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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>Modeling Bicyclist Behavioral Patterns and Multi-Faceted Decision-Making Strategies in Urban Settings with Limited Infrastructure: Guidance for Future Development</title>
      <link>https://rip.trb.org/View/2691667</link>
      <description><![CDATA[While bicycling is an essential mode of urban transportation, most parts of U.S. cities lack adequate infrastructure to keep bicyclists safe and allow them to travel efficiently. This research aims to model how psychological, street, and infrastructure characteristics influence bicyclist behavior in urban settings with inadequate bicycling infrastructure, such as in the Greater Houston area (Houston-The Woodlands-Sugar Land), Texas. This research will integrate quantitative and qualitative methods to develop a model that supports adaptive decision-making for bicycling in urban areas with limited infrastructure. The project will recruit 40 adult bicyclists to participate in surveys and bicycle simulator testing. A realistic urban network will be simulated in the bicycle simulator to replicate bicycling conditions under varying (infrastructure quality, traffic volume, visibility, etc.), psychological (risk perception, motivation, and attitudes, etc.), and operational (route choice, adaptation, and interaction with other modes of transportation, etc.) scenarios. Various techniques, including both qualitative and quantitative methods, can be used to identify key drivers of route choice and to develop optimal strategies for efficiency and safety. The findings will inform action-oriented urban planning and policy recommendations for enhancing bicycle infrastructure and safety. The outcomes have the potential to offer a replicable methodology for implementation in similarly challenged cities, providing active urban transportation and improved public health.]]></description>
      <pubDate>Sun, 12 Apr 2026 23:34:35 GMT</pubDate>
      <guid>https://rip.trb.org/View/2691667</guid>
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    <item>
      <title>Pedestrian Route Choice and Traffic Exposures: A Case Study in Downtown Atlanta</title>
      <link>https://rip.trb.org/View/2652174</link>
      <description><![CDATA[Walkable neighborhoods and active mobility provide human health benefits through exercise, stress relief,  and local sustainability, but concerns over near-roadway traffic-related air pollution remain significant (Luo, 2018). Exposure to pollutant concentration exposure assessments downwind from roadway sources not only undermines the health advantages of walking, but also poses economic burdens, as indicated by the substantial costs associated with air pollution-related mortality (World Bank, 2020). Recent studies are increasingly focused on understanding how pedestrian route choice influences exposure to traffic related air pollutants (Khreis, 2020), and integrating these exposure metrics into pedestrian route planning may improve health outcomes. Shortest path, impedance-based routing tools, such as SidewalkSim, can be used to predict pedestrian paths through the sidewalk network. Route selection can account for pedestrian asset design and condition, and even route wheelchair users around crossings that are missing a curb ramp (which imposes a significant impedance on the crossing link). When second-by-second pedestrian route data are combined with spatiotemporal predictions of pollutant concentrations downwind from roadways, analysts can assess how exposure accumulates over the course of each walking trip. Because the routing tools are impedance-based, pollutant concentrations can be converted to link impedance and potentially used to route pedestrians on slightly longer routes that result in much lower pollutant exposure. This project will apply impedance methods in the context of pedestrian travel in downtown Atlanta, testing a variety of impedance functions from the assessments to evaluate the tradeoffs between route circuity and pollutant exposure across these functions. The project will develop a framework that supports healthier, lower-exposure pedestrian pathways (while maintaining reasonable routes and convenience for all users). ]]></description>
      <pubDate>Tue, 13 Jan 2026 15:38:52 GMT</pubDate>
      <guid>https://rip.trb.org/View/2652174</guid>
    </item>
    <item>
      <title>Empirical Investigation of Post-Disaster Travel Behavior to Points of Distribution of Relief Supplies</title>
      <link>https://rip.trb.org/View/2553159</link>
