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    <title>Transport Research International Documentation (TRID)</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>Transport Research International Documentation (TRID)</title>
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      <link>https://trid.trb.org/</link>
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    <item>
      <title>Evaluating Safety Benefits of Ramp Metering By Leveraging Connected Vehicle Data: Case Study of Indiana Roadways</title>
      <link>https://trid.trb.org/View/2685739</link>
      <description><![CDATA[Traditionally, crash data, crash risk models, video recordings and user surveys have been utilized by agencies to measure the safety benefits of ramp metering technology. Connected vehicle data can now provide an agile evaluation alternative for quantifying impact of ramp meter deployments. Furthermore, in contrast to crash data, connected vehicle near miss events occur much more frequently, so the before-after evaluation can be conducted over a much shorter time period consisting of a few months, or perhaps even a few weeks. Indiana deployed ramp meters on the southeast section of I-465 around Indianapolis, on or around May 14, 2024, which were then active primarily during the morning and evening peak hours. Hard-braking events, a surrogate safety performance measure, were estimated from high-frequency connected vehicle data available at 3-second fidelity for vehicles passing through the metered ramps and the adjacent mainline interstate. A before-after analysis for the 4-5 PM peak hour showed approximately a 61% reduction in hard-braking events on mainline merge areas adjoining the metered ramps on the inner loop of I-465. Spatial analysis also showed a 70%, 41% and 33% median reduction in mild, moderate and severe hard-braking events per 0.1-mile segment in the entire 7.5-mile mainline corridor adjacent to metered ramps. The methodologies and performance measures provided in this paper demonstrate how connected vehicle data scales well to systematically assess and document the performance of new ramp metering deployments.]]></description>
      <pubDate>Mon, 17 Aug 2026 08:27:42 GMT</pubDate>
      <guid>https://trid.trb.org/View/2685739</guid>
    </item>
    <item>
      <title>Utilizing Emergency Management Audio Messages for Traffic Incident Management Performance Measures</title>
      <link>https://trid.trb.org/View/2736568</link>
      <description><![CDATA[Timely and accurate information regarding roadway incidents is critical for Traffic Management Centers (TMCs) to maintain safety and mobility. While Intelligent Transportation Systems (ITS) cameras and crowdsourced data provide significant visibility, rural interstate corridors often suffer from coverage gaps, or "blind spots," where incidents go undetected until significant queuing occurs. Public safety radio communications serve as the primary coordination method for first responders, creating a "verbal infrastructure" that contains vital information regarding incident location and severity. This study evaluates the feasibility of using Automatic Speech Recognition (ASR) to bridge these gaps by monitoring and analyzing radio traffic. The project developed and validated a prototype system capable of capturing, transcribing, and geolocating public safety radio transmissions for Traffic Incident Management (TIM) along a 71-mile segment of I-65 between Indianapolis and Chicago. The findings include a system latency assessment, transcription accuracy analysis, noise filtering, and a case study. Further recommendations are made based on the findings for improving results and larger implications.]]></description>
      <pubDate>Fri, 14 Aug 2026 15:04:11 GMT</pubDate>
      <guid>https://trid.trb.org/View/2736568</guid>
    </item>
    <item>
      <title>Rampcast Phase II: Connected Vehicles Traffic Management Application on Indiana Highways</title>
      <link>https://trid.trb.org/View/2727584</link>
      <description><![CDATA[This project advances connected vehicle applications by developing and testing an enhanced RampCast system, a comprehensive traffic management system using Cellular Vehicle-to-Everything (C-V2X) technology for Indiana highways. The system features a dual-mode architecture integrating both short-range (PC5) and long-range cellular (Uu) C-V2X communication pathways, utilizing commercial-grade Cohda MK6 hardware and adhering to SAE J2735 standards to ensure interoperability. A key innovation is the integration of an Artificial Intelligence (AI)-based prioritization framework, which leverages a large language model to enhance the contextual relevance of traffic messages. This AI system introduces two intelligent agents: one to dynamically estimate the appropriate display distance for an event based on its severity, and another to prioritize the order of messages based on urgency and potential driver impact. Field tests conducted on I-65 and I-70 in Indianapolis validated the system's hybrid design. Results confirmed that the PC5 link provides very low latency (around 25 ms), ideal for time-critical alerts, while the Uu link ensures highly reliable coverage in complex environments, albeit with higher latency (around 45 ms). The AI framework was successfully shown to reorder and present messages based on real-time context, improving the clarity and usefulness of information provided to the driver. These findings support a hybrid C-V2X architecture as a robust model for future smart highway deployments.]]></description>
