Influences of Neighborhood Characteristics and Personal Attitudes on University Commuters’ Public Transit Use

2015 ◽  
Vol 2500 (1) ◽  
pp. 93-101 ◽  
Author(s):  
Mi Namgung ◽  
Gulsah Akar
Keyword(s):  
Built Environment ◽  
Travel Behavior ◽  
Public Transit ◽  
Principal Component ◽  
Neighborhood Characteristics ◽  
Single Family ◽  
Mixed Use ◽  
Personal Attitudes ◽  
Intersection Density ◽  
Transit Use

This study examined the links between attitudes, the built environment, and travel behavior on the basis of data from the Ohio State University's 2012 Campus Transportation Survey. The analysis results indicated that attitudes might have explained travel behavior better than the built environment. Survey respondents were asked questions about their attitudes on public transit use, and their answers were grouped into new attitudinal factors by using principal component analysis. Then, new neighborhood categories were created by K-means cluster analysis by means of built-environment and land use variables (population density, employment density, housing density, median age of structures, percentage of single-family housing, and intersection density). As a result of this analysis, discrete neighborhood categories, such as urban high-density and residential neighborhoods, and urban low-density and mixed-use neighborhoods, were created. Then, differences in attitudes toward public transit were analyzed across these new neighborhood categories. Binary logit models were estimated to determine the influence of these neighborhood categories as well as personal attitudes on public transit use after sociodemographic characteristics were controlled for. The results indicated that attitudes were more strongly associated with travel behavior than with neighborhood characteristics. The findings of this study will aid in the formation of a better understanding of public transit use by highlighting the effects of attitudes and neighborhood characteristics in transit use as well as differences in attitudes between neighborhood types.

Density and Captivity in Public Transit Success: Observations from the 1995 Nationwide Personal Transportation Study

2000 ◽  
Vol 1735 (1) ◽  
pp. 10-18 ◽  
Author(s):  
Steve E. Polzin ◽  
Xuehao Chu ◽  
Joel R. Rey
Keyword(s):  
Travel Behavior ◽  
Public Transit ◽  
Good Time ◽  
Transit Planning ◽  
Economic Changes ◽  
The Public ◽  
Transit Market ◽  
Personal Transportation ◽  
Transit Use ◽  
In Transit

The new millennium provides a good time to reflect on transportation-industry trends in some fundamental external factors that influence transportation behavior and planning response. In the public-transit industry, urban density and transit captivity have long been fundamental conditions driving transit planning and service and facility investment decisions. In light of demographic and economic changes, it is useful to revisit the issue of the importance of these factors to the transit market. Findings from a comprehensive analysis of the 1995 Nation-wide Personal Transportation Study (NPTS), which explored current transit-travel behavior, are reported. Two key findings reflect on two historical axioms in transit: ( a) the extent to which density influences transit use and ( b) the importance of the transit-dependent market. The research findings reiterate the significant influence that development density has on public transit mode share and bring to light some revealing data on the influence of urban-area size on transit use. The importance of transit dependency on transit use is documented, and trends in transit dependency over the past few decades are revealed. Finally, the implications of these trends for the public-transit industry are discussed.

Does Land Use Matter? Understanding Homicide Counts Beyond the Effects of Social Disorganization

2019 ◽  
Vol 24 (4) ◽  
pp. 311-332
Author(s):  
Alana R. Inlow
Keyword(s):  
Land Use ◽  
Social Disorganization ◽  
Negative Binomial ◽  
Positive Association ◽  
Criminological Theory ◽  
Neighborhood Characteristics ◽  
Single Family ◽  
Mixed Use ◽  
The Relationship ◽  
Binomial Models

This study assesses the relationship between land use, measured as percent zoning designation per square kilometer in a census tract, and homicide counts in Portland, Oregon, while controlling for other neighborhood characteristics. Negative binomial models are implemented to account for the overdispersed homicide count indicator. Results suggest that some land use variables—specifically, mixed-use residential (positive association) and single-family residential (negative association)—have significant predictive value for homicide counts beyond neighborhood characteristics and socioeconomic variables deemed important by criminological theory and research.

scholarly journals Public transit use in the United States in the era of COVID-19: Transit riders’ travel behavior in the COVID-19 impact and recovery period

2021 ◽  
Author(s):  
Madeleine E.G. Parker ◽  
Meiqing Li ◽  
Mohamed Amine Bouzaghrane ◽  
Hassan Obeid ◽  
Drake Hayes ◽  
...  
Keyword(s):  
United States ◽  
Travel Behavior ◽  
Public Transit ◽  
Recovery Period ◽  
The United States ◽  
Transit Use

scholarly journals The Effects of Neighborhood Built Environment on Walking for Leisure and for Purpose Among Older People

