Gulsah Akar

Bicycling Choice and Gender Case Study: The Ohio State University

ABSTRACT This article brings two emerging research areas together: gender differences in travel behavior and travel patterns on college campuses; with a focus on bicycling. Detailed analysis and choice model estimations present the significant effects of gender, travel times, and personal attitudes on the decision to bike. Although men and women experience similar environmental opportunities and constraints, their perceptions in terms of safety and feasibility of alternative transportation modes differ. The models indicate women are more sensitive to being close to bicycle trails and paths. Results reveal different policy and infrastructure changes may be required to encourage more women to bicycle.

Travel Choices and Links to Transportation Demand Management: Case Study at Ohio State University

This study examined the transportation choices of individuals in a large campus setting at the Ohio State University, Columbus. A web-based survey was conducted to understand the travel patterns of the campus community and to inform recommendations to reduce single-occupancy vehicle travel there. Mode choice and the most important factors that affected it were the focus of the survey. The survey included questions about respondent attitudes toward auto use and the factors that would encourage them to carpool and take transit to campus. Discrete choice models were estimated to analyze commuter mode choices to travel to campus. The results showed that proximity to bicycle lanes and trails and bus stops increased the propensity to choose these modes. Students were more likely to travel by alternative modes than faculty and staff members. Individuals concerned about travel time, flexibility of departure time, safety, and the ability to stop on the way to and from campus were more likely to drive alone to campus. Strategies to increase the modal share of alternative modes are presented in the paper on the basis of the descriptive findings and the model results. This studys findings may be used by practitioners and campus transportation planners to understand better the factors that cause people not to choose alternative transportation modes and to evaluate transportation improvements to be considered for implementation.

Influence of neighborhood types on trip distances: Spatial error models for Central Ohio

ABSTRACT This study examines the relationships between land-use, transportation infrastructure, household, and individual characteristics and the resulting average and daily trip distances using data from the 1999 Mid-Ohio Area Household Travel Survey. Spatial error models are developed to analyze the determinants of average and daily trip distances at the person level. First, new neighborhood categories are created using K-means cluster analysis and several land-use and built-environment variables. Then, these new neighborhood categories (such as central city residential areas, medium-density suburbs, low-density suburbs, etc.) are used as independent variables to explain the resulting average and total trip distances, while controlling for sociodemographics, access to transit and bicycle facilities, and spatial autocorrelation. The results indicate that the residential location characteristics are a significant factor in explaining trip distances; people who live in areas that are away from urban centers and with a low mix of employment and population travel longer distances. For the Central Ohio Region, residing in lowest-density suburbs may add up to 44% to average trip distances and 33% to daily trip distances.

StreetSeen Visual Survey Tool for Determining Factors That Make a Street Attractive for Bicycling

In one of the first visual preference surveys to use Google Street View, adult students viewed a series of paired images of city streets and were asked to choose which image they preferred for riding a bicycle. Subsequent analyses showed differences by continent in respondents’ preferences. Discrete choice models were used to demonstrate the extent to which certain segment-level factors, such as presence of trees along the street, width of the street, presence of sidewalks, and other features, were preferred. The models revealed that increasing the vehicle traffic, number of lanes, tree density of the streetscape, or number of parking lots decreased the probability of image choice. The presence of sidewalks, pedestrians, trees set back from the street, and traffic-calming devices were positively associated with respondents’ preferences. The tree-related results may relate to perceived safety; for example, dense trees close to a street may limit visibility along a roadway. The models also revealed significant differences in preferences by respondent location. This method was effective in capturing information about bicycling preferences. The survey method and the analysis techniques introduced in this study can help city planners design streets that bicyclists prefer.

How Travel Attributes Affect Planning Time Horizon of Activities

This study focuses on incorporating the travel attributes (time, cost, and availability of modes) in the activity planning time horizon analysis and presents mixed logit models estimated separately for different activity groups. Although the effects of many other variables are estimated, the focus is on the effects of location (in-home versus out-of-home) and log-sum (measure of travel characteristics) variables. The inclusion of several other variables (household and individual characteristics and activity attributes) allows controlling for many factors affecting this choice and increases the explanatory power of the models. The results of the models reveal that the effects of location and travel characteristics on planning time horizon choice vary among different activity types. Toward a better understanding and quantification of the effects of location and travel attributes, the probabilities of planning the activities in different time horizons are estimated separately for in-home and out-of-home, and with an incremental increase in the logsum. Results reveal that the household obligations and active activities are very sensitive to travel characteristics; therefore, significant changes in the planning and execution of these activities may be expected with changes in the transportation system characteristics. The results of this study enhance the understanding of relationships between travel time, cost, availability of transportation modes, and the activity planning process.