      <pubDate>Tue, 21 Jul 2026 09:48:50 GMT</pubDate>
      <guid>https://trid.trb.org/View/2727584</guid>
    </item>
    <item>
      <title>Research Support to INDOT on I-465 Southeast Variable Speed Limit and Ramp Meter Project</title>
      <link>https://trid.trb.org/View/2698405</link>
      <description><![CDATA[Traditionally, crash data, crash risk models, video recordings and user surveys have been utilized by agencies to measure the safety benefits of ramp metering technology. Connected vehicle data can now provide an agile evaluation alternative for quantifying impact of ramp meter deployments. Furthermore, in contrast to crash data, connected vehicle near miss events occur much more frequently, so the before-after evaluation can be conducted over a much shorter time period consisting of a few months, or perhaps even a few weeks. Indiana deployed ramp meters on the southeast section of I-465 around Indianapolis, on or around May 14, 2024, which were then active primarily during the morning and evening peak hours. Additionally, Indiana deployed Variable Speed Limit (VSLs) on September 10, 2024 at 25 locations on I-465. This study was initiated to evaluate the impact of those deployments. Hard-braking events, a surrogate safety performance measure, were estimated from high-frequency connected vehicle data available at 3-second fidelity for vehicles passing through the metered ramps and the adjacent mainline interstate. A before-after analysis for the 4-5 PM peak hour showed approximately a 61% reduction in hard-braking events on mainline merge areas adjoining the metered ramps on the inner loop of I-465. Spatial analysis also showed a 70%, 41% and 33% median reduction in mild, moderate and severe hard-braking events per 0.1-mile segment in the entire 7.5-mile mainline corridor adjacent to metered ramps. A before-after analysis of the 10 AM–1 PM hours showed little to modest impact on speeds in 0.3-mile evaluation sections with VSL deployments. The methodologies and performance measures provided in this paper present a framework that scales well to systematically assess and document the performance of new ramp metering and VSL deployments.]]></description>
      <pubDate>Mon, 18 May 2026 14:04:39 GMT</pubDate>
      <guid>https://trid.trb.org/View/2698405</guid>
    </item>
    <item>
      <title>What determines travel time and distance decay in spatial interaction and accessibility?</title>
      <link>https://trid.trb.org/View/2464951</link>
      <description><![CDATA[The concept of ‘distance decay’ curves is used in spatial interaction and accessibility analysis to represent the diminishing likelihood of visiting places with increasing travel impedance, mainly distance and travel time. The shape of the resulting impedance decay curves varies by several factors, but these influential factors are often dismissed in favor of just travel mode. In this study, the authors examine which factors should be used to distinguish impedance functions for use. Using data from a large national travel survey, they first show that the impedance curves are well approximated by functions of the exponential family and the related Tanner function. They use two methods – variable-wise function fit and Shapley additive explanations – to conclude the importance of four factors for developing impedance functions. These are travel mode, trip purpose, urbanity class of trip origin and destination, and the socioeconomic status grouping of the travelers. They then show that the use of a generalized impedance function can significantly over- or underestimate spatial accessibility compared to factor-specific impedance function, with up to 80 % overestimation on average in the case of public transit and 16 % overestimation for low socio-economic status travelers. These findings highlight the importance of the choice of impedance function which has applications in spatial economics, transportation planning, and human mobility analyses.]]></description>
      <pubDate>Mon, 30 Dec 2024 11:16:01 GMT</pubDate>
      <guid>https://trid.trb.org/View/2464951</guid>
    </item>
    <item>
      <title>Fleet sizing and static rebalancing strategies for shared E-scooters: A case study in Indianapolis, USA</title>
      <link>https://trid.trb.org/View/2446781</link>
      <description><![CDATA[With the rapid development of shared e-scooters, it is essential to understand their usage patterns for formulating informed e-scooter fleet management policies. This study first analyzes the usage pattern of shared e-scooters in Indianapolis, USA, by mining big e-scooter trip data. The analysis reveals an oversupply of shared e-scooters relative to actual user demand. Thus, a minimum fleet sizing algorithm is proposed to determine the required minimum e-scooter fleet size with the objective of reducing total operation cost, while ensuring demand coverage. Furthermore, three heuristic algorithms are proposed to address the static e-scooter rebalancing problem, focusing on minimizing rebalancing distance cost and rebalancing time. These algorithms consider practical operational constraints, including the number of rebalancing vehicles, their capacity, and the frequency of visits to e-scooter stations by rebalancing vehicles. The proposed algorithms are applied to e-scooter rebalancing scenarios with comparisons between the minimum and actual fleet sizes. The case study results in Indianapolis, USA demonstrate that the rebalancing distance cost with the minimum fleet size is significantly lower than that with the actual fleet size. What’s more, the rebalancing time can be reduced by about 12.34% to 27.80% when using the minimum fleet size. The findings of this study offer valuable policy implications and managerial insights for shared e-scooter operators and policymakers in developing effective e-scooter management strategies.]]></description>
