Event Type: PhD Defense

Society has an interest in reducing pollutants emitted from the vehicles used for transporting people and goods. The main goal of heavy-duty natural gas vehicle (NGV) incentive projects is to offer upfront monetary incentives to reduce greenhouse gas emissions and the production of regulated pollutants in the state. However, these incentives are often based on vehicle weight and do not account for environmental impacts. In addition, although heavy-duty NGVs are being used in a variety of vocation types, conventional emission models only support a limited number of these vocation types. Because of this, it is challenging to assess the precise impacts of the heavy-duty NGV (HD NGV) adoption and predict the specific environmental benefits per given operational conditions and vocation type. If government agencies realize the environmental benefits of alternative fuel vehicles (AFVs), like NGVs, with respect to vocation type and operating characteristics, it would be beneficial to design cost-effective incentive structures and implementation plans. This study primarily focused on the operational characteristics and environmental impacts of the HD NGVs incentivized in California. This study conducted pattern clustering and classification analyses to obtain drive mode compositions (DMC) over duty cycles and showed the heterogeneity of operational and emission characteristics of the vocational HD NGVs. The vocational impact analysis computed the adoption impact of 40 NGVs operating in California across ten different vocation types. The proposed evaluation framework included life-cycle nitrogen oxides (NOx) and carbon dioxide (CO2) emissions of natural gas, renewable natural gas and diesel fuel pathways and compared the lifetime NOx emission reduction potential of the considered vocation type vehicles. The resulting emission benefits of the fuel pathways were used to determine the most incentive-effective vocation types among the considered NGV applications. The multi-criteria decision-making analysis prioritized the fuel pathways based on multiple criteria which are related to an incentive effectiveness index as well as life cycle emissions. Refuse truck and transit bus pathways are likely to achieve the highest return for the total incentive granted when the vehicles are renewable natural gas (RNG)-powered. For compressed natural gas (CNG) fuel pathways, school and transit buses take the highest ranks over the various analysis scenarios. Each vocation type showed different incentive effects and emission reduction potential, which means that some vocational vehicles can play a critical role in the state’s funding and emission reduction plans. The suggested decision-making tool and assessment framework can provide useful reference data to improve the performance of future alternative fuel vehicle incentive programs.

Airports are a source of greenhouse gases (GHG) and air pollutants such as fine particulate matter with an aerodynamic diameter under 2.5 μm (PM2.5), which adversely affect the climate and human health. This pollution is worsening with increasing aircraft congestion. Even though aviation is the second largest source of GHG emissions in the transportation sector, it was excluded from the recent COP21 Paris Agreement. Little is known about the climate change and adverse health impacts from increasing airports congestion. The purpose of this study is to start filling this gap.

In this dissertation, I estimate congestion, health, and climate benefits from airport congestion pricing for Los Angeles International Airport (LAX), the fourth busiest airport in the world by passenger numbers in 2018. I first derive the optimal congestion fee for airports like LAX that primarily serve local and regional markets. To quantify the impacts of airport congestion pricing, I analyze one year of airport operations (2014), which corresponds to 593,547 flights (both inbound and outbound). My simulation results suggest that hourly congestion pricing would on average reduce waiting time by 2.9 minutes per flight and annual PM2.5 emissions by 11.4 percent, thus decreasing the environmental impacts from aircraft landing and takeoff operations (LTO), which extend as far as 19 km downwind from the airport.
An analysis of the health gains from implementing a congestion fee that accounts for air pollution cost shows that it would annually reduce premature mortality from PM2.5 exposure by 4.6 cases, avoided hospital admissions for cardiovascular diseases by 167 cases, and avoid 8,539 lost work days. The corresponding monetary value of these health gains are $45.8 million, $21.9 million, and $1.4 million respectively (all in 2014 dollars). For my climate change analysis, I consider both the country-level social cost of carbon (CSCC; $36 per tonne) and the global social cost of carbon (GSCC; $417 per tonne). While pricing GHG emissions with the CSCC only has a minor impact, using the GSCC helps further reduce aircraft congestion and its associated health impacts. Indeed, an aircraft congestion fee with GHG based on the GSCC would reduce premature
mortality by 6 cases each year, avoid hospital admissions by 221 cases, and avoid 11,528 lost work days (95% CI: 4,995, 18,060). The corresponding monetary value of these health gains are $60.7 million, $27.7 million, and $1.9 million respectively.

