Event Type: PhD Defense

As population densities in urban areas increase, the associated demand on transportation infrastructure continues to exacerbate impacts on surrounding communities. These demands create a number of socioeconomic burdens including housing price impacts when communities are regularly exposed to excessive noise levels. Although noise impacts are not as commonly recognized or assessed in comparison to other environmental issues such as air, ground, or water pollution, it has been well documented in the literature that a wide range of health issues exist when communities are exposed to noise from transportation infrastructure. From a research perspective, the correlation of these health issues to the presence of impactful noise is difficult to quantify, as noise level impacts are subjective and require translation into varying degrees of annoyance to deem them as detrimental from both health and economic perspectives. This dissertation utilizes spatial hedonic price (HP) models to estimate individuals’ marginal willingness-to-pay (MWTP) to reside in noise-impacted areas. These MWTP values can then be used to both valuate economic impacts and as a noise annoyance level proxy to identify zones that are at-risk due to excessive transportation noise exposure.

The first analysis in this dissertation reviews salient transportation noise-related papers that have been published since Navrud’s comprehensive 2002 transportation noise literature review. In a review of recent literature, this dissertation found that transportation noise research has evolved to include advanced Geographic Information System data, and leverages increasingly powerful processors and statistical analysis programs. In addition, although significant transportation noise research has been conducted in Europe following EU Environmental Noise Directive 2002/49/EC, a minimal number of studies have been conducted in the United States — especially in Southern California, revealing a research gap that this dissertation helps to address.

The second analysis investigates the impacts of aircraft operations around Los Angeles International Airport. Using a subset of 2010-2014 single-family home sales data from the Los Angeles County Office of the Assessor (LACOA), HP spatial autoregressive models with autoregressive disturbances (SARAR) were estimated. The study hypothesizes and confirms that a negative impact value would be observed for homes being located within noise-mapped zones around the airport, along with an improvement in estimation values compared to previous fixed spatial effects ordinary least squares techniques.

The third analysis in this dissertation investigates two important topics. First, it hypothesizes negative home value impacts from nearby freight rail operations in the densely populated South Bay region of Los Angeles County. Noise from freight rail lines are analyzed using an HP SARAR model and confirm negative valuation impacts to homes located near these rail lines. Second, it hypothesizes that by using a subset of the master LACOA dataset above, varying levels of spatial homogeneity can be comparatively analyzed between two samples that use similar data and modeling techniques. Results indicate that when neighboring zones have distinct differences in jurisdiction, fixed spatial effect delineations remain statistically significant. However, when neighboring zones have similar jurisdictional or demographic characteristics, spatial model parameters are able to account for the fixed delineations.

The emerging and rapidly changing landscape of autonomous vehicles and shared mobility technologies bring up possibilities for a paradigm shift in how we model and analyze mobility. Transportation and mobility systems can now be connected continuously and seamlessly, which can make them more flexible and shareable. What can make this possible? Put simply, it would require integrating various mobility options so that travelers can freely hop among them. The demarcation lines among modes can then become increasingly hazy, as every individual trip may include multiple modes to various degrees. This implies that the paradigm shift is in how we view the travel modes. What were traditionally considered as limited discrete mode options, need to be seen as part of a continuum. In turn, we should focus more on mode combinations rather than individual travel modes. In this dissertation, we propose shifting the focus to the new idea of a mode option pool with an associated structure. The option pool would include every type of travel option in a continuous spectrum. This observation motivates the phrase ‘travel-option chain (TOC)’ mode proposed in this dissertation as a combination of travel options in a continuous spectrum.

Shared use of vehicles – either time-shared, or seat-shared – expands the travel option pool. Autonomous vehicle technology makes even more time-shared use of vehicles possible, as the driver constraint is also removed, and thus further expands the travel mode option pool. Then the question is on how to make such a larger option pool available for a large number of users, to improve their level of mobility and the productivity of the vehicles as well as the associated infrastructure. One aspect that needs to be addressed is that people cannot be individually owning the vehicles and infrastructure involved in all of the mobility options they use from the pool. Different people may partially or fully own different components such as, for instance, vehicles or spaces where they are parked. Some ownership may be time-shared as well. Publicly provided transit systems with purchased tickets will naturally be part of many TOC modes. Subscription- type ownership is a possibility, if mobility service providers offer the options for purchase, and they can be bundled options as well, similar to phone plans. This fits within Mobility-as-a-Service (MaaS) platforms that have been proposed in recent years. In this dissertation, a powerful user- side concept, ‘mobility portfolios’ is proposed that encompasses MaaS platforms, subscriptions, ownership, bundled plans and selection of optimal TOCs from a continuum spectrum of modes.