      <pubDate>Wed, 13 Nov 2024 13:16:58 GMT</pubDate>
      <guid>https://trid.trb.org/View/2446781</guid>
    </item>
    <item>
      <title>A latent class analysis of public perceptions about shared mobility barriers and benefits</title>
      <link>https://trid.trb.org/View/2391023</link>
      <description><![CDATA[The United States faces urban issues like congestion and pollution due to heavy car dependency, with over 91% owning cars and 87% driving to work. In response to these challenges, various shared mobility solutions have been integrated into transportation systems, encompassing bike-sharing, e-scooter sharing, and ride-hailing, but have not seen widespread adoption. To this end, this study investigates the perception of these services in the context of benefits and barriers. A survey was conducted in Indianapolis, Indiana and distributed online, gathering 424 responses. Latent class analysis (LCA) was used to determine groups of individuals with similar perceptions. The LCA identified three classes regarding perceptions of shared mobility benefits, and three others in relation to barriers. Regarding benefits, the classes include “Casual observers of benefits,” predominantly comprising older, less active females; “Benefits proponents,” featuring a majority of young, well-educated, higher-income males who prioritize health; and “Non-believers in benefits,” primarily consisting of older individuals, often identifying as black, with lower incomes and less active commuting. In terms of barriers, the classes are “Indifferent about barriers,” mainly comprising younger, lower-income individuals; “Shared mobility bystanders,” primarily characterized by older individuals with infrequent work commutes; and “Barrier conscious,” mostly including younger, well-educated, racially diverse individuals with complex commuting patterns. In both the benefits and barriers categories, the largest classes consist of individuals with neutral perspectives on shared mobility, signifying that a substantial portion of the population has not fully embraced these services. The study recommends policies to promote shared mobility to this significant population segment.]]></description>
      <pubDate>Tue, 25 Jun 2024 10:31:59 GMT</pubDate>
      <guid>https://trid.trb.org/View/2391023</guid>
    </item>
    <item>
      <title>A physics-informed machine learning for generalized bathtub model in large-scale urban networks</title>
      <link>https://trid.trb.org/View/2385144</link>
      <description><![CDATA[Traffic management strategies play a crucial role in mitigating urban congestion by enhancing the efficiency of urban road networks. Network traffic flow models are fundamental in elevating the efficacy of these strategies, as they estimate traffic states and depict traffic dynamics. While these models have strong theoretical foundations, current network traffic flow models grapple with accurately reflecting intricate and evolving real-world traffic patterns, particularly the variance and heterogeneity seen in expansive urban systems. These challenges arise from the innate dynamics of traffic flows and external factors such as fluctuating travel demands and traffic control measures. Although numerous studies have employed machine learning (ML) techniques to accurately estimate traffic states, these ML approaches often lack interpretability as the interplay among variables remains concealed. To address this shortfall, the authors introduce a hybrid model called the Physics-Informed Machine Learning with Generalized Bathtub Model (PIML-GBM). This model harmoniously combines the explanatory power of physical models with the robust modeling capabilities of ML. The authors evaluated the PIML-GBM using mobile location data from a comprehensive road network in Indianapolis, Indiana, United States. Evaluated through extensive mobile location data (14.4 million unique devices and 4.8 billion records) from Indianapolis’s comprehensive road network (396.61 mi2), the PIML-GBM demonstrates a remarkable performance improvement over traditional GBM and pure multi-layer neural network (PMNN) without physics knowledge. It achieved a significantly lower Mean Absolute Error (MAE) of 0.0226 compared to GBM’s 0.3775 and PMNN’s 0.0463, illustrating its effectiveness in accurately capturing urban traffic dynamics and predicting trip volumes over various trip distances. This study not only contributes significantly to the field of urban transportation planning but also presents a practical framework for traffic monitoring and prediction, highlighting the real-world applicability of integrating machine learning with traditional traffic flow principles.]]></description>
      <pubDate>Mon, 17 Jun 2024 09:40:42 GMT</pubDate>
