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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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      <title>Physics-Informed Deep Learning and Governance Framework for Traffic Applications with Sparse Sensor Networks</title>
      <link>https://rip.trb.org/View/2606408</link>
      <description><![CDATA[This research develops a physics-informed deep learning (PIDL) framework to address critical data blind spots in transportation networks caused by limited sensor coverage, which undermines infrastructure planning and investment decisions required for federal Highway Performance Monitoring System reporting. The study combines classical traffic estimation models with data-driven deep learning to provide accurate traffic state estimation in sensor-sparse regions, helping State Departments of Transportation overcome prohibitive sensing infrastructure costs. The methodology integrates novel Fourier feature embedding algorithms to capture spatiotemporal variations, location-based trainable adjustment parameters for localized flow disruptions, and targeted collocation sampling near critical network features. The research addresses shortcomings of existing approaches where traditional physics-based models struggle with network complexity while deep learning methods require extensive data unavailable in sparse sensor environments. A collaborative pilot study with Delaware Department of Transportation will test the framework in real-world conditions with limited sensor coverage. The interdisciplinary approach brings together transportation engineers, network scientists, and public policy experts to develop both technical solutions and governance frameworks that align transportation management ecosystems with enhanced data collection capabilities for improved decision-making and infrastructure investments.]]></description>
      <pubDate>Thu, 02 Oct 2025 15:16:16 GMT</pubDate>
      <guid>https://rip.trb.org/View/2606408</guid>
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    <item>
      <title>BikePed Portal: Pedestrian Volume Estimation Based on Push Button Actuations from Signals Data</title>
      <link>https://rip.trb.org/View/2361978</link>
      <description><![CDATA[This project translates research from Oregon DOT's "Active transportation counts from existing on-street signal and detection infrastructure" (SPR 857), into a practical application on BikePed Portal. ]]></description>
      <pubDate>Tue, 02 Apr 2024 13:42:35 GMT</pubDate>
      <guid>https://rip.trb.org/View/2361978</guid>
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      <title>Real-Time Freeway Speed Prediction Based on Deep Learning in Connected And Autonomous Vehicles Environment</title>
      <link>https://rip.trb.org/View/1881792</link>
      <description><![CDATA[In the last few years, there has been a significant increase in the research of the connected autonomous vehicles (CAV) across the globe, perhaps due to an exponential increase in the popularity and usage of the artificial intelligence techniques in various applications. CAVs can greatly help traffic engineers manage the flow and mitigate traffic congestion on road networks by using the cooperative adaptive cruise control (CACC). 
For CAV to act more efficiently and improve mobility as well as alleviate traffic congestion, timely prediction of traffic flow is undoubtedly a critical component. A comprehensive review of the existing literature clearly suggests that research on CAVs has shifted from traditional optimization and statistical models to adaptive machine learning techniques. However, existing machine learning models may not be easily developed and directly applicable in this environment due to non-linear complex relationship between spatial and temporal data collected from the surroundings during the aforementioned adaptive decisions taken by the vehicles.
In this project, the research team will develop a traffic prediction framework based on various deep learning models for CAVs and compared these models with respect to their applicability in modern smart transportation systems. This research will also establish the simulation environment for CAVs in mixed traffic scenarios with different market penetration rates of CAVs. The results of this study can greatly help traffic engineers and stakeholders better understand how CAV affect traffic flow and therefore improve its management and control.
]]></description>
      <pubDate>Mon, 04 Oct 2021 10:56:43 GMT</pubDate>
      <guid>https://rip.trb.org/View/1881792</guid>
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      <title>Development and Tech Transfer of an Integrated Robust Traffic State and Parameter Estimation and Adaptive Ramp Metering Control System</title>
      <link>https://rip.trb.org/View/1700595</link>
      <description><![CDATA[The research team proposes a comprehensive effort to resolve several common issues associated with prevailing traffic state estimation algorithms based on discrete-time first-order kinematic wave traffic flow models and to develop an adaptive ramp metering control system based on the improved traffic estimation scheme. The effort is composed of traffic flow model modifications, development of a multi-modal adaptive filtering approach to traffic state and parameter estimation scheme, development of measures to integrate emerging traffic data with conventional fixed-point measurements, development of a measure for on-line estimation of capacity-drop-proportion, development of an adaptive discrete switching feedback controller for ramp metering, and implementations of the proposed schemes in both macroscopic numerical simulation and microscopic traffic simulation platform.]]></description>
      <pubDate>Tue, 05 May 2020 09:14:15 GMT</pubDate>
      <guid>https://rip.trb.org/View/1700595</guid>
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    <item>
      <title>Develop Local Functional Classification VMT and AADT Estimation Method</title>
      <link>https://rip.trb.org/View/1564419</link>
      <description><![CDATA[Rapid growth in population over the past two decades has led to an increase in travel demand, resulting in congestion and an exponential increase in conflicts that arise because of human interaction, off- and on network characteristics, and other associated factors. To better cater the increase in demand and reduce congestion, a federally-funded, state-administered program known as Highway Safety Implementation Program (HSIP) is legislated. The goal of HSIP is to achieve a significant reduction in fatalities and serious injuries on public roads. One of the requirements of HSIP for state agencies is to report Annual Average Daily Traffic (AADT) on all paved public roads (includes functionally classified major and local roads) and develop safety performance measures. A significant amount of resources (time and money) are spent by agencies to collect AADT on these road links. However, resource constraints limit agencies from collecting AADT data for all the links, particularly local functionally classified public roads. Such limitations can be offset using robust models that help estimate AADT on functionally classified major and local roads. 

