Pedestrian and bicyclist collision risk assessment offers a powerful and informative tool in urban planning applications, and can greatly serve to inform proper placement of improvements and treatment projects. However, sufficiently detailed data regarding pedestrian and bicycle activity are not readily available for many urban areas, and thus the activity levels and collision risk levels must be estimated. This study builds upon other current work by Murphy et al. (1) regarding pedestrian and bicycle activity estimation based on centrality and accessibility metrics, and extends the analysis techniques to estimation of pedestrian collision risk. The Safety In Numbers phenomenon, which refers to the observable effect that pedestrians become safer when there are more pedestrians present in a given area, i.e. that the individual per-pedestrian risk of a collision decreases with additional pedestrians, is a readily observed phenomenon that has been studied previously. The effect is investigated and observed in acquired traffic data, as well as estimated data, in Minneapolis, Minnesota.
Non-motorized transportation, particularly including walking and bicycling, are increasingly becoming important modes in modern cities, for reasons including individual and societal wellness, avoiding negative environmental impacts of other modes, and resource availability. Institutions governing development and management of urban areas are increasingly keen to include walking and bicycling in urban planning and engineering; however, proper placement of improvements and treatments depends on the availability of good usage data. This study attempts to predict pedestrian activity at 1123 intersections in the Midwestern, US city of Minneapolis, Minnesota, using scalable and transferable predictive variables such as economic accessibility by sector, betweenness network centrality, and automobile traffic levels. Accessibility to jobs by walking and transit, automobile traffic, and accessibility to certain economic job categories (Education, Finance) were found to be significant predictors of increased pedestrian traffic, while accessibility to other economic job categories (Management, Utilities) were found to be significant predictors of decreased pedestrian traffic. Betweenness centrality was not found to be a significant predictor of pedestrian traffic, however the specific calculation methodology can be further tailored to reflect real-world pedestrian use-cases in urban areas. Accessibility-based analysis may provide city planners and engineers with an additional tool to predict pedestrian and bicycle traffic where counts may be difficult to obtain, or otherwise unavailable.
This study disentangles the impact of financial and physical dimensions of transit service operators on net transit accessibility for 46 of the 50 largest metropolitan areas in the United States. To investigate this interaction along with the production efficiency of transit agencies, two types of analysis are used: a set of linear and quadratic regressions and a data envelopment analysis. We find that vehicle revenue kilometers and operational expenses play a pivotal role in enhancing the accessibility to jobs by transit. The bivariate linear regression models indicate a 1% increase in operational expenses and vehicle revenue kilometers increase the number of jobs that can be reached within 30 minutes by 0.96 and 0.95%, respectively. The results of the quadratic functional form, also, show transit services may have both increasing and decreasing accessibility returns to scale depending on system size, and the results are sensitive to the model used. Overall, the highest system efficiency (access produced per input) is found in the New York, Washington, and Milwaukee metropolitan areas, while Riverside, Detroit, and Austin perform with the lowest efficiency.
Keywords: Public transit; Accessibility; Envelope of output; Returns to scale; Metropolitan area
The current research provides a test framework to understand whether and to what extent increasing public transit use and accessibility by transit affect health. To this end, the effect of transit mode share and accessibility by transit on general health, body mass index, and height are investigated, while controlling for socioeconomic, demographic, and physical activity factors. The coefficient-p-value-sample-size chart is created and effect size analysis are conducted to explore whether the transit use is practically significant. Building on the results of the analysis, we found that the transit mode share and accessibility by transit are not practically significant, and the power of large-sample misrepresents the effect of transit on public health. The results, also, highlight the importance of data and variable selection by portraying a significant correlation between transit use and height in a multivariate regression analysis. What becomes clear from this study is that in spite of the mushrooming interdisciplinary studies in the nexus of transportation and health arena, researchers often propose short- and long-term policies blindly, while failing to report the inherent explanatory power of variables. We show that there is a thin line between false positive and true negative results. From the weakness of p-values perspective, further, we strove to alert both researchers and practitioners to the dangerous pitfall deriving from the power of large- samples. Building the results on just significance and sign of the parameter of interest is worthless, unless the magnitude of effect size is carefully quantified post analysis.
