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.

scholarly journals Public transit network planning in small cites considering safety and convenience

2020 ◽  
Vol 12 (1) ◽  
pp. 168781402090235 ◽  
Author(s):  
Changxi Ma ◽  
Dong Yang
Keyword(s):  
Traffic Congestion ◽  
Public Transit ◽  
Network Planning ◽  
Optimization Method ◽  
Transit Oriented Development ◽  
Transit Planning ◽  
Transit Network ◽  
The Public ◽  
Traffic Generation ◽  
Interval Value

Scientific and rational public transit network planning, not only can effectively alleviate city traffic congestion, but also can reduce the risk of accidents. First, based on the data of residents’ travel survey, this article employs the multiple regression method to forecast the traffic generation and adopts the double-constrained gravity model to forecast the residents’ travel distribution of small cites. Second, by aiming at public transit planning objectives, the unsafe roads for public transit are screened, and the public transit trip-mode sharing rate is set as the interval value. According to the interval value, the public transit trip-mode sharing rate is divided into three cases, and the three alternatives of public transit network are calculated based on the network optimization method and the public transit-oriented development model. Next, the alternatives are evaluated by the set pair analysis method, and the optimal scheme is selected. Finally, this article takes the public transit network planning of Huaiyuan County in Anhui Province as an example, and the results show the proposed method is feasible.

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.

scholarly journals Identifying Public Transit Commuters Based on Both the Smartcard Data and Survey Data: A Case Study in Xiamen, China

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Shichao Sun ◽  
Dongyuan Yang
Keyword(s):  
Machine Learning ◽  
Survey Data ◽  
Public Transit ◽  
Learning Algorithm ◽  
Travel Patterns ◽  
Transit Planning ◽  
Transit System ◽  
Location Data ◽  
Machine Learning Approach ◽  
Transit Use

Understanding the travel patterns of public transit commuters was important to the efforts towards improving the service quality, promoting public transit use, and better planning the public transit system. Smartcard data, with its wide coverage and relative abundance, could provide new opportunities to study public transit riders’ behaviors and travel patterns with much less cost than conventional data source. However, the major limitation of smartcard data is the absence of social attributes of the cardholders, so that it cannot clearly extract public transit commuters and explain the mechanism of their travel behaviors. This study employed a machine learning approach called Naive Bayesian Classifier (NBC) to identify public transit commuters based on both the smartcard data and survey data, demonstrated in Xiamen, China. Compared with existing methods which were plagued by the validation of the accuracy of the identification results, the adopted approach was a machine learning algorithm with functions of accuracy checking. The classifier was trained and tested by survey data obtained from 532 valid questionnaires. The accuracy rate for identification of public transit commuters was 92% in the test instances. Then, under a low calculation load, it identified the objectives in smartcard data without requiring travel regularity assumptions of public transit commuters. Nearly 290,000 cardholders were classified as public transit commuters. Statistics such as average first boarding time and travel frequency of workdays during peak hours were obtained. Finally, the smartcard data were fused with bus location data to reveal the spatial distributions of the home and work locations of these public transit commuters, which could be utilized to improve public transit planning and operations.

Shared Use of Railway Corridors by Public Transit Vehicles

2011 ◽  
Author(s):  
Alexandre J. M. Eyquem ◽  
Joseph Lee Hutchins ◽  
Christopher Taylor ◽  
John Falcetta
Keyword(s):  
Capital Investment ◽  
Transportation Systems ◽  
Point Of View ◽  
Transit Planning ◽  
Distant Future ◽  
The Public ◽  
Public Purpose ◽  
Transit Services ◽  
In Transit ◽  
Transit Routes

As transportation corridors become an ever more important asset, existing rail freight corridors are under increasing pressure to be shared for transit purposes. In defining the expanded corridor use a comprehensive look at factors was undertaken on behalf of Transport Canada by AECOM. Understanding the issues is imperative in navigating this complex subject. In locations where the transit network and expected ridership-generation points are compatible with existing railway networks, the best use of a network of corridors for freight and transit should be one of the first steps in municipal and regional planning. Finding a best use of existing network typically does not happen for two reasons. First, the public entities typically do not observe the complete “freight network” and have a limited stake in its function and complexity. Unlike public roads or transit, public entities tend to look at single opportunities such as little used freight segments, or specific corridors that they can afford to pursue with a specific public purpose. As a result, freight railways often find themselves responding to a specific request for a segment in the context of their network and whether it serve a functional need. Second, the private freight railways similarly do not have opportunity or patience to investigate the public contribution to their networks. The point of view of what might be done if the potential transit services were understood and what it could mean to freight business with capital investment in an improved and/or rationalized freight delivery system may be hard to define, and the benefits may only occur in the distant future. They have a priority to pursue their immediate business model and therefore tend to wait until the public entity comes forward and asks about a specific corridor or opportunity. The issue of assets inventory, rationalization paired with transit planning, is the first step in evaluating efficient transportation systems through urban centres. Furthermore, the ability of some transit vehicles to be used on both on dedicated railway transit corridors, where it makes sense, as well as for street services, provide for greater flexibility in transit networks. What is missing is a review of freight networks from a regional perspective relative to all potential transit routes. The first question to be asked when looking at the freight network of an urban area is: where is it going, why is it going there, and could it be handled more effectively, and if so what are the overall benefits to all parties.

