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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>Learning from geometry-aware near misses to real-time COR: A corridor-wide grouped random parameters GEV framework</title>
      <link>https://trid.trb.org/View/2687376</link>
      <description><![CDATA[Real-time corridor-wide crash-occurrence risk (COR) prediction is challenging, since existing near-miss EVT models oversimplify collision geometry, neglect vehicle–infrastructure (V–I) interactions, and fail to adequately account for spatial heterogeneity in traffic and roadway conditions. To do so, this study develops a geometry-aware 2D-TTC near-miss extraction and integrates it with a hierarchical Bayesian structure grouped random parameters (HBSGRP–UGEV) to estimate short-term COR in urban corridors. Building on prior grouped EVT formulations while explicitly accommodating both V–V and V–I near-miss processes within a unified corridor-wide modeling framework. High-resolution trajectories from the Argoverse-2 dataset were analyzed across 28 sites on Miami’s Biscayne Boulevard to extract extreme near-miss events. The model incorporates vehicle dynamics and roadway features as covariates, with partial pooling across segments and intersections to capture corridor-wide heterogeneity. Results show that the HBSGRP–UGEV framework outperforms fixed-parameter HBSFP-UGEV models, reducing DIC by up to 7.5% (V–V) and 3.1% (V–I). Predictive validation using ROC–AUC confirms strong accuracy (0.89 for V–V segments, 0.82 for intersections, 0.79 for V–I segments, and 0.75 for intersections). Grouped random-parameters (HBSGRP) framework indicate that relative (speed, distance, and deceleration) dominate V–V near-miss risk on segments, whereas V–I segment risk is primarily associated with relative distance; at intersections, V–V risk is driven by relative (speed and distance), while V–I dynamics exhibit no statistically significant effects. These findings demonstrate the value of a geometry-aware, spatially adaptive framework for proactive corridor safety management, supporting both real-time interventions and long-term Vision Zero goals.]]></description>
      <pubDate>Mon, 27 Apr 2026 14:57:56 GMT</pubDate>
      <guid>https://trid.trb.org/View/2687376</guid>
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    <item>
      <title>Case Study: Using Pop-Up Protected Bike Lanes to Encourage Community Support for Safe Streets</title>
      <link>https://trid.trb.org/View/2642354</link>
      <description><![CDATA[Problem, research strategy, and findings: Protected and separated bike lanes create low-stress, family-friendly bicycle infrastructure and community connectedness, increasing rider safety and parental acceptance of bicycling to school. However, the creation of such infrastructure is often beset with difficulty due to public perception and transportation priorities. In this case study, we examined the creation of widespread community support toward protected bike lane projects by combining a Bike to School Day event with a tactical, urbanism pop-up bike lane as a Bike Lane to School Day, based on advocacy efforts by the University of Miami BikeSafe Program at Coconut Grove Elementary School in Miami (FL). With the support of local stakeholders, including school district personnel, local community members, and transportation officials, these events resulted in more than 120 family participants riding to school, concurrent with a reduction in automotive drop-off traffic of up to 30% during the intervention. A total of 73 parents completed a post-event online survey, which provided feedback regarding the role such infrastructure improvements could provide for their children. Takeaway for practice: The combination of Bike to School Day with a pop-up, protected bike lane—a Bike Lane to School Day—can lead to an increase in the number of families and youths riding bikes to school, a decrease in automobile traffic, and favorable community and leadership support for providing permanent bicycling facilities to benefit youth mobility.]]></description>
      <pubDate>Wed, 11 Mar 2026 16:58:59 GMT</pubDate>
      <guid>https://trid.trb.org/View/2642354</guid>
    </item>
    <item>
      <title>Roadway traffic crash during extreme heat days: Insights from hazards-exposure-vulnerability-adaptation</title>
      <link>https://trid.trb.org/View/2647526</link>
      <description><![CDATA[The escalating frequency of extreme heat events poses a potential threat to roadway safety, yet the spatial patterns of crash risk from more multi-dimensional perspectives remain underexplored. Using the Hazards-Exposure-Vulnerability-Adaptation paradigm, this study examines how traffic crash rates on extreme-heat days vary across roadway segments and road design characteristics in the City of Miami, Florida. This study analyzed traffic exposure and crash rates across three yearly extreme heat thresholds (90th, 95th, and 97th percentiles) from 2011 to 2015. A CatBoost model, interpreted via SHAP analysis, is used to identify key roadway and contextual features associated with higher or lower crash rates during extreme-heat days. The key findings are as follows: 1) Crash risk on extreme-heat days shows a threshold-dependent pattern across the examined percentiles. As the heat threshold intensifies from the 90th to the 97th percentile, the average network-wide crash rate increases, while the number of road segments with above-average crash rates follows a V-shaped pattern—first declining and then rising sharply at the highest threshold. This suggests that inherent adaptive characteristics of many roadways may be sufficient to moderate crash risk under moderately extreme heat but become increasingly inadequate once heat reaches very abnormally high threshold (e.g., the 97th percentile). 