Phd Dissertation

Exploring Delivery Services Substituting Household Shopping Trips: Implications for Travel, Transportation Networks, and Fleet Optimization, and Insights on the Potential of Autonomous Vehicles

Publication Date

March 11, 2024

Author(s)

Abstract

This dissertation delves into the intersection of two critical elements shaping the future of transportation: opportunities and the challenges presented by shopping delivery services, particularly same-day delivery (SDD), and the necessity to anticipate and explore the forthcoming transportation paradigm with the new possibilities offered by Autonomous Vehicles (AVs). This study investigates the transformative potential of SDD services facilitated by a fleet of shared autonomous vehicles (SAVs) to reshape daily shopping trips and activities.With a dual focus on both the network and household layers, the dissertation addresses the viability of SDD services, considering vehicle miles traveled (VMT) savings and operational strategies for efficient fleet management on one side, and the impacts on travel patterns on the other. Leveraging real-world data for the network of Irvine, CA, and employing optimization methodologies, this dissertation (i) investigates the potential VMT savings from SDD compared to the base scenario where households conduct their own shopping activities, (ii) analyzes the optimal fleet size needed to achieve significant VMT reductions, and (iii) evaluates operational strategies for cost-effective and efficient service delivery. In this dissertation, I analyze the optimal fleet size and system design settings needed to achieve significant VMT reductions without losing profitability and I evaluate operational strategies for cost-effective and time-sensitive service delivery.At the network layer, the system is modeled as a multi-Vehicle and Multi-Depot Pickup and Delivery Problem with Time Windows (m-MDPDPTW), which was implemented in Google OR-Tools. The depots are assumed to be at the warehouse locations from where shopping goods deliveries are made. An analysis is presented for a delivery service comprising an AV fleet serving households on their daily shopping trips for the case study of the City of Irvine, CA. The results indicate these services can significantly decrease the distance traveled and the time spent for shopping trips. The dissertation tests several scenarios to determine how varying possible service operation parameters as well as demand characteristics would affect the results. Scenarios involving varying percentage of the service demand, time window for deliveries, loading/unloading time, and warehouse distribution are considered.At the household layer, the dissertation examines how the SDD service influences household travel patterns and savings, using output from the California Statewide Travel Demand Model (CSTDM) for the City of Irvine. The time saved is used as an accessibility measure. Using the Household Activity Travel Pattern Problem (HAPP), formulated as a pickup and delivery problem with time windows for household daily activities, time saved is compared over four distinct scenarios: a base (existing) case with CSTDM patterns, the HAPP-optimized version of the base case, the base case excluding shopping trips, and its HAPP-optimized version. HAPP-based analysis sheds light on new opportunities in travel and activity planning enabled by AVs as well as insights into future activity patterns shaped by subscription services that may lead to more optimized travel patterns. High Performance Computing is used to tackle the NP-Hard computational problem involved in HAPP in the real-world case study with a large set of households.This research is also intended to establish the viability of operationalizing a HAPP-methodology for analyzing realistic travel network contexts, for transportation policies that involve innovative vehicle usage and routing patterns. A HAPP solution is not a model for the actual household-level travel behavior, but rather a constraint-driven optimal version of it. Nonetheless, with the availability of rich individual level activity data now and in the future, HAPP can indeed become an optimizer for households, if computational problems can be surmounted. This dissertation establishes that computational problems are not insurmountable with current cloud and advanced computing options, even for 4-member households with activities substitutable across individuals, which past research had generally avoided. The research illustrated that, for a real-world network that has an individual and household-level activity-based planning model, or at least a synthesized model of that kind, policy analysis for future transportation options can be done using HAPP to find an optimized implementation of the policy when the behavioral response to such policy is not available in the existing activity models or data. The dissertation also points to future research possibilities involving faster optimizations that can be achieved if HAPP can be implemented with starting feasible solutions that may be developed from existing networks.

