A Copula-Based Joint Model of Commute Mode Choice and Number of Non-Work Stops during the Commute

At the time of publication A. Portoghese, E. Spissu, and I. Meloni were at University of Cagliari, and C.R. Bhat and N. Eluru were at the University of Texas at Austin.In this paper, in the spirit of a tour-based frame of analysis, we examine the commute mode choice and the number of non-work stops during the commute. Understanding the mode and activity stop dimensions of weekday commute travel is important since the highest level of weekday traffic congestion in urban areas occurs during the commute periods. The paper employs a copula-based joint multinomial logit – ordered modeling framework in which commute mode choice is modeled using a multinomial logit formulation and the number of commute stops is modeled using an ordered response formulation. The data used in this study are drawn from the “Time use” multipurpose survey conducted between 2002 and 2003 by the Turin Town Council and the Italian National Institute of Statistics (ISTAT) in the Greater Turin metropolitan area of Italy. The results highlight the importance of accommodating the inter-relationship between commute mode choice and commute stops behavior. The results also point to the stronger effect of household responsibilities and demographic characteristics in the Italian context compared to the US context.Civil, Architectural, and Environmental Engineerin

How bicycling sharing system usage is affected by land use and urban form: analysis from system and user perspectives

There is a rapid growth of bicycle-sharing systems (BSS) around the world. Cities are supporting these systems as a more sustainable transport mode for short trips. Given the relatively recent adoption of BSS, there is substantial interest in understanding how these systems impact urban transportation. In this paper, we examine the functioning of the hugely successful New York City CitiBike system. We focus on the interaction of BSS with land-use and built environment attributes and the influence of weather condition and temporal characteristics on BSS usage. Towards this end, CitiBike system is analyzed along two dimensions: (1) at the system level, we examine the hourly station level arrival and departure rates using a linear mixed model and (2) at the trip level, we investigate users’ destination station choice preferences after they pick up a bicycle from a station employing a random utility maximization approach. The results highlight clear spatial and temporal differences in the usage of CitiBike by users with annual membership and users with temporary passes. Overall, our analysis provides a framework and useful insights for cities that are planning to install a new bicycle sharing system or to expand an existing syste

How Bicycling Sharing System Usage is Affected by Land Use and Urban Form: Analysis from System and User Perspectives

There is a rapid growth of bicycle-sharing systems (BSS) around the world. Cities are supporting these systems as a more sustainable transport mode for short trips. Given the relatively recent adoption of BSS, there is substantial interest in understanding how these systems impact urban transportation. In this paper, we examine the functioning of the hugely successful New York City CitiBike system. We focus on the interaction of BSS with land-use and built environment attributes and the influence of weather condition and temporal characteristics on BSS usage. Towards this end, CitiBike system is analyzed along two dimensions: (1) at the system level, we examine the hourly station level arrival and departure rates using a linear mixed model and (2) at the trip level, we investigate users\u27 destination station choice preferences after they pick up a bicycle from a station employing a random utility maximization approach. The results highlight clear spatial and temporal differences in the usage of CitiBike by users with annual membership and users with temporary passes. Overall, our analysis provides a framework and useful insights for cities that are planning to install a new bicycle sharing system or to expand an existing system

How land-use and urban form impact bicycle flows: Evidence from the bicycle-sharing system (BIXI) in Montreal

ABSTRACT Installed in 2009, BIXI is the first major public bicycle-sharing system in Montreal, Canada. The BIXI system has been a success, accounting for more than one million trips annually. This success has increased the interest in exploring the factors affecting bicycle-sharing flows and usage. Using data compiled as minute-by-minute readings of bicycle availability at all the stations of the BIXI system between April and August 2012, this study contributes to the literature on bicycle-sharing. We examine the influence of meteorological data, temporal characteristics, bicycle infrastructure, land use and built environment attributes on arrival and departure flows at the station level using a multilevel approach to statistical modeling, which could easily be applied to other regions. The findings allow us to identify factors contributing to increased usage of bicycle-sharing in Montreal and to provide recommendations pertaining to station size and location decisions. The developed methodology and findings can be of benefit to city planners and engineers who are designing or modifying bicycle-sharing systems with the goal of maximizing usage and availability

Developing advanced econometric frameworks for modeling multidimensional choices : an application to integrated land-use activity based model framework

