Event Type: PhD Defense

This dissertation provides modifications and extensions to the Household Activity Pattern Problem (HAPP) to help move existing formulations from a laboratory prototype toward a more useable activity-based demand modeling product. Previous research on HAPP has been based on a pickup and delivery problem with time window constraints (PDPTW), which does not lend itself easily to application that is compatible with an activity-based forecasting model. Meanwhile, other research on activity based modeling lacks of the integration of household decisions regarding time-of-day arrival, activity duration and traffic congestion effects on travel. We borrow concepts from economic research and consider that each household member tries to obtain maximum utility by choosing arrival time of activities, choosing activity duration while minimizing travel times and travel costs throughout the course of the day. Chapter 1 provides the introduction and motivation of this research. Chapter 2 reviews pertinent literature relative to the activity-based approach, the HAPP model, and positions the dissertation research relative to the existing state-of-the-art. In Chapter 3 we propose extensions to HAPP (UHAPP) that incorporate time of day activity arrival utility and the utility of activity duration into HAPP as decision variables. In Chapter 4 we introduce the travel time-dependent household activity pattern problem model (TUHAPP), which extends the ability of HAPP to capture the time-of-day (TOD) difference in travel times and costs. In Chapter 5 we develop a framework using TUHAPP (UHAPP) as a regional activity-based demand model with a household travel survey. Chapter 6 provides conclusions and future research.

The present thesis is comprised of four essays that address important gaps in passenger- and cargo-airline research. Seminal studies in airline economics that rely on cross-section methods make critical homogeneity assumptions and preclude time-specific effects. The essays in this thesis use panel data, which allow for certain assumptions made by cross-sectional studies to be relaxed, while shedding light on the intertemporal features of air transport.

The first chapter investigates the cost structure of air cargo carriers by applying a total cost model used in passenger-airline studies. Using quarterly panel data (2003-2011) on the domestic operations and costs of FedEx Express and UPS Airlines, empirical results indicate that the air cargo industry exhibits increasing returns to traffic density and constant returns to scale. Accounting for carrier-specific differences in cost structure and network size, FedEx is found to be more cost efficient than UPS (a finding that is reversed when network size is not controlled). Individually, UPS exhibits substantial economies of density and constant returns to scale while FedEx’s cost structure is characterized by weak economies of density and constant returns to scale. Both carriers exhibit economies of size.

The next three chapters embody papers that use quarterly panel data of city-level air traffic, airline delay, and socioeconomic variables. Spanning 10 years (2003-2012), the panel structure of the data permits the use of fixed effects to control for city-specific heterogeneity.

The second chapter presents a paper prepared for the Airport Cooperative Research Program (ACRP). The study demonstrates the within-city traffic impacts of urban size, employment composition, and wages, providing new insights into the determinants of passenger and air cargo traffic. The essay also confirms that airport traffic is proportional to population, and that service-sector employment and higher wages induce passenger travel and goods movement. A city’s share of manufacturing employment, however, only impacts air cargo traffic. Passenger enplanements exhibit more sensitivity to the proportion of urban workers providing non-tradable services, compared to the share of workers in tradable service jobs.

The third chapter, co-authored with Andre Tok, examines the determinants of air cargo traffic in California. The study uses a shorter 7-year panel (2003-2009), and shows that service and manufacturing employment impact the volume of outbound air cargo. Total (domestic) air cargo traffic is found to grow faster than (proportionally to) population, while wages play a significant role in determining both total and domestic air cargo movement. Metro-level air cargo tonnage are also forecasted for the years 2010-2040, indicating that California’s total (domestic) air cargo traffic will increase at an average rate of 5.9 percent (4.4 percent) per year in that period.

The final chapter is co-authored with Volodymyr Bilotkach, and it provides the first evidence on the impact of airline delays on urban-sectoral employment. Controlling for unobserved city-specific differences, the empirical estimates of the effects of air traffic on total employment are comparable to previously reported measures. However, service-sector employment is found to be less sensitive to air traffic than other studies suggested. New evidence confirming that delays have a negative impact on employment is also provided, a finding that is robust to various model specifications.

The introduction of Senate Bill (SB 375) in 2008 stimulated more research linking travel behavior to the built environment. Smart growth tools mandated by this bill aim to reduce vehicle miles traveled (VMT), greenhouse gas (GhG) emissions and promote alternative modes to motorized travel. These tools encompass an array of land use improvements that are expected to influence active travel. Potential changes in the built environment may impact the frequency, amount and even the selection of routes for walking.

Data used in this dissertation was obtained from Phase I of the Expo Study, a three-phase travel survey of residents living near the Expo Light Rail Line in Los Angeles, CA. Respondents carried GPS devices and accelerometers to track locations and activity levels; and completed seven-day trip logs. Phase I of the survey was administered in Fall 2011, prior to the introduction of the Expo Line in April 2012.

This dissertation is comprised of three essays. The first essay uses a “place-oriented” approach to examine where active travel occurs in neighborhoods adjacent to the Expo Light Rail Line. This essay is based on the Behavioral Model of Environments, which emphasizes the influence of the physical environment on individuals’ travel behavior and route choices. Results indicate that the routes selected by pedestrians have higher densities of commercial and retail centers and better access to more transit stations.

