Browsing by Author "Murphy, Daniel"

Now showing 1 - 3 of 3
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    Enhancing Managed Lanes Equity Analysis
    (Minnesota Department of Transportation, 2023-04) Douma, Frank; Fonseca-Sarmiento, Camila; Lari, Adeel; Murphy, Daniel; Morris, Paul; Zhao, Jerry
    Planning and environmental studies involving managed lanes still have difficulty determining how to effectively evaluate project alternatives from an equity perspective. To most people, "equity" is ubiquitous with income, but this is a narrow focus that limits the scope of what can be considered equity, and indeed this can be true when it comes to managed lanes. As the Minnesota Department of Transportation analyzes the expansion of E-ZPass corridors, it is imperative it evaluates project alternatives from an equity perspective. The results of this study suggest that E-ZPass lane users are more racially diverse than users in the travelsheds. In two out of the four E-ZPass lane corridors, a higher proportion of E-ZPass lane users have household incomes below $100,000 compared to the travelsheds. Overall, there is a lower percentage of people with disabilities among E-ZPass lane users than those in the travelsheds. These results are driven by the makeup of E-ZPass lane users. In addition, this research project demonstrates the feasibility of incorporating quantitative and qualitative equity measures into the alternatives analysis process. The demonstration shows that the quantitative measures are all feasible with existing tools, provide meaningful information to the alternatives analysis process, and can be put into practice immediately.
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    Hennepin County Pedestrian Crash Study
    (2023) Ackerman, Ryan; Johnson, Isak; Murphy, Daniel; Trejo, Tristan
    Our study analyzed historical pedestrian crashes throughout Hennepin County and ranked crash locations based on crash occurrence over a ten-year period (2012-2021). For analysis purposes, crashes were split into two categories: intersections and midblocks. Crashes primarily occurred in urban areas, and collisions resulting in fatal injuries were rare. We created a tiered ranking system to group together locations with similar levels of crash occurrence to guide potential county improvement projects. Using ArcGIS Pro, we developed crash point maps to spatially represent crash locations and severity in each Hennepin County Commissioner District. We then created Safety Performance Functions (SPFs) by conducting a statistical analysis of crash data using a Negative Binomial Regression model. The variables we chose for statistical analysis were identified in previous studies as statistically significant variables that influenced pedestrian crashes. We used our SPFs to predict future crash locations and crash severity at intersections and midblocks over the next ten years. Our SPFs predicted fewer crashes at intersections and midblocks over the next ten years than the actual number of crashes over the tenyear study period. This can be partially attributed to our model, which was relatively weak, but can also be attributed to a lack of data. In particular, pedestrian count data would likely have increased the accuracy of our model, but this is not easily accessible. Our study opens the door to future research by transportation planning professionals who can make proactive, informed decisions about reducing pedestrian crash risk throughout Hennepin County based on our research.
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    Predicting Risk for Acute Kidney Injury in the Outpatient Setting: a Continuous Risk Prediction Equation and Two Binary Tests for Identifying High-risk Patients
    (2021-02) Murphy, Daniel
    Background: Risk-factors for acute kidney injury (AKI) in the hospital have been well studied. Yet, risk-factors for AKI occurring and managed in the outpatient setting are unknown and may differ. Methods: A development cohort for modelling risk of AKI without concurrent or subsequent hospitalization was defined by repeated primary care encounters in a single urban healthcare system using electronic health record data. An external validation cohort was similarly defined in the Veterans Health Administration nationally. Logistic regression with bootstrap sampling for backward stepwise covariate elimination was used to develop a model for outpatient AKI over an 18-month outcome period. The model was then transformed into two binary tests: one identifying high-risk subjects for potential research and another identifying patients for additional clinical monitoring or intervention. Results: Outpatient AKI was seen in 4,611 (3.0%) and 115,744 (2.4%) patients in the development and validation cohorts, respectively. The model produced C-statistics of 0.717 (95% confidence interval (CI): 0.710-0.725) and 0.722 (95% CI: 0.720-0.723) in the development and validation cohorts, respectively. The research-test, identifying the top 5.2% most at-risk patients in the validation cohort, had sensitivity of 0.210 (95% CI: 0.208-0.213) and specificity of 0.952 (95% CI: 0.951-0.952). The clinical-test, identifying the top 20% most at-risk, had sensitivity of 0.494 (95% CI: 0.491-0.497) and specificity of 0.806 (95% CI: 0.806-0.807). Conclusions: The outpatient AKI-risk prediction model performed well in both the development and validation cohorts and was transformed into two binary tests, one for potential use in research and another in clinical care. Multiple novel risk-factors were identified.