The methodology presented in this study is widely applicable. It provides engineers, planners, and policymakers a tool for reducing airport congestion and for quantifying the resulting health and climate benefits.

The ability to model the disruptions of adverse events on various systems, such as infrastructural and social, is an important tool to assessing these systems’ resilience. While previous research on system resilience concentrated on physical infrastructure such as transportation systems, two recent research topics include social resilience and dependencies across many infrastructure systems. For example, transportation is dependent on such systems as power, communications, and the workforces that are key to restoring these infrastructure systems. This dissertation contains three disruption modeling studies that have followed the evolution of resilience research over the past decade from physical systems to interrelated topics. The first study evaluates seismic risk of potential travel time increases from earthquake damage to bridges in a roadway network using mesoscopic traffic simulation. This analysis successfully obtained system risk curves of network-wide travel time increases. The second study shifts focus towards workforces that participate in restoring infrastructure systems. It identifies transportation and communications workers and calculates these workers’ exposure to the Peak Ground Accelerations (PGAs) of a 7.8 magnitude Southern California scenario earthquake. Indeed, for this scenario, transportation workers are exposed to statistically significant higher PGAs than non-transportation workers, and communication workers to significantly lower PGAs. The third study proposes a model for the travel time of information along communication-equipped vehicles physically traveling in a network. Vehicles are sampled as equipped vehicles, then their trajectories are analyzed to (1) estimate equipped vehicle link flow and turning movement counts and (2) estimate the frequency of equipped vehicles encountering each other on links and at nodes. This study compares two scenarios: the baseline scenario and a work zone scenario that corresponds to a bridge being damaged in the network. Preliminary results suggest a difference in expected path travel times when (1) the representation of a specified subpath within the sample is increased and (2) when vehicles are routed along currently unused subpaths. This dissertation concludes with a discussion of the implications of all three studies.

When Transportation Network Company (TNC) services first emerged, there was extensive discussion in the popular press and among academics about the benefits that these “shared” services would bring. TNC as a form of ground transportation to and from the airport in contrast, is less often studied or permitted. At Los Angeles International Airport for example, Uber and Lyft could not conduct pick-ups until about seven years after they were founded. TNC operations at airports are highly controversial, yet research on both airports and TNC’s rarely intersect. This dissertation aims to fill the gap in the literature and address such questions as: which and how many airports have various types of TNC service (standard, pooled)? How do they impact other modes, vehicle-occupancy, congestion, and access at airports? Can their service be modified (i.e. through pricing or service improvement) to encourage higher uses of shared modes? Using Uber and Lyft websites, it documents all airports in the U.S. and internationally that permit TNC service and the types of services available. It analyzes airport passenger surveys to evaluate how much TNC replaces and complements transit and the net effects at several airports. Also using the passenger survey, Google Maps Directions API, and other sources, it estimates travel time and costs of the different modes to the airport, builds a discrete choice model of the access mode choices, and simulates various scenarios. Some of the scenarios are a TNC price increase (to match the cost of taxis) or a price cut and travel time increase (to mimic Uber Pool and Lyft line which are carpool versions of TNC’s). Finally, it assesses how a pooled TNC service to the airport would operate. We apply the pick-up and delivery problem to airport access requests (formed based on the airport passenger survey) and measure the number of private trips that would be eliminated when passengers are pooled. The motivation for understanding the consequences of making private TNC’s more expensive, or pooled TNC’s less expensive and more efficient (with shorter detours or travel time) is to identify effective tools to encourage modal shifts to vehicles with higher occupancy.

Experience of daily commuters shows that stationary traffic patterns can be observed during peak periods in urban freeway networks. Such stationary states play an important role in many traffic flow studies. Theoretically, studies on the impact of capacity drop and design of traffic control strategies have been built on the assumption of stationarity. Mathematically, the existence and stability of stationary road networks have been proved within the framework of kinematic wave theories. Empirically, near-stationary states have been utilized for calibration of fundamental diagrams and investigation of traffic features at freeway bottlenecks. Therefore, an imperative need for real-world near-stationary data has been recognized to better understand and explore such above studies. However, there lacks an efficient method to identify near-stationary states.

In this research, an automatic method has been developed to efficiently identify near-stationary states from large amounts of inductive loop-detector data to fill the gap. The method consists of four steps: first, a data pre-processing technique is performed to select healthy datasets with sufficient congestion periods and normalize vehicle counts and occupancies to the same scale; second, a PELT changepoint detection method is applied to partitioning time series into candidate intervals; third, informative characteristics of each candidate, including duration and gap, are calculated; finally, near-stationary states are selected from candidates based on two well-designed selection criteria.

