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    <title>Research in Progress (RIP)</title>
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    <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>
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      <title>Research in Progress (RIP)</title>
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      <link>https://rip.trb.org/</link>
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    <item>
      <title>Estimating Daytime Population for Data-Driven Urban Planning</title>
      <link>https://rip.trb.org/View/2458991</link>
      <description><![CDATA[The COVID-19 pandemic and the subsequent increase in remote work have changed how people move around cities and where people spend their time during the day, shifting the daytime population to different streets and neighborhoods. Accurately estimating this daytime population is essential for data-informed urban planning, as it impacts infrastructure, transportation service needs, and land use decisions. Partnering with the New York City Department of City Planning, (NYC DCP), this project aims to leverage Artificial Intelligence (AI) and computer vision to analyze video data from over 900 traffic cameras across NYC to estimate daytime population. The primary objectives include classifying cameras by street hierarchy, extracting vehicle and vulnerable road user (e.g., pedestrian) information (e.g., density) on both street level and community level, and using spatial analysis to explore the collective impact of various factors on traffic congestion and urban dynamics. This will provide timely insights for improving urban infrastructure, land use planning, and decision-making, enhancing accessibility, and reducing traffic congestion.]]></description>
      <pubDate>Thu, 21 Nov 2024 17:20:10 GMT</pubDate>
      <guid>https://rip.trb.org/View/2458991</guid>
    </item>
    <item>
      <title>A Data-Driven Approach to Transportation Equity: Acquiring a dataset of heterogeneous pedestrian crossing profiles</title>
      <link>https://rip.trb.org/View/2458994</link>
      <description><![CDATA[The project’s goal is to collect a dataset of heterogeneous pedestrian crossing profiles in New York City. The research team intends to do so by deploying high-resolution, high-frame-rate audiovisual sensors at intersections in the city. Unlike past attempts, the data collection process will focus on optimizing the resulting dataset to represent a diverse population of pedestrians at different ability levels. Furthermore, the research team will collect data representing different environmental conditions (sunny and rainy days) and various traffic infrastructures (presence or lack of curb ramps, presence or lack of clearly-painted crosswalks, and timing of traffic lights, among other factors described in this project). By exploring these differences, the research team hopes to accomplish a more accurate profiling of users (motorized and not) of all mobility levels at these intersections and allow comparison of crossing behavior across underrepresented groups, such as people with limited walking ability. This profiling will then serve for data-driven insights to improve safety, flow, and accessibility in these urban environments.]]></description>
      <pubDate>Thu, 21 Nov 2024 17:16:08 GMT</pubDate>
      <guid>https://rip.trb.org/View/2458994</guid>
    </item>
    <item>
      <title>Red Light Camera Expansion in New York City: Spillover Effect, Behavioral Insights and Strategic Allocation</title>
      <link>https://rip.trb.org/View/2459059</link>
      <description><![CDATA[Red Light Cameras (RLCs) are being deployed across various cities in the U.S. as tools to combat red-light running behavior at intersections on arterial roads which can lead to severe crashes and fatalities. Launched in 1994, New York City (NYC)'s Red Light Camera Program has played a vital role in enhancing traffic safety over the past three decades. The current deployment consists of 150 locations (1% of city intersections), each issuing a $50 fine for red light violations. According to the NYC Department of Transportation (NYC DOT), red light running has been reduced by 73% at locations with cameras, T-bone collisions have dropped by 65%, and rear-end collisions have fallen by 49%. In June 2024, New York State passed a renewal and expansion of the Red Light Camera Program to cover 5% of intersections (~600 locations). This provides an opportunity to evaluate the expansion’s impact and determine if it could further improve traffic safety and compliance, potentially leading to other benefits such as reduced congestion due to fewer incidents from red light running. This research project, in partnership with NYC DOT, aims to provide timely analytical support for the expansion of NYC's Red Light Camera Program. Through spatiotemporal analysis of historical RLC data combined with transportation and demographic data, the project will provide insights on the longitudinal effectiveness of the current program. Moreover, predictive analysis based on machine learning and spatial models will be developed to estimate the RLC network spillover effect and recommend strategic allocation of cameras to achieve the desired impact. This will help NYC DOT make data-driven decisions to maximize the program's benefits throughout the expansion. The expanded RLC network is expected to reduce intersection incidents, improve traffic flow, and decrease congestion, contributing to overall mobility and safety. The findings will also provide valuable insights for other states and communities with red light safety camera programs.]]></description>
      <pubDate>Thu, 21 Nov 2024 16:54:25 GMT</pubDate>
      <guid>https://rip.trb.org/View/2459059</guid>
    </item>
    <item>
      <title>The Effects of Changing Commutes on Home Delivery Activity</title>
      <link>https://rip.trb.org/View/2440043</link>
      <description><![CDATA[Since the COVID-19 pandemic, New York, like most US and global cities, has seen rapid evolution of (1) work location and time flexibility and (2) adoption of online shopping alternatives for diverse commodities by varying shopper populations.  It is expected that changes in work location – particularly the increased opportunity for some individuals to work from home at least a few days per week – could have profound impacts on the choice of location for shopping activities and on the likelihood of receiving home deliveries. 

