Trick or Treat(ment)? : Impact of Route-level Features on Walk and Bike Decisions

Trick or Treatment? Impact of Route-Level Features on Decisions to Walk or Bike Summary: Some travel routes attract people walking and cycling, while others may scare them away. What features of street environments are most important, and how do available routes affect decisions to bike or walk on a specific trip? Research to date has focused on either large-scale areal measures like miles of bike lane nearby or else has considered only shortest path routes. Neither method is suited to capturing the impact of targeted route-level policies like neighborhood greenways. This session will present a new technique for measuring bike and walk accessibility along the most likely route for a given trip. The method is applied to travel data, and results provide new insight into the relationship between route quality and travel mode choice.https://pdxscholar.library.pdx.edu/trec_seminar/1018/thumbnail.jp

Incorporate Emerging Travel Modes in the Regional Strategic Planning Model (RSPM) Tool

Performance-based planning helps local and state decision makers to understand the potential impacts of policy decisions, supporting cost-effective investments and policy choices that can help achieve policy goals. In addition, it can enable monitoring of progress and facilitate needed adjustments, help them communicate to the public, and assist them with meeting federal regulations and the intent of MAP21. The Regional Strategic Planning Model (RSPM) is a performance-based planning tool first developed by Oregon State DOT (as GreenSTEP) and later adapted for use by other states in the form of the Federal Highway Administration (FHWA) Emissions Reduction Policy Analysis Tool (EERPAT) and the underlying basis of the SHRP2 C16 Smart Growth Area Planning software (SmartGAP). As the popularity of the RSPM tool grows and application cases expand, there is recognition that a deeper understanding is needed to determine how mode choices and mode share may be impacted by policy and investment decisions and how these mode choices further influence performance outcomes of the transportation system. This is particularly important when the tool is applied in a broader base of planning and decision-making processes to truly understand what may be the best decisions for the entire multi-modal and inter-modal transportation system. ODOT is sponsoring a first phase research project led by this research team to incorporate broad stroke multi-modal travel choices in the RSPM tool. This proposed project hopes to leverage the ODOT and NITC funding to further study, along with existing modes, emerging travel modes, including car sharing, bike sharing, and autonomous vehicles, with stated preference (SP) experiments, and incorporate these new options into the RSPM tool. These modes have been rapidly gaining popularity worldwide, which will have long-term implications for car ownership decisions, fleet characteristics, travel patterns, and further system-wide performance outcomes. By incorporating these modes in the mode choice module, this project will make the RSPM tool sensitive to policies and investment targeted to shift mode share and enable it to evaluate futures in which these modes may become the mainstream, besides contributing to the emerging body of research that aims to better understanding these modes

A Bayesian Network Driven Approach to Model the Transcriptional Response to Nitric Oxide in Saccharomyces cerevisiae

The transcriptional response to exogenously supplied nitric oxide in Saccharomyces cerevisiae was modeled using an integrated framework of Bayesian network learning and experimental feedback. A Bayesian network learning algorithm was used to generate network models of transcriptional output, followed by model verification and revision through experimentation. Using this framework, we generated a network model of the yeast transcriptional response to nitric oxide and a panel of other environmental signals. We discovered two environmental triggers, the diauxic shift and glucose repression, that affected the observed transcriptional profile. The computational method predicted the transcriptional control of yeast flavohemoglobin YHB1 by glucose repression, which was subsequently experimentally verified. A freely available software application, ExpressionNet, was developed to derive Bayesian network models from a combination of gene expression profile clusters, genetic information and experimental conditions

Comparative transcriptomic analysis reveals similarities and dissimilarities in saccharomyces cerevisiae wine strains response to nitrogen availability

