Event Type: PhD Defense

Traffic safety is a major public health issue worldwide. The Kingdom of Saudi Arabia (KSA) is facing alarming road safety concerns with traffic-related injuries being the third leading cause of fatalities in the country. A better understanding of factors influencing injury severity outcomes of traffic crashes is vital for the proactive and effective implementation of suitable countermeasures. Various non-parametric machine learning (ML) based techniques have been increasingly used lately to address the drawbacks of statistical schemes for modeling the injury severity of traffic crashes. The dissertation examines the application of six different ML algorithms for injury severity prediction of traffic crashes based on three years data on interstate rural highways in KSA. Injury severity modeling performance of proposed algorithms was assessed in terms of various evaluation indices. Experimental results revealed that some models outperformed others based on the selected evaluation metrics. To address the ML model’s non-interpretability issue, feature importance and SHAP analysis were also employed. An analysis of several significant factors which aggravate the chances of fatal and severe injuries was done. The study also proposes the application of the Information Root Node Variation (IRNV) technique for extraction of significant decision rules highlighting the circumstances for the categorization of specific crash injury severity instances. For comparison purposes, multinomial logit (MNL) models were also developed and the consistency of injury severity risk factors between MNL and ML models was investigated. The outcomes and findings of the current study can yield valuable insights to safety practitioners for timely and effective implementation of suitable mitigation measures.

Apart from the COVID-19 pandemic, two chronic problems affecting Californians are high housing costs and road congestion. Although high housing costs and the determinants of commuting have separately received a lot of attention from academic researchers, to my knowledge very few papers have analyzed the linkage between them. In this dissertation, I present three essays that will enhance our understanding on the relationship between commuting, land use, housing costs, and the impact of COVID-19 on telecommuting. In all three essays, I use Structural Equation Model (SEM).

In my first essay, I propose a framework for understanding the impact of housing costs on commuting time and commuting distance in one worker-households in Los Angeles County, which is the most populous county in the US. After analyzing data from the 2012 California Household Travel Survey (CHTS), I find that households who can afford more expensive neighborhoods have on average a commute 3.1% shorter per additional $100k to their residence median home values.

In my second essay, I analyze the commutes of two-worker households to understand some of the trade-offs they need to make, since two-worker households have dual work constraints. My data for this essay come from 2017 National Household Travel Survey (NHTS) respondents who reside in five U.S. MSAs (San Francisco, Los Angeles, Dallas, Houston, and Atlanta). Results show that women do not commute as far as men on average, although their commuting time is not necessarily shorter than men’s, and that the commuting times of men and women are weakly positively correlated. Moreover, households have faster commutes by 14.5% for men and 22.7% for women per additional $1000 to their residence median monthly housing cost.

My third essay investigates the impact of the COVID-19 pandemic on telecommuting by analyzing a unique dataset collected at the end of May 2021 by IPSOS via a random survey of California members of KnowledgePanel®. I find that an additional 4.2% of California workers would engage in some level of telecommuting and more educated workers are expecting to telecommute more (0.383* for bachelor’s degree) post-pandemic.

Teasing out the impact of high housing costs on commuting is important at a time when concerns about the environmental impacts of transportation have turned reducing vehicle-miles traveled (VMT) into a policy priority. More generally, a better understanding of the determinants of commuting is critical to inform housing and transportation policy, improve the health of commuters, reduce air pollution, and achieve climate goals.

Public transportation in the U.S., including in California, is under siege. Over the last two decades, ridership has been steadily declining, possibly because traditional transit users gained more accessibility to private cars and because of the emergence of transportation network companies (TNCs, i.e., Uber and Lyft). The COVID-19 pandemic worsened a bad situation. Public transportation officials are now confronted with the challenge of restoring the health of public transit so it can contribute to a more equitable and sustainable transportation system. Therefore, in this dissertation, I first investigate how Transportation Network Companies (TNCs, e.g., Uber and Lyft) impacted transit ridership pre-pandemic, before analyzing how COVID-19 affected transit and other modes. I rely on both discrete choice and quasi-experimental models to analyze data from the 2009 and 2017 National Household Travel Surveys and from a California survey administered in May 2021 by Ipsos.

In Chapter 2, my results for the U.S. show that individuals/households who use either public transit or TNCs share socio-economic characteristics, reside in similar areas, and differ from individuals/households who use neither public transit nor TNCs. In addition, individuals/households who use both public transit and TNCs tend to be Millennials or belong to Generation Z, with a higher income, more education, no children, and fewer vehicles than drivers. To the best of my knowledge, this is the first nationwide study to contrast public transit and TNC users that relies on cross-nested logit structures. My second contribution here is a comparison between individual and household-level models to account for intra-household dependencies of mode choice.

In Chapter 3, I quantify the impact of TNCs on household transit use by comparing travel for households from the 2017 NHTS (who had access to both transit and TNCs) matched with households from the 2009 NHTS (who only had access to transit) using propensity score matching. Overall, I find an 18.2% daily reduction in transit use for the entire U.S., with a 21.8% drop for weekdays and a 15.2% decrease for weekends. My main contribution here is to tease out the causal link between the emergence of TNCs and the decline of transit at the household level using propensity score matching, as previous studies relied either on descriptive statistics, correlation analyses, or considered aggregate ridership changes.

