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

Development of a Multi-Class Bicyclist Route Choice Model Using Revealed Preference Data

Existing regional travel forecasting systems are not typically set up to forecast usage of bicycle infrastructure and are insensitive to bicyclists\u27 route preferences in general. We collected revealed preference, GPS data on 162 bicyclists over the course of several days and coded the resulting trips to a highly detailed bicycle network model. We then use these data to estimate bicyclist route choice models. As part of this research, we developed a sophisticated choice set generation algorithm based on multiple permutations of labeled path attributes, which seems to out-perform comparable implementations of other route choice set generation algorithms. The model was formulated as a Path-Size Logit model to account for overlapping route alternatives. The estimation results show compelling intuitive elasticities for route choice attributes, including the effects of distance and delay; avoiding high-volumes of vehicular traffic, stops and turns, and elevation gain; and preferences for certain bike infrastructure types, particularly at bridge crossings and off-street paths. Estimation results also support segmentation by commute versus non-commute trip types, but are less clear when it comes to gender. The final model will be implemented as part of the regional travel forecasting system for Portland, Oregon, U.S.A

Rerouting Mode Choice Models: ​H​ow Including Realistic Route Options Can Help Us Understand Decisions to Walk or Bike

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 successfully model bike and walk travel. In the past five years, we have learned a lot about where people prefer to bike and walk, but what can that tell us about whether people will bike or walk in the first place? The research presented here is designed to start bridging the gap between choice of route and choice of travel mode (walk, bike, transit, drive, etc.). A mode choice framework is presented that acknowledges the importance of attributes along specific walk and bike routes that travelers are likely to consider for a given trip. Adding route quality as a factor in mode choice decisions is new, and shows promise for: (1) improving prediction of pedestrian and cycling trips, (2) increasing sensitivity of models to finer-grained policy scenarios--testing the impact of a single proposed facility or design change on bike and walk mode shares, and (3) identifying separately the effects of pedestrian and cycling facilities on decisions to walk or ride from which route to take. The proposed framework is applied to revealed preference, GPS travel data collected from 2010-2013 in Portland, Oregon. Key results include: (1) specific walk and bike facilities are significant factors for mode as well as route choice, (2) lower traffic-stress routes may be more important for women than men when choosing whether to bike, (3) available routes on a specific trip may have independent impacts from more traditional measures of land-use and built environment such as density, and (4) the importance of walking and biking environments appears to remain even when controlling for neighborhood self-selection.https://pdxscholar.library.pdx.edu/trec_seminar/1036/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

Factors Influencing Bike Share Among Underserved Populations: Evidence from Three US Cities

There is evidence that lower-income and people of color (POC) in the U.S. do not use bike share as much as higher-income and white people. Using data from residents living near stations in New York, Chicago, and Philadelphia, our analysis examines reasons for these disparities. While smaller shares of POC are members (vs higher-income white people), large shares of POC are interested in bike share. Among POC, having positive attitudes about bicycling and having family and friends that use bike share are strong predictors of interest in bike share. POC are also motivated to use bike share for recreational reasons. Receiving information from interactive sources may be effective at increasing bike share use and interest, though it is not clear whether these efforts have affected POC. Cost is a barrier for people who have tried bike share and are interested in using it in the future but are not members

Data From: Active Transportation Counts from Existing On-Street Signal and Detection Infrastructure

This study’s objective was to use data from existing traffic signal infrastructure to estimate pedestrian volumes. Pedestrian push-button actuations were collected from signal controller logs at 49 intersections in western Oregon and an additional 16 intersections in eastern Oregon. These actuations were then compared to observed pedestrian counts, totaling over 34,000 people, obtained from video recordings. After exploring various options, a simple quadratic relationship was modeled using a single measure of pedestrian signal activity: the number of push-button presses (filtered to remove multiple presses within 15 seconds). The model’s predictions showed a correlation of 0.86 with observed pedestrian volumes and had an average error of ±2.4 pedestrians per hour. These results suggest that existing traffic signal infrastructure data can be used to estimate pedestrian volumes in Oregon with reasonable accuracy. Using such pedestrian volume estimates can lead to improvements in pedestrian traffic monitoring, safety assessments of exposure, and equity and health analyses

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