2019 ◽  
Author(s):  
Zachary J Christman ◽  
Maureen Wilson-Genderson ◽  
Allison Heid ◽  
Rachel Pruchno
Keyword(s):  
Older Adults ◽  
Built Environment ◽  
Older People ◽  
Ecological Model ◽  
Neighborhood Characteristics ◽  
Single Family ◽  
Walking Behavior ◽  
Novel Method ◽  
Neighborhood Built Environment ◽  
Design And Methods

Abstract Background and Objectives Characteristics of a neighborhood’s built environment affect the walking behavior of older people, yet studies typically rely on small nonrepresentative samples that use either subjective reports or aggregate indicators from administrative sources to represent neighborhood characteristics. Our analyses examine the usefulness of a novel method for observing neighborhoods—virtual observations—and assess the extent to which virtual-based observations predict walking among older adults. Research Design and Methods Using Google Street View, we observed the neighborhoods of 2,224 older people and examined how characteristics of the neighborhood built environments are associated with the amount of time older people spend walking for leisure and purpose. Results Multilevel model analyses revealed that sidewalk characteristics had significant associations with both walking for purpose and leisure. Land use, including the presence of multifamily dwellings, commercial businesses, and parking lots were positively associated with walking for purpose and single-family detached homes were negatively associated with walking for purpose, but none of these characteristics were associated with leisure walking. Gardens/flowers were associated with walking for leisure but not purpose. Garbage/litter was not associated with either type of walking behavior. Discussion and Implications Virtual observations are a useful method that provides meaningful information about neighborhoods. Findings demonstrate how neighborhood characteristics assessed virtually differentially impact walking for leisure and purpose among older adults and are interpreted within a social-ecological model.

scholarly journals Exploring Effective Built Environment Factors for Evaluating Pedestrian Volume in High-density Areas: A New Finding for the CBD in Melbourne, Australia

2021 ◽  
Author(s):  
Jiacheng Jiao ◽  
John Rollo ◽  
Baibai Fu ◽  
Chunlu Liu
Keyword(s):  
Land Use ◽  
Built Environment ◽  
Stepwise Regression ◽  
Public Transit ◽  
Principal Component ◽  
Central Business District ◽  
High Density ◽  
Walking Distance ◽  
Cross Sectional ◽  
Transit System

Previous studies have mostly examined how sustainable cities try to promote non-motorized travel by creating a walking-friendly environment. Such existing studies provide little research that identifies how the built environment affects pedestrian volume in high-density areas. This paper presents a methodology that combines person correlation analysis, stepwise regression, and principal component analysis for exploring the internal correlation and potential impact of built environment variables. To study this relationship, cross-sectional data in the Melbourne central business district were selected. Pearson’s correlation coefficient confirmed that visible green index and intersection density were not correlated to pedestrian volume. The results from stepwise regression showed that land-use mix degree, public transit stop density, and employment density could be associated with pedestrian volume. Moreover, two principal components were extracted by factor analysis. The result of the first component yielded an internal correlation where land-use and amenities components were positively associated with the pedestrian volume. Component 2 presents parking facilities density, which negatively relates to the pedestrian volume. Based on the results, existing street problems and policy recommendations were put forward to suggest diversifying community service within walking distance, improving the service level of the public transit system, and restricting on-street parking in Melbourne.

Propensity Score Weighting with Generalized Boosted Models to Explore the Effects of the Built Environment and Residential Self-Selection on Travel Behavior

2019 ◽  
Vol 2673 (4) ◽  
pp. 373-383 ◽  
Author(s):  
Yiling Deng ◽  
Yadan Yan
Keyword(s):  
Propensity Score ◽  
Built Environment ◽  
Hierarchical Clustering ◽  
Travel Behavior ◽  
Public Transit ◽  
Average Treatment Effect ◽  
Propensity Score Weighting ◽  
Self Selection ◽  
Environment Variables ◽  
Trip Frequency

Many studies have examined the association between the built environment, residential self-selection, and travel behavior. However, few studies have quantified the relative contribution of the built environment itself. Using the 2012 Nanjing Household Travel Survey data, this study applied hierarchical clustering and propensity score weighting to study the effects of the built environment and residential self-selection on travel behavior. First, residents’ household locations were classified into four built environment patterns using hierarchical clustering based on six built environment variables by loosely following the “5Ds” (i.e., density, diversity, design, destination accessibility, and distance to transit). Second, a powerful machine learning method, generalized boosted model (GBM), was employed to obtain propensity scores. Propensity score weighting, which is more effective for multiple treatments than matching or stratification, was used to control for residential self-selection. Lastly, the observed effect (OBE), the average treatment effect on the population (ATE), and the built environment proportion (BEP) were calculated for the walking trip frequency, bicycle trip frequency, public transit trip frequency, and vehicle kilometers traveled (VKT) of six pairs of built environment patterns. The results show that a high-density, mixed-use, walkable, and transit-accessible built environment is associated with more walking trips and lower VKT but has no impact on bicycle trips and has an inconsistent impact on public transit trips. The effects of some built environment variables on bicycle and public transit trips are tangled. The residential self-selection effect has the greatest impact on VKT (BEP: 48%–77%), followed by the walking trip frequency (BEP: 62%–74%) and the public transit frequency (BEP: 90%–107%).