textThe overall goal of the dissertation is to contribute to the growing literature on the activity-based framework by focusing on the modeling of choices that are influenced by land-use and travel environment attributes. An accurate characterization of activity-travel patterns requires explicit consideration of the land-use and travel environment (referred to as travel environment from here on). There are two important categories of travel environment influences: direct (or causal) and indirect (or self-selection) effects. The direct effect of travel environment refers to how travel environment attributes causally influence travel choices. This direct effect may be captured by including travel environment variables as exogenous variables in travel models. Of course, determining if a travel environment variable has a direct effect on an activity/travel choice of interest is anything but straightforward. This is because of a potential indirect effect of the influence of the travel environment, which is not related to a causal effect. That is, the very travel environment attributes experienced by a decision maker (individual or household) is a function of a suite of a priori travel related choices made by the decision maker. The specific emphasis of the current dissertation is on moving away from considering travel environment choices as purely exogenous determinants of activity-travel models, and instead explicitly modeling travel environment decisions jointly along with activity-travel decisions in an integrated framework. Towards this end, the current dissertation formulates econometric models to analyze multidimensional choices. The multidimensional choice situations examined (and the corresponding model developed) in the research effort include: (1) reason for residential relocation and associated duration of stay (joint multinomial logit model and a grouped logit model), (2) household residential location and daily vehicle miles travelled (Copula based joint binary logit and log-linear regression model), (3) household residential location, vehicle type and usage choices (copula based Generalized Extreme Value and log-linear regression model) and (4) activity type, travel mode, time period of day, activity duration and activity location (joint multiple discrete continuous extreme value (MDCEV) model and multinomial logit model (MNL) with sampling of alternatives). The models developed in the current dissertation are estimated using actual field data from Zurich and San Francisco. A variety of policy exercises are conducted to illustrate the advantages of the econometric models developed. The results from these exercises clearly underline the importance of incorporating the direct and indirect effects of travel environment on these choice scenarios.Civil, Architectural, and Environmental Engineerin

A joint econometric analysis of seat belt use and crash-related injury severity

This study formulates a comprehensive econometric structure that recognizes two important issues in crash-related injury severity analysis. First, the impact of a factor on injury severity may be moderated by various observed and unobserved variables specific to an individual or to a crash. Second, seat belt use is likely to be endogenous to injury severity. That is, it is possible that intrinsically unsafe drivers do not wear seat belts and are the ones likely to be involved in high injury severity crashes because of their unsafe driving habits. The preceding issues are considered in the current research effort through the development of a comprehensive model of seat belt use and injury severity that takes the form of a joint correlated random-coefficients binary-ordered response system. To our knowledge, this is the first instance of such a model formulation and application not only in the safety analysis literature, but in the econometrics literature in general. The empirical analysis is based on the 2003 General Estimates System (GES) data base. Several types of variables are considered to explain seat belt use and injury severity levels, including driver characteristics, vehicle characteristics, roadway design attributes, environmental factors, and crash characteristics. The results, in addition to confirming the effects of various explanatory variables, also highlight the importance of (a) considering the moderating effects of unobserved individual/crash-related factors on the determinants of injury severity and (b) seat belt use endogeneity. From a policy standpoint, the results suggest that seat belt non-users, when apprehended in the act, should perhaps be subjected to both a fine (to increase the chances that they wear seat belts) as well as mandatory enrollment in a defensive driving course (to attempt to change their aggressive driving behaviors)Civil, Architectural, and Environmental Engineerin

Joint Model of Participation in Nonwork Activities and Time-of-Day Choice Set Formation for Workers

At the time of publication M. Castro, and C.R. Bhat were at the University of Texas at Austin; N. Eluru was at McGill University; and R.M. Pendyala was at Arizona State University.Non-work activity and travel participation is an important component of overall travel demand that is complex to model as the greater degrees of flexibility associated with such travel induces larger variability and randomness in this behavior. This paper aims to offer a framework for modeling the participation in and travel mileage allocated to non-work activities during various time periods of the day for workers. Five time-of-day blocks are defined for workers based on the period of the day in relation to the work schedule. Individuals can choose to pursue non-work activities in one or multiple time blocks and travel miles to accomplish the activities. A multiple discrete-continuous extreme value (MCDEV) modeling approach is employed to model this phenomenon. A unique element of the paper is the addition of a latent choice set generation model as a first component in the model system. This choice set generation model can be used to determine the set of time-of-day periods that each individual will consider for the pursuit of non-work activities, while recognizing the fact that the consideration choice set is not explicitly observed (and is therefore latent) by the analyst. Thus, the model system presented in this paper is capable of modeling non-work activity engagement and associated travel mileage by time-ofday period while incorporating varying choice sets across individuals. The two-component model system is applied to a survey sample drawn from the San Francisco area of the United States, and shown to perform substantially better than a pure MDCEV model that assumes a constant choice set across the sample.Civil, Architectural, and Environmental Engineerin

A Note On Generalized Ordered Outcome Models

While there is growing application of generalized ordered outcome model variants (widely known as Generalized Ordered Logit (GOL) model and Partial Proportional Odds Logit (PPO) model) in crash injury severity analysis, there are several aspects of these approaches that are not well documented in extant safety literature. The current research note presents the relationship between these two variants of generalized ordered outcome models and elaborates on model interpretation issues. While these variants arise from different mathematical approaches employed to enhance the traditional ordered outcome model, we establish that these are mathematically identical. We also discuss how one can facilitate estimation and interpretation while building on the ordered outcome model estimates - a useful process for practitioners considering upgrading their existing traditional ordered logit/probit injury severity models. Finally, the note presents the differences within GOL and PPO model frameworks, for accommodating the effect of unobserved heterogeneity, referred to as Mixed Generalized Ordered Logit (MGOL) and Mixed Partial Proportional Odds Logit (MPPO) models while also discussing the computational difficulties that may arise in estimating these models