published journal article

Measuring the inconvenience of operating an alternative fuel vehicle

Transportation Research Part D: Transport and Environment

Publication Date

March 1, 2014
Suggested Citation
Jee Eun Kang and Will W. Recker (2014) “Measuring the inconvenience of operating an alternative fuel vehicle”, Transportation Research Part D: Transport and Environment, 27, pp. 30–40. Available at: 10.1016/j.trd.2013.12.003.

published journal article

Particulate matter, traffic-related air pollutants, and circulating C-reactive protein levels: The Multiethnic Cohort Study

Environmental Pollution

Publication Date

September 1, 2023

Author(s)

Meera Sangaramoorthy, Johnny Yang, Chiuchen Tseng, Jun Wu, Beate Ritz, Timothy V. Larson, Scott Fruin, Daniel O. Stram, Seri Park, Adrian A. Franke, Lynne R. Wilkens, Jonathan M. Samet, Loïc Le Marchand, Salma Shariff-Marco, Christopher A. Haiman, Anna H. Wu, Iona Cheng

Abstract

Inhaled particles and gases can harm health by promoting chronic inflammation in the body. Few studies have investigated the relationship between outdoor air pollution and inflammation by race and ethnicity, socioeconomic status, and lifestyle risk factors. We examined associations of particulate matter (PM) and other markers of traffic-related air pollution with circulating levels of C-reactive protein (CRP), a biomarker of systemic inflammation. CRP was measured from blood samples obtained in 1994–2016 from 7,860 California residents participating in the Multiethnic Cohort (MEC) Study. Exposure to PM (aerodynamic diameter ≤2.5 μm [PM2.5], ≤10 μm [PM10], and between 2.5 and 10 μm [PM10-2.5]), nitrogen oxides (NOx, including nitrogen dioxide [NO2]), carbon monoxide (CO), ground-level ozone (O3), and benzene averaged over one or twelve months before blood draw were estimated based on participants’ addresses. Percent change in geometric mean CRP levels and 95% confidence intervals (CI) per standard concentration increase of each pollutant were estimated using multivariable generalized linear regression. Among 4,305 females (55%) and 3,555 males (45%) (mean age 68.1 [SD 7.5] years at blood draw), CRP levels increased with 12-month exposure to PM10 (11.0%, 95% CI: 4.2%, 18.2% per 10 μg/m3), PM10-2.5 (12.4%, 95% CI: 1.4%, 24.5% per 10 μg/m3), NOx (10.4%, 95% CI: 2.2%, 19.2% per 50 ppb), and benzene (2.9%, 95% CI: 1.1%, 4.6% per 1 ppb). In subgroup analyses, these associations were observed in Latino participants, those who lived in low socioeconomic neighborhoods, overweight or obese participants, and never or former smokers. No consistent patterns were found for 1-month pollutant exposures. This investigation identified associations of primarily traffic-related air pollutants, including PM, NOx, and benzene, with CRP in a multiethnic population. The diversity of the MEC across demographic, socioeconomic, and lifestyle factors allowed us to explore the generalizability of the effects of air pollution on inflammation across subgroups.

Suggested Citation
Meera Sangaramoorthy, Juan Yang, Chiuchen Tseng, Jun Wu, Beate Ritz, Timothy V. Larson, Scott Fruin, Daniel O. Stram, Sung-shim Lani Park, Adrian A. Franke, Lynne R. Wilkens, Jonathan M. Samet, Loïc Le Marchand, Salma Shariff-Marco, Christopher A. Haiman, Anna H. Wu and Iona Cheng (2023) “Particulate matter, traffic-related air pollutants, and circulating C-reactive protein levels: The Multiethnic Cohort Study”, Environmental Pollution, 332, p. 121962. Available at: 10.1016/j.envpol.2023.121962.

published journal article

Traffic-related air pollution and Parkinson's disease in central California

Environmental Research

Publication Date

January 1, 2024

Author(s)

Dayoon Kwon, Kimberly C. Paul, Yue Yu, Kenan Zhang, Aline D. Folle, Jun Wu, Jeff M. Bronstein, Beate Ritz

Abstract

Background Prior studies suggested that air pollution exposure may increase the risk of Parkinson’s Disease (PD). We investigated the long-term impacts of traffic-related and multiple sources of particulate air pollution on PD in central California. Methods Our case-control analysis included 761 PD patients and 910 population controls. We assessed exposure at residential and occupational locations from 1981 to 2016, estimating annual average carbon monoxide (CO) concentrations – a traffic pollution marker – based on the California Line Source Dispersion Model, version 4. Additionally, particulate matter (PM2.5) concentrations were based on a nationwide geospatial chemical transport model. Exposures were assessed as 10-year averages with a 5-year lag time prior to a PD diagnosis for cases and an interview date for controls, subsequently categorized into tertiles. Logistic regression models were used, adjusting for various factors. Results Traffic-related CO was associated with an increased odds ratio for PD at residences (OR for T3 vs. T1: 1.58; 95% CI: 1.20, 2.10; p-trend = 0.02) and workplaces (OR for T3 vs. T1: 1.91; 95% CI: 1.22, 3.00; p-trend <0.01). PM2.5 was also positively associated with PD at residences (OR for T3 vs. T1: 1.62; 95% CI: 1.22, 2.15; p-trend <0.01) and workplaces (OR for T3 vs. T1: 1.85; 95% CI: 1.21, 2.85; p-trend <0.01). Associations remained robust after additional adjustments for smoking status and pesticide exposure and were consistent across different exposure periods. Conclusion We found that long-term modeled exposure to local traffic-related air pollution (CO) and fine particulates from multiple sources (PM2.5) at homes and workplaces in central California was associated with an increased risk of PD.

