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Understanding who in a community has access to its resources—parks, libraries, grocery stores, etc.—has profound equity implications, but typical methods to understand access to these resources are limited. Travel time buffers require researchers to assert mode of access as well as an arbitrary distance threshold; further, these methods do not distinguish between destination quality attributes in an effective way. In this research, we present a methodology to develop utility-based accessibility measures for parks, libraries, and grocery stores in Utah County, Utah. The method relies on passive location-based services data to model destination choice to these community resources; the destination choice model utility functions in turn allow us to develop a picture of regional access that is sensitive to: the quality and size of the destination resource; continuous (non-binary) travel impedance by multiple modes; and the sociodemographic attributes of the traveler. We then use this measure to explore equity in access to the specified community resources across income level in Utah County: the results reveal a discrepancy between which neighborhoods might be targeted for intervention using space-based analysis.

On-demand microtransit services are frequently seen as an important tool in supporting first and last mile operations surrounding fixed route high frequency transit facilities, but questions remain surrounding who will use these novel services and for what purposes. In November 2019, the Utah Transit Authority launched an on-demand microtransit service in south Salt Lake County in partnership with a private mobility operator. This paper reports the results of an expressed preferences survey of 130 transit riders in the microtransit service area that was collected before and immediately after the service launched. There is not a clear relationship between current transit access mode and expressed willingness to use microtransit, although some responses from new riders indicate the novel service competes most directly with commercial transportation network company operations. The survey responses also reveal younger passengers express a more than expected willingness to use microtransit, middle-aged passengers a less than expected willingness, and older passengers neutral or no expressed opinion. The results suggest additional relationships between household size and transit use frequency, but further research is necessary. The effect of other user characteristics, including income and automobile availability, is less statistically clear and requires further research.

Recent research has underscored the potential for public green spaces to influence individual and societal health outcomes, but empirical measurements of such influences have yielded mixed results to date, with particular disagreement surrounding how access to parks ought to be defined while controlling for alternate explanations. In this paper, we apply a comprehensive measure of park accessibility drawn from random utility choice theory, which avoids arbitrary assertions of proximity while incorporating potentially numerous amenities and attributes of both the parks and the population. We apply this utility-based accessibility measure to correlate Census tract-level obesity and physical activity rate estimates from the Centers for Disease Control and Prevention’s 500 Cities project with tract-level American Community Survey socioeconomic data in New York City, paired with geographic open space data from New York City. Controlling for the socioeconomic variables and spatially correlated error terms, we show a positive and significant relationship between park access and physical activity rates. The data also suggest a negative relationship between park access and obesity rates, beyond what is expected through physical activity and socioeconomics. In doing so, this research contributes a more comprehensive modeling approach for measuring the impact of park access on health, and may improve our understanding of the role parks and access to them can serve in furthering public health objectives.

We report the results of an experiment to evaluate the impact of transit signal priority (TSP) on headway adherence for a bus rapid transit (BRT) system in Provo / Orem, Utah. The bus requests TSP based on its unpublished schedule, but users perceive only a headway. Quantile regression models estimated on raw timepoint data from the BRT system reveal that TSP significantly improves headway adherence, after controlling for peak times, direction, and cumulative trip dwell time. We also find that requiring the bus to be 2 minutes late before requesting TSP improves headway adherence more than allowing all buses to request TSP.

Electric scooters (e-scooters) are an increasingly popular form of transportation in urban areas. While research on this topic has focused primarily on injuries, there are multiple mechanisms by which e-scooter share programs may impact health. The aim of this study is to explore the health-related behaviors of e-scooter users and to discuss their implications for public health. Data were collected using an online survey emailed to registered e-scooter users. A total of 1070 users completed the survey. Descriptive variable statistics and chi-squared analysis were performed to determine variable dependent relationships and equality of proportions. The most common destinations reported were &ldquo, just riding around for fun&rdquo, home, and dining/shopping. The two most common modes of transportation that would have been used if e-scooters were not available were walking (43.5%) and using a personal vehicle (28.5%). Riding behavior was equally mixed between on the street, on the sidewalk, and equal amounts of both. e-Scooters in Provo are likely having both positive (e.g., air pollution) and negative impacts on health (e.g., injuries, physical inactivity). Future research should further explore patterns of e-scooter use and explicitly examine the linkages between e-scooters and areas of health beyond just injuries.

Understanding homeowners’ marginal willingness-to-pay (MWTP) for proximity to public transportation infrastructure is important for planning and policy. Naive estimates of MWTP, however, may be biased as a result of spatial dependence, spatial correlation, and/or spatially endogenous variables. In this paper we discuss a class of spatial autoregressive models that control for these spatial effects, and apply them to sample data collected for the Atlanta, Georgia housing market. We provide evidence that a general-to-specific model selection methodology that relies on the generality of the spatial Durbin model (SDM) should be preferred to the classical specific-to-general methodology that begins with an assumption of no spatial effects. We show that applying the SDM raises the estimate of MWTP for transit proximity in Atlanta but also widens its confidence interval, relative to ordinary linear regression. This finding may have implications for risk estimations in land value capture forecasts and transportation policy decisions.

