published journal article
Archives: Research Products
published journal article
Association of urban green space with metabolic syndrome and the role of air pollution
Landscape and Urban Planning
Publication Date
Author(s)
Suggested Citation
Yi Sun, Yunli Chen, Yuanyuan Huang, Yan Luo, LiPing Yan, Sailimai Man, Canqing Yu, Jun Lv, Chuangshi Wang, Jun Wu, Heling Bao, Bo Wang, Liming Li and Hui Liu (2024) “Association of urban green space with metabolic syndrome and the role of air pollution”, Landscape and Urban Planning, 248, p. 105100. Available at: 10.1016/j.landurbplan.2024.105100.published journal article
Asymptotic traffic dynamics arising in diverge–merge networks with two intermediate links
Transportation Research Part B: Methodological
Publication Date
Author(s)
Suggested Citation
Wen-Long Jin (2009) “Asymptotic traffic dynamics arising in diverge–merge networks with two intermediate links”, Transportation Research Part B: Methodological, 43(5), pp. 575–595. Available at: 10.1016/j.trb.2008.10.002.Phd Dissertation
A dynamic household alternative-fuel vehicle demand model using stated and revealed transaction information.
Publication Date
Author(s)
Abstract
Forecasting the demand for alternative-fuel vehicles (AFVs) is quite important for manufacturers, fuel suppliers and environmental planners. AFVs have attributes such as reduced range and limited refueling options that are very different from existing vehicles. Therefore stated preference (SP) data is necessary for demand models. Previous work by Brownstone, Bunch, and Train (1998) shows that there are serious biases in these stated preference data. Another source of households’ vehicle preference, is households’ actual observed transaction behavior (Revealed preference (RP) data). I develop a dynamic stated and revealed preference vehicle transaction model which uses the RP data to control for the biases of using pure SP data in order to better forecast households’ demand for AFVs for California. I implement a “scale factor” to specify the relationship of the different variances of the RP and SP data. Moreover, I examine the nested structure over different fuel-type vehicle choices and estimate both the multinomial logit (MNL) and nested logit (NL) models. In addition, I conduct forecast using the pure SP and joint SP-RP MNL models under the scenario consisting of new vehicle technologies for year 1998. Compared to the new vehicle sales statistics, it is obvious that the joint SP-RP model provides more reasonable forecasts. I also examine the different substitution patterns implied by the pure SP MNL and NL models when new vehicle choices are introduced. The NL model predicts more realistic substitution pattern. I also add the used vehicle choices to the forecast scenario to make the forecast more realistic because the used vehicle market is taken into consideration. Large panel data sets have been collected by the California Alternative-Fuel Vehicle Demand Forecast Project since May 1993. These data contain extensive information on households’ stated and revealed preference vehicle transactions, vehicle utilization and households’ socioeconomic characteristics. This study serves as an example of how to forecast new products or technologies that mark considerable departures from existing products or technologies.
Suggested Citation
Hongyan Sheng (1999) A dynamic household alternative-fuel vehicle demand model using stated and revealed transaction information.. PhD Dissertation. UC Irvine. Available at: https://uci.primo.exlibrisgroup.com/permalink/01CDL_IRV_INST/17uq3m8/alma991035093101304701.research report
Using UMTA Section 15 data for triennial reviews
Publication Date
Associated Project
Author(s)
Final Report
Areas of Expertise
Abstract
Illustrates the application of the Irvine Performance Evaluation Method (IPEM) to triennial reviews of transit performance conducted by the Urban Mass Transportation Administration under provisions of the Surface Transportation Assistance Act of 1982
Suggested Citation
Gordon J. Fielding, Mark. Yamarone and Marcy Jaffe (1987) Using UMTA Section 15 data for triennial reviews. Final Report CA-06-0213-3. Washington, D.C. : Springfield, Va.: U.S. Dept. of Transportation, Urban Mass Transportation Administration, Office of Grants Management ; Available through the National Technical Information Service. Available at: https://catalog.hathitrust.org/Record/102497535.conference paper
An analysis of train emissions and their health impacts in California's alameda corridor
Proceedings of INFORMS, san diego, CA
Publication Date
Author(s)
Suggested Citation
J. Saphores, M. Sangkapichai, S. Ritchie, G. Lee, I. You and R. Ayala (2009) “An analysis of train emissions and their health impacts in California's alameda corridor”, in Proceedings of INFORMS, san diego, CA.published journal article
Analysing non-linearities and threshold effects between street-level built environments and local crime patterns: An interpretable machine learning approach
Urban Studies
Publication Date
Author(s)
Areas of Expertise
Abstract
Despite the substantial number of studies on the relationships between crime patterns and built environments, the impacts of street-level built environments on crime patterns have not been definitively determined due to the limitations of obtaining detailed streetscape data and conventional analysis models. To fill these gaps, this study focuses on the non-linear relationships and threshold effects between built environments and local crime patterns at the level of a street segment in the City of Santa Ana, California. Using Google Street View (GSV) and semantic segmentation techniques, we quantify the features of the built environment in GSV images. Then, we examine the non-linear relationships and threshold effects between built environment factors and crime by applying interpretable machine learning (IML) methods. While the machine learning models, especially Deep Neural Network (DNN), outperformed negative binomial regression in predicting future crime events, particularly advantageous was that they allowed us to obtain a deeper understanding of the complex relationship between crime patterns and environmental factors. The results of interpreting the DNN model through IML indicate that most streetscape elements showed non-linear relationships and threshold effects with crime patterns that cannot be easily captured by conventional regression model specifications. The non-linearities and threshold effects revealed in this study can shed light on the factors associated with crime patterns and contribute to policy development for public safety from crime.
