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    <title>Transport Research International Documentation (TRID)</title>
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    <atom:link href="https://trid.trb.org/Record/RSS?s=PHNlYXJjaD48cGFyYW1zPjxwYXJhbSBuYW1lPSJkYXRlaW4iIHZhbHVlPSJhbGwiIC8+PHBhcmFtIG5hbWU9InN1YmplY3Rsb2dpYyIgdmFsdWU9Im9yIiAvPjxwYXJhbSBuYW1lPSJ0ZXJtc2xvZ2ljIiB2YWx1ZT0ib3IiIC8+PHBhcmFtIG5hbWU9ImxvY2F0aW9uIiB2YWx1ZT0iMCIgLz48L3BhcmFtcz48ZmlsdGVycz48ZmlsdGVyIGZpZWxkPSJpbmRleHRlcm1zIiB2YWx1ZT0iJnF1b3Q7TGFzIFZlZ2FzICAoIE5ldmFkYSApICZxdW90OyIgb3JpZ2luYWxfdmFsdWU9IiZxdW90O0xhcyBWZWdhcyAoTmV2YWRhKSZxdW90OyIgLz48L2ZpbHRlcnM+PHJhbmdlcyAvPjxzb3J0cz48c29ydCBmaWVsZD0icHVibGlzaGVkIiBvcmRlcj0iZGVzYyIgLz48L3NvcnRzPjxwZXJzaXN0cz48cGVyc2lzdCBuYW1lPSJyYW5nZXR5cGUiIHZhbHVlPSJwdWJsaXNoZWRkYXRlIiAvPjwvcGVyc2lzdHM+PC9zZWFyY2g+" rel="self" type="application/rss+xml" />
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    <language>en-us</language>
    <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>
    <image>
      <title>Transport Research International Documentation (TRID)</title>
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      <link>https://trid.trb.org/</link>
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    <item>
      <title>Observational Intersection Traffic Safety Analysis</title>
      <link>https://trid.trb.org/View/2655705</link>
      <description><![CDATA[While planners and engineers design intersections with safety in mind, the intended use and actual use of facilities do not always align. This misalignment can lead to increased safety risks for all intersection participants, particularly non-motorized users including pedestrians and cyclists. Although facility utilization mismatches can be detected through observation, typical monitoring occurs only during limited peak hours, failing to fully capture comprehensive usage patterns and emerging safety concerns.

This research proposes long-term intersection monitoring to uncover emerging facility utilization patterns and assess inherent intersection safety. The approach leverages existing traffic camera infrastructure combined with modern deep learning techniques for accurate detection and tracking of vehicles, bicycles, and pedestrians. As an explicit use case, the study examines unprotected left-turns to characterize both vehicle-vehicle conflicts through time gap analysis and trajectory conflicts involving other road users. The project develops a computer vision system capable of processing trajectories to quantify left-turns with insufficient gaps, instances where vehicles fail to yield appropriately, and average time gaps, collectively providing metrics to characterize intersection safety.

This interdisciplinary project combines computer vision algorithm development expertise from the University of Nevada, Las Vegas (UNLV) with programming support from Howard University. System evaluation will occur at intersections in both the Washington, DC area and the Las Vegas metropolitan area, utilizing purpose-built high-resolution monitoring equipment for short-term deployment as well as existing lower-resolution traffic cameras for long-term analysis. The project leverages intersection equipment acquired through NSF Award Number 2216489.