Nitrogen levels in grape-juices are of major importance in winemaking ensuring adequate yeast growth and fermentation performance. Here we used a comparative transcriptome analysis to uncover wine yeasts responses to nitrogen availability during fermentation. Gene expression was assessed in three genetically and phenotypically divergent commercial wine strains (CEG, VL1 and QA23), under low (67 mg/L) and high nitrogen (670 mg/L) regimes, at three time points during fermentation (12h, 24h and 96h). Two-way ANOVA analysis of each fermentation condition led to the identification of genes whose expression was dependent on strain, fermentation stage and on the interaction of both factors. The high fermenter yeast strain QA23 was more clearly distinct from the other two strains, by differential expression of genes involved in flocculation, mitochondrial functions, energy generation and protein folding and stabilization. For all strains, higher transcriptional variability due to fermentation stage was seen in the high nitrogen fermentations. A positive correlation between maximum fermentation rate and the expression of genes involved in stress response was observed. The finding of common genes correlated with both fermentation activity and nitrogen up-take underlies the role of nitrogen on yeast fermentative fitness. The comparative analysis of genes differentially expressed between both fermentation conditions at 12h, where the main difference was the level of nitrogen available, showed the highest variability amongst strains revealing strain-specific responses. Nevertheless, we were able to identify a small set of genes whose expression profiles can quantitatively assess the common response of the yeast strains to varying nitrogen conditions. The use of three contrasting yeast strains in gene expression analysis prompts the identification of more reliable, accurate and reproducible biomarkers that will facilitate the diagnosis of deficiency of this nutrient in the grape-musts and the development of strategies to optimize yeast performance in industrial fermentations

The Bacterial and Viral Complexity of Postinfectious Hydrocephalus in Uganda

Postinfectious hydrocephalus (PIH), often following neonatal sepsis, is the most common cause of pediatric hydrocephalus world-wide, yet the microbial pathogens remain uncharacterized. Characterization of the microbial agents causing PIH would lead to an emphasis shift from surgical palliation of cerebrospinal fluid (CSF) accumulation to prevention. We examined blood and CSF from 100 consecutive cases of PIH and control cases of non-postinfectious hydrocephalus (NPIH) in infants in Uganda. Genomic testing was undertaken for bacterial, fungal, and parasitic DNA, DNA and RNA sequencing for viral identification, and extensive bacterial culture recovery. We uncovered a major contribution to PIH from Paenibacillus , upon a background of frequent cytomegalovirus (CMV) infection. CMV was only found in CSF in PIH cases. A facultatively anaerobic isolate was recovered. Assembly of the genome revealed a strain of P. thiaminolyticus . In mice, this isolate designated strain Mbale , was lethal in contrast with the benign reference strain. These findings point to the value of an unbiased pan-microbial approach to characterize PIH in settings where the organisms remain unknown, and enables a pathway towards more optimal treatment and prevention of the proximate neonatal infections. One Sentence Summary We have discovered a novel strain of bacteria upon a frequent viral background underlying postinfectious hydrocephalus in Uganda

Paenibacillus infection with frequent viral coinfection contributes to postinfectious hydrocephalus in Ugandan infants

Postinfectious hydrocephalus (PIH), which often follows neonatal sepsis, is the most common cause of pediatric hydrocephalus worldwide, yet the microbial pathogens underlying this disease remain to be elucidated. Characterization of the microbial agents causing PIH would enable a shift from surgical palliation of cerebrospinal fluid (CSF) accumulation to prevention of the disease. Here, we examined blood and CSF samples collected from 100 consecutive infant cases of PIH and control cases comprising infants with non-postinfectious hydrocephalus in Uganda. Genomic sequencing of samples was undertaken to test for bacterial, fungal, and parasitic DNA; DNA and RNA sequencing was used to identify viruses; and bacterial culture recovery was used to identify potential causative organisms. We found that infection with the bacterium Paenibacillus, together with frequent cytomegalovirus (CMV) coinfection, was associated with PIH in our infant cohort. Assembly of the genome of a facultative anaerobic bacterial isolate recovered from cultures of CSF samples from PIH cases identified a strain of Paenibacillus thiaminolyticus. This strain, designated Mbale, was lethal when injected into mice in contrast to the benign reference Paenibacillus strain. These findings show that an unbiased pan-microbial approach enabled characterization of Paenibacillus in CSF samples from PIH cases, and point toward a pathway of more optimal treatment and prevention for PIH and other proximate neonatal infections