In the past few years, numerous mobile applications have made it possible for public transit passengers to find routes and/or learn about the expected arrival time of their transit vehicles. Though these services are widely used, their impact on overall transit ridership remains unclear. The objective of this research is to assess the effect of real-time information provided via web-enabled and mobile devices on public transit ridership. An empirical evaluation is conducted for New York City, which is the setting of a natural experiment in which a real-time bus tracking system was gradually launched on a borough-by-borough basis beginning in 2011. Panel regression techniques are used to evaluate bus ridership over a three year period, while controlling for changes in transit service, fares, local socioeconomic conditions, weather, and other factors. A fixed effects model of average weekday unlinked bus trips per month reveals an increase of approximately 118 trips per route per weekday (median increase of 1.7% of weekday route-level ridership) attributable to providing real-time information. Further refinement of the fixed effects model suggests that this ridership increase may only be occurring on larger routes; specifically, the largest quartile of routes defined by revenue miles of service realized approximately 340 additional trips per route per weekday (median increase of 2.3% per route). Although the increase in weekday route-level ridership may appear modest, on aggregate these increases exert a substantial positive effect on farebox revenue. The implications of this research are critical to decision-makers at the country’s transit operators who face pressure to increase ridership under limited budgets, particularly as they seek to prioritize investments in infrastructure, service offerings, and new technologies.

This study explores the relationship between historical exposure to the built environment and current vehicle ownership patterns. The influence of past exposure to the built environment on current vehicle ownership decisions may be causal, but there are alternative explanations. Households may primarily select to live in neighborhoods that facilitate their vehicle ownership preferences, or they may retain preferences that they have developed in the past, irrespective of their current situations. This study seeks to control for these alternative explanations by including the built environment attributes of households’ past residences as an influence on vehicle ownership choices. We use a dataset from a credit reporting firm that contains up to nine previous residential ZIP codes for households currently living in the 13-county Atlanta, Georgia, metropolitan area. Results show that past location is significant, but of marginal influence relative to the attributes of the current location. From a practical perspective, our results suggest that models that include current but not past neighborhood attributes (also controlling for standard socioeconomic variables) can forecast vehicle ownership decisions reasonably well. However, models that include both current and past neighborhood attributes can provide a more nuanced understanding of the built environment’s potentially causal influences on vehicle ownership decisions. This better understanding may provide more realistic forecasts of responses to densification or other travel demand management strategies.

Many U.S. cities use vehicle emissions testing programs to improve air quality by identifying gross polluting vehicles and requiring their owners to make emissions-related repairs. All vehicles that meet certain criteria must pass an emissions test as part of the vehicle registration process. States use different criteria to determine which vehicles must be tested; however, the equity impacts associated with various screening criteria are unknown. This is due to difficulties researchers have faced in linking vehicle and household characteristics. We investigate the relative influence of vehicle and household characteristics on emissions failures in Atlanta, Georgia, by linking its emissions testing database to a targeted marketing database; the latter contains information about vehicle owners. We use count and hurdle models to predict vehicle emissions failures. Our model finds a relationship between sociodemographic characteristics and emissions failures after controlling for vehicle characteristics; that is, given two identical vehicles, the one owned by a low-income or minority household is more likely to fail emissions. We use our model to investigate the impacts of different emissions testing policies by income and ethnic groups. (C) 2013 Elsevier Ltd. All rights reserved.

The rapid growth of online social networking over the past decade has generated substantial amounts of data about individuals and their social relationships. Recent research studies investigating social relationships and travel behavior have sought connections between individuals' social networks and socially related travel; however, to the authors' knowledge, no study has pursued the use of online social networking data to do so. This study explores the use of online social network data in characterizing individuals' air travel behavior. Data were collected through a web-based survey including information about individuals' air travel history and online social network information, specifically participants' Facebook networks. The data were then analyzed to address a series of hypotheses about the association between online social network characteristics (specifically Facebook) and air travel behavior, in particular, travel distance, leisure-related travel, and trip generation. This study found a positive...

To quantify the effect of unnecessary motorist hesitation in free right-turn channels and its impact on engineering delay models, the authors observed peak-hour operations at two free right-turn channelized intersections in Utah County, Utah. Regression analysis demonstrated that perceived vehicle conflicts would significantly increase individual vehicle delay at free right-turn channels, and that existing traffic prediction models did not adequately describe this delay. On average, motorists yield about 2seconds for each conflicting vehicle. This finding raises questions about the validity of current delay assumptions for free rightturn channels, or indicates the driving public may need more adequate signage. © 2011 Published by Elsevier Ltd.

Free right-turn channels are sometimes used to alleviate vehicle delay at signalized intersections, but motorists often yield unnecessarily when using these channels. To better understand why motorists do not proceed when they have an available path, the authors conducted a national survey. Approximately one thousand survey participants viewed flashcards of intersection treatments and were asked to identify the proper driving behavior from a list of multiple choices. The answers to the questions were compared in Chi-squared cross tabulation analysis. The results showed that statistically significant proportions of people incorrectly indicate that right-turn behavior is similar at standard right-turn pockets or lanes, yield right-turn channels, and free right-turn channels. Men and women were also noted to have statistically different responses. The survey results further indicate that motorists may not distinguish between intersection treatments, prompting better signage and motorist education or the revision of right-turn delay models for different intersection types.