Browsing by Author "Johnson, Isak"

Now showing 1 - 3 of 3
  • Thumbnail Image
    Item
    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.
  • Thumbnail Image
    Item
    Towerside Innovation District: Building Equity and Economics for a Resilient Towerside
    (Resilient Communities Project (RCP), University of Minnesota, 2021) Christianson, Mark; Robb, Max; Stewart, Gustave; Zielinski, Jake; Beckner, Meyer; Paddock, Henry; Davis, Ruby; Siegel-Garcia, Diana; Shebesta, Timothy; Phan, Kevin; Lehman, Joseph; Turner, Anna; Boudlali, Jamila; Fransen, Elena; Lohse, Maxwell; Bergum, Maddy; Menke, Alex; Jacobs, Tia; Benson, Rachel; Franklin, Lila; Hesari, Elham; Bakken, Noelle; Bretheim, Laura; Flannery, Katlyn; Needham, Revee; Do, Don; King, Robbie; Krause, Laura; San Juan, Carmel; Harsch, Trey; Sheikh, Maya; Harrington, Ben; Berger, Jacob; Johnson, Isak; Paquin, Jarred; Trejo, Tristan
    This project was completed as part of a partnership between Towerside Innovation District and the University of Minnesota’s Resilient Communities Project (http://www.rcp.umn.edu). The goal of this project was to answer key questions around district assets and how Towerside can align with potential industry and partners’ goals to guide future strategic planning and attract further investment. Towerside Innovation District project lead Sabina Saksena collaborated with teams of students in Dr. Fernando Burga’s course PA 5211 to learn from other successful innovation districts, explore possible land use solutions and scenarios in Towerside, and consider climate change and racial justice implications. A final student report containing 10 posters is available.
  • Thumbnail Image
    Item
    The Value of Dedicated Right of Way (ROW) to Transit Ridership and Carbon Emissions
    (Center for Transportation Studies, University of Minnesota, 2023-12) Cao, Jason; Tao, Tao; Johnson, Isak; Huang, Hannah
    Transit agencies have adopted various types of right of way (ROW) for transit routes, including mixed traffic, semi-exclusive ROW, exclusive ROW, and grade separation, but few empirical studies have quantified their impacts on ridership and carbon emissions. Using data collected from transit agencies in the US, this research aimed to examine the impacts of dedicated ROW. We applied the gradient boosting decision tree method to estimate the nonlinear relationships between yearly route-level transit ridership and five types of independent variables, with a focus on ROW. The results showed that ROW contributes 18% of the power to predicting transit ridership, which is the largest among all the independent variables. Upgrading from mixed traffic to semi-exclusive ROW could boost ridership by 70,000, on average. A further upgrade to an exclusive ROW could add 3.68 million passengers. Moreover, the number of stops, transit route commence year, population density, signal priority, number of park-and-ride facilities, headway, network density, and route length all have non-trivial contributions to predicting ridership. Upgrading the operating environment could substantially reduce carbon emissions, up to 6.37 million pounds of CO2e. Overall, elevating ROW levels could notably enhance transit ridership and reduce carbon emissions, locating transit routes in the areas with adequate population density and network density could improve their performance, deploying signal priority and improving transit frequency also help, and increasing the share of electric buses could further decrease carbon emissions.