To calibrate two critical parameters of the method, a multi-objective optimization problem is formulated to consider the quantity and quality of near-stationary states as objective functions. Then a game theory approach is designed to convert the problem into a non-cooperative game. Further a game theory search algorithm with a built-in modified hill-climbing technique is developed to solve the game and obtain a unique Nash equilibrium solution. In an extended paradigm, a five-player game is built to achieve better performance on the near-stationary flow-occupancy pattern in the congested regime.

In an application, a calibration method of multi-lane multi-class fundamental diagrams with unifiable and non-FIFO properties is performed using identified near-stationary states. Results show that the calibrated multi-lane multi-class fundamental diagrams are well-fitted, physically meaningful, and have robust performance on the estimation and prediction of commodity flow-rates.

An unfortunate feature of freeway traffic flow at merge bottlenecks is the capacity drop (CD) phenomenon. It refers to a drop in the bottleneck outflow when a queue forms upstream to that bottleneck compared to the outflow observed before the formation of the queue. While its causes and exact mechanism are still open questions, this research concerns in the impacts of CD and how to mitigate them.

The distinct features of CD in a freeway corridor are assessed based on the behavior of equilibrium states in a model capable of replicating CD. The impacts are unveiled by comparing the system properties with and without the CD. The main finding is that the highest outflow occurs under uncongested equilibrium; however, it may not be reachable depending on the demands and initial conditions.

The local ramp metering control is investigated into more details. CD imposes a hysteresis on the system response with respect to the demand level. Also, we analyze the system in closed loop considering ALINEA, a well-known control algorithm. We establish the stability range with respect to parameters which is a necessary requirement for the controller to be effective. Further, we propose an extension of ALINEA to enlarge the stability range mitigating a performance loss that occurs when the on-ramp and the bottleneck are far apart.

Essential aspects of ramp metering are better captured with microscopic models; however, there were few evidences that such models can replicates CD. To that end, we propose a parameter calibration procedure that ensures the underlying model properly captures CD. The approach is tested with loop detector data from a merge bottleneck in which the CD is consistently observed. Finally, the analytical results are validated, and further practical aspects, such as detector placement and the effect of the controller sample time, are studied.

All results with different approaches point to the direction that the existence of CD imposes additional challenges on the system control. Fortunately, in most cases the effects of CD can be mitigated with a properly designed control strategy, such as the ones tested and proposed in this research.

This dissertation proposes a set of coherent cognition-based paradigms to allow greater sensitivity and adaptability to emerging technologies and behavioral policies. These paradigms are derived from a cognition-based framework that explicates information source, medium, sensation, perception, and learning. The feasibility is demonstrated on an analytical example of multi-stakeholder decision processes and human-machine systems where the two types of entities can be incorporated with the same modeling scheme. This reduces the challenges in object-oriented programming in agent-based modeling practice, such as information intractability and data redundancy.
The first paradigm strictly models information as changes of uncertainty, which is applied in quantifying traveler information for the evaluation of dynamic message boards with various contents at various candidate locations near Downtown Los Angeles.

The second paradigm proposes to capture the prevailing and systemic bias in travel survey studies with a quantum logic. In a sense, this is a utilitarian extension to the quantum probability theory to resolve issues challenging for the classical probability theory. The paradigm is applied to quantify the inconsistency between the stated intentions and the reported preferences.

The third paradigm is developed for a utility-based decision model under risk around the proposed concept of Elastic Surprise. This concept makes feasible the differentiation between probability misperception and perceived uncertainty. It is shown that conventional methods on decision under risk such as Expected Utility Theory and Cumulative Prospect Theory are special cases. In addition, a specific form of Elastic Surprise under certain assumption on human’s cognition leads to Shannon’s information entropy and, hence, connects with the first paradigm. The method is tested in conjunction of the Cumulative Prospect Theory on travel time equivalency under risk in a survey study. The results show improvement in data fitting and cognition-wise result interpretability.

Finally, guided by the framework and the specificity of the paradigms are tested on a case study of multi-class multi-criteria dynamic traffic assignment where heterogeneous travelers’ risk preference on travel time is explicitly modeled.

