Phd Dissertation

Incorporating Individual Activity Arrival and Duration Preferences within a Time-of-day Travel Disutility Formulation of the Household Activity Pattern Problem (HAPP)

Publication Date

September 4, 2014

Author(s)

Abstract

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.

working paper

Determinants of Air Cargo Traffic in California

Publication Date

August 14, 2014

Abstract

Studies on the economic impact of air cargo traffic have been gaining traction in recent years. The slowed growth of air cargo traffic at California’s airports, however, has raised more pressing questions amongst airport planners and policy makers regarding the determinants of air cargo traffic. Specifically, it would be useful to know howCalifornia’s air cargo traffic is affected by urban economic characteristics surrounding airports. Accordingly, this study estimates the socioeconomic determinants of air cargo traffic across cities in California. We construct a 7-year panel (2003-2009) using quarterly employment, wage, population, and traffic data for metro areas in the state. Our results reveal that the concentration of service and manufacturing employment impacts the volume of outbound air cargo. Total air cargo traffic is found to grow faster than population, while the corresponding domestic traffic grows less than proportionally to city size. Wages play a significant role in determining both total and domestic air cargo movement. We provide point estimates for the traffic diversion between cities, showing that 80 percent of air cargo traffic is diverted away from a small city located within 100 miles of a large one. Using socioeconomic and demographic forecasts prepared for California’s Department of Transportation, we also forecast metro-level total and domestic air cargo tonnage for the years 2010-2040. Our forecasts for this period indicate that California’s total (domestic) air cargo traffic will increase at an average rate of 5.9 percent (4.4 percent) per year.

Phd Dissertation

Essays on Air Cargo Cost Structures, Airport Traffic, and Airport Delays: Panel Data Analysis of the U.S. Airline Industry

Publication Date

August 14, 2014

Author(s)

Abstract

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.

Phd Dissertation

Active travel, built environment and transit access : a micro-analysis of pedestrian travel behavior

Publication Date

August 13, 2014

Author(s)

Abstract

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 research topics. The first topic uses a “place-oriented” approach to examine where active travel occurs in neighborhoods adjacent to the Expo Light Rail Line. This chapter 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 research topic 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 segment-level and quarter-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 research topic 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 more for GPS-tracked routes than for 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

Phd Dissertation

Inferring and replicating activity selection and scheduling behavior of individuals

Publication Date

August 6, 2014

Abstract

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. In this dissertation, we develop 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, capability of integrating modal components. The data used for the analysis is the California Household Travel Survey data, 2000-2001, (Caltrans, 2002). After preprocessing (which includes queries to match, clean, and prepare data), the final cleaned data is consisted of activity patterns of 26,269 individuals. In the model-building process, we initially cluster individuals in the sample based on their reported (one-day) activity patterns. Later, we argue and demonstrate that clustering activity/travel patterns in terms of such activity characteristics as type, duration, scheduling, and location can be an effective tool to capture preferential distributions of arrival time, departure time, and duration, which are unobservable inputs to activity-based travel models. Representative patterns are found based on two measures of dissimilarities between activity patterns, Sequence Alignment Method and Agenda dissimilarity, resulting in 8 clusters. A decision tree based on socio-demographics of individuals is fitted to infer the cluster to which each individual belongs. Inference on agenda formation in each cluster is based on ensemble of three different modules–“multivariate probit model,” “Markov chains with conditional random fields,” and “adaptive boosting”– applied to individuals within each cluster. In each of these modules, the inputs are socio-demographic attributes of individuals, and the outputs are discrete outcomes indicating participation in each activity type. Arrival time and activity duration inference for each activity type in each cluster, is performed using the adaptive boosting algorithm. Having identified the type of activities, and their arrival time and duration, activities are scheduled in the agenda using two approaches: decision rules, and Household Activity Pattern Problem (HAPP: a variation of pickup and delivery problem with time windows, (Recker, 1995) ). 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; correct activities during 867 minutes of 1080 awake minutes in a day was predicted.

published journal article

Strategic Hydrogen Refueling Station Locations with Scheduling and Routing Considerations of Individual Vehicles

Abstract

A hydrogen refueling station siting model that considers scheduling and routing decisions of individual vehicles is presented. By coupling a location strategy of the set covering problem (SCP) and a routing and scheduling strategy of the household activity pattern problem, this problem falls into the category of location routing problems. It introduces a tour-based approach to refueling station siting, with tour-construction capability within the model. There are multiple decision makers in this problem: the public sector as the service provider and the collection of individual households that make their own routing decisions to perform a given set of out-of-home activities together with a visit to a refueling location. A solution method that does not require the full information of the coverage matrix is developed to reduce the computational burden. Compared to the point-based SCP the results indicate that the minimum infrastructure requirement may be overestimated when vehicle (refueling demand)-infrastructure (refueling supply) interactions with daily out-of-home activities are excluded.

