About RIDE

Improving upon inequities and disparities in transportation that disproportionately harm underserved and underrepresented groups is of paramount importance. In addition to these disparities, it has been well documented that mobility leads to equity - ensuring a reliable, affordable, and consistent form of transportation leads to greater employment, higher education, quality health care and food, social and civic engagement, and participation in everyday activities.

The Research for Inclusivity and Driving Equity (RIDE) Research Experience for Undergraduates (REU) Site will provide undergraduate students an immersive and interdisciplinary experience in community engaged research focused on improving the transportation experience for underserved and underrepresented communities. REU students will participate in research in civil engineering, health policy, industrial engineering, information management, legal studies, psychology, and regional planning.

The RIDE REU will run from June 12 - August 11, 2023.

Faculty and PhD Student Mentors

Program Details

In addition to learning and growing as a researcher, you will also:
  • Receive a stipend of $550/week for 9 weeks
  • Have UMass housing
  • Receive travel to and from UMass
  • Go on field trips
  • Attend professional development seminars

  • Eligibility Details

    You must be:
  • A US Citizen, national, or permanent resident
  • An incoming freshman through junior enrolled at a college, university, or community college for Fall 2023 semester

  • There are no minimum GPA requirements and prior research experience is not required. As long as you have a passion and eagerness for doing research, please apply!

    The priority application deadline is February 15, 2023.

    Summer 2023 Projects

    • 1: Adolescent driving and risky decision making/behavior

    • Adolescence is a period of rapid social, emotional, behavioral, cognitive, and neurobiological change and development that includes shifts in riskier decision-making and behavior that are connected to potentially dangerous driving (e.g., speeding, driving under the influence). There remains a need for improving understanding of the risky decision making and behavioral processes underlying dangerous behaviors, in diverse populations, and at different points of development across adolescence. A core set of research questions arise: Do developmental changes in risky decision making and behavior operate in the same way or in distinct ways for females and males, for youth across the socioeconomic spectrum, and for youth who hold distinct racial and ethnic identities? For this project, students will analyze two existing longitudinal datasets (funded by NIH DA036017; NIH HD054805) that include large, diverse US and international samples of youth from 12-19 years of age; cognitive, behavioral and/or neurobiological measures of risky decision making; and direct measures of driving risks (e.g., a driving simulation “stoplight” task [see figure] and survey items), along with other closely related health risk behaviors (e.g., substance use). Students will learn to apply a variety of data analysis techniques to answer research questions or test competing hypotheses, to draw conclusions with translational significance and broader impacts for diverse populations of youth and communities. Students should have some prior exposure to and understanding of basic probability and statistics (e.g., mean, standard deviation, correlation), and interest in and eagerness to learn new statistical techniques. No prior statistical software experience is required (students will learn this as part of the experience).
    • Number of REU students: 2
    • Mentors: Prof. Kirby Deater-Deckard, Christina Bertrand, MS, and Ann Folker, MS
    • Community Partner

      Public Health Institute of Western Massachusetts
    • 2: Developing a Campus Accessibility Map

    • According to the 2010 census in the United States, more than 3.6 million people with disabilities use a wheelchair for mobility. Sidewalks are indispensable, as they connect different destinations and provide wheelchair users essential physical access to their communities. Although many sidewalks have been put in place to enable accessibility for wheelchair users and improve their mobility, many physical barriers have simultaneously been unintentionally created because of inadequate construction and the deteriorating condition of sidewalks.

      The research lab of Dr. Chengbo Ai has focused on the integration of data acquisition hardware (e.g., low-cost mobile LiDAR) and the development of processing algorithms for automatically mapping sidewalk locations, evaluating surface conditions, and assessing ADA compliances. In this proposed REU project, Dr. Ai plans to leverage the strength of his lab and work with the REU students to develop a navigation-enabled sidewalk inventory for colleges and universities and to create an equitable and barrier-free environment. For Phase 1 of this project (Summer 2023), the research team plans to work with the campus planning office and the disabled community at UMass to enhance the existing campus accessibility map by digitizing detailed ADA-compliant features and facilities across the campus. For the future phases of this project, the research team plans to expand such an effort on campuses across the country.

    • Number of REU Students: 1
    • Mentors: Prof. Chengbo Ai and Bryan Remache-Patino

    • Community Partner

    • 3: Quantifying the Impact of Bridge and Road Condition on Drivers and Residents in Vulnerable Communities

    • Critical transportation infrastructure such as bridges and roads are in various states of deterioration due to aging. The risk of infrastructure failure in deteriorated infrastructure is high. As a result, the nation has recently passed legislation to fund the rehabilitation of road and bridge infrastructure. However, these funds must be equitably distributed throughout communities with varying needs. The REU student will examine Massachusetts Environmental Justice Communities and exposure to deteriorated infrastructure. This will be done through a geospatial analysis using GIS.

      Skillset: Some knowledge with GIS would be a plus but it is not required. Basic excel skills would be appreciated.

