Do your neighbors affect your bicycling choice? A spatial probit model for bicycling to The Ohio State University

Neighborhood social effects have recently become a focus of interest in transportation research, whereby transportation mode choice is not only affected by an individual’s characteristics and transportation system conditions, but also by the mode choices of that individual’s social neighbors. This study supports the neighborhood social effects argument, using a spatial econometrics approach and data from The Ohio State University (OSU) 2012 Campus Transportation Survey. A spatial probit model of commuters’ mode choices (bicycling versus non-bicycling) is estimated, accounting for spatial autocorrelation. The results show that the more OSU-affiliated bicycle riders are residing around an individual OSU commuter, the more attractive bicycling becomes, controlling for other factors such as gender, status, proximity to campus, bicycle infrastructure and attitudes. The results indicate that students and males are more likely to commute by bicycles. The probability of choosing bicycles decreases with distance from campus. In addition, proximity to bicycle infrastructure and physical environment both encourage respondents to bicycle. Feeling of safety, travel cost and concern for the environment also affect bicycling choice.     

The influence of attitudes on university bicycle commuting: Considering bicycling experience levels

This study examined attitudes towards bicycling among bicycle users with different experience levels and how these attitudes influence the bicycle use. The study area is The Ohio State University’s main campus and we used the 2015 Campus Transportation Survey that asked questions about different commuting modes to the campus, bicycling experience levels, attitudes toward bicycle use, and demographic characteristics. For the empirical analysis, we grouped 20 attitudinal statements on bicycling into eight factors by using principal component analysis: perception of living in a bicycle-friendly community; perception of bicycling barriers; bicycling willingness upon facility availability; awareness of bicycling benefits; familiarity with local bicycling information; preference for bicycling; sensitivity to bicycle security; and perception of the availability of campus bicycle facilities. We ran t-test analyses to examine whether the attitudes toward bicycling vary by bicycling experience levels. Then, we employed binary logit analyses to estimate the effects of the attitudes differentiated by bicycling experience levels on being a bicyclist. The empirical analyses show that experienced bicycle users have more positive and favorable attitudes toward bicycling while less experienced bicycle users perceive greater bicycling barriers. We also found that the availability of bicycle facilities has a greater importance for less experienced bicycle users than for experienced bicycle users.     

Non-motorized transport and university populations: an analysis of connectivity and network perceptions

This research examines local bicycle and pedestrian networks in the vicinity of the University of Alabama campus to assess the utility of these networks for travel to the university by students and employees. Network connectivity is examined using Geographic Information Systems (GIS) and the results compared to a survey of 3731 university students and employees. Results indicate that areas within one mile of the University of Alabama’s campus have the highest levels of bicycle and pedestrian network connectivity and accessibility. The survey results show that an individual’s positive perception of the bicycle and pedestrian networks is related to their travel behavior, and that this knowledge decreases within an increase in commute distance to campus. Increases in connectivity can be expected to lead to an increase in non-motorized travel, but it is also clear that lack of knowledge of driving and cycling laws is a deterrent to many.     

Active commuting in a university setting: Assessing commuting habits and potential for modal change

This paper describes the results of an online survey that examined commuting patterns, potential for change and barriers and motivators affecting transport decisions in a University population (n=1040 students, n=1170 staff). Overall, 21.5% of staff and 46.8% of students at The University of Western Australia regularly used active modes, and potentially an additional 30% of staff and students would switch to active modes. The results suggested that reducing barriers to using active modes, in particular reducing actual and perceived travel time by bus and bicycle would have the greatest impact on commuting patterns. Some policy applications appeared to hold particular promise, including an implementation of a subsidised public transport pass (U-Pass), increased student housing on or near campus, increased cost of parking, and improved bus services and cycle networks.     

Accessibility evaluations for nighttime walking and bicycling for low-income shift workers

This paper addresses an important issue related to nighttime commuting of low-income shift workers who walk and/or bike to their workplace using public transit. A shift worker is anyone who follows a work schedule that is outside of the typical daytime working hours of a business day and commute after dark - by walking or bicycling to a transit stop. However, poor visibility conditions on sidewalks and bicycle lanes often thwart safety of their walking and bicycling activities. Therefore, this paper develops two simple scores - nighttime accessibility score for walking (NASW) and nighttime accessibility for bicycling (NASB) - for evaluating nighttime infrastructure for pedestrians and bicyclist. The scores consider the employment data, travel time and the physical distribution of streetlight poles along the sidewalks and bicycle lanes. Data from the city of El Paso in Texas is used to demonstrate the applicability of the two scores. Employment data from three prominent service industry sectors known to employ low-income shift workers – i) Retail Trade, ii) Accommodation and Food Services, and iii) Health Care and Social Assistance – are used for demonstration purposes. It is observed that amongst the three sectors analyzed, both NASW and NASB values are higher for regions in El Paso with low-income employment concentrations from the Health Care and Social Assistance sector. It is also observed that some prominent regions in north-east, south-east and west of El Paso need improvements in streetlight systems, sidewalks and bicycle network to facilitate walking and bicycling amongst low-income shift workers employed in Retail Trade, and Accommodation and Food Services industry sectors to access transit stops at night.     