      <guid>https://trid.trb.org/View/2385144</guid>
    </item>
    <item>
      <title>The Impact of COVID-19 on User Perceptions of Public Transit, Shared Mobility/Micro-Mobility Services, and Emerging Vehicle Types</title>
      <link>https://trid.trb.org/View/2317368</link>
      <description><![CDATA[The objective of this project is to investigate the impact of COVID-19 on public perceptions of public transit, shared mobility services, and micro-freight delivery services. As transportation systems were at the forefront of the COVID-19 pandemic, it is critical to examine the changes in habits and overall travel behavior of users of shared modes and/or emerging services to best plan for transportation policies in the long run. Three surveys were conducted in select communities with different levels of transit and smart mobility usage [Indianapolis (low), Minneapolis (medium), and Chicago(heavy)] to assess public perceptions for public transit, emerging technologies such as ride-hailing, micro-mobility, and micro-freight delivery services in the COVID era. This study evaluates the relationship between certain demographics and travel preferences during the pandemic. The study also draws a comparison between the three urban settings with regards to travel habits and intentions. The research results can help transportation agencies enhance their plans and policies towards any future pandemics. Overall, the study contributes to the ongoing national dialogue on transportation, accessibility, and mobility in the post-COVID-19 era.]]></description>
      <pubDate>Mon, 08 Jan 2024 12:19:16 GMT</pubDate>
      <guid>https://trid.trb.org/View/2317368</guid>
    </item>
    <item>
      <title>Behavioral Intention to Ride in AVs and Impacts on Mode Choice Decisions, Energy Use and GHG Emissions</title>
      <link>https://trid.trb.org/View/2315215</link>
      <description><![CDATA[The objective of this project is to examine the potential effects of high-level vehicle automation on energy demand and greenhouse gas (GHG) emissions from vehicles. To achieve this, improved projections of future travel demand and patterns of autonomous vehicles (AVs) were obtained using a stated preference survey distributed in Indianapolis, Indiana, and the associated energy consumption and carbon intensity levels were estimated. Also, a two-stage simulation framework based on an agent-based model was proposed. Different scenarios were designed to examine the impact of the size and composition of fleets of AVs offering single-passenger rides, on GHG emissions, air pollutants, and energy consumption.]]></description>
      <pubDate>Tue, 02 Jan 2024 15:51:22 GMT</pubDate>
      <guid>https://trid.trb.org/View/2315215</guid>
    </item>
    <item>
      <title>What motivates the use of shared mobility systems and their integration with public transit? Evidence from a choice experiment study</title>
      <link>https://trid.trb.org/View/2237987</link>
      <description><![CDATA[Shared mobility, including bike-sharing, shared e-scooter, and ride-hailing, could improve transportation sustainability when substituting private car use and integrating with public transit. However, if shared mobility competes with other green modes, it cannot guarantee sustainability benefits. The competing and synergistic relationships between conventional modes and shared mobility are complex and not well-studied to date. Understanding users’ preferences in mode choice decisions among shared mobility, conventional modes, and multimodal systems can help better evaluate the impact of shared mobility adoption and support related policies. This paper presents the design and results of a stated-preference choice experiment study conducted in Indianapolis, Indiana. An integrated choice and latent variable (ICLV) model was estimated to identify attributes that affect travelers’ mode choices for both non-commuting and commuting purposes. Results show that (1) travel cost and travel time are significant variables and their impact on mode choice for system integration and competition is elastic; and (2) user heterogeneity can be observed through three latent variables (perceptions of shared mobility, travel attributes importance, and social values) to identify traveler’s preferences and concerns for mode choice. The contributions from this study include: (1) developing quantitative models to estimate mode choice behavior with regard to shared mobility use, and (2) identifying a set of policy guidelines for system development to encourage multimodal usage and decrease car dependency.]]></description>
      <pubDate>Wed, 06 Sep 2023 15:14:35 GMT</pubDate>
      <guid>https://trid.trb.org/View/2237987</guid>
    </item>
    <item>
      <title>A multi-group analysis of the behavioral intention to ride in autonomous vehicles: evidence from three U.S. metropolitan areas</title>
      <link>https://trid.trb.org/View/2148603</link>
