Kimon Proussaloglou

The choice of air carrier, flight, and fare class

This paper reports the development and interpretation of air traveler choice models to gain insights into the tradeoffs air travelers make when they choose among different carriers, flights, and fare classes. Such insights can be used to support carrier decisions on flight scheduling, pricing, seat allocation, and ticket restrictions. This paper develops a conceptual framework and applies it to the choice of carrier, flight, and fare class as a basis for analyzing air travel demand in a competitive market. Model estimation results are used to quantify the importance of carrier preferences, market presence, quality of service, frequent flyer program membership, schedule convenience, and fares on carrier travel demand. The empirical results provide measures of the premium that business and leisure travelers are willing to pay to avoid schedule delays, to choose a carrier in which frequent-flyer program they participate, and to obtain the amenities and freedom from travel restrictions associated with higher fare classes.

Time-of-Day Modeling in a Tour-Based Context: Tel Aviv Experience

Insights into the time-of-day preferences of individuals are crucial for accurately quantifying vehicle emissions and for understanding time-shifting behavior in response to traffic congestion and road pricing. Most urban models account for travel demand by time of day by using fixed factors or by incorporating the choice among aggregate time periods such as a.m., p.m., and off-peak. These methods are inadequate for examining traveler response to congestion mitigation strategies. To address some of these concerns, FHWA recently conducted a research project to develop innovative methods of modeling travel by time of day. This paper extends the FHWA methodology to the Tel Aviv tour-based model system that Cambridge Systematics is currently developing for Israels Ministry of Transport. The purpose of this paper is threefold: first, it discusses the modeling framework used in Tel Aviv; second, it describes the data inputs required for modeling time-of-day decisions; and finally, it describes the model estimation procedure and empirical results from Tel Aviv. The main features of the modeling approach include using half-hour time intervals, accounting for schedule delay in the absence of desired arrival and departure time data, and modeling the 24-h cycle. This paper serves as a proof of concept for the FHWA methodology and demonstrates that using the commonly available household survey data and some basic level-of-service data makes it possible to develop time-of-day models that are more detailed and better suited for policy testing.

Wisconsin Passenger and Freight Statewide Model: Case Study in Statewide Model Validation

This paper reports the model validation process for a passenger and a freight statewide model developed for the Wisconsin Department of Transportation. These policy-sensitive planning models are used to understand and quantify passenger and freight flows in Wisconsin and to support system-planning analyses at a statewide level. Examples of policies being tested include the impact of different land use scenarios and transportation projects on highway traffic, the diversion of traffic along key corridors, and the ridership potential of enhanced intercity bus service. The passenger model was estimated by using the Wisconsin add-on for the 2001 National Household Travel Survey (NHTS), and the freight model was estimated by using Transearch commodity flow data. Model validation relied on the NHTS data, statewide automobile and truck traffic counts, intercity transit ridership estimates, and 2000 U.S. Census data including the Census Transportation Planning Package, FHWAs validation manual, and NCHRP Report 365. The Wisconsin statewide models are presented as case studies to highlight the data sources, model estimation and validation methodologies, and results obtained at a statewide level. The passenger and freight validation results support the robustness of the models at a statewide and a corridor level. The methodology and standards discussed for the Wisconsin statewide models provide another data point to help establish guidelines for statewide model validation.

Transit district customers in San Mateo county, California: Who, why, where, and how

Faced with declining transit market share and increasing population and jobs, transit agencies must go beyond the traditional approach of trying to find the balance between maximizing ridership and meeting the needs of those who depend most on transit. Innovative approaches that account for customer perceptions and attitudes toward transit must make transit a more attractive option for customers. The efforts of the San Mateo County (California) Transit District (Sam Trans) to understand customer attitudes and perceptions and create market segments that reflect and account for traveler attitudes are reported. The study uses a structural equation modeling approach along with cluster analysis to identify market segments in the population that can be targeted for new services to be offered by Sam Trans. In addition, the results of the market segmentation are applied spatially so that Sam Trans can customize its response to address individual market areas. The results of the market segmentation were applied based on a mode choice model that determines the market share for competing automobile, bus, and enhanced transit modes. Sam Trans is provided with the means of identifying the spatial and modal distribution of their service market on the basis of customer needs. This approach allows Sam Trans to design transit services that compete more effectively in the target geographic markets addressing the needs of individual market segments.

Investigating Contextual Variability in Mode Choice in Chicago Using a Hierarchical Mixed Logit Model

In this paper, a hierarchical random-coefficient mixed logit model is applied to quantify variability in commuters’ mode choice in the Chicago metropolitan area, especially concerning the contextual variability by the traits of census tract of residence. It is found that individual mode choice behaviour varies considerably across residential locations. Moreover, the contextual effects are found to modify the marginal utility of mode choice. Especially, in-vehicle travel time and gasoline cost are significant covariates of census tract traits (such as percentage of blue-collar residents, ethnicity). Furthermore, random variation is present even after both contextual and individual traits are controlled for, suggesting intrinsic randomness in individual mode choice. The hierarchical structure of quantifying contextual variability proves to be a useful tool in capturing intrinsic heterogeneity in mode choice. The study findings have important implications for integrated land use and transport planning especially at the geographical levels below that of the region.

Modeling the Impacts of Bicycle Facilities on Work and Recreational Bike Trips in Los Angeles County, California

Metropolitan planning organizations and other planning agencies increasingly are asked to evaluate proposed bicycle facilities and to select the most promising facilities for funding. The objective was to develop models of utilitarian and recreational bicycling and to implement these models in a unified geographic information system–based framework that the Los Angeles County, California, Metropolitan Transportation Authority could use for planning purposes. Statistical models that evaluate bicycling to work and bicycling for recreation were estimated and implemented in the framework. A logistic regression model was used to estimate bicycle-to-work mode share at the level of census tracts or traffic analysis zones. Furthermore, a pair of disaggregate models was estimated to evaluate bicycle trips made for recreation. The bicycle-to-work model estimation generated parameter estimates that were consistent with previous literature and added to the literature by demonstrating the importance of well-connected and accessible bicycle networks for utilitarian bicycling. The recreational models provide a new way to estimate the impacts of bicycle facilities on leisure or fitness-based bicycle trips. Like the work trip model, the recreational bicycling models show the importance of proximity to trails and the impact of distance as a barrier to reaching the nearest trails on the decision to bicycle for recreation.

Assessing the Impact of App-Based Ride Share Systems in an Urban Context: Findings from Austin

App-driven ridesharing platforms are gaining popularity and are transforming urban movement patterns in cities throughout the world. Because of privacy and business considerations, their owners have released little information about riders’ trip-making characteristics. This lack of data prevents planners and modelers from understanding and quantifying the impact of these new modes on regional travel patterns. In 2016, RideAustin, a not-for-profit company, was established to provide mobility-on-demand services in the Austin region. RideAustin released its dataset of over one million trips to researchers to support transportation planning through a better understanding of urban travel flows. This paper presents findings from an in-depth analysis of this dataset and summarizes key aspects of interest to the transportation research community such as the number of riders, drivers, and trips; total vehicle miles including deadhead miles; and terminal times. The paper also presents findings from two case studies that show the competitiveness of RideAustin versus transit and the utilization of the RideAustin system during the South by Southwest festival. While some of the metrics cannot be readily transferred to other regions, several findings can be used by planners and modelers as they integrate rideshare systems within their planning and modeling frameworks. We also believe that some of the research findings may provide insights into a future system of autonomous and shared vehicles.