Relying on the New York City Department of Transportation’s forthcoming 2022 Citywide Mobility Survey (CMS) and publicly-available land-use and employment data, this project will explicitly investigate the relationship between work-related travel activity (or lack thereof) and propensity for home delivery.  This study will distinguish individuals based on demographic characteristics, home and work built environments (e.g. land uses and building types) and commute characteristics (e.g. frequencies, modes, times of day), and will evaluate shopping frequencies for several specific categories of goods - including groceries, prepared food, and parcels. Results are expected to provide insights on the expected impacts of changing work on local delivery activity, to inform the design of future urban freight infrastructure and city logistics strategies in work- and residence-oriented communities, and to provide insights for potential implications for local travel and retail activity.
]]></description>
      <pubDate>Sun, 13 Oct 2024 16:13:49 GMT</pubDate>
      <guid>https://rip.trb.org/View/2440043</guid>
    </item>
    <item>
      <title>Real-time Information Dissemination for Efficiency in a Robo-taxi System (RIDERS)</title>
      <link>https://rip.trb.org/View/2321642</link>
      <description><![CDATA[The Real-time Information Dissemination for Efficiency in a Robo-taxi System (RIDERS)
project addresses the emerging challenges in shared-use mobility, particularly the congestion and efficiency issues posed by ride-hail services like Uber and Lyft, and the rise of autonomous ride-hailing or robo-taxis. It explores how a robo-taxi fleet, equipped with connected and automated vehicles (CAVs), can mitigate traffic congestion and improve safety by collecting and sharing real-time traffic information in urban networks. The project's goal is twofold: first, to enable continuous, widespread traffic data collection and system monitoring without hindering passenger service; and second, to enhance overall transportation system efficiency and safety through informed operational and routing decisions. This interdisciplinary effort involves collaboration between transportation engineering and computer science, with a focus on developing smart infrastructure and connected systems. Using simulation-based experiments and real-world data from New York City, the project aims to devise effective strategies for vehicle repositioning and routing that account for traffic conditions and service quality. The outcomes will contribute to smarter, safer urban transportation, with findings shared through academic publications, presentations, and a dedicated project webpage.]]></description>
      <pubDate>Fri, 12 Jan 2024 10:57:23 GMT</pubDate>
      <guid>https://rip.trb.org/View/2321642</guid>
    </item>
    <item>
      <title>An AI-reinforced Traffic Digital Twin for Testing Emergency Vehicle Interventions</title>
      <link>https://rip.trb.org/View/2278553</link>
      <description><![CDATA[Emergency vehicle (EMV) response times have degraded due to increasing urbanization and resulting congestion. Evaluating interventions to mitigate this degradation is too costly to be done in the field. This project will build a traffic digital twin (TDT) to be developed in collaboration with FDNY as a virtual test bed to evaluate interventions and support decision-making and planning in a safe simulation environment. The TDT will be built on the open source Simulation of Urban Mobility (SUMO) microscopic continuous traffic simulation. Key challenges are incorporating AI to learn non-EMV driver responses to EMV signals (sirens, V2X technologies) and to train the TDT to different traffic states using historical traffic data and dispatch data from FDNY. The scope of work can be summarized as: (1) development and calibration of a baseline SUMO simulation for FDNY district M6 in Harlem, NYC; (2) combining traffic data and camera data at the same time to develop an AI model for traffic state prediction in the digital twin; (3) combining EMV global positioning system (GPS) data and the traffic state data to statistically learn non-EMV behavioral responses (response reaction time, etc.); and (4) developing simulation-based intervention optimization and test using out-of-sample observations
]]></description>
      <pubDate>Sat, 28 Oct 2023 19:49:04 GMT</pubDate>
      <guid>https://rip.trb.org/View/2278553</guid>
    </item>
    <item>
      <title>Exploring Cost-effective Computer Vision Solutions for Smart Transportation Systems</title>
      <link>https://rip.trb.org/View/1953288</link>