Travel behavior of university students who live on campus: A case study of a rural university in Asia

With their irregular class schedules and considerable freedom in the campus environment, university students are an example of a social group that tends to have complex and unique travel behavior. This study examined travel patterns of 130 students who study and live on campus in a rural university of Thailand. All survey participants completed a travel diary for seven consecutive days in a typical school week. Other than overall travel patterns, such as trip generation, mode split, distance traveled, and travel time, this study also investigated the differences in traffic patterns of four student groups, categorized by their gender and whether they own a private vehicle or not. It was found that students of both genders appeared to have similar travel patterns in all aspects. Whether they own a private vehicle does not appear to impact daily trip generation nor the total distance traveled of the students, but it does have an effect on the travel modes used by students. Those students who own a private vehicle mostly rely on driving the vehicle, while those who do not own a vehicle rely on three modes of travel: primarily being a passenger on or in a friend’s private vehicle, and to a lesser extent, driving a friend’s vehicle, and taking a bus (the only form of public transport on the campus). The results indicate a high social interdependency among university students, which makes the development of a model to simulate travel behavior of university students a complicated task.     

Can the built environment explain gender gap in cycling? An exploration of university students' travel behavior in Toronto,Canada

The benefits of cycling as a healthier and more sustainable transportation alternative to private automobile is emphasized in both literature and policy. One key policy challenge in improving cycling rates is the significant gender gap in cycling that exists across urban regions in North America. In this study, travel behavior of >10,000 students attending four universities in Toronto, Canada, was analyzed to explore gender-based differences in cycling uptake. The mode share for cycling was higher for non-commute trips (9%) when compared to commuting trips to universities (7.6%). In addition, men had higher cycling rates than women, for both commute and non-commute trips. Results from binomial logistic regression models indicate that the built environment-related correlates were different between male and female students, and between commute and non-commute trips. Access to bicycle lanes or cycle tracks was found to increase the odds of female commuter cycling. This effect, however, was moderate in the neighborhoods with higher land use mix. Further, high-speed traffic was a significant barrier to cycling among female commuters. Noticeably, our analysis did not find major gender-based differences in the coefficients relating to travel attitudes and preferences. The findings provide a Canadian comparison to the limited international research on this topic, as well as offer new insights particularly relating to cycling for non-commute trips. The results identify potential avenues for policy intervention regarding the promotion of healthy and sustainable travel behavior among post-secondary students, and more broadly, the millennial generation.     

Toward a spatial understanding of active transportation potential among a university population

Universities and surrounding communities stand to benefit when active travel mode choices are elevated. Despite this, there is little research on travel mode choice at commuter universities and, in particular, the nonlinear spatial relationships among active travel potential and various contextual and compositional factors. The purpose of this study was to examine and visualize linkages among personal, household, density, diversity, and design factors, and active travel (bicycling, walking, and mass-transit modes) among a commuter-university population residing throughout southeastern Michigan, USA. This was accomplished by employing exploratory spatial data analysis (ESDA), ordinary least squares (OLS) regression, and a geographically weighted regression (GWR) model. The GWR model outperformed the traditional OLS model in terms of goodness of fit (R² = .534 and R² = .461, respectively). A novel cartographic mapping technique was employed to depict where statistically significant parameter estimates negatively or positively influenced active travel. The main finding was that personal, household, density, diversity, and design estimates varied in both magnitude and spatiality throughout the university's study area. Interestingly, distance was not a universal barrier to active travel potential. These variations emphasize the importance of promoting active transportation through localized interventions as well as coordinating efforts among universities and surrounding communities.     

Commuting patterns and bicycle level of traffic stress (LTS): Insights from spatially aggregated data in Franklin County,Ohio

This study analyzes the relationships between bicycle network design and commute mode shares in Franklin County, Ohio. We first adopt the bicycle level of traffic stress (LTS) criteria to measure bicycle network design. Then, we develop a fractional multinomial logit model to simultaneously estimate the determinants of the shares of commuters who regularly go to work by carpooling, riding public transit, bicycling, and walking. The analytical results show that increasing the proportion of low-stress road segments (LTS 2) is statistically significant and positively associated with the share of bicycle commuters at the census block group level. However, we do not observe a significant relationship between the proportion of very low-stress road segments (LTS 1) and the share of bicycle commuters. This study suggests that social and cultural relevant factors are more important predictors of reductions in automobile use as compared to those factors representing road conditions.     