In Chapter 4, I analyze how Californians changed transportation modes due to COVID-19 and explore their intentions to use different modes after the pandemic. I find that driving but especially transit and TNCs, could see substantial drops in popularity after the pandemic. Many Hispanics, African Americans, Asians, lower-income people, and people who would like to telecommute more intend to use transit less. Key obstacles to a resurgence of transit after COVID-19 are insufficient reach and frequency, shortcomings that are especially important to younger adults, people with more education, and affluent households (“choice riders”). In addition to addressing these concerns, effective transit policies must be integrated into a comprehensive framework designed to achieve California’s social and environmental goals.

Overall, my findings highlight the danger for public transit to enter into outsourcing agreements with TNCs, neglect captive riders (people with no alternatives to transit), and to expose choice riders to TNCs. Key priorities for transit agencies should therefore be to increase the frequency of their service (as appropriate), and extend their reach to solve the “first and last mile problem”, possibly by creating partnerships with micromobility providers.

Keywords: Public Transit; Transportation Network Companies; Active Modes; COVID-19; Cross-Nested Logit; Generalized Ordered Logit; Propensity Score matching

Trucking electrification combined with connected and automated technologies promises to cut the cost of freight transportation, reduce its environmental footprint, and make roads safer. If electric trucks are powerful enough to cease behaving as moving bottlenecks, they could also increase road capacity and reduce the demand for new infrastructure, a consequence that has so far been overlooked by the literature. In this dissertation, I study the traffic and infrastructure demand impacts of electrifying and connecting (via cooperative adaptive cruise control, CACC) heavy-duty drayage trucks (HDDTs) that serve the San Pedro Bay Ports (SPBP; the ports of Los Angeles and Long Beach, which is the largest port complex in the U.S), quantify the resulting health, environmental, and Environmental Justice impacts, and explore how to maximize the benefits of connected vehicles with active demand management.

In Chapter 2, I explore the potential traffic and infrastructure implications of replacing conventional HDDTs that serve the SPBP with electric and/or connected HDDTs. I rely on microscopic simulation on a freeway and arterial network centered on I-710, the country’s most important economic artery. My results show that 1,000-hp electric/hydrogen trucks can be a substitute for additional road capacity. Accounting for the traffic impacts of new vehicle technologies is critical in infrastructure planning, and my results suggest shifting funding from building new capacity to financing zero-emission (ZE) 1,000 hp HDDTs until the market for these vehicles has matured.

In Chapter 3, I quantify the health and GHG reduction benefits of replacing the HDDTs serving the SPBP with ZE-HDDTs. I simulate ZE-HDDTs on a regional freeway network to analyze their PM2.5 and CO2 emissions in 2012 and 2035 using MOVES3 emission factors. I then estimate their contribution to PM2.5 concentrations with InMAP and health impacts with BenMAP. I find that despite technology improvements and air quality regulations, SPBP HDDTs would still cause 106 premature deaths (valued at $1.3 billion in $2022) and 2,142 asthma attacks (>2/3 accruing to disadvantaged communities) in 2035 due to population and drayage traffic growth, not to mention at least $220 million in climate costs. With ZE-HDDTs becoming attractive from a total cost of ownership point-of-view, the main cost of achieving ZE road drayage is a scrappage program for non-ZE-HDDTs. My results justify implementing this program by 2035.

In Chapter 4, I study the performance impacts of lane management strategies implemented on I-710 to support the deployment of CACC-enabled vehicles and their potential to absorb the 2035 projected growth in cargo demand at the SPBP. I find that a designated lane for CACC-enabled vehicles can decrease congestion by creating more platooning opportunities, thus maximizing CACC benefits.

Mobility systems are undergoing a major transformation due to emerging autonomous and shared mobility technologies. A primary aspect of such technologies consists of improving the mobility system inefficiencies via a reduction in the number of vehicles needed to fulfill the transportation needs. This would impact the use of vehicles and their expected lifetime. This dissertation is focused on the importance of the increased usage of vehicles and how the system can benefit from an optimization with a vehicle point of view. The improvements come from mainly two aspects of shared mobility – carsharing and ridesharing – which are both implemented in the modeling and optimization framework. An analysis of the current vehicle ownership and trip distributions is presented. A vehicle usage cost function is designed to incorporate the changed relative importance of fixed and usage-based variable costs. It presents a framework that analyzes the interactions between all the elements, including a pricing scheme for benefit-cost analysis and optimizations from a service provider perspective. With shared mobility, ownership paradigms can also change to subscription-based use
of vehicles from fleet service providers, as included implicitly in the interaction framework. Modeling is carried out for idealized networks, as well as a real-world network of a reasonable size from the city of Irvine, CA. The results capture the increased use of shared and/or autonomous vehicles (SAV) and the benefits of optimizing the system with properly updated costs. Results and conclusions are provided on the viability of service provider plans as well as on system benefits in terms of the replacement ratio indicating how many personal vehicles can be removed using autonomous fleets.

Traffic congestion is known to have many negative impacts both for travelers and society as a whole (e.g., emissions, noise). This dissertation is centered on the modeling and analysis of vehicle and trip flow dynamics. Several transportation systems are considered, from a single bottleneck to a network region, in order to propose effective management strategies to reduce traffic congestion. Since different transportation systems are considered, different models and space paradigms are needed to model each system’s dynamics. At the local level, it is reasonable to study the traffic flow in the physical road and how it evolves spatially and temporally with traditional traffic flow theory models. In contrast, when the network is highly complex (e.g., corridors with many on- and off-ramps or urban cores), it is reasonable to disregard the detailed vehicle-network interaction at the distinct physical locations and model traffic dynamics with more aggregated models in the relative space.

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.