      <description><![CDATA[This project aims to understand the travel behavior of disaster-affected populations when seeking relief supplies in the aftermath of a natural disaster. Relief supply-seeking travel behavior and related decisions influence the demand for relief supplies at the points of distribution set up during the emergency response stage. Therefore, understanding these behaviors can significantly impact the success and efficiency of disaster relief operations and the adequate and equitable distribution of relief supplies. Previous research on travel behavior in the context of disasters has mainly focused on evacuation and migration, which typically occur during the emergency stages of preparedness and recovery. However, there is a significant knowledge gap regarding travel behavior and human decisions made during the response stage, i.e., those related to post-disaster aid-seeking behaviors, including traveling and searching for disaster relief supplies. This project is an exploratory and empirical investigation of the choices, attitudes, and perceptions of disaster-affected populations when traveling to and from points of distribution of disaster relief supplies. The aim is to understand relief supply-seeking travel behavior, focusing on destination, mode, and route choices. A mixed-method approach will be used comprising a quantitative component, leveraging surveys, and a qualitative component, with focus groups. Furthermore, the project will evaluate the effects of socioeconomic characteristics, urbanistic levels, and multimodal transportation infrastructure on relief supply-seeking travel behaviors, providing insights into vulnerability, accessibility, and equity-related implications. The project will be the first step in explaining relief supply-seeking travel behaviors in the aftermath of disasters and developing a family of people-centric models for effective and equitable disaster relief distribution.]]></description>
      <pubDate>Thu, 15 May 2025 14:27:38 GMT</pubDate>
      <guid>https://rip.trb.org/View/2553159</guid>
    </item>
    <item>
      <title>Utilizing Big Data to Verify and Enhance Route Choice Models in Travel Demand Modeling</title>
      <link>https://rip.trb.org/View/2553840</link>
      <description><![CDATA[Virginia Department of Transportation (VDOT) has invested roughly $5M to maintain and improve its long-range travel demand models, which are needed to help analyze candidate transportation projects.  A key step within these models is “trip assignment” where the path vehicles follow is represented in the network.  High-resolution global positioning system (GPS)-level data offers the potential to improve the accuracy of this step.  However, VDOT staff are finding some discrepancies between route choice as currently represented in demand models and route choice as observed by GPS data—and it is not immediately clear how to resolve this discrepancy.  This research effort will determine the best way to use the GPS data to enhance model accuracy.  Note that a substantial amount of effort needed to make this determination has already been completed through an earlier research effort, and thus, this new study adds additional work to address this trip assignment step.]]></description>
      <pubDate>Thu, 15 May 2025 09:16:33 GMT</pubDate>
      <guid>https://rip.trb.org/View/2553840</guid>
    </item>
    <item>
      <title>A Pilot Experimental Project for Predicting Pedestrian Flows using Computer Vision and Deep Learning</title>
      <link>https://rip.trb.org/View/2440260</link>
      <description><![CDATA[Walking for transportation, health, and pleasure is an essential part of people’s lives in most cities. Knowing where people linger, the destinations that attract them, and how those places are accessed could assist in optimizing business locations and providing better security. In addition, predicting and sharing congestion times and locations (perhaps in real-time as in Waze for cars) could also provide useful information to travelers who can then choose appropriate travel routes and improve travel efficiency. Yet, far less is known about the spatial and temporal variations in pedestrian volumes than is known about vehicular movement. While pedestrian route choice has been an active area of research, few studies have attempted to predict pedestrian flows from unbiased pedestrian count data. Pedestrian route choice models assume that people choose their walking routes based on their perceived path attributes. Statistical path choice models identify people’s behavior related to route attributes on the selected path. These models hypothesize that the fundamental utility attribute is path length or travel time, which pedestrians generally minimize. These models also consider that people are willing to deviate to longer routes if the preferred path is comparatively safe, comfortable, and aesthetically pleasing. Yet, these models are inefficient for pedestrian traffic planning since they require prohibitive amounts of information about individual walkers. The research team develops a graph convolutional network model (GCN) based only on pedestrian counts at various intersections and segments to predict pedestrian traffic flows.
