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    <title>Research in Progress (RIP)</title>
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    <language>en-us</language>
    <copyright>Copyright © 2026. National Academy of Sciences. All rights reserved.</copyright>
    <docs>http://blogs.law.harvard.edu/tech/rss</docs>
    <managingEditor>tris-trb@nas.edu (Bill McLeod)</managingEditor>
    <webMaster>tris-trb@nas.edu (Bill McLeod)</webMaster>
    <image>
      <title>Research in Progress (RIP)</title>
      <url>https://rip.trb.org/Images/PageHeader-wTitle-RIP.jpg</url>
      <link>https://rip.trb.org/</link>
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    <item>
      <title>Evaluating the Public Engagement Processes Used in State Transportation Planning and Design</title>
      <link>https://rip.trb.org/View/2470851</link>
      <description><![CDATA[This project examines public engagement processes in state transportation planning and project design across Delaware, Maryland, Virginia, and West Virginia. Building on our Year 1 study of decision-making in the Delaware Department of Transportation (DelDOT), this Year 2 research expands the scope to assess engagement strategies used by state Departments of Transportation (DOTs) in the region. Using a multiple case study design, we will conduct a document review and informational interviews with key informants to analyze the consistency, variation, and effectiveness of public engagement practices. Key research questions include: (1) How effective are public engagement processes in state transportation planning and design? (2) What methods are used to engage different communities? and (3) How have engagement strategies evolved, particularly in response to the COVID-19 pandemic? Rather than critiquing state agencies, this study aims to document engagement processes constructively and provide insights for improving participation efforts. Findings will be shared with state DOT leadership and local government officials, contributing to both transportation planning research and practice. This study will also inform a potential Year 3 project that identifies engagement challenges and evaluates strategies to enhance public participation in transportation decision-making.]]></description>
      <pubDate>Fri, 06 Dec 2024 16:30:30 GMT</pubDate>
      <guid>https://rip.trb.org/View/2470851</guid>
    </item>
    <item>
      <title>Crime Prevention through Environmental Design (CPTED) for Public Transit Stations: Year 2</title>
      <link>https://rip.trb.org/View/2446989</link>
      <description><![CDATA[This project continues exploring Crime Prevention through Environmental Design (CPTED) principles in public transit stations to enhance community safety. Through site assessments in Wilmington, Philadelphia, and Baltimore, the study identifies and addresses gaps in CPTED implementation at vulnerable transit locations. The project aims to catalog existing CPTED practices, evaluate additional transit stations, and develop actionable recommendations to reduce crime risk and improve station accessibility, fostering safer, community-centered transit environments that encourage transit use.]]></description>
      <pubDate>Wed, 30 Oct 2024 14:33:26 GMT</pubDate>
      <guid>https://rip.trb.org/View/2446989</guid>
    </item>
    <item>
      <title>SafeSpeed: Enhancing Work Zone Safety through Speed Enforcement 
</title>
      <link>https://rip.trb.org/View/2440014</link>
      <description><![CDATA[The large number of work zone crashes has been a significant concern of transportation agencies and researchers. In the US, a work zone crash occurred every five minutes during 2015-2019. One approach for transportation agencies to reduce work zone crashes is to lower the speed within work zones, for example, posting speeding limits and installing speeding cameras. This approach is supported by studies that highlighted that average traffic speed is associated with crash risk. However, the findings of the relationship between traffic speed and crashes are inconsistent, which could lead to conflicting or even misleading interventions with the speed enforcement in work zones. Work zone presence could lead to the reduction of actual traffic speed that influences crash risk and, at the same time, directly impose effects on crash risks.  It is challenging to rigorously separate these direct and indirect impacts. Furthermore, the actual impact of speed enforcement countermeasures on work zone crash risk has been rarely studied among the literature, providing limited knowledge on whether these countermeasures are effective in reducing crash risk near work zones in practice.

In this research project, the research team will apply a comprehensive causal analysis and Web-Geographic Information Systems (GIS) approach to enhance work zone safety through speed enforcement in Pennsylvania and Maryland. It contains three core initiatives. First, it develops a causal inference model to analyze the impact of work zones on crash risk controlling for traffic speed with the equational g-estimation and regression discontinuity design (RDD), using multiple large-scale and high-granular data sets. Second, it examines the work zone impact on crash risk under different speed enforcement countermeasures. Lastly, the research team creates an interactive Web-GIS platform for comprehensive traffic safety analysis in work zones, enabling stakeholders to access and analyze crashes related to work zones, speed enforcement measures, and other important crash contributors, with continuous data updates planned until 2025. This platform aims to identify high-risk areas and provide insights for safety improvements in work zones.