Catalysts and magnets: Built environment and bicycle commuting

What effects do bicycle infrastructure and the built environment have on people’s decisions to commute by bicycle? While many studies have considered this question, commonly employed methodologies fail to address the unique statistical challenge of modeling modes with small mode shares. Additionally, personal characteristics that are not adequately accounted for may lead to overestimation of built environment impacts.This study addresses these two key issues by using an ordered probit Heckman selection model to jointly estimate participation in and frequency of commuting by bicycle, controlling for demographics, residential preferences, and travel attitudes. The findings suggest a strong influence of attitudinal factors, with modest contributions of bicycle accessibility. Bicycle lanes act as “magnets” to attract bicyclists to a neighborhood, rather than being the “catalyst” that encourages non-bikers to shift modes. The results have implications for planners and policymakers attempting to increase bicycling mode share via the strategic infrastructure development.     

Determination of active travel speed for minimum air pollution inhalation

A higher active travel speed has offsetting impacts on air pollution inhalation dose through higher breathing rate but shorter exposure duration. The net effect of speed choice on inhalation dose for pedestrians and bicyclists has not been established. This paper derives equations for pedestrian and bicycle steady-state minimum-dose speed (MDS). Parameter distributions from the literature are applied to a synthetic population of travelers to calculate individual MDS. Results strongly support the existence of a definable MDS, which is near observed travel speeds for urban pedestrians and bicyclists. For a wide range of travelers, the MDS is 2–6 km/h while walking and 12–20 km/h while bicycling, decreasing with road grade at a rate similar to observed speeds. On level ground, pedestrian and bicycle MDS corresponds to a moderate-intensity physical activity level (3–6 MET). Small deviations from the MDS have little effect, but large deviations (by more than 10 km/h for bicycling) can more than double inhalation dose over a fixed distance. It appears that pedestrians and bicyclists choose travel speeds that approximately minimize pollution inhalation dose, although pollution is unlikely a primary motivation.     

Road environments and bicyclist route choice: The cases of Davis and San Francisco,CA

Examining bicyclists' route choices provides valuable insights into the importance of road environments for bicycling. In this study, we examine the role of road factors, individual factors, and preference heterogeneity on route choice using two diverse and extreme cases in the U.S. The first case is bicycling to the University of California, Davis campus by students, faculty, and staff. This case represents the most bike friendly environment in the U.S. which affords a diverse bicycling population. The second case is bicycling to many destinations for many purposes in San Francisco, CA. It is more representative of a large U.S. city, but also has a relatively large bicycling mode share. It serves as an important case for examining the new innovative type of bicycling infrastructure that has been installed in North American cities over the past decade. Results suggest substantial within-city between-person heterogeneity in preference for road attributes and bicycling facilities as well as differences between contexts. Davisites show strong preferences for bike lanes and off-street paths and consistently choose routes of similar length to shortest routes indicating the need for suitable routes with minimal detours to support a large bicycling mode share. San Franciscans show strong preferences for conventional bike lanes on minor arterials, even stronger preferences for separated and protected bicycling facilities, and are willing to detour considerable distances to ride on them. Given large between-person differences within cities, we suggest usual valuations of bicycling facilities from elasticities and marginal rates of substitutions at the mean may need rethinking when applied to bike infrastructure planning.     

How vehicle ownership affect time utilization on study,leisure, social activities,and academic performance of university students? A case study of engineering freshmen in a rural university in Thailand

This study aims to investigate how the ownership of a private vehicle influences time utilization of university students, and whether it impacts their academic performance. This research analyzes travel/activity patterns of 130 engineering freshman students at a rural university in Thailand. An analysis of travel/activity data shows that vehicle ownership seems to play an important role in university student’s time utilization on various activities. It was found that those students who own a vehicle tend to spend less time for academic purposes, and more on leisure and social activities than non-owner students. Nevertheless, a further study using regression analysis on academic performance shows that the ownership of a vehicle does not seem to have a significant impact on the grade point average of students, once the cognitive ability and gender are accounted for. The findings imply that campus policies/measures that restrict the ownership or usage of a private vehicle in rural universities would improve the campus learning environment by influencing university students to put more attention on school-related activities, but such policies do not seem to impact on the academic performance of the college students.     