      <description><![CDATA[This paper proposes a well-grounded theoretical model to assess the factors influencing the intention to ride in autonomous vehicles (AVs). The model is based on the Theory of Planned Behavior (TPB), which has been decomposed to account for key components of the Diffusion of Innovation (DoI) theory and extended to include other influential attitudinal components (such as driving-related sensation seeking, safety perceptions, environmental concerns, and affinity to innovativeness). The extent to which these factors are expected to affect the diffusion of AVs uniformly across different urban settings is also examined. Data were collected through stated preference surveys targeting adult residents in three metropolitan statistical areas, Chicago (Illinois), Indianapolis (Indiana), and Phoenix (Arizona). Confirmatory factor analysis was conducted to test the validity and reliability of the components included in the theoretical model, followed by the estimation of a multi-group structural equation model. The findings of the measurement model show that the survey questions are measured equally across the three areas, and hence, the theoretical model is transferrable. The results of the structural model suggest that the synergistic effects between TPB and DoI can better explain the behavioral intention to ride in AVs. It was also found that the effect of the TBP components is similar across various areas; however, this is not the case for the DoI components. In general, the findings reinforce the need for wider testing of AV technology in urban areas coupled with public education campaigns to harvest public awareness and acceptance.]]></description>
      <pubDate>Tue, 25 Apr 2023 09:49:40 GMT</pubDate>
      <guid>https://trid.trb.org/View/2148603</guid>
    </item>
    <item>
      <title>Public Acceptance and Socio-Economic Analysis of Shared Autonomous Vehicles: Implications for Policy and Planning</title>
      <link>https://trid.trb.org/View/2134845</link>
      <description><![CDATA[Shared transportation has grown significantly as renewed interest in urbanism and growing social and economic concerns have strengthened the need for sustainable alternatives. Shared autonomous vehicles (SAVs) are emerging as an alternative mode of transportation that could improve mobility and accessibility. However, the implications of SAVs on social equity are still under research and uncertainty exists regarding the potential adoption and market penetration within transportation-disadvantaged populations. The objective of this study is to assess the extent to which transportation-disadvantaged groups intend to adopt SAVs at two study areas with different density and travel characteristics, and to identify the potential geographical areas where SAVs could be effectively deployed. Public acceptance towards SAVs was assessed via stated preference surveys while a multi-spatial perspective approach was adopted to identify transportation disadvantaged groups in the two study areas. The results of the spatial market segmentation analysis showed that most of the respondents located in areas in Indianapolis identified as transportation disadvantaged are classified as early adopters and innovators, while the opposite conclusion was reached for the respective areas in Chicago, except for those closer to the downtown area. The results of this study could be useful to three stakeholders: ridesharing service providers, for their marketing and pricing-scheme decisions; public transportation planning agencies, for their policy making and investment decisions; and transportation planners, for infrastructure preparations towards the emergence of SAVs.]]></description>
      <pubDate>Mon, 20 Mar 2023 09:35:40 GMT</pubDate>
      <guid>https://trid.trb.org/View/2134845</guid>
    </item>
    <item>
      <title>Highly Transportation Disadvantaged Areas Indianapolis [supporting dataset]</title>
      <link>https://trid.trb.org/View/2134843</link>
      <description><![CDATA[This file contains areas designated as highly transportation disadvantaged in Indianapolis. These two areas were outlined using three different measures and publicly available data. The methodology is replicable and can be used in other cities.]]></description>
      <pubDate>Mon, 20 Mar 2023 09:35:40 GMT</pubDate>
      <guid>https://trid.trb.org/View/2134843</guid>
    </item>
    <item>
      <title>Multinomial Logit Models Raw Data for Chicago and Indianapolis [supporting dataset]</title>
      <link>https://trid.trb.org/View/2134959</link>
      <description><![CDATA[These two files contain information regarding a travel survey when respondents share travel habits and opinions regarding travel easiness. It is also the first National Household Travel Survey (NHTS) effort to include health-related data with travel data. This data was obtained from the National Household Survey for Chicago and Indianapolis (https://nhts.ornl.gov/). The authors processed this data to be able to estimate Multivariate logistic regressions. These models predict the relationships between dependent and independent variables. It calculates the probability of something happening depending on multiple sets of variables. For this project, the authors used this data and the modeling to understand these two cities' substitution or complementing trip patterns when analyzing bike, walk, public transit, and ridesharing use (as a proxy of shared autonomous vehicles). These four modes were the independent variables. The rest of the database was used as dependent variables or factors related to the probability of choosing those four modes of transportation in Chicago and Indianapolis.]]></description>
      <pubDate>Mon, 20 Mar 2023 09:35:40 GMT</pubDate>
      <guid>https://trid.trb.org/View/2134959</guid>
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