published journal article
published journal article
The increase in the availability of GPS-based movement data has enabled the exploration of mobility patterns in urban transportation networks. Understanding the relationship between social vulnerability and transportation flows from big data during natural disasters is crucial for utilities and policymakers for decision-making purposes, such as evacuation and restoration planning. In this study, we explore the geographic variation of changes in trip times of taxi trips in New York City (NYC) before and after Hurricane Sandy (2012) using GPS trajectory data in relation to the underlying socio-economic distribution of impacted populations using localized regression technique with GWR. The findings reveal how the spatial patterns of trip change times with respect to SVI, income levels and population density in NYC.
published journal article
Phd Dissertation
This dissertation is composed of three chapters on estimation of vehicle choice and utilization models, simulated likelihood estimation, and Bayesian non-parametric additive methods for neighborhood effect models. The first chapter exploits differences in fuel efficiency between hybrid vehicles and their gasoline counterparts to investigate two behavioral questions relating to fuel economy standards: how car buyers value fuel economy (the energy paradox) and whether improved fuel efficiency increases travel (the rebound effect). Emphasis is placed on handling methodological and data issues that are typically ignored in prior studies, such as partially observed choice, endogeneity, and measurement error. Estimates of the rebound effect and consumer valuation of fuel economy remain imprecise despite the use of the most detailed household level data available and sound methodology to handle limitations with these data. The inability to precisely estimate these important policy questions suggests it is a worthwhile endeavor to obtain reliable, detailed data on household vehicles. The following chapter (joint with Ivan Jeliazkov) presents techniques, based on Markov chain Monte Carlo (MCMC) theory, for construction of the likelihood function in a broad class of hierarchical models where direct evaluation of the likelihood function is not possible. We review existing estimators, introduce new MCMC estimators, and examine their performance in applications to the Poisson-log normal and mixed logit models. The MCMC techniques outperform existing methods in both settings, with the existing methods performing especially poorly in the Poisson-log normal case. The final chapter applies Bayesian semiparametric additive methods to a neighborhood effects model. The baseline model assumes all covariates enter linearly, whereas the approach in this paper allows for flexible functional forms. An efficient Markov chain Monte Carlo (MCMC) algorithm that exploits the properties of banded matrices is proposed for estimation. The efficiency gains offered by the banded matrix algorithm are critical, as they permit the estimation of applications with large sample sizes. The model and estimation methodology are used to examine foreclosure contagion in California. The results reveal the impact of neighborhood effects on foreclosure rates as nonlinear, where the relationship resembles a tipping point phenomenon.
policy brief
After years of research and development, companies are now operating fully driverless shared-use automated vehicle-enabled mobility services (SAMS) or “robo-taxis“ in Arizona and California. SAMS offer several potential benefits to travelers and society including reducing vehicle ownership, parking demand, congestion, crashes, energy consumption, and emissions, as well as increasing roadway capacity, mobility, and accessibility. Moreover, previous research by our team found that SAMS can provide significant job accessibility benefits to workers in California. To better understand the equity implications of the job accessibility benefits from SAMS, we analyzed the distribution of SAMS benefits across different segments of the population (e.g., low- vs. high-income, young vs. old). To measure the accessibility benefits of SAMS, we use the logsum of a hierarchical work destination and commute mode choice model—a monetary measure of consumer surplus consistent with microeconomic and utility maximization theories. If a new commute mode (e.g., SAMS) is made available to travelers, and that new mode is competitive with existing modes in terms of travel time and travel cost, then the new mode will improve a traveler’s job accessibility. For more information, please see our previous study on measuring the job access benefits of SAMS2.
published journal article
In order to meet the increasing demand for low carbon and renewable transportation fuels, a methodology for systematically establishing build-out scenarios is desirable. In an effort to minimize initial investment costs associated with the development of fueling infrastructure, the analytical hierarchy process (AHP) has been developed and applied, as an illustration, to the case of hydrogen fueling infrastructure deployment in the State of California. In this study, five parameters are selected in order to rank hydrogen transportation fuel generation locations within the State. In order to utilize meaningful weighting factors within the AHP, expert inputs were gathered and employed in the exercising of the models suite of weighting parameters. The analysis uses statewide geographic information and identifies both key energy infrastructure expansion locations and critical criteria that make the largest impact in the location of selected sites.
conference paper
conference paper
published journal article
By March of 2020, most cities worldwide had enacted stay-at-home public health orders to slow the spread of COVID-19. Restrictions on nonessential travel had extensive impacts across the transportation sector in the short term. This study explores the effects of COVID-19 on shared e-scooters by analyzing route trajectory data in the pre- and during-pandemic periods in Austin, TX, from a single provider. Although total shared e-scooter trips decreased during the pandemic, partially owing to vendors pulling out of the market, this study found average trip length increased, and temporal patterns of this mode did not meaningfully change. A count model of average daily trips by road segment found more trips on segments with sidewalks and bus stops during the pandemic than beforehand. More trips were observed on roads with lower vehicle miles traveled and fewer lanes, which might suggest more cautious travel behavior since there were fewer trips in residential neighborhoods. Stay-at-home orders and vendor e-scooter rebalancing operations inherently influence and can limit trip demand, but the unique trajectory data set and analysis provide cities with information on the road design preferences of vulnerable road users.
MS Thesis
In 2015 bike-sharing has become a viable transportation mode in the urban core of many large cities worldwide. Notably lacking is research on the bike-sharing/transit connection. Bike-sharing provides an excellent solution to the “first-last mile” problem experienced by transit networks but data is difficult to collect due to the independent operation of each network. This thesis proposes an optimization algorithm of user mode choice based on minimizing cost. Required system characteristics for this optimization program are at least two bike-sharing market areas, transit links between the areas and a realistic potential for the vehicle network to become congested. The results show the optimal mode choice by Origin-Destination (OD) pair. This model was applied to trips from downtown Pasadena to downtown Los Angeles in California. These two areas are expected to have a bike-sharing system as soon as 2016 operated by the Los Angeles County Metropolitan Transportation Authority (METRO). Based on congestion from 1x to 4.25x the free-flow time, bike-sharing provides increasing value to commuters between these two areas. The simple parameters of this application including value-of-time and cost of use could be easily updated to reflect a deeper consideration of user cost.