Keywords: Public transit; BRFSS data; ACS data; Accessibility to jobs; p-hacking
Social equity is increasingly incorporated as a long-term objective into urban transportation plans. Researchers used accessibility measures to assess equity issues, such as determining the amount of jobs reachable by marginalized groups within a defined travel time threshold and compare these measures across socioeconomic categories. However, allocating public transit resources in an equitable manner is not only related to travel time, but also related to the out-of- pocket cost of transit fares, which can represent a major barrier to accessibility for many disadvantaged groups. Therefore, this research proposes a set of new accessibility measures that incorporates both travel time and transit fares. It then applies those measures to determine whether people residing in socially disadvantaged neighborhoods in Montreal, Canada experience the same levels of transit accessibility as those living in other neighborhoods. Results are presented in terms of regional accessibility and trends by social indicator decile. Travel time accessibility measures estimate a higher number of jobs that can be reached compared to combined travel time and cost measures. However, the degree and impact of these measures varies across the social deciles. Compared to other groups in the region, residents of socially disadvantaged areas have more equitable accessibility to jobs using transit; this is reflected in smaller decreases in accessibility when fare costs are included. Generating new measures of accessibility combining travel time and transit fares provides more accurate measures that can be easily communicated by transportation planners and engineers to policy makers and the public since it translates accessibility measures to a dollar value.
Abstract: This study measures the variability of job accessibility via automobile for the Minneapolis-St. Paul region. The accessibility analysis uses cumulative opportunity measures. The travel times on the network are tested at various level (10th percentile speed, 50th percentile speed, 90th percentile speed) using the TomTom speed data for 2010. It is shown that accessibility varies widely day-to-day as travel speeds on the network vary. Some parts of the region (a ring around the core) have more volatility in accessibility (and are thus less reliable) than others.
In 1863, the Metropolitan Railway of what came to be known as the London Underground successfully opened as the world’s first subway. Its high ridership spawned interest in additional links. Entrepreneurs secured funding and then proposed new lines to Parliament for approval, though only some were actually approved. While putative rail barons may have conducted some economic analysis, the final decision lay with Parliament, which did not have modern transportation, economic, or geographic analysis tools available. How good were the decisions that Parliament made in approving Underground lines? This paper explores the role accessibility played in the decision to approve or reject proposed early London Tube schemes. It finds that maximizing accessibility to population (highly correlated with revenue and ridership) per expenditure largely explains Parliamentary approvals and rejections.
A new set of Forthcoming papers in the Journal of Transport and Land Use have been made available online. These are mostly papers that have been presented at the 2014 WSTLUR Conference in Delft and completed a comprehensive peer review process. More are coming.
JTLU is an open access journal, so unlike much research, this is not behind a firewall. These will be assigned volumes and issues over the coming months, but these are out now.
Owen, Andrew and David M. Levinson (2014) Modeling the commute mode share of transit using continuous accessibility to jobs Transportation Research Part A: Policy and Practice Volume 74, April 2015, Pages 110–122
Accessibility to jobs by transit is calculated for departures at each minute.•
Detailed spatial resolution more accurately reflects walking trip components.
Higher transit mode share is associated with higher average transit accessibility.
Higher transit mode share is associated with lower variation in transit accessibility.
This paper presents the results of an accessibility-based model of aggregate commute mode share, focusing on the share of transit relative to auto. It demonstrates the use of continuous accessibility – calculated continuously in time, rather than at a single of a few departure times – for the evaluation of transit systems. These accessibility calculations are accomplished using only publicly-available data sources. A binomial logic model is estimated which predicts the likelihood that a commuter will choose transit rather than auto for a commute trip based on aggregate characteristics of the surrounding area. Variables in this model include demographic factors as well as detailed accessibility calculations for both transit and auto. The mode achieves a ρ2 value of 0.597, and analysis of the results suggests that continuous accessibility of transit systems may be a valuable tool for use in modeling and forecasting.