Expected outcomes include research contributions in computer vision and machine learning for trajectory analysis, workforce development through student training across both institutions, and technology transfer through publications on intersection safety scoring and practitioner engagement for field deployment.]]></description>
      <pubDate>Mon, 19 Jan 2026 16:30:17 GMT</pubDate>
      <guid>https://trid.trb.org/View/2655705</guid>
    </item>
    <item>
      <title>Safer, Faster, Smarter: Pairing Cloud-Based Vehicle Preemption and AI Intersection Video Analytics [supporting dataset]</title>
      <link>https://trid.trb.org/View/2620573</link>
      <description><![CDATA[The Regional Transportation Commission of Southern Nevada's (RTC’s) Safer, Smarter, Faster smart technology traffic signal project utilized a cloud-based signal timing optimization system that supports transit signal priority (TSP) and enhances safety using artificial intelligence intersection analytics (AIA). The limits for Stage 1 pilot project encompass UMC hospital and downtown Las Vegas. Intersection analytics were deployed at 20 signalized intersections within the project limits. TSP was deployed along RTC Transit Route 206 at 17 signalized intersections along Charleston Boulevard and Casino Center Boulevard. This route often experiences lower on-time performance (OTP) scores than many other routes in the region, while being the 3rd highest passenger route.  Evaluation results showed weekday on-time performance improved by 3–6%, with delays reduced 7–13% and average savings of up to 78 seconds per trip. The AIA system achieved 92.2% accuracy for traffic detection and 97.6% precision in identifying red-light running and near-miss events.]]></description>
      <pubDate>Mon, 24 Nov 2025 10:22:32 GMT</pubDate>
      <guid>https://trid.trb.org/View/2620573</guid>
    </item>
    <item>
      <title>Safer, Faster, Smarter: Pairing Cloud-Based Vehicle Preemption and AI Intersection Video Analytics: Data Management Plan</title>
      <link>https://trid.trb.org/View/2620572</link>
      <description><![CDATA[The Regional Transportation Commission of Southern Nevada's (RTC’s) Safer, Smarter, Faster smart technology traffic signal project utilized a cloud-based signal timing optimization system that supports transit signal priority (TSP) and enhances safety using artificial intelligence intersection analytics (AIA). The limits for Stage 1 pilot project encompass UMC hospital and downtown Las Vegas. Intersection analytics were deployed at 20 signalized intersections within the project limits. TSP was deployed along RTC Transit Route 206 at 17 signalized intersections along Charleston Boulevard and Casino Center Boulevard. This route often experiences lower on-time performance (OTP) scores than many other routes in the region, while being the 3rd highest passenger route. Evaluation results showed weekday on-time performance improved by 3–6%, with delays reduced 7–13% and average savings of up to 78 seconds per trip. The AIA system achieved 92.2% accuracy for traffic detection and 97.6% precision in identifying red-light running and near-miss events.]]></description>
      <pubDate>Mon, 24 Nov 2025 10:22:32 GMT</pubDate>
      <guid>https://trid.trb.org/View/2620572</guid>
    </item>
    <item>
      <title>Evaluation of Remote Driver Performance in Urban Environment Operational Design Domains</title>
      <link>https://trid.trb.org/View/2591511</link>
      <description><![CDATA[Remote driving has emerged as a solution for enabling human intervention in scenarios where Automated Driving Systems (ADS) face challenges, particularly in urban Operational Design Domains (ODDs). This study evaluates the performance of Remote Drivers (RDs) of passenger cars in a representative urban ODD in Las Vegas, focusing on the influence of cumulative driving experience and targeted training approaches. Using performance metrics such as efficiency, braking, acceleration, and steering, the study shows that driving experience can lead to noticeable improvements of RDs and demonstrates how experience up to 600 km correlates with improved vehicle control. In addition, driving efficiency exhibited a positive trend with increasing kilometers, particularly during the first 300 km of experience, which reaches a plateau from 400 km within a range of 0.35 to 0.42 km/min in the defined ODD. The research further compares ODD-specific training methods, where the detailed ODD training approaches attains notable advantages over other training approaches. The findings underscore the importance of tailored ODD training in enhancing RD performance, safety, and scalability for Remote Driving System (RDS) in real-world applications, while identifying opportunities for optimizing training protocols to address both routine and extreme scenarios. The study provides a robust foundation for advancing RDS deployment within urban environments, contributing to the development of scalable and safety-critical remote operation standards.]]></description>