The second essay uses an ecological modeling approach. Multilevel analysis of the effects of the built environment on active transport was performed in three geographic levels of aggregation near respondents’ homes. Examination of land uses at the half-mile extent yield the least number of significant results. In contrast, land uses examined at the 100-meter and half-mile distance from homes emphasize the importance of street connectivity and green space on increasing transport-related physical activity (TPA). This suggests the importance of analyzing the data at finer geographic levels.

The third essay proposes a practical methodology of pedestrian route analysis in which observed GPS-tracked routes were examined and compared to GIS-simulated shortest paths. The two route types were compared over deviations in trip-level travel indices, respondents’ socio-demographic traits, time of day variations and differences in objectively measured built environment features along both sets of routes. Results suggest that observed routes diverged more from shortest paths with increasing distance and were more circuitous beyond the 2.4-mile threshold. Most walks were completed after the AM Off Peak time. With the exception of the Evening time, observed routes were found to be much longer in all time periods especially in the AM Peak time. Moreover, higher densities of commercial centers, local businesses and green spaces were observed for GPS-tracked routes than shortest paths. These routes also had more street intersections and transit stops. Overall, results imply that pedestrians selected routes that were longer than the respective shortest paths and that may have been due to greater access to amenities and activity centers.

Understanding the choices that each individual in the population makes regarding daily plans and activity participation behavior is crucial to forecasting spatial-temporal travel demand in the region. The focus of this dissertation is developing a comprehensive mathematical/statistical framework to infer and replicate travel behavior of individuals in terms of their socio-demographic profiles. The framework comprises series of distinct modules that employ statistical segmentation, Bayesian econometrics, data mining, and optimization techniques to predict individuals’ activity types, activity frequencies, and the travel linkages that make them possible.

The key advantages of the model are: first; providing the likely content of activity agenda as part of the inference procedure, second; integrating transportation network topology within activity scheduling step, and third; integrating modal components. As part of the dissertation, a Graphical User Interface was developed for practical application of the model in transportation agencies. The data used for the analysis is the California Household Travel Survey data, 2000-2001. Testing the entire modeling system on an out-of-sample population—15% of the entire sample— shows that the model is able to predict on average 80.3% of daily activities of individuals correctly.

Freight operations are critical to our prosperity, but they also generate substantial external costs in the form of additional congestion, air pollution, and health impacts. Unfortunately these external costs are not well understood. In this dissertation, I focus on the drayage trucks that serve the San Pedro Bay Ports (or SPBP, i.e. the Ports of Los Angeles and Long Beach in Southern California), which is the largest port complex in the country. Freight routes providing access to the SPBP comprise a major rail-line (the Alameda Corridor) flanked by the I-110 and I-710 freeways, which both carry thousands of trucks per day. A number of policies have been implemented to reduce emissions on the ocean-side (e.g., limiting ship speeds and managing their queues) and in the Ports (e.g., providing power to docked ships so they do not have to run their engines). On the land-side, two policies were implemented: the Clean Trucks Program, which regulates drayage truck emissions and provides funds for their upgrade, and the PierPASS program (the focus of my dissertation), which shifts drayage trucks traffic from mid-day and peak hours to the evening and night hours. However, external costs from drayage trucks remain a major concern for communities adjacent to the ports because they bear a disproportionate fraction of the health impacts (respiratory and cardiovascular illness, cancer, and premature death) associated with the pollution generated by ports operations. In this context, my dissertation analyzes some of the benefits of shifting freight traffic to off-peak periods with an emphasis on congestion, air pollution (NOx, and PM) and related health impacts, using an innovative approach that expands microscopic traffic simulation model. My results will inform policy makers concerned with crafting cleaner logistics policies.

This dissertation assesses costs and benefits of two recent public rail transit systems in Kaohsiung, Taiwan’s second largest city: Kaohsiung’s mass rapid transit (MRT) system, which was completed and inaugurated in 2008 and Kaohsiung light rail transit (LRT) loop line, which is now under construction. I first focus on the benefits of the opening of Kaohsiung’s MRT system as reflected in the price of apartments with elevators. I combine two stage least squares with geographically weighted regression to analyze transactions of apartments with elevators in 2007 and 2009. This approach allows accounting for the joint determination of time-on-market information (TOM) and price while allowing hedonic parameters to vary spatially. Results show that the opening of the MRT had a statistically significant and positive impact on the value of apartments with elevators. However, accounting for TOM has a negligible impact on my results.

Second, I apply the theory of real options to capture uncertainty in operating revenues and costs in the context of build-operate-transfer (BOT) and operate-transfer (OT) contracts for Kaohsiung’s LRT loop line project. Unlike the traditional net present value (NPV) approach, real options analysis includes option values embedded in a project. Here, I rely on the binomial pricing approach to explore the value of the options to abandon and to expand the project. My findings show that the options to abandon or expand the LRT system are not sufficient to make a BOT contract attractive to a private firm, even under the best case scenario; however, accounting for the value of these options makes an OT contract at least 10% more attractive. These results show that accounting for uncertainty in large urban transportation projects can be important although the value of flexibility may not be sufficient to offset large construction costs.