    • Number of REU Students: 1
    • Mentors: Prof. Jessica Boakye and Joshua Govina

    • Community Partner

    • 4: Addressing transit accessibility inequities in the Pioneer Valley

    • Inequitable access to jobs, health care services, education and food have been shown to be significant contributors to health disparities. Data from a variety of sources can be used to identify gaps in accessibility, but there remains a need to systematically identify these gaps and the actions that can be taken by public officials to address them. This REU project will focus on identifying accessibility gaps within the Pioneer Valley located in Western Massachusetts. This region is diverse in that it includes minority and non-minority populations, as well as both urban and rural areas, and is served by the Pioneer Valley Transit Authority (PVTA), which is our community partner. The REU student will assist with various aspects of the project that might differ from one year to the next as the research progresses, namely: 1) mapping of available datasets to allow for identification of accessibility gaps, 2) mapping and understanding of PVTA’s transit network, and potentially 3) engaging with focus groups in communities in Pioneer Valley to document barriers to the use of transit.

      Skillset: Some knowledge with GIS would be a plus but it is not required. Basic excel skills would be appreciated.
    • Number of REU Students: 1
    • Mentors: Prof. Eleni Christofa and Efthymia (Fay) Kostopoulou
    • Community Partner

      Pioneer Valley Transit Authority
    • 5: Microtransit in Berkshire County: Transit for Rural Communities

    • This project will involve collecting and analyzing data to assess the demand and utilization of a new microtransit service that is being launched in Berkshire County, Massachusetts, in early 2023. Microtransit is a flexible bus service that uses technology to route and schedule vehicles on demand. Although fixed-route transit services can provide efficient, high-capacity mobility service for corridors and communities with dense demand, over 70% of Americans live outside urban centers. It is particularly challenging to provide transit service in small towns and rural communities, yet rural households typically face significantly higher transportation costs than their urban counterparts. The goal of this project will be to support the Berkshire Regional Planning Commission by collecting and analyzing data from the new microtransit service in towns of Great Barrington, Egremont, and Stockbridge. The REU student will assist a graduate student and faculty member in collecting data from multiple sources, including surveying passengers. The REU student will work on analyzing ridership, operations, and costs to compare the pilot microtransit program with projections and existing models that suggest that microtransit services are more resilient and efficient for serving demand in low density communities.
    • Number of REU Students: 1
    • Mentors: Prof. Eric Gonzales and Tate Coleman

    • Community Partner

      Berkshire Regional Planning Commission
    • 6: Bicycle Simulation Development

    • The Human Performance Lab is developing a bicycle simulator platform based on the Unity 3D programming environment. This platform will be used to evaluate bicyclists' safety performance when encountering novel bicycle infrastructure. This specific project will contribute to this platform by developing virtual environments related to bicycle infrastructure. This project will thus include (1) the identification of bicycling infrastructure via literature reviews, and (2) the design and development of various physical infrastructure types in a virtual reality simulation. Depending on time, the students can also contribute to human subject data collection using this platform.

      Expected Skill Sets: (A) Familiarity with or willingness to learn 3D authoring environments (Unity, Unreal, etc.) and 3D software (Blender, Sketchup, 3DStudioMax, etc). Students must have good programming skills (specific programming language unimportant). (B) Communication skills and the ability to work in a team environment.

    • Number of REU Students: 1
    • Mentors: Prof. Anuj Pradhan and Prof. Eleni Christofa

    • Community Partner


    • 7: Black Maternal Health and Transportation in Western Massachusetts
    • Black women are three to four times more likely to die while pregnant or within 1 year postpartum than their White and Latina counterparts. There are a myriad of reasons why this is the case, but appropriate, timely, and accessible prenatal care is widely cited as a means to reduce maternal mortality. Though we know that mobility leads to equity, there are specific issues related to Black pregnant women in Western Massachusetts that need to be identified before their transportation access can be improved. The goal of this project is to identify those issues using a mixed method approach, that includes interviews, surveys, secondary data analysis, and “ride-alongs”. Working with the faculty and PhD student mentor, the undergraduate student for this project will focus on the “ride-alongs”: traveling with pregnant women to (and from) their prenatal appointments to better understand the lived experience of Black women’s transportation options. The undergraduate student will record the experience and analyze the data in the hopes of not only identifying transportation barriers, but also identifying innovative transportation solutions.
    • Number of REU Students: 1
    • Mentor: Prof. Shannon Roberts and colleagues from Nursing and Nutrition
    • Community Partner

    • 8: Human Factors in Transportation
    • Description is coming soon!
    • Number of REU Students: 1
    • Mentor: Prof. Shannon Roberts
    • Community Partner


    Contact Us

    Amherst, MA

    Phone: 413-545-2165

    Email: scroberts@umass.edu

    The priority application deadline is February 15, 2023.

    This project is supported by the National Science Foundation under grant number 2150204. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors/PI and do not necessarily reflect the views of the National Science Foundation.

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