First, the team will establish a rigorous causal inference model to infer the causal impact of work zones on crash risk when the traffic speed is controlled with high-granular and multi-source data sets. The team proposes to use an innovative approach, i.e., the combination of the sequential g-estimation and RDD, to examine the causal effect of the presence of work zones on crash occurrences when the traffic speed is controlled. The sequential g-estimation removes the effect of traffic speed on crash risk. RDD mitigates the potential confounding bias caused by roadway characteristics. The proposed method will be implemented using high-granular and multi-source data of thousands of work zones in Pennsylvania (PA) and Maryland (MD) between 2018 and 2023 to control for the complex built and natural environments and reduce the associated bias of the estimation. The results can provide insights for most desired and actual traffic speeds to reduce work zone crash risk.

Second, the team will examine the impact of work zones on crash risk under different speed enforcement countermeasures. The team will apply the same framework in the first step to examine the heterogenous causal impact of work zones on crash risk under different speed enforcement countermeasures, including no speed enforcement, posting speed limit, and posting speed limit along with enforcement (e.g., automated speed enforcement and high-visibility enforcement), and compare the impacts for the work zones in PA and MD. In addition, the team will further estimate these heterogenous impacts (by speed enforcement countermeasure) under various work zone characteristics, time of day, and traffic volumes. The results can offer information on how different speed enforcement countermeasures modify the causal impact of work zones on crash risk and, accordingly, provide implications for better deploying these countermeasures.

Third, the team will build an interactive Web-GIS platform for work zone traffic safety analysis using the safety data in PA and MD. The digital platform provides users with an online interactive interface to explore all work zones in PA and MD by multiple aspects, including speed enforcement countermeasures, average speed, traffic volumes, roadway characteristics. In addition, the platform can help users identify high-risk locations, highlight potential crash contributors, and offer suggestions on how to improve work zone safety for each work zone based on their characteristics and locations.  In addition, the team will continue to collect and archive up-to-date data from various data providers in both PA and MD from 2024 to 2025 and enhance the web platform. The safety data providers include Pennsylvania Department of Transportation (PennDOT), Maryland Department of Transportation (MDOT SHA), Waze, NOAA, and private data sources, including INRIX, TomTom, and Replica. The team will integrate and analyze large-scale crash data and develop an additional function to the platform to visualize and forecast crash types, frequencies, and severity for each road segment in the two states, especially those with work zones and different speed enforcement countermeasures. With that said, the platform allows transportation agencies and other related stakeholders, such as urban planning departments, local communities, consulting firms, and academic institutions, to access historical, real-time, and forecasted traffic safety metrics for all work zones. The team will continue to interview various data providers to enhance the quality and quantity of massive data in both states.
]]></description>
      <pubDate>Sat, 12 Oct 2024 12:18:07 GMT</pubDate>
      <guid>https://rip.trb.org/View/2440014</guid>
    </item>
    <item>
      <title>Determinants of Electric Vehicle and Public Charging Infrastructure Adoption in Baltimore, MD: Investigation and Policy Implications</title>
      <link>https://rip.trb.org/View/2359163</link>
      <description><![CDATA[In an effort to reduce the environmental impacts of the transportation sector and to reduce reliance on fossil fuels, policies supporting electric vehicle (EV) adoption are being implemented at all levels of government across the nation. Past studies have noted that the most important location for EV charging is at home, followed by work, and then public locations. However, in Baltimore City, where only 14% of Baltimore’s housing stock are single family detached homes and dedicated parking is limited, home-based charging may be an option. Thus, the objectives of this study are to understand factors such as residential location and socioeconomic characteristics that increase the likelihood of EV adoption in Baltimore City and quantify the utilization and users of public EV charging stations. To accomplish the objectives, a GIS EV user and EV public charging station accessibility and equity analysis will be conducted at the census tract level. Additionally, a survey will be distributed to EV users in Baltimore city to understand the factors that lead to the purchase of an EV. Video surveillance will be used to collect information on the utilization of public EV charging stations. By linking license plate data to residential location, we can imply the rate of home-based and non-home-based charging trips. Finally, the results of the study will be used to develop a prioritization metric for determining future public EV charging locations.