2) Models based on higher extreme-heat thresholds provide clearer insight into vulnerability patterns. Compared to the 90th and 95th percentile models, the 97th percentile model more clearly isolates roadway and contextual features most strongly associated with elevated crash rates on extreme-heat days, whereas lower thresholds appear more affected by noise from other coincident factors. 3) Roadway investment emerges as the primary adaptive factor associated with reduced risk. Physical attributes such as construction cost and geometric design are the most influential correlates of crash vulnerability on extreme-heat days, with higher-quality roadway investment linked to substantially lower crash rates. In contrast, the observed associations for safety control measures and emergency service accessibility are comparatively limited. These findings characterize which roadway environments are more vulnerable when extreme-heat conditions occur.]]></description>
      <pubDate>Fri, 06 Feb 2026 08:45:32 GMT</pubDate>
      <guid>https://trid.trb.org/View/2647526</guid>
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    <item>
      <title>Cognitive Task Analyses of Air Traffic Management (ATM) Workforce to Inform Human Performance Modeling</title>
      <link>https://trid.trb.org/View/2658080</link>
      <description><![CDATA[This report documents a comprehensive Cognitive Task Analysis (CTA) conducted by The MITRE Corporation’s Center for Advanced Aviation Systems Development (CAASD) for the Federal Aviation Administration (FAA) Human Factors Division (ANG-C1). The primary objective was to analyze key air traffic workforce positions at Air Route Traffic Control Centers (ARTCCs)—including Radar (R) and Radar Associate (RA) controllers, Operations Supervisors (OSs), Traffic Management Coordinators (TMCs), Supervisory Traffic Management Coordinators (STMCs), and Oceanic controllers—to inform operational changes, improve procedures, training, interfaces, and decision support aids. The research adopted a holistic CTA approach, spanning systems and capturing the overall operational workflow rather than focusing on system-specific tasks. Data collection involved approximately 60 hours of facility observations and guided discussions at Seattle (ZSE), Oakland (ZOA), and Miami (ZMA) ARTCCs, enabling the team to document both observable actions and underlying cognitive processes. MITRE developed multi-level CTA models using flowcharts for task flows and Goals, Operators, Methods, and Selection Rules (GOMS)-based cognitive models to represent decision-making criteria, automation interaction, and communication. Key findings highlight the complex interplay of attention, vigilance, communication, and perceptual skills required across all ARTCC positions, with unique operational characteristics observed in oceanic areas and supervisory roles. The CTA results provide a baseline for discrete event task network modeling, supporting the FAA’s efforts to anticipate cognitive performance issues and improve individual and team performance. Recommendations include further research to expand efforts to the Terminal Radar Approach Control (TRACON) and Airport Traffic Control Tower (ATCT), further validate model applicability across additional ARTCC facilities and expanding supervisor task analysis to include both operational and administrative responsibilities to inform insights beyond the scope of this effort such as those focused on overall workload experience. Next steps involve formatting task flow diagrams for integration with the Improved Performance Research Integration Tool (IMPRINT) and extending CTA research to TRACON and ATCT facilities.]]></description>
      <pubDate>Mon, 02 Feb 2026 14:13:56 GMT</pubDate>
      <guid>https://trid.trb.org/View/2658080</guid>
    </item>
    <item>
      <title>Efficient system reliability assessment of shoreline seawalls: Applications to SEAHIVE (UM)</title>
      <link>https://trid.trb.org/View/2663225</link>
      <description><![CDATA[Seawalls play a critical role in protecting coastal transportation systems from erosion, flooding and storm surges. Yet their performance is deteriorating due to changes in structural capacity and increasing external demands, posing growing threats to coastal safety. Evaluating the reliability and risk of seawalls along the shoreline is essential for informed maintenance and repair decisions. However, the large scale of shoreline seawalls and the complex coastal and geotechnical conditions in Miami present significant challenges for system reliability analysis. This is a collaborative research project conducted in partnership with Texas State University. The objective of this research project is to develop an efficient and practical framework that integrates interdisciplinary expertise in geotechnical asset management, seawall design and construction, and reliability analysis to perform system reliability analysis of shoreline seawalls.