]]></description>
      <pubDate>Thu, 10 Oct 2024 16:01:37 GMT</pubDate>
      <guid>https://rip.trb.org/View/2440260</guid>
    </item>
    <item>
      <title>Shipper’s utility functions according to commodity groups and freight modal split</title>
      <link>https://rip.trb.org/View/2422979</link>
      <description><![CDATA[The United States freight transportation network consists of railroads, highways, waterways, pipelines, and airways. It carries the flow of commodities along the supply chains from the origin of production to the destination of consumption. The already congested transportation system faces fast-growing freight demands. The annual freight volume is expected to grow from 19 billion tons in 2022 to at least 29 billion tons in 2050, according to the US Bureau of Statistics. Imports and exports account for over 13% of the total freight volume and will grow at a much faster rate than domestic freight. The mode and route choices of shippers will play an important role in network performance and in the planning consideration of planners and public policy makers because they determine the freight volumes on the network according to their modes, and therefore determine congestion and delay on the routes. From a microscopic and operational perspective, to address bottlenecks and choke points on the network requires understanding of the shipper’s choice of mode and routes. Shippers make their rational choices based on the premise of maximizing their utility functions. The research team proposed to study the utility functions and other means for route and mode choices by the shippers. However, shipper’s choices also have to do with the commodity types. For example, higher value commodities such as machinery or electronic products may be less sensitive to the price charged than bulk commodities. The goal of the project is to propose a framework for predicting the network commodity flows that policy makers and planners may use. The midterm goal of the effort is twofold. The first is to explore the format of the shipper’s utility function in their decision of mode and route choices. The Second is to study the differences between the utility functions of different commodity groups by using a few groups that have data available. This project focuses on exploring the shippers’ utility function: the format and calibration by using example commodity groups specifically.]]></description>
      <pubDate>Thu, 29 Aug 2024 17:00:14 GMT</pubDate>
      <guid>https://rip.trb.org/View/2422979</guid>
    </item>
    <item>
      <title>Dynamic Origin-Destination estimation (DODE) under incidents using individual trajectories data</title>
      <link>https://rip.trb.org/View/2247598</link>
      <description><![CDATA[Travel behavior in route choices under incidents will be modeled based on a disutility function for individuals and the calibrated regional network model. The research team will design a methodology to simulate the traffic and estimate dynamic origin-destination (O-D) demand on the real time basis. The simulation adopts the historical traffic demand as an initial (base) demand and their pre-scribed route choices from the dynamic network model in the existing meso-scopic simulation tool (MAC-POSTS). ]]></description>
      <pubDate>Fri, 15 Sep 2023 15:31:42 GMT</pubDate>
      <guid>https://rip.trb.org/View/2247598</guid>
    </item>
    <item>
      <title>Impacts of Shared Mobility on Infrastructure Usage, Greenhouse Gas Emissions, and Accessibility</title>
      <link>https://rip.trb.org/View/2232147</link>
      <description><![CDATA[This project will develop a new integrated mode choice and route choice model incorporating shared mobility options. The model will directly predict changes in travel behavior and traffic congestion. The outputs will be post-processed to produce maps of accessibility changes from shared mobility, in particular demonstrating the locations of services like grocery stories and medical facilities. The Environmental Protection Agency's (EPA’s) MOtor Vehicle Emission Simulator (MOVES) will be used to predict the impacts on greenhouse gas emissions.]]></description>
      <pubDate>Wed, 23 Aug 2023 10:45:38 GMT</pubDate>
      <guid>https://rip.trb.org/View/2232147</guid>
    </item>
    <item>
      <title>Safety Service Patrol Standardization and Management Practices</title>
      <link>https://rip.trb.org/View/1894844</link>
      <description><![CDATA[The primary objective of this Pooled Fund Study study will be to gain technical information related to Safety Service Patrol (SSP0 program management, standards associated with SSP response protocol and the implementation of traffic control, and references and guidance related to staffing, training, and resource allocations within SSP programs. The goals include: (1) Assemble best practices and lessons learned from existing programs; (2) Develop guidance documents based on lessons learned from existing programs; (3) Reference or create tools that will help agencies make informed program decisions such as route selection, staffing levels, and resource allocation.]]></description>