Phd Dissertation

Investigation of LiDAR for Traffic Monitoring with emphasis on Heavy Duty Trucks

Abstract

Traffic Monitoring is at the center of any Intelligent Transport System. Current traffic monitoring sensors are challenged to deliver in the evolving landscape of connected, autonomous and alternative fuel transportation systems. This dissertation explores the feasibility of emerging LiDAR technology for traffic monitoring, in an infrastructure-based, side-fire LiDAR configuration. LiDAR technology was investigated in terms of providing both the core data elements of existing traffic monitoring systems such as vehicle counts and speeds, as well as more high-resolution data elements required for future connected and autonomous vehicles such as relative positions of vehicles on a roadway, vehicle lateral and longitudinal positions within a lane, physical attributes of individual vehicles, and vehicle microscopic trajectories. LiDAR sensors were deployed at both dense urban corridors and rural highway locations. At the urban location, the LiDAR estimate of vehicle counts across lanes was between 87% to 110% of a baseline calibrated sensor’s vehicle counts. At the rural highway location, microscopic trajectories for vehicles were derived at 0.1 second resolution, enabling detection of anomalies in vehicle behavior. In addition, the precise lateral positions of heavy-duty vehicles were derived at the urban corridor location, with a particular interest being future safety assessment for loss of control of autonomous heavy-duty trucks. The high-resolution traffic data elements derived from this research can assist in detecting anomalous behavior of vehicles, whether from impaired driving or loss of effective autonomous control, with road safety assessments, and in providing inputs for microscopic road emission modeling. 

research report

Natural Gas Vehicle Incentive Program

Abstract

This report presents the results of the Natural Gas Vehicle Incentive Program administered by the Institute of Transportation Studies at the University of California, Irvine under agreement number 600-14-006 with the California Energy Commission. Program development and administration is described, including discussion of outreach efforts and engagement with stakeholders to improve program operations. Performance statistics for the Natural Gas Vehicle Incentive Program are presented describing the characteristics of the 916 vehicles incentivized under the project, and the 87 distinct entities that received incentive funding. Recommendations are offered for future vehicle incentive programs to resolve some of the problems that arose during the administration of the program that were mostly due to structural characteristics of the voucher process itself. The report also details the findings of two major research efforts conducted under the agreement. The first research thrust targeted developing a better understanding of alternative fuel demand from the perspective of fleet operations. This included both fleet purchase behavior as it relates to alternative fuel heavy duty vehicles and also a detailed study of how heavy-duty vehicle operating cycles impact their emissions and suitability for alternative fuel deployments. The second research thrust addressed the implications of scaling specific features of the California Sustainable Freight Action Plan to statewide operations. Using results from the California Statewide Freight Forecasting Model, a case study on optimizing the deployment of overhead catenary electric highway infrastructure around the state in terms of either maximizing vehicle miles traveled coverage or maximizing the accrued benefits to disadvantaged communities.

journal article preprint

A Comparison of Time-use for Telecommuters, Potential Telecommuters, and Commuters during the COVID-19 Pandemic

Abstract

Throughout the ongoing COVID-19 pandemic, changes in daily activity-travel routines and time-use behavior, including the widespread adoption of telecommuting, have been manifold. This study considers how telecommuters have responded to the changes in activity-travel scheduling and time allocation. In particular, the research team considers how workers utilized time during the pandemic by comparing workers who telecommuted with workers who continued to commute. Commuters were segmented into those who worked in telecommutable jobs (potential telecommuters) and those who did not (commuters). Our empirical analysis suggested that telecommuters exhibited distinct activity participation and time use patterns from the commuter groups. It also supported the basic hypothesis that telecommuters were more engaged with in-home versus out-of-home activity compared to potential telecommuters and commuters. In terms of activity time use, telecommuters spent less time on work activities but more time on caring for household members, household chores, eating, socializing, and recreation activities than their counterparts. During weekdays, a majority of telecommuters did not travel and in general this group made fewer trips per day compared to the other two groups. Compared to telecommuters, potential telecommuters made more trips on both weekdays and weekends while non-telecommutable workers made more trips only on weekdays. The findings of this study provide initial insights on time use and the associated activity-travel behavior of both telecommuter and commuter groups during the pandemic.

policy brief

What Does the Prevalence of Telecommuting Mean for Urban Planning?