The proposed project builds on two lines of prior works. First, an effective and well-defined inspection rating system was developed to evaluate the conditions of mechanically stabilized earth (MSE) walls at Texas State University. Second, SEAHIVE®, a novel seawall composed of concrete perforated hexagonal prisms, was developed at the University of Miami and has been implemented in the Miami area for its ability to dissipate wave energy and protect habitats. Leveraging these advances, the proposed project will establish a unified framework for reliability assessment of shoreline seawalls.
The project consists of two phases: component-level and system-level reliability analysis. At the component level, the research team will develop an efficient and effective method to evaluate the reliability analysis of individual SEAHIVE® components. First, using available analytical models and experimental data, the team will define limit states that specify the conditions under which SEAHIVE® components perform adequately or fail. Second, the inspection rating method originally developed for MSE walls will be recalibrated for SEAHIVE® in the Miami area, following procedures established in prior work. Finally, these calibrated ratings will then serve as inputs to the defined limit states, enabling the calculation of reliability indices. The expected outcome of this phase is a practical guideline for engineers to quickly rate the seawall and determine the component reliability index.
Since seawalls function as interconnected systems rather than isolated units, the next phase is system-level analysis. Specifically, the team will elicit statistical correlations in seawall deterioration and soil conditions across different locations using inspection, measurement, and simulation data. An efficient system reliability analysis will then incorporate these correlations into component-level reliability analysis to compute the overall reliability index of seawalls along the shoreline. Together, the two phases will yield a practical decision support tool to efficiently inspect the shoreline seawalls and estimate the system reliability index in support of risk management and maintenance prioritization for seawalls.
]]></description>
      <pubDate>Sat, 31 Jan 2026 11:03:15 GMT</pubDate>
      <guid>https://trid.trb.org/View/2663225</guid>
    </item>
    <item>
      <title>Measuring exposure to extreme heat in public transit systems</title>
      <link>https://trid.trb.org/View/2589290</link>
      <description><![CDATA[Public transit users are among the most vulnerable to extreme heat due to the urban heat island effect and longer outdoor exposure. However, few studies have provided detailed measurement of transit riders' heat exposure and discussed the resilience of transit systems in responses to heat exposure. Using 1 m-by-1 m microclimate simulations and transport network analysis, this paper introduces the Transit Heat Exposure Index (THEI) to gauge high-fidelity heat exposure for transit riders. A case study of THEI's application in Miami, one of the hottest US cities, shows that downtown Miami has lower heat exposure due to better transit access, despite higher local feels-like temperature. Walking is the primary source of heat compared to waiting, and a few streets and stops contribute most exposure. The methodology developed in this study provides a valuable tool to enhance transit resilience to heat and develop effective mitigation strategies.]]></description>
      <pubDate>Thu, 13 Nov 2025 13:32:30 GMT</pubDate>
      <guid>https://trid.trb.org/View/2589290</guid>
    </item>
    <item>
      <title>Marine Investigation Report: Fire aboard Yacht Flagship, April 28, 2024</title>
      <link>https://trid.trb.org/View/2588963</link>
      <description><![CDATA[​On April 28, 2024, at 1031 local time, a fire started on board the uncrewed yacht Flagship while it was docked at an enclosed bay of a shipyard on the Miami River in Miami, Florida. Shoreside firefighters moved the vessel to a nearby sea wall, where they extinguished the fire. The vessel eventually sank at the sea wall. There were no injuries, and no pollution was reported. The Flagship was declared a total loss, valued at $5 million. The National Transportation Safety Board (NTSB) determined that the probable cause of the fire on the yacht Flagship was the thermal runaway and explosion of the 24-volt lithium-ion battery bank due to the inoperable battery management systems, resulting in the practice of manually charging the lithium-ion batteries with a portable battery charger, which compromised the safe monitoring of the vessel’s lithium-ion battery systems.​]]></description>
      <pubDate>Tue, 19 Aug 2025 16:34:02 GMT</pubDate>
      <guid>https://trid.trb.org/View/2588963</guid>
    </item>
    <item>
      <title>A Trip-Chain-based approach to generate travel demands for shared autonomous vehicle systems modeling</title>
      <link>https://trid.trb.org/View/2540382</link>