      <pubDate>Wed, 01 Dec 2021 17:13:59 GMT</pubDate>
      <guid>https://rip.trb.org/View/1894844</guid>
    </item>
    <item>
      <title>Assessment of driver route decision-making during a range of incident-induced traffic flow disruptions</title>
      <link>https://rip.trb.org/View/1889097</link>
      <description><![CDATA[Drivers typically plan and carry out travel to most effectively utilize their time. A key component of travel planning is to select routes, times, and modes that minimize both travel duration and delay. However, such plans are based on prior experience under routine travel conditions. When infrequent, yet inevitable, incidents occur that cause congestion and delay, many drivers make decisions to increase the efficiency of their trip. Although one of the most common driver strategies is to divert travel to alternative routes, relatively little is known about the motivation of this decision-making nor the characteristics that most acutely effect driver choice. The goal of this research is to address the need for a better understanding of route-diversion behavior by assessing driver decision making under a range of incident, traffic, and guidance conditions. The
result of this research is expected to advance both research and practice.]]></description>
      <pubDate>Fri, 29 Oct 2021 15:36:44 GMT</pubDate>
      <guid>https://rip.trb.org/View/1889097</guid>
    </item>
    <item>
      <title>Prioritizing People - Mixed Equilibrium Assignment for AV Based on Occupancy (Phase II)</title>
      <link>https://rip.trb.org/View/1881798</link>
      <description><![CDATA[Autonomous Vehicles (AV) have the potential to revolutionize transportation operations mode choice. In June 2017, Connecticut Public Act No. 17-69 “An Act Concerning Autonomous Vehicles” authorized the testing of AVs on Connecticut roads. In April 2018, Connecticut launched the Fully Autonomous Vehicle Testing Pilot Program (FAVTPP), which set the permitting and testing requirements for AVs on public roads. Although there is optimism that introduction of AVs will mitigate traffic congestion and vastly improve safety, the transition to a completely AV fleet - which will take time - presents non-trivial problems. In the United States, automobiles did not begin to outnumber horses on roadways until the late 1920’s, twenty years after the first Model T rolled off the production line. If a similar timeline for AV deployment and market penetration holds, the public won’t see AVs outnumber human-driven vehicles until sometime in the 1930’s and won’t see a completely autonomous fleet until somewhat later. This means that for the next 20+ years the public will be operating in a mixed traffic environment including human-driven vehicles, occupied AVs and unoccupied AVs.
Some AVs will operate as part of a centrally owned, shared autonomous fleet in which vehicles are routed according to real-time requests similar to current human-driven e-hailing services.
However, a not insignificant portion of AVs will continue to be owned by a single household. The availability of an AV in a household may allow them to own fewer vehicles at a considerable cost savings, as a single AV could be used to meet multiple household members’ tripmaking needs provided it could reach the next household member in time to get them to their destination on
time. This means that a significant portion of the AV travel time will be unoccupied, depending on the tripmaking needs of the household. These unoccupied AVs will impact the travel times of
occupied AV and human-driven vehicles.
It seems obvious that the travel needs of occupied vehicles (AV and human-driven) should be prioritized, and that empty AVs should be routed to minimize the impacts on occupied vehicles. However, if unoccupied AVs are assigned a route that is too circuitous, it may not be able to meet a household’s tripmaking needs – requiring additional vehicles and eliminating the cost savings for the household of owning an AV.
The central research question of this proposal is: How can unoccupied AVs be routed to minimize the impacts on occupied vehicles without disproportionally hurting households that own an AV?
The proposed research will focus on the following topics:
(1)	Mitigating travel delays experienced by occupied vehicles by minimizing the impact of empty AV route choice.
(2)	Differential route assignment for occupied versus unoccupied vehicles while considering impacts of unoccupied AV route choice on AV owners.