What drives sustainable student travel? Mode choice determinants in the Greater Toronto Area

Examination of mode choice behavior is an important step in accurately predicting future travel demand. Despite having somewhat unique travel needs and challenges, there is a lack of knowledge in understanding the mode use behavior of university student population. The existing studies on university populations relied on a relatively smaller sample in investigating the behavior. Therefore, using world's largest university student's travel database, this study examines the factors affecting the mode choice behavior of a diverse university student population with student samples from four universities and their seven campuses located across the Greater Toronto Area (GTA) in Canada. Additionally, stratifying this diverse population using their attitudinal responses towards numerous travel modes, this study also estimates three additional mode choice models to obtain a more comprehensive understanding of how students in different markets, with different latent attitudes towards transportation, vary in terms of sustainable mode choice. A cluster analysis based on fourteen attitudinal responses, was conducted to stratify the sample whereas the popular multinomial logit approach was used to estimate the mode choice models. This study finds transit pass and bike ownership as important determinants that govern sustainable mode choice among the students in the region. The findings of this study could facilitate the sustainability offices at the four universities in making an informed policy decision in shifting the mode use behavior of students towards sustainable modes.     

Invisible commuters: assessing a university’s eco-friendly transportation policies and commuting behaviours

To assess the efficacy of a private university’s environmentally friendly transportation policies, we administered a transportation and energy-use phone survey to a random sample of faculty, non-professional staff, and off-campus students. Statistical findings confirm that our sample population exhibits the Environmental Attitudes & Behaviours (A-B) and Behaviours & Behaviours (B-B) Splits observed in past studies. Those most likely to report environmentally friendly attitudes/behaviours are also the most likely to commute longer distances and in larger cars. Moreover, the least-compensated university sub-population, non-professional staff (i.e., office support, grounds keepers), is significantly more likely to contribute to a dramatically larger per-capita percentage of the university’s carbon footprint from commuting. University pro-environment transportation alternatives at the time of this study did not consider directly this commuter sub-group’s structural disadvantages. This incongruence in goals and execution reflects an institutional variant of the A-B Split explained by the New Institutionalism perspective.     

Analysis of travel mode choice and trip chain pattern relationships based on multi-day GPS data: A case study in Shanghai,China

As the propensity to link multiple intermediate stops in a trip chain (a sequence of journeys that starts and ends at home, includes visiting one or more locations) is more prevalent, the relationship between travel mode choice and trip chain pattern aroused the attention of academics. This paper examines two distinct structures to identify the decision process of travelers between travel mode choice and trip chain pattern: one structure in which trip chain pattern organization precedes travel mode choice, another structure in which travel mode choice decision precedes the organization of trip chain pattern. To accommodate multi-day behavioral variability and unobserved heterogeneity in personal characteristics ignored by traditional travel surveys, multi-day GPS data collected in Shanghai is employed to estimate these two structures within Nested Logit (NL) model. The Monte Carlo (MC) method simulates the switch of trip-chaining and mode choice under possible Transportation Demand Management (TDM) strategies based on estimation results. The findings of this study are as follows: (1) trip chain pattern decision precedes travel mode choice, which means trip-chaining is organized first and affects travel mode choice; (2) complex trip chain is related to higher automobile dependency, and it is a barrier to the tendency to adopt public transit; (3) people who generally travel by automobiles might switch to public transit when private cars are unavailable, and an increase in household bicycle ownership enhances competition between the bicycle and public transit which leads people to turn to cycling. These findings help implement TDM strategies to develop sustainable transportation systems and optimize the urban trip structure.     

How does the inclination to bicycle sway the decision to ride in warm and winter seasons?

Many cities are creating policies and programs aimed at expanding bicycling mode shares. Attitudes towards bicycling in combination with weather conditions, however, can strongly influence the decision to ride. In locations with wide annual seasonal variation, attitudes can radically alter demand on bicycle networks across the year. Though researchers have looked at weather impacts on bicycling, the link between attitudinal factors that might impact riding decisions and seasonal variation remains understudied. This paper investigates heterogeneous taste preferences about the inclination to bicycle for riders who ride only in the warm weather and those who ride all year long (including during severe winters). This research relies on survey data from Edmonton, Canada and presents results from a hybrid discrete choice model. After controlling for age, sex, education, income, the supply of bike lanes, and the latent variable “bicycling inclination,” the results indicate that attitudes have a significantly positive impact on the decision to ride across seasons. The findings suggest that public education and season-specific training programs—particularly aimed at adults and women—have the potential to increase bicycling all year around.     