      <description><![CDATA[To inform decision making and guide the development of smart transportation systems towards urban sustainability, it is critical to model how travelers may use shared autonomous vehicles (SAV). Such models need two key components − travel demands with high spatiotemporal resolutions and travelers’ sociodemographic information – to determine travelers’ acceptance and participation in SAV system. Existing SAV operations models used travel demand generation methods that either lack travelers’ demographics or only generate trips at a zonal level on a case-by-case basis. A scalable approach that can generate travel demands with higher resolution and linked household- and person-level sociodemographic is needed to enable better analysis of trips’ shareability and support SAV operations modeling. To address this gap, the authors propose a Household and Individual Trip-chain-based (HIT) travel demand generation model. The travel demands of household members are generated as chains of trips with spatial and temporal details that match the travel patterns of the individual’s as well as the household’s demographic profile. Using Miami as a case study city, the authors compared the proposed HIT model with a state-of-the-art activity-based model (ABM) to demonstrate its feasibility and validity. Results show that HIT model captures more complex travel patterns. The authors also used the travel demands generated by both methods as inputs to simulate SAV operation and found that using ABM to input travel demands in SAV operation models may overestimate the benefits of SAVs. Additionally, the proposed HIT model has the advantage of only requiring publicly available data as inputs, making it scalable nationwide.]]></description>
      <pubDate>Wed, 28 May 2025 16:23:17 GMT</pubDate>
      <guid>https://trid.trb.org/View/2540382</guid>
    </item>
    <item>
      <title>Equitable Restoration Strategies for Bridge and Road Infrastructure Networks after Hurricanes in Coastal Communities</title>
      <link>https://trid.trb.org/View/2534041</link>
      <description><![CDATA[The functionality of Bridge and Road Infrastructure Networks (BRIN) is indispensable for facilitating the recovery process of low-lying coastal communities in the aftermath of hurricanes. By ensuring the efficient distribution of disaster supplies and providing access to essential services for affected residents, BRIN plays a critical role in restoring normalcy post-disaster. Despite the existence of various optimization methods aimed at expediting post-hurricane recovery, a considerable research gap persists, particularly concerning equity considerations across diverse population groups and geographical regions. In response to this gap, this project endeavors to address the disproportionate impacts of hurricanes on underserved communities, with a specific focus on the densely populated metropolitan area of Greater Miami. The proposed tool represents a novel approach by integrating equity considerations directly into transportation infrastructure restoration decisions. Key tasks encompass the development of a comprehensive flooding map utilizing historical data from the Federal Emergency Management Agency (FEMA), the optimization of restoration models to prioritize equity, and the active engagement of multisector stakeholders in the planning and implementation of recovery efforts. Research activities include understanding the impacts of hurricanes on infrastructure, with a particular emphasis on identifying and mitigating disparities in access and service provision. By prioritizing equity in restoration plans and collaborating closely with local stakeholders, the project aspires to ensure the inclusivity, sustainability, and resilience of transportation infrastructure in the face of natural disasters. Ultimately, the goal is to mitigate the disproportionate impact of hurricanes on underserved communities and foster the development of more equitable and resilient infrastructure systems.]]></description>
      <pubDate>Mon, 14 Apr 2025 17:07:03 GMT</pubDate>
      <guid>https://trid.trb.org/View/2534041</guid>
    </item>
    <item>
      <title>Out-of-home activity adaptations of commuters and non-workers to the power outage at home induced by hurricane Irma</title>
      <link>https://trid.trb.org/View/2522099</link>
      <description><![CDATA[Compared to hurricane evacuation travel, considerably less is known about travel by those who remain in the at-risk area and experience utility/infrastructure disruptions. How do people adapt their activities/travels in the aftermath especially with the impact of a power outage at home? With data collected after Hurricane Irma (2017), this study focuses on understanding commuters’ and non-workers’ activity participation and the interrelationships among those activities, by testing the significance of variables typically used in activity-based travel demand modeling studies. Its purpose is to compare the modeling results with those from past studies made in normal situations so that potential behavior changes (reflected by the parameter significance) can be observed. This study employs structural equation modeling (SEM) to capture the complex interrelationships among activities. Two major findings from this study are that (1) people are more likely to engage in multiple out-of-home discretionary activities (include dining, social visits, and leisure activities) during a hurricane-induced power outage at home and (2) commuters provided with flexible working arrangements (such as telecommuting) are more likely to work a shorter day during the period. Increased out-of-home discretionary activity participation and decreased working duration are likely to cause activity/trip pattern changes from an aggregate view. Such changes can affect the decision-making of public agencies, such as the priority placed on different locations for debris removal and power restoration. This study serves as a starting point and contributes to future studies making more in-depth investigations into post-impact travel and travel during infrastructure disruptions.]]></description>
