Abstract
This dissertation uses large-scale data to analyze shared micromobility systems (SMS), such as dockless scooters and bikes. Through a combination of meta-analysis, spatial modeling, and equity assessments, I identify key factors driving SMS ridership and evaluate how equitably these services are distributed across neighborhoods in U.S. cities. The research reveals insights into the roles of sociodemographics, urban infrastructure, and land use in shaping SMS usage and producing disparities in access to SMS vehicles. These findings aim to inform more inclusive and efficient urban mobility strategies, promoting equitable and sustainable transportation solutions for diverse communities.
Privately owned autonomous vehicles (PAVs) introduce new travel behaviors, such as remote parking, returning home, and serving other household members, potentially increasing vehicle miles traveled (VMT). This dissertation examines PAV travel patterns, assesses their impact on transportation systems, and proposes design and policy measures to enhance mobility. While the exact performance of AVs on road networks remains uncertain, the findings indicate that PAV deadheading could negatively affect transportation systems. As the AV era approaches, planners must develop strategies that minimize deadheading miles. The advancements and insights in this dissertation provide a foundation for addressing these challenges and preparing for the impact of AVs.
Traditional traffic control has been based on collective stop-and-go movements for over a century, but should we still hold the presumption that it is best in the future as well, when collision risks may be less? Beginning with the simple idea of scheduling for individual vehicle movements, a new paradigm for next-generation traffic control can be developed to avoid forced vehicle stoppage and queuing that is inherent in current traffic control. This leads to control that is link-based, in contrast to the traditional node-based control. The new scheme can drastically improve travel times and throughput, and also lead to smoother eco-driving. The research presented here develops optimized schemes to schedule movements that use traffic stream gaps and also proposes a mathematical model for traffic safety analysis. Simulations demonstrate that these models significantly improve traffic efficiency, reduce environmental impact, save fuel, and potentially enhance traffic safety.
Traffic Monitoring is at the center of any Intelligent Transport System and the current traffic monitoring devices are challenged to deliver in the evolving landscape of connected, autonomous and alternative fuel transportation systems. LiDAR sensor, an emerging traffic monitoring device is being used to derive the core data elements provided by existing traffic monitoring systems such as vehicle count, physical attributes of individual vehicles, their microscopic trajectories, and speed. Along with the traditional data elements, the futuristic data elements required for connected and autonomous vehicles such as real-world relative positions of vehicles on the road and lateral positions within a lane. The high-resolution traffic data elements derived from this work can act as input for microscopic road emission models, road safety assessment models to aid in key decision making.
The sensor is deployed at a dense urban corridor and the estimate of vehicle counts across lanes is within 87% to 110% of another calibrated sensor’s vehicle counts. The microscopic trajectories for vehicles are derived at 0.1 second resolution from a sensor deployed at a rural highway and are assessed for any anomalies. Also, precise lateral positions of heavy-duty vehicles are derived for the urban corridor to enable future safety assessment of autonomous trucks.
This dissertation delves into the intersection of two critical elements shaping the future of transportation: the necessity to anticipate and explore the forthcoming transportation paradigm with the new possibilities offered by Autonomous Vehicles (AVs), and the challenges and opportunities presented by shopping delivery services, particularly same-day delivery (SDD). This study investigates the transformative potential of SDD services facilitated by a fleet of shared autonomous vehicles (SAVs). With a dual focus on both the network and household layers, the dissertation addresses the viability of SDD services encompassing impacts on travel patterns, vehicle miles traveled (VMT) savings, and operational strategies for efficient fleet management in one side, as well as the impacts on travel patterns. Leveraging real-world data for the Irvine network and employing optimization methodologies, it investigates the potential VMT savings compared to the base scenario where households conduct their own shopping activities, analyzes the optimal fleet size needed to achieve significant VMT reductions, and evaluates operational strategies for cost-effective and efficient service delivery. It analyzes the optimal fleet size and system design settings needed to achieve significant VMT reductions without losing profitability and evaluates operational strategies for cost-effective and time-sensitive service delivery.
The network layer is modeled as a multi-Vehicle and Multi-Depot Pickup and Delivery Problem with Time Windows (m-MDPDPTW), implemented in Google OR-Tools. An analysis is presented for a delivery service comprising an AV fleet serving households on their daily shopping trips for the case study of Irvine. The results indicate these services can significantly decrease the distance traveled and the time spent for shopping trips. The study tests several scenarios involving varying percentage of the service demand, time window for deliveries, loading/unloading time, and depots distribution are considered. The household layer analysis is based on the California Statewide Travel Demand Model (CSTDM) data for the Irvine population, with travel time saved as the accessibility measure. Using the Household Activity Travel Pattern Problem (HAPP), formulated as a pickup and delivery problem with time windows for household daily activities, this measure is compared over different scenarios, to shed light on new opportunities in travel and activity planning enabled by AVs. High Performance Computing is used to make the NP-Hard HAPP’s application possible for a large-scale case study.
