published journal article
published journal article
Phd Dissertation
The deregulation experience in airline, banking, and telecommunication suggests that the heterogeneity in consumers’ preferences has important policy significance. However, the varied nature in motorists’ preferences has been hardly recognized in urban passenger transportation sector. In this public sector, the public authority generally offers a uniform class of services to all potential users. This dissertation employs the new advances in econometrics on survey data sets from road pricing experiment in Los Angeles area to study the diversity in motorists’ preferences for travel time and travel time reliability. The empirical findings are used to explore the efficiency and distributional effects of road pricing that accounts for users’ heterogeneity. This dissertation found substantial heterogeneity in motorists’ preferences for both travel time and travel time reliability. Furthermore, based on a simulation model, this dissertation found that road pricing policies catering to varying preferences can substantially increase efficiency while maintaining the same political feasibility as the current experiments. This dissertation also explores how to apply the recent developments in Bayesian econometrics to estimate the multinomial probit models combining different sources of data, which can be used to estimate the diversity in peoples’ preferences with more flexibility in model specification.
published journal article
Public transit is considered a sustainable mode of transport that can address automobile dependency and provide environmental, economic, and societal benefits. However, with typical temporal and spatial constraints such as fixed routes and schedules, transfer requirements, waiting times, and access/egress issues, public transit offers lower accessibility and mobility services than private vehicles and thus it is considered a less attractive mode to many prospective users. To improve the performance of transit and in turn to increase its usage, a broader understanding of the daily activity-travel patterns of transit users is fundamental. In this context, this study analyzed transit-based activity-travel patterns by classifying users via Latent Class Analysis (LCA). Using data from the 2017 National Household Travel Survey, the LCA model suggested that transit users could be split into five distinct classes where each class has a representative activity-travel pattern. Class 1 constituted employed white males who made transit-dominant simple work tours. Class 2 was composed of employed white females who made complex work tours. Employed white millennials comprised Class 3 and made multimodal complex tours. Transit Class 4 were non-white younger or older adult groups who made transit-dominant simple non-work tours. Last, Class 5 members made complex non-work tours with recurrent transit use and comprised single older women. This study provided insights regarding the variations of activity-travel patterns and the associated market segments of transit users in the United States. The results can assist transit agencies in identifying transit user groups with particular activity patterns and to consider market strategies that can address their travel needs.
published journal article |
Apr 2023
research report
published journal article
conference paper
policy brief
Transportation agencies increasingly rely on detailed trip data to analyze traffic patterns and plan infrastructure improvements. However, traditional data collection methods require extensive personal information about travelers’ origins, destinations, and routes, raising serious privacy concerns. Current “big data” approaches can track individual movements with alarming precision, often without explicit consent. As privacy regulations tighten and public concerns grow, transportation planners need alternative methods that balance analytical needs with privacy protection. To address this challenge, the research team evaluated the “bathtub model” as a privacy-preserving alternative to traditional traffic data collection methods. This simple, network-level approach treats all trips in a region as part of one system. Instead of tracking each person’s path, a bathtub model represents trips by how much distance they have left to travel. This allows for analyzation of network performance while protecting privacy.
policy brief
Recent commercialization of advanced low-nitrogen oxides (NOx) Compressed Natural Gas (CNG) engines for medium- (MDV) and heavy-duty (HDV) vehicles has garnered significant interest due to the potential air quality benefits. Further, utilizing renewable natural gas (RNG) in advanced CNG engines from sources such as biomass and/ or biogas can achieve reductions in greenhouse gas (GHG) relative to using petroleum fuels and fossil CNG. However, the regional air quality and GHG reduction benefits of large‐scale deployment of advanced CNG trucks are currently unclear. Further, more information is required regarding RNG production potential from California instate biofuel resources, including potential supply volumes and production pathways that provide maximum GHG reductions. The UC Irvine Advanced Power and Energy Program assessed the air quality and GHG implications of transitioning to advanced CNG engines in MDVs and HDVs in California by developing and comparing different future adoption scenarios. The research team also leveraged prior research of biogas and biomass resources in California to consider different options for producing RNG in-state. Key findings from this research are highlighted in the following section.
conference paper