      <pubDate>Mon, 31 Mar 2025 16:15:15 GMT</pubDate>
      <guid>https://trid.trb.org/View/2522099</guid>
    </item>
    <item>
      <title>Improving the Efficiency of Traffic Outside Intermodal Facilities: A Proof of Concept of Operations</title>
      <link>https://trid.trb.org/View/2389880</link>
      <description><![CDATA[Intermodal facilities, including port terminals, play a significant role in the economic framework of the United States by making substantial contributions to the country’s GDP, but face challenges managing increased freight volumes. However, increased transportation time within port facilities leads to higher costs, emissions, and impacts on efficiency and sustainability. This research aims to develop a concept of operations (ConOps) for improving the efficiency of heavy truck movement outside port facilities, with goals of reducing congestion, considering greenhouse gas (GHG) emissions, and addressing truck drivers’ satisfaction. The study proposes integrating technological solutions to streamline heavy truck traffic at intermodal port facilities, including scheduled truck arrivals and departures, truck stop and rest areas, real-time traffic information, implementation of dedicated truck lanes, and autonomous truck platooning. The focus is improving communication, efficiency, and safety for trucking companies, operations managers, and truck drivers. Using microsimulation modeling a traffic impact study is also conducted, focusing on a case study near the port of Miami. Different traffic scenarios are implemented to evaluate different strategies, such as dedicated and exclusive truck lanes, freeway lane restrictions, and autonomous truck platooning. Simulation findings emphasize the positive impact of these strategies on travel times and delays, and forecast scenarios account for increased truck volumes. Dedicated truck lanes and truck platooning demonstrate promising results in improving overall traffic flow. This research supports decision-making for government officials and logistics service providers in sustainable and efficient intermodal freight planning.]]></description>
      <pubDate>Fri, 28 Jun 2024 13:59:48 GMT</pubDate>
      <guid>https://trid.trb.org/View/2389880</guid>
    </item>
    <item>
      <title>Guidelines for Activating Ramp Metering Signals in Response to Non-Recurrent Congestion during Off-Peak Hours Using a Statistical Method</title>
      <link>https://trid.trb.org/View/2386146</link>
      <description><![CDATA[Ramp metering is a transportation systems management and operations strategy that utilizes signals installed at freeway on-ramps to dynamically manage traffic entering the freeway. Ramp metering signals (RMSs) are usually activated during peak hours to alleviate recurrent congestion. However, recurrent congestion during peak hours constitutes less than half of all congestion. It is the non-recurrent congestion resulting from traffic incidents, work zones, adverse weather conditions, special events, and so forth, that adversely affects the performance of the freeway. Thus, this study used a three-regime model to develop guidelines to activate and deactivate RMSs during off-peak hours in response to non-recurrent congestion caused by incidents. A 10-mi section of I-95 in Miami, Florida, was used as the case study. The findings indicated that the RMS immediately upstream of the incident location might be activated when the average speed on the mainline drops below 45?mph and deactivated when the incident has been cleared and the average mainline speed reaches 45?mph for a consistent 5-min period. The RMS immediately downstream of the incident location may be activated when the average speed on the mainline drops below 35?mph for a consistent 5-min period and deactivated when the incident has been cleared and the average mainline speed reaches 35?mph for a consistent 5-min period. The proposed guidelines will enable transportation agencies to use ramp metering to improve traffic operations and safety during off-peak hours.]]></description>
      <pubDate>Wed, 05 Jun 2024 15:18:17 GMT</pubDate>
      <guid>https://trid.trb.org/View/2386146</guid>
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    <item>
      <title>Phase Ⅱ: Field Load Testing of Shallow Foundations in Florida Limestone</title>
      <link>https://trid.trb.org/View/2342096</link>