Role of Gender and Attitudes on Public Transportation Use

This study aimed to evaluate gender differences in public transportation–related attitudes and their effects on transit use. How did attitudes affect peoples transit use? Did public transit–related attitudes differ by gender in general and by status group (faculty, staff, and students)? This research aimed to address these questions. The analysis was based on data collected from the 2012 campus transportation survey at Ohio State University. The survey questionnaire covered individuals’ sociodemographic characteristics, commute mode choices, and attitudes toward driving and taking public transit, including reliability, safety, flexibility, convenience, accessibility, and comfort. After the descriptive analysis of perceptions that were segmented on the basis of gender and status (faculty, staff, undergraduates, and graduate students), binary logit models were estimated to assess the influences of individuals’ attitudes on transit use while controlling for other factors. First a binary logit model measuring the effects of respondents’ status (student, staff, or faculty), car ownership, ethnicity, proximity to bus stops, and distance to campus was estimated. Then respondents’ attitudes were added to the existing model. Results indicated that including attitudes significantly increased the explanatory power of the model, and the results revealed the significant connections between attitudes related to public transportation and public transit use. The findings of this study can help transportation planners understand the ways attitudes affect transit use and the differences across genders. Although the study used data from Ohio State University, the findings can help in developing plans for increasing alternative transportation use on other campuses, as well as in surrounding areas.

Another Look at Vehicle Miles Traveled: Determinants of Vehicle Use in Two-Vehicle Households

This study analyzed the determinants of vehicle miles traveled (VMT) with data from the 2009 National Household Travel Survey. First, total VMT models were estimated across all households. Next, the survey sample was segmented by the number of vehicles owned, and separate models were estimated for each sample segment. Finally, with a focus on two-vehicle households, a seemingly unrelated regression model was formulated to analyze total household VMT and the VMT share of each vehicle. Household increases in income and number of vehicles, workers, adults, and children all led to higher VMT. Population density and gasoline cost negatively affected VMT. Other findings of the total VMT models were related to telecommuting and vehicle characteristics. Results indicate that having the option to telecommute and owning SUVs, pickup trucks, vans, or hybrid vehicles increase VMT. If the driver of a vehicle was a female, older, unemployed, or did not hold a bachelors degree, that vehicles share of the total household VMT decreased. If this vehicle was an SUV, a pickup truck, a van, or a hybrid, it was likely to be used more. These significant effects have important implications for understanding the substitution patterns in multivehicle households.

Street Intersection Characteristics and Their Impacts on Perceived Bicycling Safety

Safety concerns are among the main issues that deter people from bicycling in the U.S. Earlier studies have explored the associations between characteristics of intersection design and bicyclists’ safety perceptions. Research shows that there are significant links between bicycling choice, safety perceptions, bicycling experience levels, and socio-demographics. Yet the existing bicycling safety-rating models do not control for individuals’ socio-demographics and their levels of bicycling experience, which are known to affect bicycling choice. This study develops a Perceived Bicycling Intersection Safety (PBIS) model which helps engineers, planners, and decision makers to understand better how a wide range of intersection features contribute to bicyclists’ safety perceptions, controlling for socio-demographics and bicycling experience. The empirical analysis is based on an online visual survey conducted at the main campus of The Ohio State University through March and April 2017. Results showed that visual surveys are effective in capturing information about bicycling preferences. The paper concludes with recommendations for infrastructure decisions and suggestions for future research. The results of this study can help planners design street intersections that bicyclists will prefer. Our model can be applied elsewhere to test the effects of different intersection and street features.

Who is Interested in Carpooling and Why: The Importance of Individual Characteristics, Role Preferences and Carpool Markets:

Carpooling is an informal form of commuter ride-sharing that can mitigate traffic congestion and parking demand. While ride-sharing and carpool formation require both a driver and at least one passenger, there has been a lack of studies that explore potential users of carpooling and their role preferences (driver versus passenger); perceived incentives, attitudes and current mode choices may affect their willingness to carpool and their role preferences. This study examined the factors affecting individuals’ carpooling decisions and carpool role preferences using data from Ohio State University’s 2012 Campus Travel Pattern Survey. Based on individuals’ stated interests and choices, three market segments were identified: current carpool users, potential carpool users, and not-interested commuters. The analyses revealed that incentives and deterrents to carpooling significantly differ by role preference. People favoring the passenger role emphasized safety, flexibility and parking cost-savings, whereas those favoring the driver role tended to find value in the convenience and opportunities for socializing through a carpool trip. People receptive to both roles emphasized flexibility and all types of cost reductions. Although most people did not elect technological aids as a meaningful intervention, potential carpool users, particularly those favoring the passenger role, were more likely to seek these aids. Using probit and bivariate probit regressions, carpool interest and choice were found to be associated with commute distance, university affiliation, marital status, current travel mode, and personal attitude. Concern for the environment, and the propensity to make stops while commuting were positively associated with both carpooling interest and decision to carpool.

Geographically Weighted Regression Approach to Investigate Spatial Variations in Activity Space

This study examined individual activity space focusing on whether and how the effects of the built environment vary spatially across the study region. With the use of the 2012–2013 Northeast Ohio Regional Travel Survey data, a global regression model and a geographically weighted regression (GWR) model were estimated to analyze these effects after controlling for sociodemographics and travel time. The global results indicate that people living in areas characterized by high densities, a variety of jobs and populations, and high access and connectivity generally have smaller activity spaces. The GWR results report better fit than the global model and present spatial variations in the relationships between the six built environment variables and individuals’ activity spaces. Relevant transportation and land use policies, such as transit-oriented development and smart growth, may directly affect the built environment and therefore change individual access and sizes of activity spaces. The spatial variations suggest that the effects of these policies may vary. These findings are of great assistance to policy makers as they point out the target areas for effective policy interventions and infrastructure provisions.