The question then ensues on how we can find the optimal TOC modes. From an analytical standpoint, this can be solved with a ridematching problem formulation of matching paths in a time-expanded multimodal network. A more vexing problem is how people can travel on these TOC modes unless they have paid for it in a certain way. The mobility portfolio scheme proposed in this dissertation is geared to make it possible for them to pay for it in an efficient way and in a shareable manner with enough flexibility. This dissertation defines mobility portfolio as a “grouping of the number of hours/cost/resources that can be spent on each distinct travel options, so as to fit within a time/cost/resource constraint specified for a given time period”. The portfolio approach compartmentalizes the travel options that are chained, and allocates appropriate “quantities” of them, when we view them as consumable travel commodities and resources. The portfolio scheme incorporates pricing for the commodities and are expected to bring in efficiency and cost savings while increasing shared mobility participation. This is a good approach for controlling TOC mode change travel behaviors and it subsumes currently envisaged ideas such as MaaS mobility bundles in a smart and shared mobility system with subscription options.

The focus of this dissertation is on the user level decisions on selecting the TOC modes from their mobility portfolios scheme. Innovative options such as users offering their own resources (e.g., owned vehicles) and their own services (e.g., potential driving for shared rides) are incorporated in the portfolios. We develop an iterative framework which is rooted on a learning-based travel cost perception update model, so as to provide the best mobility portfolio solutions. Performing simulated case studies on a real network, we confirm that the proposed framework converges to the optimum mobility portfolio state for system participants and improves the performance of the system by inducing people to use shared mobility options more.

During the past two decades, and especially during the Covid-19 pandemic, e-shopping has become increasingly popular, changing the way people shop and travel. With increasing concerns about the environmental impacts of transportation, particularly on regional air quality and on emissions of greenhouse gases (GHG), it is important to understand how e-shopping has affected household travel behavior. In this dissertation, I investigated the influence of e-shopping before, during, and after the pandemic by analyzing data from the 2009 & 2017 U.S. National Household Travel Surveys (NHTS) and from an IPSOS survey of Californians conducted in late May 2021. Understanding changes in shopping is essential to business owners, logistics managers (for adapting supply chains), transportation planners (for mitigating the impacts of warehousing and of additional residential freight deliveries), and policymakers (for helping at-risk and under-served groups).

This dissertation has three parts. In the first part, I estimated zero-inflated negative binomial models to analyze factors that affected residential deliveries before the pandemic based on the 2009 and 2017 NHTS. I found that U.S. e-shoppers were more varied in 2017 than in 2009. Households with more females, higher incomes, and more education, received more deliveries. I also analyzed the 2017 American Time Use Survey to explore factors that influence grocery shopping. I found that in-store grocery shoppers are more likely to be female and unemployed but less likely to be younger, to have less than a college education, and to be African American. In contrast, online grocery shoppers are more likely to be female.

In the second part, I studied the impact of e-shopping on household travel using propensity score matching. My analysis of 2017 NHTS data showed that before the pandemic, greater online shopping was associated with more frequent trips and slightly more travel. Furthermore, the extent to which an increase in the number of activities translated into more travel depends on population density, the day of the week, the frequency of online shopping, and the type of activity.

In the third part, I analyzed the impact of the Covid-19 pandemic on grocery shopping frequencies in-store, online with home delivery (e-grocery), and online with curbside or store pickup (click-and-pick), to understand how they changed due to the pandemic, and how they may change after, using ordered models and structural equation models. My results showed that Californians kept shopping for groceries in brick-and-mortar stores during the pandemic but less frequently than before. The pandemic accelerated the adoption of e-grocery and click-and-pick with some strong generation effects: younger generations were more likely to experiment with e-grocery and click-and-pick, while older generations relied more on in-store shopping. Education also made a difference, but thankfully race did not impact the use of e-grocery and click-and-pick, or intentions to rely on them after the pandemic (but it did affect in-store grocery shopping before). My results also illustrated the heterogeneity of Hispanics and non-linear income effects. As expected, tech-savvy households were much more likely to embrace e-grocery and click-and-pick.

Alternative fuel adoption by heavy-duty vehicle (HDV) fleets can bring substantial benefits to both current local communities and future generations by reducing air pollutants and greenhouse gas emissions. However, the penetration rate of alternative fuel vehicles (AFVs) is still very low in the HDV sector. Revealing HDV fleet operator perspectives towards alternative fuels can serve as the basis for developing effective policies for accelerating the diffusion of these technologies. This dissertation aims to fill a key knowledge gap, where such fleet operator perspectives have rarely been addressed, by exploring alternative fuel adoption behavior of HDV fleets. 