scholarly journals Exploring the Role of Socio-Economic and Built Environment Driving Factors in Shaping the Commuting Modal Share: A Path-Analysis-Based Approach

2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Soufiane Boukarta ◽  
Ewa Berezowska-Azzag
Keyword(s):  
Built Environment ◽  
Path Analysis ◽  
Public Transit ◽  
Principal Component ◽  
Explanatory Variables ◽  
Causal Pathways ◽  
Bivariate Correlation ◽  
Exploratory Stage ◽  
Private Car

AbstractThis paper explores the role of the built environment and socio-economic drivers in shaping the modal share of commuting. For this, we have identified through our literature review 67 potential variables categorised into two groups; the built environment and the households’ socio-economic characteristics. We have considered the city of Djelfa as a case study and used the questionnaire as a data collection tool. The questionnaire processing of the 700 questionnaires provided to the households allowed us to select 184 questionnaires for our analysis. The sensitivity analysis protocol is designed for two stages; (i) an exploratory stage, conducted by principal component analysis and bivariate correlation analysis; (ii) and a confirmatory stage conducted by a path analysis. The first step allowed us to hypothesise several causal pathways that could explain, directly or indirectly, the modal share of commuting. The results of the path analysis show that the modal shares of walking, private car and public transit are controlled by 13, 16 and 12 explanatory variables, respectively. Overall, the socio-economic characteristics of households discourage walking and transit use, and encourage private car commuting. On the other hand, the variables identified in this paper related to the built environment discourage walking, but encourage the use of public transit rather than private cars for commuting.

Automobile and Public Transit Use in the United States and Canada: Comparison of Postwar Trends

1996 ◽  
Vol 1521 (1) ◽  
pp. 3-11
Author(s):  
Paul Schimek
Keyword(s):  
United States ◽  
Public Transportation ◽  
Public Transit ◽  
Unit Cost ◽  
The United States ◽  
Gasoline Prices ◽  
Single Family ◽  
Per Capita ◽  
Transit Use ◽  
Automobile Use

Differences in automobile and public transportation use in Canada compared with that in the United States are described. In Canada public transit use is almost twice as high per capita as in the United States. Automobile use is almost 20 percent lower per capita, or about the same as the U.S. level of the 1970s. Gasoline prices, which have been about US$0.13/L (US$0.50/gal) higher in Canada than in the United States since 1984, slowed the growth in Canadian automobile ownership and driving and created a more efficient automobile fleet, resulting by the early 1990s in 40 percent less highway fuel consumption per capita compared with that in the United States. One explanation for the higher level of transit use in Canada is more compact urban densities, as evidenced by the significantly lower share of single-family detached houses. The influx of public subsidies for transit in the 1970s in both countries had different effects: a much larger increase in service in Canada and deeper fare cuts in the United States but similar increases in unit cost of service. Each new transit trip in 1992 beyond the 1970 ridership level cost about four times as much to attract in the United States as it did in Canada.

scholarly journals When I’m 64: Assessing Generational Differences in Public Transit Use of Seniors in Montreal, Quebec, Canada

2017 ◽  
Vol 2651 (1) ◽  
pp. 31-41 ◽  
Author(s):  
Lesley Fordham ◽  
Emily Grisé ◽  
Ahmed El-Geneidy
Keyword(s):  
Travel Behavior ◽  
Generational Differences ◽  
Baby Boomer ◽  
Public Transit ◽  
Age Groups ◽  
Developed Countries ◽  
Baby Boomer Generation ◽  
The Difference ◽  
Strong Attachment ◽  
Transit Use

The growth rate of adults older than 65 in Canada is increasing more rapidly than the population as a whole. This increase is reflective of the aging baby boomer population. That population is known to have a strong attachment to automobiles, which might be reflected in their travel behavior as they move toward different stages in their older life. The purpose of this paper is to contribute to the understanding of the travel behavior, mainly public transit usage, of Canada’s older population relative to younger cohorts. A pseudocohort analysis was conducted in Montreal, Quebec, Canada, of residents who were 50 or older to follow changes in public transit use of similarly aged respondents from 1998 to 2013. The results revealed that older generations used public transit more than younger generations did at the same age. In addition, the most recent survey year showed a stagnation of transit use across all age groups. Differences in transit use between males and females were more pronounced in earlier cohorts, but the difference was decreasing in more recent years. These findings add to the growing body of work suggesting that the nature of transportation behavior in seniors is changing, and accordingly planners and engineers cannot expect the baby boomer generation to behave the same way as previous generations. Addressing the transportation needs of seniors around the world will be an important challenge for planners and engineers, as the population of seniors is growing more rapidly than the population as a whole in the majority of developed countries. This growth imposes new challenges on the transportation system because of differences in the travel behavior of today’s older adults compared with that of previous cohorts of seniors.

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