Publication Date

January 6, 2024

Abstract

Researchers at the University of California, Irvine, are looking into what may become the “new normal” in work and work-related travel and the consequences that could have on traffic conditions, efforts to address climate change, and the future of our urban areas, as well as our daily lives. They find, for instance, that current research is largely equivocal about the consequences of telecommuting on where individuals choose to live, their day-to-day travel, and urban/metropolitan development. Equally unclear is how increased telecommuting may impact efforts to create more sustainable and inclusive communities. In light of this uncertainty, they suggest planners and researchers need to pay more attention to the changing nature of urban commuting and how it can play an important role in shaping a more desirable future.

research report

Telecommuting and the Open Future

Publication Date

January 6, 2024

Abstract

The COVID-19 pandemic has generated renewed interest in how telecommuting can alter the workings of cities and regions, but there is little guidance on how to align planning practice with the new reality. This report synthesizes the research on telecommuting and its consequences to help planners better understand what effects may occur from the proliferation of telecommuting and what lessons can be drawn from research findings. Emphasis is on the broad relevance of telecommuting to many domains of planning, including housing, land use, community development, and inclusive place-making, while attention is paid to changes in travel demand, vehicle miles traveled (VMT), and greenhouse gas emissions. The research suggests that telecommuting can occur in a variety of ways, and its impacts are largely dependent not only on the type/schedule of telecommuting but also on the built environment, transit accessibility, and other amenities/opportunities the location provides. The varying impacts reported in the research can be seen as an encouragement for planners to actively create a better future rather than merely responding to the rise of telecommuting. Given the breadth of telecommuting’s impacts, systematic coordination across various planning domains will be increasingly important. This report also calls for collaboration across cities to guide the ongoing transformation induced by telecommuting not in a way that leads to more residential segregation but in a way that provides more sustainable and inclusive communities.

policy brief

COVID-19 Vaccination Rates Influenced Bus Ridership Recovery

Abstract

COVID-19 has had lasting effects on transit ridership, with the worst declines seen in high-income, better educated, urban neighborhoods. However, declines among immigrant and/or low-income households was well documented prior to the pandemic, as more gained access to private vehicles. This has created a unique challenge for transit agencies to bring riders back to transit in cases where they may have already switched to traveling by car or consciously chose to make fewer trips. To better understand ridership during the pandemic, we documented the recovery of bus ridership in Los Angeles County and its relationship with COVID-19 vaccinations between April and December 2021, before the Omicron COVID-19 wave. We then developed a statistical model that relates LA Metro bus ridership as a percentage of October 2019 levels with the percent of adults fully vaccinated by ZIP code. We tested whether the relationship between vaccinations and bus ridership varied by two events: first, the full reopening of businesses in California and second, the wave of COVID transmission caused by the subsequentDelta variant.

Phd Dissertation

Exploring Trip Chaining Behavior in Activity-Transport Systems: Trip Chain Classification, Peak-period Travel Implications, and Ride-hailing's Role

Abstract

An activity-travel chain is a series of consecutive trips to multiple destinations. By influencing activity decisions (e.g., activity location, duration, and start time) and travel decisions (e.g., trip mode, route, and departure time), activity-travel chaining can directly impact roadway congestion, vehicle miles traveled by mode, transit ridership, energy consumption, and emissions of harmful pollutants. In this context, my dissertation uses the 2017 National Household Travel Survey (NHTS) and 2018-2019 Household Travel Survey from four Metropolitan Planning Organizations (MPOs) to (i) identify distinct activity-travel chain types, (ii) quantify the effect of activity-travel chaining propensity on peak and off-peak person-miles traveled (PMT), and (iii) explore how activity-travel chain makers use emerging transportation modes (i.e., ride-hail). To perform these three analyses, I employ several statistical modeling techniques, including Latent Class Analysis (LCA), multi-level Poisson regression, structural equation modeling, and logistic regression. In Chapter 3, I identify four distinct types of activity-travel chains. The most representative type involves simple car-based activity-travel chains with short-duration stops, typically for maintenance activities. The classification also reveals one group that exclusively represents non-motorized transport (NMT)- and transit-based activity-travel chains. In addition to identifying distinct activity-travel chains, I also model the propensity of travelers to conduct each type of activity-travel chain. I find that travelers in households with children and older travelers more frequently make car-based activity-travel chains for maintenance activities. Moreover, travelers in single-member households, and travelers who are younger and male more frequently make NMT- and transit-based activity-travel chains for maintenance activities. I expect the identification of these distinct activity-travel chain types, and the models of propensity of travelers to perform each activity-travel chain type, to be useful in agent- and activity-based travel forecasting modeling frameworks. In Chapter 4, I investigate the structural relationship between activity-travel chaining propensity and motorized person-miles traveled (PMT) during the peak and off-peak periods of the day. Moreover, I differentiate between workers and non-workers. Using structural equation modeling techniques, and mediating factors I find that chaining of maintenance and discretionary activities increases peak motorized PMT for workers and non-workers, providing the strongest evidence in the literature that activity-travel chaining can exacerbate traffic congestion during peak travel periods. Moreover, I find possible substitution of maintenance activities (e.g., shopping, dining, etc.) in peak-hour with same/similar chained activities in off-peak hour. Finally, in Chapter 5, I analyze activity-travel chain mode choice and show that young persons, frequent transit users, and those having long-duration stops prefer ride-hailing over car. Also, activity-travel chain makers headed to healthcare and social/recreational activities have a particularly high tendency to use ride-hail. Understanding the use of ride-hailing in activity-travel chains should help in formulating policies to better align ride-hailing services with compatible activity-travel patterns and consequently improve accessibility and mobility. 