The objectives of this research project are 1) to review annual average daily traffic (AADT) and vehicle miles traveled (VMT) generation methods, 2) to survey how other state departments of transportation are meeting the Highway Safety Improvement Program (HSIP) AADT requirements, 3) to develop models to estimate AADT on local roads, 4) to validate and calibrate the models to improve their predictability, and, 5) to recommend growth factors for continuously estimating AADT and VMT on local roads. The count-based AADT at 12,899 traffic count stations on local roads in North Carolina were used to develop and validate statistical and geospatial models. The influence of road, socioeconomic, demographic, and land use characteristics was examined. The outputs from statewide models were compared with the outputs from county-level models. An error analysis was performed to identify factors influencing the predictability of these models. Sample sizes and growth factors were computed for each county. Recommendations were made to estimate AADT and VMT based on the count-based AADT at traffic count stations, model outputs, and growth factors for the reporting year.]]></description>
      <pubDate>Fri, 19 Oct 2018 16:54:52 GMT</pubDate>
      <guid>https://rip.trb.org/View/1564419</guid>
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    <item>
      <title>Measuring Traffic Performance using Passive Sensing Technologies on Signalized Arterials</title>
      <link>https://rip.trb.org/View/1404678</link>
      <description><![CDATA[Among other sensing technologies, the wireless Wi-Fi based solution to estimating travel time along arterials seems promising. Similar with the Bluetooth-based solution, a road-side virtual Wi-Fi “hotspot” will capture the unique Wi-Fi MAC address in passing vehicles and then match them at different locations. Nonetheless, the major difference between Bluetooth-based solution and Wi-Fi-based solution is that whenever in-vehicle Wi-Fi MAC addresses are captured, virtual Wi-Fi “hotspot” can immediately record its timestamp. In contrast, the road-side Bluetooth sniffers stamp all captured Bluetooth MAC addresses with the same time only when a complete scan of all possible Bluetooth sub-frequencies is finished. The objective of this project is to extensively test this new solution at different locations in various operational scenarios. The tentative research findings out of this new solution will include but are not limited to: (1) comparison of travel time sample capture rates (valid Wi-Fi-based travel time samples, Bluetooth-based travel time samples, total number of vehicles identified, etc.); (2) estimation of positioning errors and timing errors in Wi-Fi and Bluetooth measurements under various configurations and (3) accuracy of identifying vehicle arriving patterns at signalized intersections. 
]]></description>
      <pubDate>Thu, 21 Apr 2016 13:23:18 GMT</pubDate>
      <guid>https://rip.trb.org/View/1404678</guid>
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      <title>Establishment of Ideal Saturation Flow Rate for Intersection Level of Service Analysis for Washington DC</title>
      <link>https://rip.trb.org/View/1290479</link>
      <description><![CDATA[Saturation flow rate (SFR) measures the maximum rate of flow of traffic in specific lane groups on the approach to signalized intersections. SFR is used extensively in the analysis and design of signalized intersections, and is a factor in the level of service analyses for signalized intersections. SFR depends on several roadway and traffic conditions (parking, lane configuration, lane width, presence of heavy vehicles, traffic behavior, number of lanes, traffic volumes, approach grade, etc.). Most jurisdictions use default values for SFR, built into traffic operations software programs and often do not provide guidelines for selecting SFR for specific conditions at intersections. However, the use of inappropriate SFR values leads to poor estimation of the average vehicle delays, thereby leading to erroneous level of service (LOS) reporting. Due to the sensitivity of the effect on the LOS of a signalized intersection with modest changes in the value of the SFR, a number of jurisdictions (Pennsylvania, Delaware, and others) have conducted studies to determine an "average" or representative SFR value for their respective areas. This research is intended to establish an appropriate SFR for use by traffic operations engineers at the District Department of Transportation (and consultants) in the conduct of LOS analysis for all signalized intersections in Washington, DC.]]></description>
      <pubDate>Thu, 30 Jan 2014 01:00:58 GMT</pubDate>
      <guid>https://rip.trb.org/View/1290479</guid>
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    <item>
      <title>Freeway Travel Time Estimation using Existing Fixed Traffic Sensors - A Computer-Vision-Based Vehicle Matching Approach</title>
      <link>https://rip.trb.org/View/1256988</link>
      <description><![CDATA[Travel time information is of interest to both road users and road network operators. The direct travel time estimation method such as probe vehicles has high accuracy but it requires a high probe rate to collect the complete travel time information of a road network, which is too costly for the daily operation of a road network. The indirect method by point sensors assumes stable speed within a roadway segment and it has low accuracy when the traffic becomes congested. The project explores a fundamental advancement in the theoretical and practical research related to travel time estimation in a freeway network by matching vehicles in a network of traffic surveillance cameras. The accurately computed travel time will sustain the transportation system in a manner that is more effective, more efficient, and more economic competitive.]]></description>
      <pubDate>Wed, 24 Jul 2013 01:01:09 GMT</pubDate>
      <guid>https://rip.trb.org/View/1256988</guid>
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