published journal article
Area of Expertise: Unspecified
conference paper
Understanding household preferences for alternative-fuel vehicle technologies
ACSP 51st annual conference in minneapolis, minnesota
Publication Date
Author(s)
Suggested Citation
Hilary Nixon and Jean-Daniel Saphores (2011) “Understanding household preferences for alternative-fuel vehicle technologies”, in ACSP 51st annual conference in minneapolis, minnesota.working paper
Modelling the Choice of Clean Fuels and Clean Fuel Vehicles
Publication Date
Author(s)
Working Paper
Abstract
Reducing vehicle emissions levels is particularly important in the South Coast Air Basin of California, which includes the Los Angeles Metropolitan Area and the adjacent and interdependent Orange County, Riverside, and San Bernardino Metropolitan Areas. The climate and topography create ideal conditions for the area’s infamous smog; and cars, trucks and buses contribute 88 percent of carbon monoxide emissions and about 50 percent of the ozone components: oxides of nitrogen and reactive organic gases. It is apparent that air quality can be greatly improved if gasoline-powered personal vehicles can be replaced in substantial numbers by vehicles powered by electricity or alternative fuels, such as methanol, ethanol, propane, or compressed natural gas (CNG) (see Sperling, 1988 and National Research Council, 1990, for discussions of the environmental factors associated with specific alternative fuels). While none of these alternative fuels has zero-level emissions (even electricity, if generation is taken into account), they all have lower overall emissions levels than currently available gasoline and diesel fuels; they are considered “clean” fuels for the purposes of this market research study. Personal vehicles are defined for the purposes of the study to be cars or light trucks owned or leased by private individuals. The objective of this study is to determine the effect on personal vehicle purchase and fuel use of a few important attributes that potentially differentiate clean-fuel vehicles from conventional gasoline or diesel vehicles. By concentrating on quantitative estimation, it is intended that this study complement others aimed at qualitative assessments of the roles of information and uncertainty in consumer acceptance of clean-fuel vehicles (e.g., Turrentine and Sperling, 1991).
Suggested Citation
Ryuichi Kitamura, Mark Bradley, David S. Bunch and Thomas F. Golob (1991) Modelling the Choice of Clean Fuels and Clean Fuel Vehicles. Working Paper UCI-ITS-WP-91-12. Institute of Transportation Studies, Irvine. Available at: https://escholarship.org/uc/item/59c821m2.published journal article
Changes in service and associated ridership impacts near a new light rail transit line
Sustainability
Publication Date
Author(s)
Suggested Citation
Jeongwoo Lee, Marlon Boarnet, Douglas Houston, Hilary Nixon and Steven Spears (2017) “Changes in service and associated ridership impacts near a new light rail transit line”, Sustainability, 9(10), p. 1827. Available at: 10.3390/su9101827.book/book chapter
Neighborhood Change in Near-Transit Latinx 1 Communities: Challenges and Opportunities for Sustainable Development
Publication Date
Author(s)
Suggested Citation
Michelle E. Zuñiga and Douglas Houston (2022) “Neighborhood Change in Near-Transit Latinx 1 Communities: Challenges and Opportunities for Sustainable Development”, in . Erualdo González Romero, . Michelle E. Zuñiga, . Ashley C. Hernandezand . Rodolfo D. Torres (eds.) Gentrification, Displacement, and Alternative Futures. New York: Routledge, pp. 7–25. Available at: https://www.taylorfrancis.com/chapters/edit/10.4324/9780429341809-2/neighborhood-change-near-transit-latinx-1-communities-michelle-zu%C3%B1iga-douglas-houston (Accessed: October 5, 2023).published journal article
Distributed computing and simulation in a traffic research test bed
Comp-aided Civil Eng
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Author(s)
Abstract
This article describes the California Advanced Research Testbed (CART), integrated with an urban traffic network and having real-time communication capabilities with traffic control centers in Orange County, California. The test bed provides opportunities to study different components of Advanced Traveler Information Systems (ATIS) and Advanced Traffic Management Systems (ATMS). Focus of the article is on distributed computing issues and the implementation of a hybrid simulation framework.