Phd Dissertation

Modeling Commute Behavior Dynamics in Response to Policy Changes: A Case Study from the COVID-19 Pandemic

Publication Date

November 30, 2023

Author(s)

Abstract

This dissertation introduces a novel model intended for integration within an Agent-Based Model (ABM) framework to dynamically estimate and predict workers’ commuting behaviors under various policy scenarios. The model is designed to aid policy-making by providing insight into commuting patterns and their potential responsiveness to policy interventions. In particular, the focus is on changes in Working from Home behavior due to the COVID-19 pandemic. The methodology encompasses a three-step process, starting with the identification of worker commuting preference classes. Employing an unconditional latent class analysis model, the study categorizes workers into distinct groups based on their telecommuting preferences and behaviors. This classification is foundational for understanding diverse work-related travel patterns. The second step is predicting class membership. Post-classification, the study considers demographic features to determine their impact on class membership. This analysis is critical for predicting shifts in commuting behavior in relation to demographic changes. Third, estimating commuter type within each commuter type class: This concluding step uses logistic regression to estimate the likelihood of an individual being a commuter, a hybrid commuter, or a telecommuter, with adaptability to policy changes for exploring varied outcomes. The study produced several key findings. First, diverse worker classes were identified: The analysis of the ASU Covid Future Panel Survey data revealed several distinct worker classes based on telecommuting experiences and preferences. These include a telecommuter class, a regular commuter class, pre-Covid home remote worker class, and a class exhibiting significant demographic changes during the pandemic. Particularly noteworthy is a class that shows a strong propensity to shift to high-frequency telecommuting under supportive policies, despite an initial preference for hybrid or regular commuting. Distinct class characteristics and predictors were identified within each class, serving as predictors for class membership. This finding is essential for understanding and predicting changes in commuting behaviors. The study also included an intra-class commuter type estimation and factor analysis to identify the factors influencing these classifications. This provides deeper insights into the motivations and constraints affecting commuting choices. 

research report

Transport Pricing Policies and Emerging Mobility Innovations

Abstract

Transportation pricing policies aim to manage vehicular demand for parking, dense urban areas, roadways, and highway lanes. Although pricing policies take various forms, most were designed in a world before the sharing economy and ride-sourcing companies. Hence, the efficacy of existing pricing policies in a world with shared mobility services requires further consideration. Moreover, future pricing policies designed to handle private vehicles and shared ride-sourcing vehicles must consider the behavior of both sets of travelers and vehicle fleets. This study develops a conceptual framework to support systems-level analysis of pricing policies for a world with private and shared vehicle usage. It qualitatively analyzes the impact of shared vehicles on the effectiveness of various pricing policies, while also considering the role of vehicle-to-infrastructure technology. This conceptual framework will support future research that uses activity-based travel demand and dynamic network assignment models to evaluate congestion pricing policies in an era of shared mobility. Additionally, the study presents a detailed review of the literature related to transportation pricing together with a trend analysis on congestion pricing policies in Transportation Research Board annual meeting titles and abstracts.