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

Using Smart Farecard Data to Support Transit Network Restructuring: Findings from Los Angeles

2019 ◽  
Vol 2673 (6) ◽  
pp. 202-213 ◽  
Author(s):  
Pragun Vinayak ◽  
Zeina Wafa ◽  
Conan Cheung ◽  
Stephen Tu ◽  
Anurag Komanduri ◽  
...  
Keyword(s):  
Los Angeles ◽  
Time Of Day ◽  
Transit Planning ◽  
Metropolitan Regions ◽  
Trip Purpose ◽  
Transit Ridership ◽  
Market Shares ◽  
Transit Market ◽  
Transit Agencies ◽  
In Transit

Recent technological innovations have changed why, when, where, and how people travel. This, along with other changes in the economy, has resulted in declining transit ridership in many U.S. metropolitan regions, including Los Angeles. It is important that transit agencies become data savvy to better align their services with customer demand in an effort to redesign a bus network that is more relevant and reflective of customer needs. This paper outlines a new data intelligence program within the Los Angeles County Metropolitan Transportation Authority (LA Metro) that will allow for data-driven decision-making in a nimble and flexible fashion. One resource available to LA Metro is their smart farecard data. The analysis of 4 months of data revealed that the top 5% of riders accounted for over 60% of daily trips. By building heuristics to identify transfers, and by tracking riders through space and time to systematically identify home and work locations, transit trip tables by time of day and purpose were extracted. The transit trip tables were juxtaposed against trip tables generated using disaggregate anonymized cell phone data to measure transit market shares and to evaluate transit competitiveness across several measures such as trip length, travel times relative to auto, trip purpose, and time of day. Relying on observed trips as opposed to simulated model results, this paper outlines the potential of using Big Data in transit planning. This research can be replicated by agencies across the U.S. as they reverse declining ridership while competing with data-savvy technology-driven competitors.

scholarly journals Covid-19 Pandemic Impacts on Essential Transit Riders: Findings from a U.S. Survey

2021 ◽  
Author(s):  
Qian He ◽  
Dana Rowangould ◽  
Alex Karner ◽  
Matthew Palm ◽  
Seth LaRue
Keyword(s):  
United States ◽  
National Survey ◽  
Public Transit ◽  
The United States ◽  
Financing Policy ◽  
Transit Service ◽  
Transit Use ◽  
In Transit ◽  
Travel Needs

The Covid-19 pandemic has decimated public transit service across the United States and caused significant decreases in ridership. Adapting to the pandemic has been more challenging for some transit riders than for others. Little is known about the reasons for pandemic-era mode shifts and the impacts of pandemic-related transit reductions on riders’ day-to-day lives. Using a national survey of U.S. transit riders (n=500), this study examines changes in transit use since the pandemic began, the reasons for transit reductions, and the effects of reduced transit use and transit service on transit riders’ ability to meet their travel needs. The Covid-19 pandemic has exacerbated existing transportation burdens for essential transit riders, pointing to shortcomings inherent in current transit financing policy. We close with recommendations for strengthening the transit service for these groups in the long term as we recover from the pandemic.

Do people walk more in transit-accessible places?

2020 ◽  
Vol 13 (1) ◽  
Author(s):  
Yunkyung Choi ◽  
Subhrajit Guhathakurta
Keyword(s):  
Travel Behavior ◽  
Propensity Scores ◽  
Metropolitan Region ◽  
Rail Transit ◽  
Built Environments ◽  
Walking Behavior ◽  
Planning And Design ◽  
Catchment Areas ◽  
Transit Use ◽  
In Transit

While transit-oriented developments (TODs) are generally believed to promote the use of sustainable travel modes, the degree to which various components of TODs influence travel behavior is still debatable. This paper revisits Chatman’s (2013) question: “Does TOD need the T?” by addressing the effect of rail transit access in influencing walking behavior in TOD areas. In particular, we compare TODs to other similar areas, with rail transit access being the key variable, and examine whether people are more likely to walk in TODs for purposes other than transit use. This hypothesis is tested using traffic analysis zones (TAZs) in the Atlanta Metropolitan Region. First, we identify TAZs within rail catchment areas and use propensity scores to match them with other TAZs with similar built environmental characteristics except for rail transit access. We then conduct a statistical analysis comparing walking trips for both commuting and non-commuting trips in these two TAZ groups. Our results confirm that the likelihood of walking trips increases in transit-accessible TAZs compared to other similar areas without transit. Therefore, states and localities can maximize the benefits of pedestrian-friendly built environments by making rail transit access an important part of their planning and design.

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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