      <description><![CDATA[The project is focused on developing a deep learning based data acquisition and analytics tool using vision - based sensors (i.e., cameras) to understand cities with machine eyes . The team will assess the maturity of various smart city applications using computer vision and object detection (e.g., pedestrian detection, work zone identification , curb lane usage, connected and automated vehicles [CAVs] ) as well as the needs of the local agencies. The goal is to demonstrate the c ost - effectiveness of the computer vision technology to generate new stream of mobility data and provide support for planning and operational strategies , utilizing both existing transportation infrastructure and emerging probe and CAVs . More specifically, t his project aims to establish an inventory of available traffic camera systems in the U.S. and deploy two computer vision smart city applications based on stakeholder feedback that are customized for New York City (NYC) . The team will also establish a formalized pipeline for running the computer vision algorithm enhanced for NYC conditions and prototype the applications for real - world implementation.]]></description>
      <pubDate>Wed, 18 May 2022 13:26:25 GMT</pubDate>
      <guid>https://rip.trb.org/View/1953288</guid>
    </item>
    <item>
      <title>One-to-Many Simulator Interface with Virtual Test Bed for Equitable Tech Transfer</title>
      <link>https://rip.trb.org/View/1942832</link>
      <description><![CDATA[After five years of R&D, researchers have developed a number of independent simulation tools to evaluate different algorithms. A broad API will be developed to handle interfacing any simulation with a multi-agent demand simulator. This will be tested on the existing MATSim-NYC (which will be enhanced to include freight and parcel delivery activities) and a BEAM implementation, BEAM-NYC, for three use cases in electric transit, freight, and traffic, considering equity impacts on different population segments (by income level, ability, and age). The team will jointly conduct case studies in NYC and Seattle, enabling deeper insights of evaluated cases and promote tech transfer and collaboration to broader communities (including agencies, the industry, and the public).]]></description>
      <pubDate>Fri, 22 Apr 2022 11:11:16 GMT</pubDate>
      <guid>https://rip.trb.org/View/1942832</guid>
    </item>
    <item>
      <title>Evaluation of Integrated Overweight Enforcement System using High Accuracy WIM System and Non-Proprietary ALPR System</title>
      <link>https://rip.trb.org/View/1942837</link>
      <description><![CDATA[The main objective is to establish the second testbed for overweight enforcement along the BQE corridor in New York City. The team will develop the drawing for the site-specific sensor layout, install the Quartz sensors and automated-license-plate-recognition (ALPR) cameras to measure truck weight data and identify license plate and/or USDOT number, and evaluate the performance of the overweight enforcement system. The team will also estimate the impact of an extreme event using data collected for infrastructure resilience.]]></description>
      <pubDate>Fri, 22 Apr 2022 10:42:10 GMT</pubDate>
      <guid>https://rip.trb.org/View/1942837</guid>
    </item>
    <item>
      <title>Developing a Framework to Optimize FloodNet Sensor Deployments Around NYC for Equitable and Impact-based Hyper-local Street-level Flood Monitoring and Data Collection</title>
      <link>https://rip.trb.org/View/1942825</link>
      <description><![CDATA[The project will build a framework to optimize and prioritize locations for FloodNet sensor deployment for measurement of hyper-local flooding in New York City (NYC). The research team proposes using comprehensive risk and equity metrics to locate optimal placement of FloodNet sensors around NYC. The team will develop a framework and define metrics with feedback from the NYC Mayor’s offices, partners since the initial development of the FloodNet sensors, to ensure the project’s successful implementation. To this end, the team plans to build a risk-informed digital twin of NYC integrating multiple data streams. The approach is probabilistic and based on rigorous risk analysis to study various flood impacts and capture the influential uncertainties in stormwater, tidal and compound flood events. The team anticipates using risk metrics based on impacts, e.g., fatalities or subway and street disruptions, rather than relying on direct hazard metrics, such as flood depth and extent, alone. The digital twin will utilize explicit equity risk metrics, e.g., identifying neighborhoods at disproportionally higher risk, and consider the equity priorities of NYC and the FloodNet project. This project is timely as the project team plans to deploy the FloodNet network on an unprecedentedly large scale in partnership with NYC, to meet the City’s agenda for climate resilience.]]></description>
      <pubDate>Fri, 22 Apr 2022 10:27:51 GMT</pubDate>
      <guid>https://rip.trb.org/View/1942825</guid>