(3)	Application of the methodology on a Hartford, CT case study.
]]></description>
      <pubDate>Mon, 04 Oct 2021 11:52:13 GMT</pubDate>
      <guid>https://rip.trb.org/View/1881798</guid>
    </item>
    <item>
      <title>Y3R2 - Identification and Evaluation of Critical Urban
Freight Corridors</title>
      <link>https://rip.trb.org/View/1868879</link>
      <description><![CDATA[There has been a steady increase in demand for goods over the past half-century and therefore there is a
continuous need for well-organized freight transportation systems. Optimal use of roadways as the primary
and fundamental sector of the freight transportation system is essential. Efficient movement of freight is
vital to the rivaling economies of cities and metropolitan areas, and truck highway corridors comprise an
essential ingredient of the regional freight transportation system, along with rail and intermodal facilities,
river-port barge terminals, and air cargo facilities. To achieve efficient, reliable and robust freight
movement, the Fixing America’s Surface Transportation (FAST) Act requires the Federal Highway
Administration (FHWA) to establish a National Highway Freight Network (NHFN) to strategically direct
Federal resources and policies toward improved performance of the NHFN. The main objectives of FAST
are to make the Federal surface transportation more streamlined, performance-based, and multimodal, and
to address challenges facing the U.S. transportation system, including improving safety, maintaining
infrastructure condition, reducing traffic congestion, improving the efficiency of the system and freight
movement, protecting the environment, and reducing delays in project delivery. 
This network is the focus of funding under the National Highway Freight Program (NHFP), and a
significant funding target under the Fostering Advancements in Shipping and Transportation for the Long term Achievement of National Efficiencies (FASTLANE) Grants Program (Nationally Significant Freight
and Highway Projects Program) (23 U.S.C. 117). The NHFN consists of the following four subsystems: (1)
the Primary Highway Freight System (PHFS); (2) those portions of the Interstate System not part of the
PHFS; (3) Critical Rural Freight Corridors (CRFCs); and (4) Critical Urban Freight Corridors (CUFCs).
(23 U.S.C. 167(c)). The ability to entirely understand and accurately designate freight vehicle route choices
is essential in helping to inform regional and state decisions. Specific criteria and requirements exist for
identifying and designating CRFCs and CUFCs according to FHWA. This research will focus on CUFCs
and the mobility of goods especially on the first-/last-mile links leading to them. Critical Urban Freight
Corridors (CUFC) are public roads in urbanized areas which provide access and connection to the primary
highway freight system for ports, public transportation, or other intermodal transportation facilities. After
identifying the critical urban corridors, as FHWA encourages when making CUFC designations, it is crucial
to consider first or last mile connector routes from high-volume freight corridors to freight-intensive land
and key urban freight facilities, including ports, rail terminals, and other industrial-zoned lands. Therefore,
investigating the first-/last-mile connectors is necessary to inspect the condition of the route regarding how
congested it is and figuring out the necessity of modifying the mobility of the area by innovative and cutting edge technologies.]]></description>
      <pubDate>Tue, 27 Jul 2021 09:52:27 GMT</pubDate>
      <guid>https://rip.trb.org/View/1868879</guid>
    </item>
    <item>
      <title>Evaluating the Impacts of Deploying Automated Roads for Infrastructure-Enabled Autonomous Vehicles</title>
      <link>https://rip.trb.org/View/1691455</link>
      <description><![CDATA[Autonomous driving technology is expected to bring dramatic societal, environmental, and economic benefits due to its potential for improving traffic safety, vehicle fuel economy, road capacity, travel speed, and driver productivity. However, focusing on AV technology alone may potentially slow the penetration of AVs and consequently slow the realization of societal benefits from AVs. In order to safely drive itself in various road environments, an AV needs to be equipped with expensive sensor systems and additional hardware and software. The high cost of AVs can be a significant barrier to their broad adoption. Integrating transportation infrastructure enhancement into the realization of autonomous driving can potentially promote the development and adoption of AVs. This project proposes a modeling framework for the planning and evaluation of an infrastructure-enabled autonomous driving system. The proposed project will accomplish the following two objectives:1) Develop a new network equilibrium model to describe road users' vehicle type and route choice behaviors in a transportation network with automated roads; 2) Investigate the strategic planning of automated roads in a general transportation network.]]></description>