      <pubDate>Fri, 17 Oct 2025 16:49:29 GMT</pubDate>
      <guid>https://trid.trb.org/View/2591511</guid>
    </item>
    <item>
      <title>A specialized inclusive road dataset with elevation profiles for realistic pedestrian navigation using open geospatial data and deep learning</title>
      <link>https://trid.trb.org/View/2487794</link>
      <description><![CDATA[Built environment characteristics can greatly influence pedestrians' route choices with factors beyond distance, such as accessibility, convenience, safety, and aesthetics, playing crucial roles. Although current navigation apps, such as Google Maps and Waze, have successfully provided driving directions, their navigation services are insufficient and sometimes unrealistic for addressing pedestrians' needs, largely due to the lack of dedicated pedestrian networks and the associated navigation algorithms. To address the research gaps, this paper proposes a novel approach that integrates freely available geospatial data and computer vision technology to create a specialized inclusive network dataset for outdoor pedestrian navigation. Moreover, a pedestrian navigation algorithm is developed to generate more practical “shortest” and “alternative” paths by incorporating various sidewalk attributes. The authors applied the method to create a pedestrian navigation network in Las Vegas. SpaceNet's open imagery dataset was used to extract Las Vegas's road networks. A virtual audit process assessed the visual and operational properties of the sidewalk networks using Google street-level images, evaluating factors including sidewalk presence, widths, surface types and conditions, missing curb ramps, greenery, protection from weather conditions, and lighting. Google Earth's open elevation data were used to analyze road elevation profiles as meaningful 3D indicators of sidewalk accessibility for wheelchair users. Further, additional geometric properties of the network, including road curviness, proximity to road intersections, and directional changes, were detected and analyzed. A navigation experiment conducted with individuals having varying mobility abilities, including regular pedestrians, older adults, and wheelchair users demonstrated the effectiveness of the newly developed network and algorithm in meeting the diverse needs of pedestrians.]]></description>
      <pubDate>Tue, 18 Feb 2025 10:56:05 GMT</pubDate>
      <guid>https://trid.trb.org/View/2487794</guid>
    </item>
    <item>
      <title>High Speed Rail Access Charge for the XpressWest of Nevada</title>
      <link>https://trid.trb.org/View/2447235</link>
      <description><![CDATA[Shared High-Speed Rail (HSR) networks are networks where two or more railway operators use the same railway network infrastructure for train operations. When a rail operator who owns the infrastructure allows other operators to access its infrastructure, the additional traffic will lead to an increase in the cost of operations and maintenance of the infrastructure. In such cases, it is common for the other operators to be required to pay a fee, generally referred to as “access charge.” An access charge is a fee paid by a train operator to the owner of the infrastructure to compensate for the increased expenditure and other impacts of additional traffic such as additional delays due to congestion and incidents. The study was done in two parts. Part 1 develops a new framework to calculate a reasonable value of access charges for shared HSR systems. The study describes how to calculate access charge in terms of maintenance costs, congestion costs, and costs of installing side tracks mathematically. The study develops a theoretical capacity allocation model to calculate congestion costs. Part 2  developed a framework for the analysis of train operations including the impact of incidents on the operations and determining access charges for a shared HSR system using VISSIM traffic simulation software. The California High-Speed Rail (CHSR), which is currently under construction, is used as a case study to analyze a potentially shared corridor from Palmdale to Los Angeles. XpressWest, a HSR system that plans to connect Las Vegas with Los Angeles through Palmdale, plans to utilize the CHSR network from Palmdale to Los Angeles for the California part of its operations.]]></description>
      <pubDate>Wed, 20 Nov 2024 13:08:58 GMT</pubDate>
      <guid>https://trid.trb.org/View/2447235</guid>
    </item>
    <item>
      <title>Autonomous Vehicle–Pedestrian Interaction Modeling Platform: A Case Study in Four Major Cities</title>
      <link>https://trid.trb.org/View/2394789</link>
