published journal article
published journal article
conference paper
published journal article
This article estimates a structural model of competition among hub-and-spoke airlines in order to measure the strength of economies of traffic density on individual route segments. We find that economies of density were strong during the sample period (fourth quarter 1985), stronger than previous estimates by Douglas Caves, Laurits Christensen, and Michael Tretheway derived from traditional cost-function methods. We also find that the airlines’ competitive behavior was far from collusive in the markets under study (markets requiring a connection at a hub airport). Our structural model also provides plausible estimates of demand elasticities. We use our estimates to provide a cost-based rationale for the major changes in the structure of the industry following deregulation (for example, the increase in airport and industry-wide concentration, and the increase in competition at the city-pair market level) and to simulate the effects of a merger of airlines that share a hub.
published journal article
While Google Street View (GSV) has been increasingly available for large-scale examinations of urban landscapes, little is known about how to use this promising data source more cautiously and effectively. Using data for Santa Ana, California, as an example, this study provides an empirical assessment of the sensitivity of GSV-based streetscape measures and their variation patterns. The results show that the measurement outcomes can vary substantially with changes in GSV acquisition parameter settings, specifically spacing and direction. The sensitivity is found to be particularly high for some measurement targets, including humans, objects, and sidewalks. Some of these elements, such as buildings and sidewalks, also show highly correlated patterns of variation indicating their covariance in the mosaic of urban space.
conference paper
Recognizing the limitations of previous travel mode choice models such as random utility models and multi-layer perceptron neural network models, this study develops a framework using a deep neural network with deep learning schemes, to predict travelersâ?? mode choice behavior. Deep neural networks and deep learning are relatively newer models, applied mostly so far to pattern recognition and image/voice processing, and for big data analytics. The authors develop such a choice model with a structure that is appropriate for the travel mode choice problem, and demonstrate the success of the model using an available dataset. The research also develops an important component of the model that takes into account the inherent characteristics of choice models that all individuals have different choice alternatives, an aspect not considered in the neural network models of the past that led to poorer performance. The proposed model is compared against existing mode choice models. The results prove that the new model clearly outperforms the previous mode choice models.
published journal article
MA Thesis
The role in the description of urban form attributable to specific manifestations of the trip-making behavior of spatially-defined groups of persons residing within the Detroit Standard Metropolitan Statistical Area is inductively established through exploration into regularities in selected data characterizing Minor Civil Divisions. All data were obtained from the 1965 Detroit Regional Transportation and Land Use Study. Dimensions of this role are developed mathematically through comparative principal components factor analyses of the interrelationships between the Minor Civil Divisions in terms of the flows of persons travelling among the areas and in terms of measures of the economic activities within the areas and the socio-economics, demographics and travel decisions of their respective populations. These dimenisons are then interpreted cartographically, and linkages are defined between the spatial distributions of Minor Civil Divisions with particular evaluations on the new descriptive dimensions and the location of major transportation facilities, and between the distributions and patterns of urban development. The results expand the realm of urban ecological studies to the inclusion of transportation demand and supply phenomena.
book/book chapter
conference paper
A dynamic traffic assignment (DTA) model has been widely accepted as a standard planning tool for assessing developed transportation policy and forecasting the effect of future travel demand. If excluding a problem originated from the inclusion of time concept in DTA model, the basic framework of DTA model is similar to that of static model. Using given exogenous travel demand, DTA model loads the travel demand on a network and finds an optimal solution satisfying with a pre-defined route choice rule. In standard DTA loading, the departure time pattern of given travel demand is predefined and assumed as fixed values. However, the departure time is variable depending on a network traffic condition in a real travel choice process when drivers have their preferred arrival time for their destinations. Therefore, for commuters in the morning peak, the departure time should be modeled as an endogenous variable. In this paper, the authors criticize the framework of current standard DAT model and propose an alternative approach which could overcome the shortcomings of current DTA model. The authors substitute a traditional definition for time-dependent OD table by a new definition in which the time-dependent OD table is defined as arrival time-based one. In results, we can have a new DTA model which can calculate equilibrium departure pattern without any additional module such as a schedule delay function. Three types of objective function for a new DTA framework are proposed, and the solution algorithms for the three objective functions are also explained.