      <pubDate>Fri, 06 Mar 2020 17:24:43 GMT</pubDate>
      <guid>https://rip.trb.org/View/1691455</guid>
    </item>
    <item>
      <title>Smartphone-Based Incentive Framework for Dynamic Network-Level Traffic Congestion Management (Project H3)</title>
      <link>https://rip.trb.org/View/1682040</link>
      <description><![CDATA[By leveraging advances in smartphone-based personalization, big data availability for traffic, and network-level integration through information-based connectivity, this study proposes to develop new types of tools to manage congestion in real-time in traffic networks, especially during peak period commutes and under debilitating incidents. The study seeks to develop a smartphone-based framework to develop real-time incentives (monetary, value-based, travel-related credits, etc.) to influence drivers’ en route routing decisions to manage network-level system performance in congested dynamic traffic networks. In doing so, a key objective is to ensure that the proposed incentives are behavior-consistent, in that they are tailored based on travelers’ smartphone responses. Another objective is to explore how public and private sector transportation entities can collaborate through the use of new forms of incentives that leverage the emerging transportation future, in addition to the currently-used ones. The methodology consists of a three-phase approach. Analytical models and algorithms will be developed in the first phase to identify and implement the specific incentives to be deployed in real-time, using techniques from game theory, optimization, and machine learning. Phase 2 will involve driving simulator-based experiments to analyze the responses of drivers/travelers to the real-time incentives. In the third phase, the insights from the second phase will be used to fine-tune the analytical models and develop the modules that would form the components of a smartphone-based app.]]></description>
      <pubDate>Fri, 31 Jan 2020 17:04:59 GMT</pubDate>
      <guid>https://rip.trb.org/View/1682040</guid>
    </item>
    <item>
      <title>Evaluating Detours for a Major Construction Project in the Era of Real-Time Route Guidance  (Project D3)</title>
      <link>https://rip.trb.org/View/1681324</link>
      <description><![CDATA[Major road construction projects can be significant sources of traffic congestion and motorist delays. Maintaining agencies typically attempt to mitigate these impacts by designating detour routes and providing project information to motorists. This information can be conveyed through a variety of media, from traditional static and variable roadway signage placed in the field to electronic media including web sites, radio and television advertisements, call centers, text messaging, and navigation apps. In this era of real-time traffic information and in-vehicle route guidance, it is not clear to what extent this detour information is used and which messaging components are most effective. This study used the Interstate 59/20 reconstruction project in Birmingham, AL to evaluate the detour planning process and the effectiveness of the resulting detour and information strategies. The objective was to develop recommendations and best practices that can be applied to future construction projects and allow transportation agencies to allocate project resources to greatest effect. The evaluation included a review of the transportation modeling process used to project traffic diversions and designate detour routes, a survey of motorists to determine the sources of information they used to choose detour routes during construction, and a study of traffic patterns before, during, and after the project to understand if and how detour patterns changed over the course of the one-year project. The study resulted in recommendations for conducting planning studies for large roadway projects and found that factors such as transit usage assumptions, employer work policies, and roadway capacity assumptions can have significant impacts on model accuracy. The survey found that motorists used a wide variety of information sources when selecting detour routes and that they often modified those routes based on real-time data. The travel time and traffic count analysis found that detour patterns did vary over time as the transportation system reached equilibrium. It also found that actual traffic patterns during the reconstruction project did not always match responses to the motorist survey, suggesting that motorists used designated detour routes initially but adjusted them during the course of the project.

]]></description>
      <pubDate>Thu, 30 Jan 2020 21:05:00 GMT</pubDate>
      <guid>https://rip.trb.org/View/1681324</guid>
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