysis and identify eight relatively independent automobility factors: “car dependence”, “car identity”, “driving aversion”, “societal concern”, “alternative mode norms”, “house ownership indifference”, “walkability indifference”, and “car use as social harm”. We then use regression analyses to explore the role of automobility factors in consumer interest in ride-hailing, carsharing, automated vehicles, and electric vehicles – controlling for demographics and other characteristics. Analyses indicate that each factor is significantly associated with interest in at least one innovation type. Some automobility factors are consistently significant predictors of interest in automated and electric vehicles, such as “car dependence” and “societal concern”. Some patterns vary by innovation. For example, “alternative mode norms” are strongly associated with interest in car-sharing and “driving aversion” is only positively associated with interest in fully automated vehicles (FAVs) that cannot be driven manually. We conclude that consumer engagement with automobility is correlated with interest in new mobility innovations.
Evaluate the System-Level Impact of Connected and Automated Vehicles Coupled with Shared Mobility: An Agent-based Simulation Approach
Peng Hao, University of California, RiversideShow Abstract
Chao Wang, University of California, Riverside
Guoyuan Wu, University of California, Riverside
Shams Tanvir, University of California, Riverside
Bingrong Sun, National Renewable Energy Laboratory (NREL)
Jacob Holden, National Renewable Energy Laboratory (NREL)
Andrew Duvall, National Renewable Energy Laboratory (NREL)
Jeff Gonder, National Renewable Energy Laboratory (NREL)
Matthew Barth, University of California, Riverside
With the rapid growth of information and communication technologies, Connected and Automated Vehicles (CAVs) are deemed to be disruptive with the potential to significantly improve overall transportation system efficiency, however, may bring Vehicle Miles Traveled (VMT) increase or other issues. Further, shared mobility systems are another disruptive force that is reshaping our travel patterns. To quantify the combined impact of CAV and shared mobility on travel behavior, traffic performance and energy efficiency, we develop a mesoscopic simulation-based framework for mobility and energy efficiency evaluation considering the disruptive transportation technologies. Under this framework, we develop novel models for energy intensity and modal activity, and evaluated a variety of energy scenarios for different combinations of CAV applications, various levels of automation, roadway characteristics, and traffic conditions, while also varying different vehicle types and fuel/powertrain technologies. By applying this modeling suite to a calibrated BEAM simulation network in Riverside California, it was found that cooperative automated driving in general will improve mobility, but automated vehicles, even when deployed in a shared autonomous fleet, will likely bring an increase of VMT (up to 36%) due to mode shifts and deadheading. Ride-hailing vehicles typically have better energy efficiency and a higher share of electric vehicles, which helps offset the negative impact from VMT increases when estimating the system-level energy consumption. In general, simulation results show a 6% increase in energy consumption for the scenarios with an increasing shift to ride-hailing modes.
Comparison of Federally-Surveyed Data for Gasoline and Diesel Prices to Crowdsourced and Commercially-Surveyed Data Sets
David Gohlke (firstname.lastname@example.org), Argonne National LaboratoryShow Abstract
Josephine Kelpsas, Argonne National Laboratory
Gasoline and diesel prices are key pieces of the U.S. economy, often being heavily impacted by supply and demand patterns resulting from economic dips and rises. In the new technological age, it has become easier to determine where cheaper gas prices are in a specific area using crowdsourced apps or privately sourced data sources like GasBuddy and the Oil Price Information Service (OPIS). However, this gas price data remains untested in accuracy and reliability. For use in research settings, having a trustworthy data source for gasoline prices is essential. Currently, the Energy Information Administration (EIA) releases gas price data on a weekly basis. This data is proven and provides usable information. This study compares data from both GasBuddy and OPIS to the baseline of EIA in order to determine the similarities of these data sources for use in future analysis. The conclusions of this study show that the correlations of both sources to the EIA are consistently high across the regions and states compared in this study. National average gas prices for the three sources agree to within one cent/gallon on average since May 2018. State-level data also gives general agreement, in most cases to within 2%. This suggests that either source could be used in conjunction with EIA data to give researchers more frequent data points throughout a week and higher spatial resolution.
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