Suggested Citation
Dayoon Kwon, Kimberly C. Paul, Yu Yu, Keren Zhang, Aline D. Folle, Jun Wu, Jeff M. Bronstein and Beate Ritz (2024) “Traffic-related air pollution and Parkinson's disease in central California”, Environmental Research, 240, p. 117434. Available at: 10.1016/j.envres.2023.117434.

published journal article

Mortgage Default with Asymmetric Information

The Journal of Real Estate Finance and Economics

Publication Date

January 1, 2000

Author(s)

Suggested Citation
Jan K. Brueckner (2000) “Mortgage Default with Asymmetric Information”, The Journal of Real Estate Finance and Economics, 20(3), pp. 251–274. Available at: 10.1023/a:1007885109086.

working paper

A Transaction Choice Model for Forecasting Demand for Alternative-Fuel Vehicles

Publication Date

January 1, 1996

Associated Project

Author(s)

Abstract

The vehicle choice model developed here is one component in a mlcro-slmulatlon demand forecasting system being designed to produce annual forecasts of new and used vehicle demand by vehicle type and geographic area in Cahforma. The system will also forecast annual vehicle miles traveled for all vehicles and recharging demand by ume of day for electric vehicles. The choice model specification differs from past studies by directly modehng vehicle transactions rather than vehlcle holdings. The model Is calibrated using stated preference data from a new study of 4,747 urban Califorma households. These results are potentially useful to public transportation and energy agencles m their evaluation of alternatives to current gasoline-powered vehicles. The findings are also useful to manufacturers faced with designLug and marketing alternauve-fuel vehicles as well as to utility companies who need to develop long-run demand-side management plamung strategies

working paper

Does Neighborhood Design Influence Travel? A Behavioral Analysis of Travel Diary and GIS Data

Publication Date

January 1, 1998

Associated Project

Author(s)

Randall Crane, Richard Crepeau

published journal article

Trucking industry adoption of information technology: A multivariate discrete choice model

Transportation Research Part C: Emerging Technologies

Publication Date

June 1, 2002

Abstract

The objective of this research is to understand the demand for information technology among trucking companies. A multivariate discrete choice model is estimated on data from a large-scale survey of the trucking industry in California. This model is designed to identify the influences of each of twenty operational characteristics on the propensity to adopt each of seven different information technologies, while simultaneously allowing the seven error terms to be freely correlated. Results showed that the distinction between for-hire and private fleets is paramount, as is size of the fleet and the provision of intermodal maritime and air services. (C) 2002 Elsevier Science Ltd. All rights reserved.

Suggested Citation
Thomas F. Golob and Amelia C. Regan (2002) “Trucking industry adoption of information technology: A multivariate discrete choice model”, Transportation Research Part C: Emerging Technologies, 10(3), pp. 205–228. Available at: 10.1016/s0968-090x(02)00006-2.

conference paper

Sharing is caring: Dynamic autonomous vehicle fleet operations under demand surges

Proceedings of the 97th annual meeting of the transportation research board

Publication Date

January 1, 2018

Author(s)

Michael Hyland, Hani Mahmassani

Abstract

Given the emergence and growth of ridesourcing companies, the forthcoming introduction of fully- autonomous vehicles (AVs), and the projected positive impact of AVs on the growth of mobility services, this paper aims to analyze the operational efficiency of two on-demand AV-enabled mobility services (AVeMSs). Specifically, the study compares an AV-enabled shared-ride service, with an AV-enabled traditional ridesourcing (i.e. no shared-rides) service, in terms of handling demand surges, when fleet size is fixed. The authors hypothesize that shared-ride service will significantly outperform traditional ridesourcing service because as demand increases with shared-ride service, the number of feasible shared-ride opportunities increases, effectively increasing the service rate of the shared-ride fleet. To test this hypothesis, the authors employ a dynamic agent-based simulation of travelers, AVs, and an AV fleet operator. The underlying AV fleet control problem is highly-dynamic and stochastic, as traveler requests are unknown to the fleet operator a priori. To solve the dynamic and stochastic optimization problem, the AV fleet operator repeatedly re-solves an online AV-traveler assignment problem based on the current state the system. The simulation results illustrate that under various experimental settings, shared-ride service significantly outperforms ridesourcing service in terms of handling demand surges. At low demand levels, traveler wait times are similar for shared-ride and ridesourcing services. However, as demand increases, average traveler wait times increase more rapidly under ridesourcing service. The results suggest shared-ride service allows fleet operators to better handle demand surges.

Suggested Citation
Michael F. Hyland and Hani S. Mahmassani (2018) “Sharing is caring: Dynamic autonomous vehicle fleet operations under demand surges”, in Proceedings of the 97th annual meeting of the transportation research board, p. 16p.

published journal article

Adaptive Self-Learning Framework for Resilient Vehicle Classification Through the Integration of Inductive Loops and LiDAR Sensors

IEEE Open Journal of Intelligent Transportation Systems

Suggested Citation
Yiqiao Li, Andre Y. C. Tok and Stephen G. Ritchie (2025) “Adaptive Self-Learning Framework for Resilient Vehicle Classification Through the Integration of Inductive Loops and LiDAR Sensors”, IEEE Open Journal of Intelligent Transportation Systems, 6, pp. 768–780. Available at: 10.1109/OJITS.2025.3575808.