A trip chain is a series of consecutive trips to multiple destinations. By influencing activity and travel decisions, trip 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 trip chain types, (ii) quantify the effect of trip chaining propensity on peak and off-peak person-miles traveled (PMT), and (iii) explore how trip 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. The main findings suggest the existence of distinct trip chain types and a significant association between ride-hailing and trip chaining attributes. I also find that chaining subsistence, maintenance and discretionary activities increases peak PMT for both workers and non-workers, with possible substitution effects on the off-peak PMT.
This dissertation introduces a novel model for integration into an Agent-Based Model (ABM) framework, aimed at estimating and predicting workers’ commuting behaviors in a post-pandemic context. The model is designed to inform policy-making by analyzing commuting patterns and their responsiveness to policy changes. The methodology involves three stages: first, identifying worker commuting preference classes through latent class analysis; second, predicting class membership based on demographic features; and third, estimating commuter types (commuter, hybrid commuter, or telecommuter) using logistic regression. The study utilizes the ASU Covid Future Panel Survey data to identify diverse worker classes with distinct telecommuting experiences and preferences. Key findings include the discovery of various worker classes, such as regular commuters and telecommuters, and a class inclined towards high-frequency telecommuting under supportive policies. The research also explores intra-class commuter type estimation and factors influencing commuting choices, offering valuable insights for future commuting pattern predictions and policy development.
The purpose of this study is to develop a methodology for processing vehicle trajectory data which are presented as a series of discrete positions of vehicles recorded over consecutive time intervals. The framework combines vehicle trajectory smoothing and imputation, ensuring that speeds and higher-order derivatives of positions are consistently defined as symplectic differences in positions, while adhering to physically meaningful bounds determined by traffic laws, drivers’ behaviors, and vehicle characteristics.
This dissertation explores the evolving landscape of prepared food consumption, particularly restaurant meals, and proposes optimization strategies for managing delivery fleets. In the context of COVID-19, I examined shifts in meal consumption in California using Heterogeneous ordered logit models. I analyzed meal delivery, uncovering unique dynamics across regions and times and emphasizing the role of deliveries in enhancing food access for marginalized communities. Using graph theory, I also explored fleet management optimization, comparing Hopcroft-Karp and Karp algorithms. This research informs policy interventions, aids platform operators, restaurant owners, and urban planners, and bridges academia and practice through an interdisciplinary lens.
Cities around the world vary in terms of their transportation network structure and travel demand patterns, with implications for the viability of shared mobility services. Recently, the urban mobility sector has witnessed a significant transformation with the introduction of several new types of Mobility-on-Demand (MOD) services that vary in terms of their capacity and flexibility of routes, schedules, and user Pickup and Dropoff (PUDO) locations. This dissertation proposes models and algorithms to analyze sharing in transportation networks and Mobility-on-Demand (MOD) services in two comprehensive studies. The first study addresses the lack of metrics that jointly characterize a region’s travel demand patterns and its transportation network in terms of the potential for travelers to share trips. I define sharing potential in the form of person-trip shareability and introduce ‘flow overlap’ as a fundamental shareability metric. Then, I formulate the Maximum Network Flow Overlap Problem (MNFLOP), a math program that assigns person-trips to network paths that maximize network-wide flow overlap. The results reveal that the shareability metrics can (i) meaningfully differentiate between different Origin-Destination trip matrices in terms of flow overlap, and (ii) quantify demand dispersion from a single location considering the underlying road network. Finally, I validate MNFLOP’s ability to quantify shareability by showing that demand patterns with higher flow overlap are strongly associated with lower mileage routes for a last-mile microtransit service. The second study proposes a scalable algorithm for operating shared-ride MOD services with flexible and dynamic PUDO locations (or virtual stops)—called C2C (Corner-to-Corner) services—in a congestible network. I compare four MOD service types: Door-to-Door (D2D) ride-hailing, D2D ride-pooling, C2C ride-hailing, and C2C ride-pooling by evaluating operator and user costs. The results show that ride-pooling reduces operator costs while slightly increasing user costs, whereas C2C reduces operator costs but significantly increases user costs. Combining ride-pooling and C2C further reduces operator costs and decreases vehicle miles traveled in MOD systems.