Suggested Citation
Sugie Lee, Donghwan Ki, John R Hipp and Jae Hong Kim (2025) “Analysing non-linearities and threshold effects between street-level built environments and local crime patterns: An interpretable machine learning approach”, Urban Studies, 62(6), pp. 1186–1208. Available at: 10.1177/00420980241270948.published journal article
Modeling the dynamics of passenger travel demand by using structural equations
Environment & planning A
Publication Date
Author(s)
Suggested Citation
T F Golob and H Meurs (1988) “Modeling the dynamics of passenger travel demand by using structural equations”, Environment & planning A, 20(9), pp. 1197–1218. Available at: 10.1068/a201197.Phd Dissertation
An Analysis of Carsharing and Battery Electric Vehicles in the United States
Publication Date
Author(s)
Abstract
According to the California Air Resources Board (CARB, 2020), light-duty vehicles are responsible for 13 percent of statewide NOx emissions and 28 percent of statewide greenhouse gas emissions. Scientists, policymakers, and car manufacturers have been striving to reduce the air pollution and greenhouse gas emissions from the transportation sector using various measures, ranging from cleaner engines to alternatives to driving to reduce VMT. In this dissertation, I focus on a subset of these measures: carsharing programs and Battery Electric Vehicles (BEVs). In the first part of this dissertation, I explore the profile of households engaging in carsharing by estimating zero-inflated negative binomial (ZINB) models on data from the 2017 National Household Travel Survey (NHTS). My results show that households who are more likely to carshare are those who participate in other forms of sharing, have more Silent generation members, are less educated (the highest educational achievement is a high school degree), and have fewer vehicles than drivers. Conversely, households with more young adults (18 – 20 years old), with 2 or more adults and no children, take part in carsharing program less often. Moreover, households who took more part in ridesharing and have fewer vehicles than drivers are less likely to never carshare. Furthermore, households whose annual income between $75,000 and $150,000 are more likely to never carshare. In the second part of this dissertation, I concentrate on the adoption of BEVs. More specifically, I focus on two questions: 1) What are the characteristics of households who own battery electric vehicles (BEVs)?; and 2) Does the travel behavior of these households differ from the travel of households who have motor vehicles but not BEVs? To answer those questions, I characterize three groups of households based on their vehicle holdings: BEV-only, BEV+ (i.e., households with both one or more BEV and at least one conventional vehicle), and non-BEV households. I analyze data from the 2017 NHTS using mixed methods. Results show that BEV households are more likely to be Asian, well-educated, with a higher income and to live in higher population and employment density areas. Furthermore, BEV-only households are more likely to be composed of one adult (not retired) with fewer Baby Boomers. Yet, BEV+ households are more likely to be larger households with 2 or more adults. Also, BEV+ households are more likely to have more Generation X (37-52 years old in 2017) and Z members (20 years old or younger in 2017). They are also more likely to own their home. My analysis on gender (at the individual level) concluded that BEV owners are more likely to be men. Furthermore, I find that BEV households travel as much as non-BEV households. Although carsharing and BEVs could substantially decrease the environmental footprint of transportation, they are currently far from mainstream. To promote carsharing programs, their reach could be extended, they could be made more affordable, while increasing the cost of owning and operating private vehicles. Similarly, state and federal governments could continue to provide financial incentives to lower the purchase price difference between conventional and BE vehicles, manufacturers could provide extended warranties on batteries, and the charging infrastructure needs to be developed in order to attract more customers. The Covid-19 crisis is giving governments around the world an opportunity to invest in clean technologies to jumpstart the economy. It is critical to take advantage of this crisis to reduce air pollution and greenhouse gas emissions from transportation for the good of current and future generations.