A new framework for analyzing choice set formation for route choice models is presented and an algorithm is proposed. The algorithm is tested against a sample of GPS data for heavy trucks for the State of California. The results are presented in detail along with an analysis of both their qualitative and quantitative merits. A new algorithm for the route choice problem is also presented and its results analyzed against the state of the practice and state of the art. This new algorithm, ReMULAA, is also the first known closed solution algorithm for the route choice problem using the Multinomial Logit Model (MNL) for an entire class of networks (Directed Acyclic Networks) without explicit route enumeration. A correction for the MNL model to account for route overlapping is also presented and the results compared with other state-of-the-art route choice algorithms. The results of the application of ReMULAA in areal world model are also presented and its advantages discussed.

Freight forecasting models are data intensive and may require many explanatory variables to achieve prediction accuracy. One problem, particularly in the United States, is that public data sources are usually available only at highly aggregate geographic levels, while models with more disaggregate geographic levels are required for regional freight transportation planning. A second problem is that supply chain effects are often ignored or modeled with economic input-output models which lack explanatory power. This study addresses these challenges by considering a Structural Equation Modeling approach, that is not confined to a specific spatial structure as spatial regression models would be, and allows for correlations between industries. The goal of the proposed methodology is to design a reliable and policy sensitive modeling framework for long term commodity flow forecasting that makes the best use of public available data sources. Practicality and improvement over previously available freight generation and distribution models are the highlights of this approach.

There are two primary developed in this study. The first one is a structural commodity generation model. The second model is the Structural Equations for Multi-Commodity OD Distribution (SEMCOD) model. The models are specified and estimated based on FAF3 data. It is shown that the proposed modeling framework provides a better fit to the data than independent regression models for each commodity. The three components of the models are: direct and indirect effects, supply chain elasticities at zone level and at origin-destination level, and intra-zonal supply-demand interactions. A validation of the geographic scalability of the model is conducted using a zoning system consisting of 97 county or sub-county zones in California

Transportation has been a significant contributor to greenhouse gas and criteria air pollutant emissions. Emission mitigation strategies are essential in reducing transportation’s impacts on our environment. In order to effectively develop and evaluate on-road emissions reduction strategies, accurate quantification of emissions is the critical first step. The accuracy and resolution of the traffic measures needed by the emission models will directly affect the emission estimation results. This dissertation investigates the application of traffic detection technologies to deriving the traffic measures needed for accurate on-road emissions estimation.

The inductive vehicle signature (IVS) system is identified as the most promising technology to couple with EPA’s latest MOVES emission model for estimating emissions accurately. Models and algorithms based on the IVS detection system are developed to generate the two most important traffic measures for emission estimation: vehicle mix and average speed. The performances of the models are verified using real-world field data.

Although average speed has been the most common input into emission models, the MOVES model is capable of using second-by-second vehicle speed trajectories to estimate emissions more accurately. Crowd sourced GPS data can also be used by emission models like MOVES to estimate emissions. In this study, we aim to answer two most fundamental questions: 1) how to use the GPS data, and 2) how the penetration rate of the GPS probes affects the emission results. It is found that emissions can be estimated with high accuracy and reliability with even a very small penetration rate of GPS probes.

We conclude that the IVS detection system and GPS probe data can be successfully applied to estimate accurate and reliable on-road emissions estimation. Discussions on the application of the models developed in this study to various scenarios are included.

This dissertation focuses on the integration of the Household Activity Pattern Problem (HAPP) with various locational decisions considering both supply and demand sides. We present several methods to merge these two distinct areas—transportation infrastructure and travel demand procedures—into an integrated framework that has been previously exogenously linked by feedback or equilibrium processes.

From the demand side, the Location Selection Problem for the Household Activity Pattern Problem (LSP-HAPP) is developed. LSP-HAPP extends the HAPP by adding the capability to make destination choices simultaneously with other travel decisions of household activity allocation, activity sequence, and departure time. From the supply side, the network decisions are determined as an integral function of travel demand rather than a given fixed OD matrix. The Location – Household Activity Pattern Problem (Location-HAPP), a facility location problem with full-day scheduling and routing considerations is developed. This is in the category of Location-Routing Problems (LRPs), where the decisions of facility location models are influenced by possible vehicle routings. Location-HAPP takes the set covering model as a location strategy, and HAPP as the scheduling and routing tool. The Network Design Problem is integrated with the Household Activity Pattern Problem (NDP-HAPP) as a bilevel optimization problem. The bilevel structure includes an upper level network design while the lower level includes a set of disaggregate household itinerary optimization problems, posed as HAPP or LSP-HAPP.

Utilizing the aforementioned models, two transportation sustainability studies are then conducted for the adoption of Alternative Fuel Vehicles (AFVs). From the demand, we measure the household inconvenience level of operating AFVs. From the supply side of the AFV infrastructure, Location-HAPP is applied to the incubation of the minimum refueling infrastructure required to support early adoption of Hydrogen Fuel Cell Vehicles (HFCVs).