      <description><![CDATA[Accurately evaluating the safety effects of autonomous vehicles (AVs) has become more pressing with the increased adoption rate of AVs. This study utilizes a multiagent adversarial inverse reinforcement learning (MAAIRL) framework for modeling the interactions between AVs and pedestrians in four different cities: Boston, Las Vegas, Pittsburgh, and Singapore. Multiagent actor-critic with Kronecker factors deep reinforcement learning (MACK DRL), a paradigm that extends deep reinforcement learning (DRL), was used to model the behavior of both AVs and pedestrians and to determine their policies and collision avoidance strategies. Simulated trajectories are compared to actual trajectories and the results are evaluated to analyze the behavior of both AVs and pedestrians in terms of their evasive actions such as swerving, accelerating, or decelerating. The multiagent model provides a more comprehensive insight into how road users act in situations of conflict and accounts for changes in the environment. The study also shows that the level of competition between AVs and pedestrians varies significantly across different cities. Las Vegas has the most competitive relationship between AVs and pedestrians, while Singapore has the least competitive environment. The study also highlights the importance of cooperative behavior, particularly in yielding to pedestrians, in reducing the level of competition between AVs and pedestrians. In summary, this research provides valuable insights into the behavior of AVs and pedestrians and can be used to inform the development of more efficient and safe autonomous mobility systems.]]></description>
      <pubDate>Thu, 11 Jul 2024 13:53:12 GMT</pubDate>
      <guid>https://trid.trb.org/View/2394789</guid>
    </item>
    <item>
      <title>Data-driven crash prediction by injury severity using a recurrent neural network model based on Keras framework</title>
      <link>https://trid.trb.org/View/2301386</link>
      <description><![CDATA[With the development of big data technology and the improvement of deep learning technology, data-driven and machine learning application have been widely employed. By adopting the data-driven machine learning method, with the help of clustering processing of data sets, a recurrent neural network (RNN) model based on Keras framework is proposed to predict the injury severity in urban areas. First, with crash data from 2014 to 2017 in Nevada, OPTICS clustering algorithm is employed to extract the crash injury in Las Vegas. Next, by virtue of Keras’ high efficiency and strong scalability, the parameters of loss function, activation function and optimizer of the deep learning model are determined to realize the training of the model and the visualization of the training results, and the RNN model is constructed. Finally, on the basis of training and testing data, the model can predict the injury severity with high accuracy and high training speed. The results provide an alternative and some potential insights on the injury severity prediction.]]></description>
      <pubDate>Fri, 26 Jan 2024 10:02:59 GMT</pubDate>
      <guid>https://trid.trb.org/View/2301386</guid>
    </item>
    <item>
      <title>Prioritizing Organic Waste to Energy-Renewable (POWER) Framework: Reaching the Next Technology Readiness Levels</title>
      <link>https://trid.trb.org/View/2269977</link>
      <description><![CDATA[Developed in the authors' previous CTEDD-funded project, the Prioritizing Organic Waste to Energy-Renewable (POWER) Framework (previously called the “Food & Flora Waste to Fleet Fuel (F4) Framework”) helps cities/regions assess the economic feasibility of co-digesting organic wastes for energy recovery (including renewable natural gas or electricity for fleets) using existing anaerobic digester infrastructure. Leveraging the previous work, the project described herein aimed to advance the Technology Readiness Level (TRL) of the POWER Framework from TRL 5 “Integrated components demonstrated in a laboratory environment” to TRL 8 “Technology proven in operational environment,” by accomplishing the following objectives: 1) Forming and soliciting input from a multi-disciplinary Advisory Group of state/regional government officials and industry representatives in transportation, solid waste management, wastewater, and agriculture (farm digesters), to guide advancement of the POWER Framework from TRL 5 to 8. 2) Upgrading the POWER Framework to Version 2.0 via improvements arising from the previous project, Advisory Group recommendations, and case studies (Obj. 3). 