]]></description>
      <pubDate>Mon, 25 Mar 2024 19:37:19 GMT</pubDate>
      <guid>https://rip.trb.org/View/2359163</guid>
    </item>
    <item>
      <title>Promoting Commute Equity in Maryland: A Machine Learning-Based Model Development Proposal</title>
      <link>https://rip.trb.org/View/2343827</link>
      <description><![CDATA[Unequal mobility and accessibility have been a key constraint in accessing jobs, education and healthcare and other opportunities across the nation. This is aggravated by differences in income, transport infrastructure, transit and indeed all modes, vehicle availability, class of workers and other variables which individually or collectively contribute to the commute inequity. Evaluating these can be challenging because there are types of equity and impacts to consider including horizontal and vertical commute equities and various ways to measure them. Horizontal equity assumes that people with similar needs and abilities should be treated equally; vertical equity assumes that disadvantaged groups should receive a greater share of resources. Through the utilization of cutting-edge machine learning techniques and conducting a comprehensive analysis of the factors influencing commute equity, this project aims to empower Maryland policymakers with an effective decision-making framework.]]></description>
      <pubDate>Thu, 22 Feb 2024 16:07:26 GMT</pubDate>
      <guid>https://rip.trb.org/View/2343827</guid>
    </item>
    <item>
      <title>High Spatiotemporal Passenger-Centric Transit Performance Measures using Archived GTFS-Real Time Data</title>
      <link>https://rip.trb.org/View/2343788</link>
      <description><![CDATA[Unreliable public transportation is a barrier to reaching employment, education, and other community lifeline services. Most transit agencies provide a measure of on-time performance as the percentage of time that a bus arrives within a predefined on-time window to a stop. However, automatic vehicle location (AVL) and automatic passenger count (APC) technologies enable higher resolutions performance metrics to be developed. Using archived General Transit Feed Specification (GTFS)-real-time data, this project proposes high spatiotemporal resolution, passenger-centric on-time performance measures for MTA Maryland serving the Baltimore metropolitan area. Metrics include disaggregate on-time performance, reoccurring vs non-reoccurring delay, schedule and headway adherence, and the degree of schedule adherence. These metrics will be weighted by ridership and displayed on a publicly available web-based dashboard. The approach outlined in the project may be generalized to any transit agency that utilizes APC and AVL technology.]]></description>
      <pubDate>Thu, 22 Feb 2024 16:04:34 GMT</pubDate>
      <guid>https://rip.trb.org/View/2343788</guid>
    </item>
    <item>
      <title>An Agent Based Simulation Suite for Public Transit Planning and Design</title>
      <link>https://rip.trb.org/View/2343672</link>
      <description><![CDATA[Accurate ridership estimation is a pivotal component of developing a sustainable transit system, applicable to both proposed and existing transit networks. Different methods, including travel demand models, direct ridership models, and regression models, have been used by the practitioners and researchers to estimate ridership at station and network levels. However, travel demand models, the widely used approach for new transit lines, have inherent limitations of aggregate nature and complexity based on their types. Researchers also identified other drawbacks, such as their inability to capture small spatial resolutions and specific characteristics of stations, as often these models are designed for large- scale analysis. This leads to the scope of this study. In this study, authors presented a novel approach utilizing three microscopic agent-based models to develop a travel demand modeling suite, serving as a policy-sensitive forecasting tool reducing these limitations of traditional demand model. The modeling suite incorporates three agent-based models: SILO-MITO-MATSim. The model was validated against the previous year's data and applied for the future year. It was applied to estimate network level ridership for the proposed 'Purple Line’, a light rail transit line proposed by Maryland Department of Transportation (MDOT), Maryland Transit Administration (MTA), Maryland. This line will connect with Washington D.C. Metro which is USA's fourth largest transit system, with an average daily ridership of half a million. The findings indicate a projected ridership of approximately 18,320 passengers during the opening year of 2027. The proposed model offers a robust and policy-sensitive solution empowering decision-makers to make informed choices to support sustainable transportation system.]]></description>
      <pubDate>Thu, 22 Feb 2024 15:51:10 GMT</pubDate>
      <guid>https://rip.trb.org/View/2343672</guid>
    </item>
    <item>
      <title>A data-driven framework for traffic incident duration prediction</title>
      <link>https://rip.trb.org/View/2343620</link>