To this end, an initial theoretical framework was first developed based upon existing theories and literature to conceptually understand AFV fleet adoption behavior in organizations. This initial framework consists of a five-stage adoption process as well as two levels of sub-frameworks: at the decision-making unit level and the individual (e.g., vehicle drivers) acceptance level.

Next, it was attempted to empirically improve the initial framework by investigating 20 organizations operating HDVs in the State of California via in-depth qualitative interviews and project reports. A total of 29 adoption and 42 non-adoption cases were probed across various alternative fuel technologies, including natural gas, propane, electricity, hydrogen, biodiesel, and renewable diesel options. The qualitative data was analyzed using content and thematic analyses, by which numerous themes and hypotheses were developed to build a theory explaining heavy-duty AFV fleet adoption behavior. 

Based on these qualitative inferences, this dissertation proposes a conceptual modelling framework for estimating demand of heavy-duty AFVs under different policies and technology advancement scenarios. An overall structure along with specific modules and components for this framework are presented. As an ongoing work, a stated preference choice experiment was designed to quantitatively operationalize one of the modules, which intends to estimate AFV choice probabilities. As a case study with California drayage fleets, a feasibility of this modelling is being examined by interviewing drayage fleet operators. 

Finally, the research findings contribute theoretically and empirically to a better understanding of the demand-side aspects of AFV adoption by HDV fleet operators, particularly in California and in the other US states that follow California’s environmental policies.

Crowdsourced delivery, or crowd shipping, is a delivery service in which logistics service providers contract delivery services from the public (i.e., non-employees), instead of providing delivery services exclusively with an in-house logistics workforce. Studies in the literature formulate the crowdsourced delivery problem as a Vehicle Routing Problem (VRP) and propose a variety of solution approaches for small problem instances. Conversely, this dissertation focuses on large-scale crowdsourced systems and problem instances, which have significant promise and importance, respectively, for effective planning and efficient operation crowdsourced systems in the real-world.

This dissertation provides a taxonomy of urban last-mile crowdsourced delivery, defines the crowdsourced shared-trip delivery problem, presents two separate models for the crowdsourced shared-trip delivery problem, and develops a novel decomposition heuristic tailored to solve large-scale crowdsourced delivery problems. The taxonomy identifies three types of urban last-mile crowdsourced delivery—crowdsourced trip-based delivery, crowdsourced time-based delivery, and crowdsourced shared-trip delivery. This dissertation focuses on the crowdsourced shared-trip delivery problem in which crowdsourced drivers communicate their origin and destination of an upcoming trip to the logistics provider and, if the logistics provider can identify packages for the driver to pickup and deliver without significant detours, the logistics provider assigns packages to and compensates the driver. The dissertation models the crowdsourced delivery problem by adapting VRP formulations from the literature as well as using a set partitioning formulation. The set partitioning formulation leads to an alternative solution method for large-scale instances that involves decomposing the problem into several subproblems. The novel decomposition heuristic contains a series of subproblems that are solved in this dissertation using various solution algorithms, including, budgeted k-shortest path, large scale bi-partite matching, package switching and vehicle routing. The decomposition heuristic achieves high quality results and significantly shorter computational time in comparison to an exact solution method.

In the numerical case study, the dissertation analyzes various factors that may impact the effectiveness and efficiency of urban crowdsourced delivery. The results indicate that crowdsourced shared-trip delivery service can reduce total delivery cost by between 20% to 50%, compared to a delivery service that exclusively uses its own dedicated vehicles and drivers. Vehicle miles travelled (VMT) savings depend on the origin location (i.e., home locations or the store/depot) of crowdsourced drivers that participate in the service. In addition, the results show that dedicated vehicles are still required since even a considerably large set of candidates shared crowdsourced vehicles cannot usually serve all packages.