Among all the freight modeling approaches, commodity-based model provides concerns on all travel modes and can capture the economic mechanisms driving freight movements. However, there are still challenges on how to effectively use public freight data and reflect the supply chain relations of various commodities. In this research, a commodity-based framework for freight demand forecasting using Structural Regression Model (SRM) is proposed and applied in California Statewide Freight Forecasting Model (CSFFM) using Freight Analysis Framework 4 data.
The framework developed in this study contains four innovative components: (1) mathematical approach for determining freight economic centroids; (2) aggregation of commodities using Fuzzy C-means clustering algorithm; (3) employing average travel distance by commodity group instead of highway skim to accurately represent real condition; (4) forecasting freight demand using Structural Regression Modeling method to comprehensively consider the direct effect, indirect effect and latent variable. The SRM is adopted in both total generation model and domestic direct demand model which combines the traditional generation and distribution steps. The application results are further compared with old CSFFM 1.0’s forecasts in 2012 to illustrate the advantages of proposed framework.

Transportation systems have been traditionally operated on a First-Come-First-Served (FCFS) fashion. FCFS consumption of supply naturally arises from supply being centralized by an infrastructure operator, without considering any individual-specific information from users. Thus, FCFS behaves as a status quo policy, generally considered as fair, since it is acknowledged that all users are treated equally. We know though, that there exists heterogeneity in users’ value of time and delay savings. Taking advantage of smartphones and connected vehicle environments, it is now possible to include this user heterogeneity into operations in order to increase overall system efficiency and fairness. I call this novel operational paradigm, collaborative consumption of transportation supply.

This seminar explores the idea of violating FCFS by allowing users to trade in real-time the part of supply they “own” while they participate into a transportation system. This de facto ownership emanates from the space-time region which each agent lawfully controls. This topic led me to introduce by the first time in transportation literature, the fundamental economic concept of envy-freeness as a behavioral paradigm. I also have expanded this concept to the domain of dynamic problems, which I call dynamic envy-freeness, and created a new envy-minimizing criteria family, which strongly fits into the existing axiomatic body of Welfare Economics.

Several applications of collaborative consumption are explored in this dissertation. First, I create PEXIC, Priced EXchanges in Intersection Control, in which users can pay other vehicles to reduce their delays in a fair manner. Second, Peer-to-peer (P2P) ride exchange in ridesharing systems, where trip property rights are transferred to users in such a way that they can trade their rides between each other. Third, I have studied a new operational policy in highway control: queue-jumping operations, where vehicles can jump a queue by paying the overtaken vehicles such that the resulting queue is fair and stable. Fourth, I have modelled a P2P ridesharing system as a truthful dual-role market exchange economy, which guarantees a ride-back. Finally, I present how collaborative consumption will be extended to urban systems.

More frequent vehicle movements are required for moving containers in a local area due to low unit volume that a single vehicle can handle compared with vessels and rails involved in the container supply chain. For this reason, truck operations for moving containers significantly affect not only transportation cost itself but also product price. They have inherent operational inefficiencies associated with empty container movements and container processes at facilities such as warehouses, distribution centers and intermodal terminals. One critical issue facing the trucking industry is the pressing need for truck routing plans that reduce such inefficiencies. Hence, this dissertation proposes to apply the concept of sharing resources, which is an emerging economic model, to container truck operations in order to resolve this issue. Two shareable resources – vehicles and containers – are considered.

This study extends the literature on routing and scheduling problems that arise from container movements, and examines the possible benefits of sharing resources across customers. A series of truck container routing and scheduling problems were developed by assuming different levels of resource sharing among; (1) customers of one trucking operator, (2) customers across collaborations of multiple operators, and (3) customers over multi-day operations. To enable a trucking company to operate its fleet under a shared resource environment, two operational strategies – street turning and decoupling operations – together with temporal precedence constraints – in addition to the time constraints that are typically included in the vehicle routing problem with time windows (VRPTW) – were adopted to address the proposed problems.
Two meta-heuristic algorithms based on a variable neighborhood search (VNS) scheme were developed to solve the proposed problems, including temporal precedence constraints – which are computationally more expensive – for real-world applications. To address flexible time windows resulting from temporal precedence constraints, a novel feasibility check algorithm was developed.

Results from a series of numerical experiments confirm that the proposed approach leverages the advantages of resource sharing, and the meta-heuristic algorithms are efficient solution approaches for each problem with the targeted resource sharing. Consequently, this dissertation offers a platform for the development of a decision-support tool for drayage companies by applying three different levels of resource sharing into their operations.