Bicycle Planning GIS Tool

Although currently only about one percent of US trips are done by bicycle, there is significant geographic variation. Differences across communities, along with much higher cycling rates observed in other places around the world, indicates large potential bicycling demand for daily travel in the US. In response, many communities are developing and implementing bicycle master plans that include a range of bikeway infrastructure aimed at making riding more appealing, including separated paths, protected (or separated) bike lanes, striped bike lanes, bicycle boulevards, sharrows, route signage, and intersection crossing aids. Given limited resources, planners and engineers need tools to estimate the effects of new infrastructure on behavior. How many more people will ride a bike if a city builds out their planned bicycle network? Which competing project options provide the most bang for the buck? Those are the questions communities are asking, but that our current tools do a poor job of answering. Recent research has sharply advanced our understanding of bicyclist—and potential bicyclist—preferences for different types of bikeways. This project translates that emerging research into a GIS planning tool that is relatively simple and quick to apply but also powerful enough to answer questions about how specific bicycle network changes might impact ridership. Inputs are data on bicycle networks, such as bikeway types, slope, and intersection features, along with local data on origins and destinations and/or widely available Census data. Outputs are quality of connections and predicted bicycle commute rates at Census Tract level under different planning scenario conditions. Scenarios can be compared by incorporating planned bikeway network, population, or land-use changes. The methods allow side-by-side analysis of both the overall impact of a project or plan and the geographic distribution of impacts on connectivity and bicycle commute rates. This project extends existing sketch tools by improving sensitivity to specific bicycle infrastructure changes and by explicitly linking network connectivity changes to bicycle use outcomes. The GIS tools developed seek to make available to a wider audience analysis methods formerly only available in complex regional travel demand models in a handful of regions

Travel Mode Choice Framework Incorporating Realistic Bike and Walk Routes

For a number of reasons--congestion, public health, greenhouse gas emissions, energy use, demographic shifts, and community livability to name a few--the importance of walking and bicycling as transportation options will only continue to increase. Currently, policy interest and infrastructure funding for nonmotorized modes far outstrip our ability to model bike and walk travel. To ensure scarce resources are used most effectively, accurate models sensitive to key policy variables are needed to support long-range planning and project evaluation, and to continue adding to our growing understanding of key factors driving walk and bike behavior. This research attempts to synthesize and advance the state of the art in trip-based, nonmotorized mode choice modeling. Over the past fifteen years, efforts to model the decision to walk or bike on a given trip have been hampered by the lack of a comprehensive behavioral framework and inconsistency in measurement scales and model specification. This project develops a mode choice behavioral framework that acknowledges the importance of attributes along the specific walk and bike routes that travelers are likely to consider, in addition to more traditional area-based measures of travel environments. The proposed framework is applied to a revealed preference, GPS-based travel dataset collected from 2010-2013 in Portland, Oregon. Measurement of nonmotorized trip distance, built environment, tour-level variables, and attitudinal attributes as well as mode availability are explicitly addressed. Route and mode choice models are specified using discrete choice techniques, and predicted walking and bicycling routes are tested as inputs to various mode choice models. Results suggest strong potential for predicted route measures to enhance walk and bicycle mode choice modeling. Findings also support the specific notion that bicycle and pedestrian infrastructure contribute not only to route choice but also to the choice of whether to bike or walk. For decisions to bicycle, availability of low-traffic routes may be particularly important to women. Model results further indicate that land use and built environments around trip ends and a person’s home still have important effects on nonmotorized travel when controlling for route quality. Both route and area travel environment impacts are mostly robust to the inclusion of residential self-selection variables, consistent with the idea that built environment differences matter even for households that choose to live in a walkable or bikeable neighborhood. The combination of area and route-based built environment measures alongside trip context, sociodemographic, and attitudinal attributes provides a new perspective on nonmotorized travel behavior relevant to both policy and practice

Incorporate Emerging Travel Modes in the Regional Strategic Planning Model (RSPM) Tool

Performance-based planning helps local and state decision makers to understand the potential impacts of policy decisions, supporting cost-effective investments and policy choices that can help achieve policy goals. In addition, it can enable monitoring of progress and facilitate needed adjustments, help them communicate to the public, and assist them with meeting federal regulations and the intent of MAP21. The Regional Strategic Planning Model (RSPM) is a performance-based planning tool first developed by Oregon State DOT (as GreenSTEP) and later adapted for use by other states in the form of the Federal Highway Administration (FHWA) Emissions Reduction Policy Analysis Tool (EERPAT) and the underlying basis of the SHRP2 C16 Smart Growth Area Planning software (SmartGAP). As the popularity of the RSPM tool grows and application cases expand, there is recognition that a deeper understanding is needed to determine how mode choices and mode share may be impacted by policy and investment decisions and how these mode choices further influence performance outcomes of the transportation system. This is particularly important when the tool is applied in a broader base of planning and decision-making processes to truly understand what may be the best decisions for the entire multi-modal and inter-modal transportation system. ODOT is sponsoring a first phase research project led by this research team to incorporate broad stroke multi-modal travel choices in the RSPM tool. This proposed project hopes to leverage the ODOT and NITC funding to further study, along with existing modes, emerging travel modes, including car sharing, bike sharing, and autonomous vehicles, with stated preference (SP) experiments, and incorporate these new options into the RSPM tool. These modes have been rapidly gaining popularity worldwide, which will have long-term implications for car ownership decisions, fleet characteristics, travel patterns, and further system-wide performance outcomes. By incorporating these modes in the mode choice module, this project will make the RSPM tool sensitive to policies and investment targeted to shift mode share and enable it to evaluate futures in which these modes may become the mainstream, besides contributing to the emerging body of research that aims to better understanding these modes