Exploring tertiary students' travel mode choices in Auckland: Insights and policy implications

It is crucial that policymakers and public transport operators comprehend tertiary students' travel mode choices and understand the factors that inform these choices in order to manage travel demands effectively and to optimise the use of public transport and improve its quality of service, particularly during the peak hours. This study aims to examine tertiary students' travel mode choices in Auckland, which is recognised as one of the most car-dependent cities in the world. The city is struggling with traffic congestion, particularly around its Central Business District (CBD) during peak hours. Auckland is New Zealand's largest city, with a population of 1.4 million residents. The main campuses of two major public universities, The University of Auckland (UoA) and Auckland University of Technology (AUT), are located in the CBD, attracting a large number of daily trips by both staff and students, particularly during the teaching semesters. However, there is a limited understanding of tertiary students' travel mode choices, particularly the factors that inform these choices when they travel to the universities' city campuses. A mixed methodology approach was used to comprehend the travel mode choices of students attending The University of Auckland and to identify the key factors that are drivers of these choices. The data collection included a questionnaire-based survey, which received 249 responses, and 10 semi-structured interviews with students. Thematic analysis was utilised to codify and then analyse the interviews. Despite the significant car dependency in Auckland, the survey demonstrated that most respondents utilised public transport and active modes when commuting to the university's city campus. Seven factors were identified that inform tertiary students' travel mode choices: cost, parking availability and cost, access to a car, travel time, physical environment, reliability, and attitudinal variables. The interviewees mostly argued that travel cost and lack of or limited access to a private car were the primary drivers of their travel mode choices. The study suggests that different stakeholders, such as Auckland Transport (AT) and The University of Auckland, should work collaboratively to provide an inclusive travel demand management policy. The university could rearrange classes for off-peak hours, and AT could offer tertiary students further discounts during these hours. These actions would result in the optimisation of public transport efficiency, improvement of the quality of the public transport service, and mitigation of traffic congestion around Auckland's Central Business District (CBD).     

Spatial variations in active mode trip volume at intersections: a local analysis utilizing geographically weighted regression

Geographically weighted regression (GWR) models have been employed in previous studies regarding vehicular travel demands, but few studies have locally modeled walking travel demands at intersections to address the issue of spatially varying relationships. Harnessing a comprehensive collection of walking and bicycling traffic counts over 10 years in Chittenden County, Vermont, US, along with socioeconomic characteristics, transit accessibility indices, land use attributes and characteristics of intersections and roadway networks, this study utilizes GWR models to identify whether there are spatially varying relationships between active mode travel demands and ambient built-environment attributes. One Ordinary Least Square (OLS) model and two GWR models were parametrically calibrated: a full GWR model of all local variables and a mixed GWR model of both global and local variables. K-fold cross-validation method is used to select variables that significantly influence the volume of active travel modes in the OLS model. The uniform set of variables is investigated in two GWR models. Only residuals of the mixed GWR model exhibit spatial independence. The prediction accuracy of the three models is respectively compared by means of the k-fold cross-validation method. Results show that the mixed GWR model has higher prediction accuracy, while the other two models have roughly the same level of performance. We find that not all independent variables possess a spatially varying relationship with active mode volumes. The flexibility of the mixed GWR model that allows some independent variables to be global strengthens its prediction power. With these findings, transportation planners can dynamically estimate bicycle and pedestrian volumes at widespread intersections, and this geographical realism would facilitate local transportation planning, facility design, safety enhancement and operation analysis, as well as instilling new insights into interdisciplinary spatial research domain.     

Does bicycle network level of traffic stress (LTS) explain bicycle travel behavior? Mixed results from an Oregon case study

This paper draws on census data, mode choice, and regional household travel survey data to investigate the relationship between the varying levels of traffic stress (LTS) routes and bicycle travel behavior. Specifically, does bicycle level of traffic stress explain bicycle behavior enough to warrant its use given the limitations? Bicycle level of traffic stress is a new system of bicycle infrastructure classification for cities that cannot afford existing alternatives such as bicycle level of service which is more data-intensive. The LTS criteria lacks many of the input variables found significant in predicting both route choice and mode choice, including several that are required for its more expensive alternatives. The authors select the Salem-Keizer metropolitan area, a case study representative of the kinds of small and medium-sized communities that may prefer to use the LTS to evaluate cycling infrastructure given its lower input costs. The results validate LTS on travel behavior data from the Oregon Household Activity Survey (OHAS), but not mode choice data from the American Community Survey (ACS). These results suggest the LTS criteria may not be useful for cities looking to prioritize infrastructure improvements for specifically increasing commuter cycling. That said, results suggest the system provides a valid measure of a household's propensity to cycle. Further research on a broader cross-section of communities can clarify these mixed results.