Suggested Citation
R. Jayakrishnan and Craig R. Rindt (1999) “Distributed computing and simulation in a traffic research test bed”, Comp-aided Civil Eng, 14(6), pp. 429–443. Available at: 10.1111/0885-9507.00161.policy brief
General Plan Content Related to Transportation and Land Use Varies Significantly Across Cities in Orange County
Publication Date
Author(s)
Abstract
Author(s): Kim, Jae Hong; Li, Xiangyu
Suggested Citation
Jae Hong Kim and Xiangyu Li (2020) General Plan Content Related to Transportation and Land Use Varies Significantly Across Cities in Orange County. Policy Brief. Available at: https://escholarship.org/uc/item/2g79d7gk (Accessed: October 11, 2023).published journal article
Best frenemies? A characterization of TNC and transit users
Journal of Public Transportation
Publication Date
Author(s)
Abstract
The emergence of transportation network companies (TNCs) has created new options for travelers and fierce competition for taxis and public transportation (PT). While the literature focuses either on TNCs or PT users, we contrast individuals/households who use only PT, only TNCs, or both by estimating a cross-nested logit on 2017 NHTS data. We analyzed both individuals (for consistency with most of the literature) and households (to account for intrahousehold travel dependencies). Our results show that the unit of analysis (individuals vs. households) does not matter much for our dataset. We found that individuals/households who use either PT or TNCs or both share socio-economic characteristics, reside in similar areas, and differ from individuals/households who use neither transit nor TNCs. In addition, individuals/households who use both PT and TNCs tend to be composed of Millennials and Generation Z, with a higher income, more education, no children, and fewer vehicles than drivers. Our findings highlight the danger for PT of entering into outsourcing agreements with TNCs, neglecting captive riders, and further exposing choice riders to TNCs.
Suggested Citation
Farzana Khatun and Jean-Daniel M. Saphores (2022) “Best frenemies? A characterization of TNC and transit users”, Journal of Public Transportation, 24, p. 100029. Available at: 10.1016/j.jpubtr.2022.100029.published journal article
Field tests of a dynamic green driving strategy based on inter-vehicle communication
Transportation Research Part D: Transport and Environment
Publication Date
Author(s)
Suggested Citation
Hao Yang, Lawrence Andres, Zhe Sun, Qijian Gan and Wen-Long Jin (2018) “Field tests of a dynamic green driving strategy based on inter-vehicle communication”, Transportation Research Part D: Transport and Environment, 59, pp. 289–300. Available at: 10.1016/j.trd.2018.01.009.Preprint Journal Article
Physics-Informed Deep Learning to Reduce the Bias in Joint Prediction of Nitrogen Oxides
Publication Date
Author(s)
Abstract
Atmospheric nitrogen oxides (NOx) primarily from fuel combustion have recognized acute and chronic health and environmental effects. Machine learning (ML) methods have significantly enhanced our capacity to predict NOx concentrations at ground-level with high spatiotemporal resolution but may suffer from high estimation bias since they lack physical and chemical knowledge about air pollution dynamics. Chemical transport models (CTMs) leverage this knowledge; however, accurate predictions of ground-level concentrations typically necessitate extensive post-calibration. Here, we present a physics-informed deep learning framework that encodes advection-diffusion mechanisms and fluid dynamics constraints to jointly predict NO2 and NOx and reduce ML model bias by 21-42%. Our approach captures fine-scale transport of NO2 and NOx, generates robust spatial extrapolation, and provides explicit uncertainty estimation. The framework fuses knowledge-driven physicochemical principles of CTMs with the predictive power of ML for air quality exposure, health, and policy applications. Our approach offers significant improvements over purely data-driven ML methods and has unprecedented bias reduction in joint NO2 and NOx prediction.