    </item>
    <item>
      <title>URBANO.IO V2 
The only tool that connects urban design, mobility, and public health</title>
      <link>https://rip.trb.org/View/1878047</link>
      <description><![CDATA[The current COVID-19 crisis reveals the vulnerability of urbanizing societies where cities are the current pandemic's focal points. Therefore, long-standing planning paradigms that promote urban density to enhance social life, increase efficiency, and sustainability must be re-evaluated using a data-driven approach. While U.S. transportation emissions dropped ~10% in 2020 as millions of workers stopped driving to work, the use of mass transportation may have accelerated the outbreak in specific locales like New York City. This suggests that the built environment, urban sustainability, and pandemic resilience are closely linked. To better understand how urban design choices relate to the spreading and containment of pathogens, practitioners and researchers need new tools at the nexus of urban design, mobility simulation, and public health modeling. This proposal seeks to elevate Urbano.io to version 2 by finalizing a new NHTS data-driven multimodal mobility model and by interfacing with existing public health models. The research team further develops a real-world, case study that enhances NYC OpenStreets and informs a large rezoning project to test and showcase new features in close collaboration with the executive user group.]]></description>
      <pubDate>Tue, 14 Sep 2021 16:54:29 GMT</pubDate>
      <guid>https://rip.trb.org/View/1878047</guid>
    </item>
    <item>
      <title>Autonomous Vehicle Good Citizenry Standard</title>
      <link>https://rip.trb.org/View/1844337</link>
      <description><![CDATA[In this project, the research team will develop a Responsible Autonomous Mobility framework of good citizenry for autonomous vehicles entering New York City. Based on stakeholder and expert input, the standard will behave like an LEED building certificate, but for technology like delivery robots and driverless shuttles, to incentivize providers toward metrics of equity, sustainability and responsible use of machine learning and data technologies.]]></description>
      <pubDate>Thu, 01 Apr 2021 20:10:37 GMT</pubDate>
      <guid>https://rip.trb.org/View/1844337</guid>
    </item>
    <item>
      <title>Quantifying and Visualizing City Truck Route Network Efficiency Using a Virtual Test Bed</title>
      <link>https://rip.trb.org/View/1844338</link>
      <description><![CDATA[The research team proposes to develop a citywide model of truck network flows, one that relates changes to truck routes to changes in truck tours or to time-of-day congestion pricing policies, for example, by extending MATSim-NYC for New York City Department of Transportation (NYCDOT). This will be a 1-year project working closely with the NYCDOT staff led by Diniece Mendes.]]></description>
      <pubDate>Thu, 01 Apr 2021 20:08:19 GMT</pubDate>
      <guid>https://rip.trb.org/View/1844338</guid>
    </item>
    <item>
      <title>Equitable Access To Residential (EQUATOR) EV Charging</title>
      <link>https://rip.trb.org/View/1844339</link>
      <description><![CDATA[The EQUATOR project will quantify access to charging infrastructure in New York City (NYC) and optimize access-aware investments in utility-operated charging infrastructure. First, access to charging infrastructure will be quantified in terms of its availability, affordability, quality-of-service, and environmental metrics by means of data-driven analyses of static and dynamic spatio-temporal transportation and power grid data (e.g. on a zip code and hourly basis). Second, these metrics will be used to allocate utility’s investments in electric vehicle (EV) charging under budget constraints to reduce access disparity across zip codes.]]></description>
      <pubDate>Thu, 01 Apr 2021 20:04:09 GMT</pubDate>
      <guid>https://rip.trb.org/View/1844339</guid>
    </item>
    <item>
      <title>A Comprehensive Analysis of Air Quality in the NYC Subway System</title>
      <link>https://rip.trb.org/View/1844341</link>
      <description><![CDATA[The research team will carry out a comprehensive spatial-temporal analysis of particulate matter air quality across the New York City subway system. This will be achieved through the integration of a high-resolution spatial model and temporally resolved measurements using a field deployable sensor network positioned at selected stations. Data products which will be based on rigorous statistical analysis may subsequently be used by agencies to prioritize system upgrades incorporating public health metrics.  ]]></description>
      <pubDate>Thu, 01 Apr 2021 19:57:22 GMT</pubDate>
      <guid>https://rip.trb.org/View/1844341</guid>
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