There have been laws passed in California (SB32) that would require the State to cut its Greenhouse Gas Emissions (GHG) to 40% of 1990 levels by 2030 in order to combat climate change. With cars contributing to 41% of GHG emissions in California it is clear that to reach that goal there will need to be a significant reduction in Vehicle Miles Travelled (VMT). A way to quickly reduce VMT is to invest in existing rail systems specifically commuter rails. An investigation was conducted to model the potential effects of improving commuter rail services on
a state vs. national level, station-by-station level, and a regional level. To conduct the research data was gathered from the National Transit Database, Longitudinal Employer-Household Dynamics site, and the Environmental Protection Agencies Smart Location Database (EPA-SLD) for the year 2014.
The California Model unlinked passenger trips are more sensitive to the hours of service than the National Model. Also, the California Model is more sensitive to log peak vehicles operated which would imply that the more vehicles or frequency of the vehicles servicing people can have a large impact on passenger trips.
The Station boarding and egress models were the best when there were exogenous latent variables in the regression model. The latent variables Mixed-Use Density and Work Opportunity play a significant role in transit boardings and egress by stating that if the mixed-use density increases the employment, employment entropy, and ratio of jobs accessible in 45 minutes increases.
Model 2 is superior of the SEM models created. The ridership factors that the passenger rates to all the observed variables and the measure of their satisfaction with the variables can be a tool to use for improving service quality and for planning for future services. In the long run, this could have cost savings because if there is information about the riders’ preferences there can be improvements made specific to what is valued as important. This model can be easily modified to fit other transit services in many different regions or countries because of the framework structure which can be used for analyzing any type of service from survey responses.

Understanding the spatiotemporal distribution of commercial vehicle is essential for facilitating strategic pavement design, freight planning, and policy making. Hence, analysts and researchers have been increasingly interested in collecting more diverse high granularity truck data across different truck characterization schemes to meet these variegated needs across the roadway network to better understand their distinct operational characteristics and dissimilar impacts on infrastructure and the environment. Existing truck monitoring infrastructure are limited in spatial coverage across the roadway network due to their high installation and maintenance costs. The recently developed Truck Activity Monitoring System (TAMS) by the University of California Irvine Institute of Transportation Studies provides a cost-effective solution for monitoring truck movements statewide across California along major freeways networks through existing inductive loop infrastructure enhanced with inductive signature technology. Nonetheless, it possesses three major limitations: model bias against underrepresented truck classes, spatial coverage limitation on rural highways, and system obsolescence over time.

This dissertation explored a resilient spatiotemporal truck monitoring framework using inductive loop signature and multi-array Light Detection and Ranging (LiDAR) sensor technologies to address the aforementioned issues and to improve truck monitoring capabilities across the roadway network. The designed framework comprises three major parts: Inductive loop sensors for major highway truck monitoring, multi-array LiDAR sensors for rural highway truck monitoring, and a self-learning truck classification through a sensor integration framework.

The first part of the framework was built upon the existing Truck Activity Monitoring System (TAMS) developed by ITS Irvine and addresses prediction model biasness caused by inherently imbalanced truck datasets to provide reliable truck speed estimation and truck classification data.

The second part explored non-intrusive LiDAR-based sensing technologies to fill the surveillance gap along rural highway corridors. This section developed a truck classification method using a LiDAR sensor oriented to provide a wide field-of-view of roadways.

Finally, a self-learning framework for truck classification systems was designed to address system obsolescence through the integration of inductive loop sensors and LiDAR sensors, the latter of which has been proved to have the ability to recognized the truck axle configuration in this dissertation. This framework enhances the resilience of signature-based FHWA classification model with an intelligent system update to accommodate the change of the truck designations over time and significantly reduces the overall burden of periodic model calibration by utilizing the information stored in the legacy model.

Traffic advisories to travelers are based upon traffic state information at the link level. This is due to existing infrastructure which sometimes can only provide link-level information. However, the primary justification for providing link-level data is the reluctance of Traffic Management Agencies to consider more detailed traffic state data for operational and safety reasons. However, with the advances in automotive technology, sensing equipment, and the Internet of Things (IoT), we can do better. Research shows that faster and more accurate travel paths can be obtained by using lane data rather than link data. Our contention is that for vehicles to be able to change lanes to improve their travel times, operationally, they would need to enter into Peer-to-Peer negotiations with surrounding vehicles, where they can trade their position in time and space in exchange for monetary benefits. Our work is an exploration of this idea.
We begin with a simple in-vehicle advisory control policy, partially inspired by the Kinetic theory of traffic. We then move towards an individual-level Peer-to-Peer negotiated lane change framework by first investigating its efficacy by means of microsimulation studies. We then propose an agent-based optimization framework for this system, which minimizes both travel time and the ”envy” induced among drivers when they are assigned paths that are inferior to their peers. Numerical results from running our optimization on an illustrative off-ramp network show that the proposed model converges to both envy-free and system optimum traffic states, even at a net zero budget, meaning this system can be used by transportation agencies without exacting tolls or giving subsidies.
Our proposed framework of routing vehicles on a lane to lane basis can only be realized in the field if the mediating agency (TMC, or a mobility service) has accurate information about traffic conditions. We propose multiple algorithms, including a LSTM (Long Short Term Memory) neural network architecture-based framework to estimate traffic states solely using information collected from sensor-equipped probe vehicles, without the need for any other data such as those obtained from traditional embedded loop detectors.

