conference paper
Submission: Trip Length Distribution of TNC Trips: Based on Empirical Data in ChicagoPresenter: Irene MartinezAuthors: Irene Martínez (University of California, Irvine)*; Wen-Long Jin (University of California, Irvine)
published journal article
Structural equation modeling (SEM) is an extremely flexible linear-in-parameters multivariate statistical modeling technique. It has been used in modeling travel behavior and values since about 1980, and its use is rapidly accelerating, partially due to the availability of improved software. The number of published studies, now known to be more than 50, has approximately doubled in the past three years. This review of SEM is intended to provide an introduction to the field for those who have not used the method, and a compendium of applications for those who wish to compare experiences and avoid the pitfall of reinventing previous research.
conference paper
This paper proposes a real-time adaptive control model for signalized intersections that decides optimal control parameters commonly found in modern actuated controllers, aiming to exploit the adaptive functionality of traffic-actuated control and to improve the performance of traffic-actuated signal system. This model incorporates a flow prediction process that estimates the future arrival rates and turning proportions at target intersections based on the available signal timing plan and detector information. Signal control parameters are optimized dynamically cycle-by-cycle to satisfy these estimated demands. The proposed adaptive control strategy is tested on a network consisting of thirty-eight actuated signals using microscopic simulation. Simulation results show that the proposed adaptive model is able to improve the performance of the study network, especially under off-peak traffic conditions.
published journal article
published journal article
Introduction: Few studies have assessed extreme temperatures’ impact on gestational diabetes mellitus (GDM). We examined the relation between GDM risk with weekly exposure to extreme high and low temperatures during the first 24 weeks of gestation and assessed potential effect modification by microclimate indicators. Methods: We utilized 2008–2018 data for pregnant women from Kaiser Permanente Southern California electronic health records. GDM screening occurred between 24 and 28 gestational weeks for most women using the Carpenter-Coustan criteria or the International Association of Diabetes and Pregnancy Study Groups criteria. Daily maximum, minimum, and mean temperature data were linked to participants’ residential address. We utilized distributed lag models, which assessed the lag from the first to the corresponding week, with logistic regression models to examine the exposure-lag-response associations between the 12 weekly extreme temperature exposures and GDM risk. We used the relative risk due to interaction (RERI) to estimate the additive modification of microclimate indicators on the relation between extreme temperature and GDM risk. Results: GDM risks increased with extreme low temperature during gestational weeks 20–-24 and with extreme high temperature at weeks 11–16. Microclimate indicators modified the influence of extreme temperatures on GDM risk. For example, there were positive RERIs for high-temperature extremes and less greenness, and a negative RERI for low-temperature extremes and increased impervious surface percentage. Discussion: Susceptibility windows to extreme temperatures during pregnancy were observed. Modifiable microclimate indicators were identified that may attenuate temperature exposures during these windows, which could in turn reduce the health burden from GDM.
published journal article
conference paper
Autonomous Driving (AD) technology has always been an international pursuit due to its significant benefit in driving safety, efficiency, and mobility. Over 15 years after the first DARPA Grand Challenge, its development and deployment are becoming increasingly mature and practical, with some AD vehicles already providing services on public roads (e.g., Google Waymo One in Phoenix and Baidu Apollo Go in China). In AD technology, the autonomy software stack, or the AD software, is highly security critical: it is in charge of safety-critical driving decisions such as collision avoidance and lane keeping, and thus any security problems in it can directly impact road safety. In this talk, I will describe my recent research that initiates the first systematic effort towards understanding and addressing the security problems in production AD software. I will be focusing on two critical modules: perception and localization, and talk about how we are able to discover novel and practical sensor/physical-world attacks that can cause end-to-end safety impacts such as crashing into obstacles or driving off road. Besides AD software, I will also briefly talk about my recent research on autonomy software security in smart transportation in general, especially those enabled by Connected Vehicle (CV) technology. I will conclude with a discussion on defense and future research directions.
published journal article
This paper presents an analysis of the perceptions held by for-hire and private trucking company logistics and operations managers about the impacts of congestion on their operations and the feasibility and effectiveness of actual and potential congestion mitigation policies. Responses to an extensive survey of nearly 1200 California-based or large national carriers are examined using confirmatory factor analysis. The method applied facilitates both the grouping of congestion relief policies into classes and the identification of characteristics of companies which lead them to favor one set of policies over others. This research comes at a time when California government leaders and transportation policy analysts are struggling with key resource allocation issues that will impact the short and long term future of goods movement in the state. To the greatest extent possible, insights of commercial vehicle operations users of the transportation network should be included in the policy analysis process. (C) 1999 Elsevier Science Ltd. All rights reserved.
published journal article
published journal article
The vehicle miles of travel for each vehicle in multi-vehicle households is modelled as a function of household characteristics, vehicle characteristics, and the matches of vehicle to driver in the satisfaction of travel desires. A structural equations model is developed in which principal driver characteristics, as well as vehicle miles of travel, are endogenous. There are links between how each vehicle is used and who in the household is each vehicle’s principal driver. Each vehicle’s usage can then be expressed in reduced-form equations as a function of exogenous household and vehicle type variables for forecasting purpose’s. The model is estimated on a 1993 sample of approximately 2000 multi-vehicle households in California.