      <description><![CDATA[Three full-scale shallow foundation load tests (900 tons) were performed at Miami, Fort Lauderdale, and Bell, Florida, to validate the Florida Bearing Capacity equations (Phase I – FDOT BDV31-977-51) as well develop and verify load-settlement response for service limit state. Rock coring, Standard penetration test (SPT), and seismic shear tests were performed at each site; the split tension, unconfined compression, and triaxial compression tests were performed on the recovered cores to establish strength envelope and moduli of the rock. Exact footing location at each site was selected based on the limit of load test frames and the strength envelope, which was established as a function of dry unit weight and formation. The seismic shear tests representing a larger volume of rock beneath the footing were found to characterize the mass dry unit weight of each site (validated by the cores). The subsurface information (in-situ testing), construction of load test, and the load test setup and measured results as well as predicted behavior are presented in the report.  The Florida Bearing Capacity equations were validated in all three load tests for different formations and boundaries (single rock layer and rock over sand). The load-settlement response of a single layer was shown to be predicted with Fenton and Griffiths method. For the rock-over-sand case, the Burmister method and/or Equivalent Modulus (Winkler model) method are recommended to characteristic the bilinear load-settlement response. A parametric study between the Burmister method, Winkler model, and finite element method was conducted for different footing widths, shapes, embedment depths, rock dry unit weights, and rock layer thicknesses as well as the sand modulus. Good agreement was achieved between both.]]></description>
      <pubDate>Thu, 22 Feb 2024 09:06:50 GMT</pubDate>
      <guid>https://trid.trb.org/View/2342096</guid>
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    <item>
      <title>How do attitudes and impacts of Covid-19 affect demand for microtransit?</title>
      <link>https://trid.trb.org/View/2244666</link>
      <description><![CDATA[The Covid-19 pandemic dramatically reduced demand for public transportation and other shared mobility services. While some studies have indicated that this lowered demand may continue for some time post-pandemic, more affordable forms of shared mobility may be more likely to rebound faster. In this paper, the authors concentrate on the likely effect of pandemic-related attitudes on one such service: microtransit. They applied quantitative and qualitative methods to assess this question on a behavioral survey applied to 2,400 residents in four diverse United States cities: Miami, Washington D.C., Minneapolis, and Seattle. The purpose of this survey was to investigate interest in a hypothetical microtransit, or on-demand, first-mile/last-mile shuttle service. The authors find no correlation between Covid-19 impact and interest in microtransit using structural equation modeling. Choice modeling applied on hypothetical mode choice experiments shows a positive correlation between the chances of choosing it in hypothetical scenarios and a general propensity to use shared mobility during the pandemic, but not on other pandemic-related attitudes. Finally, a qualitative analysis shows there is hardly any relation between open-ended responses related to microtransit and the pandemic. These results suggest that the pandemic may not have such a large impact on shared mobility as expected. Based on the findings, it is possible that services such as microtransit could see demand at or above pre-pandemic levels. The authors recommend transit agencies assess any expected demand shifts with as much information as possible, as it is possible that demand for services such as microtransit will rebound to pre-pandemic levels comparatively quickly.]]></description>
      <pubDate>Mon, 23 Oct 2023 16:20:04 GMT</pubDate>
      <guid>https://trid.trb.org/View/2244666</guid>
    </item>
    <item>
      <title>Shared E-Scooter User Characteristics and Usage Patterns across Four U.S. Cities</title>
      <link>https://trid.trb.org/View/2244402</link>
      <description><![CDATA[The rapid growth of shared e-scooters around the world creates an interest and a need to understand who uses shared e-scooters, trip patterns, and modal shift impacts. To shed light on these questions, this study conducted an online survey (N?=?1498) to collect information on shared e-scooter use in four U.S. cities: Birmingham, AL, Washington D.C., Los Angeles, CA, and Miami, FL. The paper provides a comprehensive view of shared e-scooter use in different cities and reveals the similarities and differences in e-scooter users’ socioeconomic characteristics. Furthermore, the results of a binary logistic model show the impact of socioeconomic and travel modal choice factors on predicting shared e-scooter usage. The findings suggest that male, younger, and more affluent users who live in smaller households owning fewer vehicles are more likely to use shared e-scooter services. The relationships between income level, race/ethnicity, or e-scooter ownership and the usage of shared e-scooters are not statistically significant. The McFadden’s R2 of the binary logistic regression model indicates an excellent fit. The results also show that compared with non-users, shared e-scooter users tend to assess travel cost as more important and travel safety as less important in their mode choice. The findings from this work can help city planners, policymakers, and other micromobility stakeholders in their efforts to promote the adoption of shared e-scooters and improve on deployment practices of shared e-scooters at their locations.]]></description>
      <pubDate>Mon, 18 Sep 2023 08:50:48 GMT</pubDate>
      <guid>https://trid.trb.org/View/2244402</guid>
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