Measuring the geographic extent of travel-activity patterns is very important to develop our knowledge on potential and actual activity spaces around individual travel routes and activity locations which will enrich our understanding of human activities. Previous activity space studies, primarily from the field of geography, demonstrate analysis techniques to characterize and assess spatial dimensions of areas that individuals come into contact within daily life. Although a handful of studies have begun to integrate activity space within the travel behavior analysis in Europe and U.S. context, few studies have measured the size, structure, and implications of human activity spaces in the context of developing countries. To address these concerns, this dissertation examines the impact of land-use characteristics, socio-demographics, individual trip characteristics, and personal attitudes on travel-activity based spatial behavior in Dhaka City, capital city of Bangladesh.
This dissertation focuses on two separate subareas: Mirpur from the Dhaka North City Corporation and Dhanmondi from the Dhaka South City Corporation based on their distinctive socio-economic and transportation characteristics. The first stage of this dissertation (presented in Chapter 2) is comprised of a household-based travel diary pilot survey that was conducted in 2017. Regular activity locations were geocoded using Geographic Information System (GIS). Network analyst based Shortest Path Network (SPN) with Road Network Buffer (RNB) was used to calculate activity space of the participants. Daily activity areas for individual respondents range from 0.38 to 6.18 square miles. Land-use mix is found to be a significant predictor of activity space size. Larger activity space is recorded for the residents of one subarea over another due to less land-use diversity. Pilot study results identify specific socio-economic and travel differences across the two study subareas (by car ownership, income, modal share, distance traveled, trip duration).
The second stage of this dissertation (presented in Chapter 3) builds on lessons learned from the pilot study and comprises of a weeklong household-based travel diary survey collected in 2018. Using Artificial Neural Network (ANN) and Regression Analysis, results show that weekly (weekdays/weekend days) activity areas for individual respondents range from 0.08 to 10.13 square miles. Dhanmondi respondents are found to have larger weekday activity space while Mirpur respondents have larger weekend activity space. Trip characteristics (distance, duration, and cost) are found to be significant predictors of individual activity space size. In case of household activity space, Density variables are found to play the most significant role. Higher density of retail shops and employment locations within a household’s activity space decrease the weekday activity space. Also, households without car have limited activity area during weekday. Unlike weekday finding, smaller households are found to have smaller activity space for weekend.
Exploratory and Confirmatory Factor Analysis shows that people’s perceptions (Perceived neighborhood amenities, Car attachment, Monetary concerns, Perceived daily travel area and environmental concern) mainly shape weekend spatial behavior. RNB activity space measure indicates that 44.4% of respondents do not have access to a recreational facility within their weekly activity space. Positive correlations are found between activity area and number of different opportunities except open space. The association is strong for hospital, retail shop and restaurant facility. Weekend activity spaces are found to be more compact than those for weekdays. Individual day-to-day variability is less during weekdays than for weekends. Also, weekday to weekend variability is found to be much larger in Dhanmondi compared to Mirpur. Female respondents and high-income people are found to have smaller activity spaces. While examining heterogeneity in activity spaces, results indicate that at an aggregate level activity spaces vary from day to day. To further analyze the impact of different indicator characteristics on this variability, Fixed Effects Panel Regression using Least Squares Dummy Variable approach, General Linear Model and Random Effects Panel Regression: Mixed Models is used. Model estimation results show that several time-varying predictors: trip characteristics (duration, distance, and cost), Density of schools and retail shops, intersection Design within the activity space, and few time-invariant predictor variables are found to significantly affect day to day activity space variability. Three attitudinal factors (Perceived neighborhood amenities, Environmental awareness, and Monetary concerns) also show significance in predicting activity space variability.
This dissertation study contributes to travel-activity space literature and planning practice in several ways. To my knowledge, this is the first study of activity space calculation in any South East Asian city and therefore contributes significantly to transportation science literature of the region. Also, only a few previous studies have assessed the influence of individual perceptions and values on activity spaces. Finally, understanding the day-to-day variability of activity spaces and examining accessibility to potential urban opportunities provide planners and policy makers specific guidance for future planning implications on spatial behavior in the study area.

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.