      <description><![CDATA[Traffic incidents pose significant challenges to the efficient flow of transportation systems, causing congestion, delays, and potential safety hazards. This research aims to utilize several data sources, including probe vehicle data, to predict traffic recovery time and analyze the impact of each traffic duration component on traffic recovery time in the State of Maryland. The results derived from the traffic recovery time prediction models can be a valuable tool for decision-makers in planning alternative routes, adjusting signal timings, or providing real-time traffic information to drivers.]]></description>
      <pubDate>Thu, 22 Feb 2024 15:46:43 GMT</pubDate>
      <guid>https://rip.trb.org/View/2343620</guid>
    </item>
    <item>
      <title>Crime Prevention through Environmental Design (CPTED) for Public Transit Stations</title>
      <link>https://rip.trb.org/View/2250687</link>
      <description><![CDATA[Crime Prevention through Environmental Design (CPTED) uses design principles to engineer safer spaces through management of both built and natural environmental features. CPTED principles aim to reduce chances and fear of criminal activity through design of spaces that both deter criminal activity and build community. Vacant lots, poor lighting, uncontrolled access, and lack of monitoring can be ameliorated to design spaces in which people feel – and are – safer. CPTED is multi-disciplinary in nature and has evolved from analysis of spaces, to include social relations and overall livability of areas. Public transportation can be an attractor of crime, and safety is cited as one barrier to public transportation. This project will examine CPTED practices in place in state DOTs and local transit agencies serving Wilmington, Delaware, Philadelphia, Pennsylvania, and Baltimore, Maryland. As part of the project, the research team will catalog CPTED practices already in use, even if outside of a comprehensive CPTED framework. The team will develop a CPTED checklist for rail and bus stations based on existing literature and analyze CPTED features in place at transit stops/stations in high and low crime areas of cities chosen in consultation with state and local stakeholders. The work will be accomplished through site visits, interviews with state DOT staff and local transportation agencies, and review of transportation station design standards. Based on findings, the team will develop a set of practices and priorities for integrating CPTED into transit station design to fill gaps identified through the study. 

]]></description>
      <pubDate>Thu, 21 Sep 2023 13:13:01 GMT</pubDate>
      <guid>https://rip.trb.org/View/2250687</guid>
    </item>
    <item>
      <title>Developing a Comprehensive System to Illustrate the Career Pathways with Maryland Department of Transportation State Highway Administration. (MDOT SHA)</title>
      <link>https://rip.trb.org/View/2118377</link>
      <description><![CDATA[Maryland Department of Transportation State  Highway Administration (MDOT SHA) is experiencing a continued workforce shortage in several critical offices in part due to the lack of clearly defined career paths. Without clearly defined career paths, where professional development can lead to advancements in career opportunities, employees are more likely to leave or may not be attracted to the agency. This project will assess the  inventory of the current Cornerstone Learning Management System application to determine the capabilities of "the system" identifying specific trainings required to move along a specific career path.]]></description>
      <pubDate>Tue, 14 Feb 2023 15:14:10 GMT</pubDate>
      <guid>https://rip.trb.org/View/2118377</guid>
    </item>
    <item>
      <title>MDOT MTA Track Intrusion Detection and Alert System</title>
      <link>https://rip.trb.org/View/2096556</link>
      <description><![CDATA[The project will allow MTA to expand its track warning and detection pilot program to five additional stations in the Baltimore Metro system. The technology will instantly alert train operators when someone is on the tracks.]]></description>
      <pubDate>Fri, 13 Jan 2023 14:49:07 GMT</pubDate>
      <guid>https://rip.trb.org/View/2096556</guid>
    </item>
    <item>
      <title>Mobile LiDAR: Modernizing Condition Assessments  An innovative approach to data acquisition</title>
      <link>https://rip.trb.org/View/2093173</link>
      <description><![CDATA[The Maryland Department of Transportation Maryland Transit Administration (MDOT MTA) will deploy and demonstrate Light Detection and Ranging (LiDAR) technology to detect, monitor, and track deficiencies and degradation of light rail assets along the Light Rail corridor. The project will provide a more accurate asset inventory of track, structural, and wayside asset conditions and enable the MD MTA to develop performance outcomes and maintenance prediction system for its light rail assets.]]></description>
      <pubDate>Tue, 03 Jan 2023 13:53:35 GMT</pubDate>
      <guid>https://rip.trb.org/View/2093173</guid>
    </item>
    <item>
      <title>Effectively Implementing Machine Learning within Office of Materials Technology</title>