Breaking Barriers to Bike Share: Insights from Bike Share Users

This report seeks to provide more information about lower-income people and people of color who engage in bike share, including why they choose to become members, how they use the system, and how they benefit. The report looks at current and past bike share members, along with those who were involved in some type of equity-based outreach program. The findings draw from a survey intended to reach lower-income and/or people of color known to have engaged in bike share, either through membership or participating in events such as organized rides, in the same three cities studied in the resident report (NITC-RR-884b) – New York (Brooklyn), Chicago and Philadelphia. With some variation by city, the survey was distributed to people who lived in or adjacent to neighborhoods targeted by equity-focused outreach efforts and had joined bike share, as well as people system-wide who participated in equity-focused programs, including discounts and events. Respondents were divided into three groups for analysis. Two groups consisted of users targeted for the equity-focused outreach efforts – lower-income individuals and people of color. One of these groups included those who took advantage of equity-focused discounts or related programs (“BBSP target users”); the other group included those who did not partake in such focused discounts or programs (“non-BBSP target users”). The third analysis group consisted of higher-income, white users. Findings suggest that people in the BBSP target user group were less likely to have exposure to bike share through their existing networks (e.g., friends and family) or through their personal experiences (including using bike share in other places). They were more likely to have exposure to bike share through some of the intervention methods used in the outreach efforts, such as finding out about bike share at events or finding out about available discounts for them. BBSP target users, in self-reported reasons for why they joined, were most likely to state either the cost savings or discounted membership, while other users were more likely to state the convenience of using bike share. This indicates that the discount programs are likely reaching people who would not otherwise join bike share. Moreover, about two-thirds of BBSP target users stated that they were “very likely” to renew their membership (the same as for the other groups), and they rode as frequently as other users. This is another indication that the discount programs are effective. Target users were more likely to pay monthly for bike share. However, in cities that offer a monthly and annual payment option, it could mean that lower-income people and people of color will be paying a higher effective rate than higher-income, white members. In terms of bike share usage, all respondents were generally frequent users, with over half indicating that they make 11 or more bike share trips per month in good weather, with a third reporting making 20 or more trips per month. This suggests that once target users become members, perhaps with the help of a discount membership, they may use bike share as often as white, higher-income users. BBSP target users were more likely to ride just for fun or for exercise. Though not a large share of bike share trips, BBSP target users were also more likely to use bike share for school, daycare or religious-related trips, as well as for trips related to looking for work or job/skill training. Overall, exercise, time savings and convenience/flexibility were the most commonly stated benefits of bike share. BBSP target users reported saving the most, with a quarter of respondents in that group reporting saving $21 or more per week, and a majority saving more than$6 per week. Most of those receiving discounts were realizing savings that exceeded the discount amount, an encouraging sign for the value of the program and for retaining those members even if discounts end. The top barrier to using bike share more in the user survey was that of distances being too far to bike, at rates similar to the resident survey. For current users, regardless of user group, more stations, bikes and docks were viewed as the things that would make them most likely to use bike share more, and a lack thereof acting as a barrier to use. Better quality bike infrastructure / routes were noted as both things that would make them ride more and (a lack of them) as key barriers. A majority of the BBSP target users, more so than the other groups, indicated that having longer time limits on bike share rides would encourage them to use it more. This may be linked to both using bike share for exercise and concerns about having to pay for longer trips. The target users were also more likely to increase their use if the fees for longer trips were lower. These findings indicate that changes to pricing structures may encourage more use among lower-income people and people of color