MS Thesis

Advising and optimizing the deployment of sustainability-oriented technologies in the integrated electricity, light-duty transportation, and water supply system

Abstract

The convergence of increasing populations, decreasing primary resource availability, and uncertain climates have drawn attention to the challenge of shifting the operations of key resource sectors towards a sustainable paradigm. This is prevalent in California, which has set sustainability-oriented policies such as the Renewable Portfolio Standards and Zero-Emission Vehicle mandates. To meet these goals, many options have been identified to potentially carry out these shifts. The electricity sector is focusing on accommodating renewable power generation, the transportation sector on alternative fuel drivetrains and infrastructure, and the water supply sector on conservation, reuse, and unconventional supplies. Historical performance evaluations of these options, however, have not adequately taken into account the impacts on and constraints of co-dependent infrastructures that must accommodate them and their interactions with other simultaneously deployed options. These aspects are critical for optimally choosing options to meet sustainability goals, since the combined system of all resource sectors must satisfy them. Certain operations should not be made sustainable at the expense of rendering others as unsustainable, and certain resource sectors should not meet their individual goals in a way that hinders the ability of the entire system to do so. Therefore, this work develops and utilizes an integrated platform of the electricity, transportation, and water supply sectors to characterize the performance of emerging technology and management options while taking into account their impacts on co-dependent infrastructures and identify synergistic or detrimental interactions between the deployment of different options. This is carried out by first evaluating the performance of each option in the context of individual resource sectors to determine infrastructure impacts, then again in the context of paired resource sectors (electricity-transportation, electricity-water), and finally in the context of the combined tri-sector system. This allows a more robust basis for composing preferred option portfolios to meet sustainability goals and gives a direction for coordinating the paradigm shifts of different resource sectors. Overall, it is determined that taking into account infrastructure constraints and potential operational interactions can significantly change the evaluation of the preferred role that different technologies should fulfill in contributing towards satisfying sustainability goals in the holistic context.

Phd Dissertation

Assessment of Constant Volume Sampler Based Test Procedure and Charging Scenarios Based Energy Impact of Plug-in Hybrid Electric Vehicles

Publication Date

June 29, 2014

Author(s)

Abstract

The advent of plug-in hybrid electric vehicles (PHEVs) introduces a new vehicle paradigm that consumes both gasoline and electricity. The new concept presents new questions. In particular, (1) what modifications need to be made for test procedures in terms of the emissions and fuel economy measurements for individual vehicles, and (2) what methodology needs to be established to evaluate the energy and emission impacts of a PHEV fleet? For the test procedure, the emission testing has been done by using the continuous sampling method for continuous diluents, the smooth approach orifice (SAO) measurement for ambient air flow, and fuel flow meter (FFM) measurement for fuel consumption in addition to the industry standard constant volume sampler (CVS) system, which faces challenges for PHEVs. Results show that the current CVS dilution factor (DF) exhibits an error resulting in higher emission mass calculation; an alternative procedure can be proposed for the charge depleting cycle to eliminate the overdilution; the CVS system has an error resulting from exhaust left in the tailpipe and CVS sampling line. For the evaluation of the energy impact of PHEVs, the South Coast Air Basin of California (SoCAB) was selected as an example by considering different charging scenarios consisting of different charging powers, locations and time. Results show that petroleum reduction is significant; the all-electric ability is crucial to cold start emission reduction; the benefit of higher power charging is small; delayed and average charging are better than immediate charging for home; and non-home charging increases peak grid load.

MS Thesis

A Performance Assessment of the Elimination of Left-Turns at Selected Intersections

Publication Date

June 29, 2014

Author(s)

Abstract

For most signalized intersections, left-turn movements are considered as a primary contributor to intersection delay. The concept of eliminating left-turn movements is now feasible with the rise of GPS-based routing which will allow the active routing of vehicles in networks with reduced left turns. This research seeks to estimate the impact of left turn reductions on overall travel time and left turn delay at intersections. The research objective is to evaluate the effect of left turn movement elimination in sample networks. The type of intersections considered is restricted to a grid network but is defined by the roadway hierarchy. A sample network was selected based on a real world network and reflecting observed volumes, travel times, and delays. The analysis approach is to eliminate left turn movements in three types of intersections by applying turn prohibitions and adjusting cycle lengths and turn penalties on other movements. Network performance is then assessed based on delay reduction, total travel time, and fuel consumption. It was concluded that selective reduction of left turn movements can improve network performance.