      <link>https://rip.trb.org/View/2071877</link>
      <description><![CDATA[Every year the Maryland State Highway Administration (SHA) invests millions of dollars into testing the states geomaterials to optimize engineering designs. There is a significant opportunity for cost savings by leveraging historic material testing data with predicative machine learning models to provide estimated values as well as gaining a better understanding of historic data. Every year MDOT invests millions of dollars into testing geomaterials and thus massive amounts of engineering datasets as well as other data such as roadway and construction data have been accumulated over a long time period. There are many advantages of the machine learning-based approaches for field inspection/testing and ARAN imagery data modeling and prediction. Significant cost savings can be achieved by leveraging historical datasets and integrating with machine learning enabled work process. If implemented, not only the material and condition characteristics of highway, and other transportation infrastructures can be estimated in the early phase of the project, but also scheduling/construction and maintenance can be optimized by data driven decision-making assistance enabled by accurate prediction with cutting edge machine learning methods and continual self-supervised learning of future incoming data.]]></description>
      <pubDate>Thu, 01 Dec 2022 10:55:06 GMT</pubDate>
      <guid>https://rip.trb.org/View/2071877</guid>
    </item>
    <item>
      <title>Performance of Ultra-Thin Bonded Wearing Course During Winter Events</title>
      <link>https://rip.trb.org/View/2071993</link>
      <description><![CDATA[Ultra-Thin Bonded Wearing Course (UTBWC) pavement surfacing was introduced in some states back in mid 1990s under the brand name, “NovaChip”. It was developed as a preventative maintenance option to extend pavement life by placing a thin open/gap-graded hot mix asphalt (HMA) lift over a polymer-modified asphalt emulsion. The open/gap-graded aggregate also provides superior safety benefits under wet pavement conditions. Despite successful use in northern climates such as Vermont and Minnesota, anecdotal feedback indicates severe icing occurs on some UTBWC surfaces in Maryland during winter storms, and that current salting practices may be insufficient. Similar concerns have been identified by other states, and thus safety benefits under snow and ice conditions recently have been in question. However, other UTBWC locations seem to have no issues. Thus, research is needed to determine how prevalent the problem is in Maryland, to assess the performance difference of UTBWC surfaces compared to adjacent non-UTBWC surfaces, and whether any issue is dependent on precipitation type, rate of fall or volume, and how volume of salt or other treatment affects performance.]]></description>
      <pubDate>Thu, 01 Dec 2022 10:53:15 GMT</pubDate>
      <guid>https://rip.trb.org/View/2071993</guid>
    </item>
    <item>
      <title>Study of  Impacts of  Technology on the Future Workforce at the Maryland Department of Transportation State Highway Administration (MDOT SHA)</title>
      <link>https://rip.trb.org/View/2072000</link>
      <description><![CDATA[Technologies have made remote work a new reality. More administrative tasks can be performed virtually from anywhere, using a variety of electronic systems. The potential for remote work has grown greater in a more advanced economy and the COVID-19 pandemic has accelerated this trend. A recent Gallup survey (2021) estimates that 60 million U.S. full-time jobs, about half of the entire workforce, can be done remotely and predicts a 37% reduction of in-person days worked per week even when the pandemic wanes.
As an alternative solution to office shutdowns during the pandemic, remote work arrangements can present challenges to organizations whose mission is customer-driven and requires frequent interactions and engagements and where job designs, policies, performance standards and organizational culture may not have been fully updated to accommodate, monitor, evaluate and reward remote work. Facing a dilemma between shifting back to normal and continuing remote work, organizations need to make long-term decisions about how to achieve an optimal remote work strategy in the aftermath of the COVID-19 pandemic. The primary focus of this study is to assess state highway administration's (SHA’s) needs for administrative assistants and business analysts in today’s workforce. The research team aims to provide recommendations on how administrative assistants and business analyst jobs may be redesigned to better meet SHA’s needs in function areas where there is a strong need for administrative assistants and business analysts. Meanwhile, to support the redesign effort the team provides a comprehensive strategy on a mix of work modes (remote/office/hybrid) based on analysis of SHA’s work requirements and worker attributes.]]></description>
      <pubDate>Thu, 01 Dec 2022 10:52:08 GMT</pubDate>
      <guid>https://rip.trb.org/View/2072000</guid>
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