3) Conducting case studies for two additional communities for conversion of organic wastes to renewable energy, including fleet fuel, and showcasing the use of the POWER Framework Version 2.0 to estimate costs, energy/fuel produced, and emission benefits. The Advisory Group included officials from states/regions (North Central Texas, Southern Nevada/Las Vegas, and Vermont) with demonstrated commitment to food waste diversion, as well as the President of the Texas Natural Gas Vehicle Alliance, and an engineer with Waste Management, Inc. POWER Tool upgrades included additional digester types (on-farm and industrial/stand alone, as well as water resource recovery facility); additional organic wastes (fats/oils/grease, manure, and crop residuals, as well as food, yard, and sludge); additional biogas end uses (grid electricity and pipeline renewable natural gas, or RNG; as well as vehicle fuel – electricity and RNG). GIS inputs were automated using the GIS Toolbox. The code for the Optimization Tool was revised to make it more flexible and to incorporate the changes in the POWER Framework (e.g. inclusion of farm digesters and stand-alone industrial digesters); a Graphical User Interface was also created for the Optimization Tool. Upgrades to the POWER Framework were tested using case studies for Vermont and Las Vegas. For the Vermont case study, the Optimization Tool narrowed the list of 17 potential sites to 7 optimal sites. For the Las Vegas case study, from the 23 existing and potential sites, the optimization chose 1-10 preferred sites, depending on the scenario. The case study results for both Vermont and Las Vegas showed the following: o More biogas was produced from digesting organics compared to landfilling; this is due to a higher fraction of gas being captured, and a higher methane content of the gas. o Digesting organic waste would reduce greenhouse gas emissions compared to the regular power mix and use of landfill gas. Traditional air pollutants from digestion were slightly higher than the regular power mix, likely due to greater impurities in digester gas, except for PM 2.5. Traditional air pollutants from digester gas combustion are lower than those from landfill gas. o “NET COSTS” for anaerobic digestion were negative, indicating that the benefits outweigh the costs. In estimating the “Total Benefits,” it was assumed that all potential credits are obtained. This may be overly optimistic for actual cases. o Net benefits for digestion were estimated to be greater than for landfilling. The Las Vegas case study showed that FOG waste has the highest overall benefit/cost savings per ton of waste digested, due to its higher energy density compared to other wastes.]]></description>
      <pubDate>Fri, 17 Nov 2023 11:25:03 GMT</pubDate>
      <guid>https://trid.trb.org/View/2269977</guid>
    </item>
    <item>
      <title>A comparison of pedestrian behavior in interactions with autonomous and human-driven vehicles: An extreme value theory approach</title>
      <link>https://trid.trb.org/View/2266667</link>
      <description><![CDATA[Autonomous Vehicle (AV) technologies are expected to result in significant safety and mobility benefits to the road system. However, one of the most important issues that autonomous vehicle technology faces is ensuring safe interactions with active road users such as pedestrians who can have unpredictable behavior. Moreover, road user behavior varies considerably across different traffic environments, which might represent a challenge to implementing AVs as they lack the intuition common in human-driven vehicles (HDV). This study proposes an approach to evaluate crash risk in vehicle–pedestrian interactions. An Extreme Value Theory (EVT) Peak Over Threshold (POT) model is used to compare the crash risk of AV-pedestrian and HDV-pedestrian interactions in four different cities, namely Boston, Las Vegas, Pittsburgh, and Singapore. A Bayesian hierarchical structure is used to incorporate the effect of several behavioral covariates, which enables estimating the crash risk of each interaction. Results show that the risk varies considerably depending on the type of interaction and the environment. For example, the impact of behavioral covariates (i.e., minimum distance between road users and maximum pedestrian speed) on the risk of AV-pedestrian interactions is greater when compared to the risk of HDV-pedestrian interactions in Boston, Las Vegas, and Singapore. This might indicate that, in busy and congested environments, road users may not be entirely comfortable with the presence of AVs. In addition, Singapore presented a higher percentage of riskier AV-pedestrian interactions when compared to the other cities. Finally, this study offers significant insights into the challenges of introducing AVs in diverse environments as behavior plays a crucial role in traffic and can influence conflict occurrence.]]></description>
      <pubDate>Wed, 15 Nov 2023 09:19:57 GMT</pubDate>
      <guid>https://trid.trb.org/View/2266667</guid>
    </item>
    <item>
      <title>Pedestrian Injury Severity Analysis at Signalized Intersections: A Survival Analysis Based Approach</title>
      <link>https://trid.trb.org/View/1990361</link>
      <description><![CDATA[In order to investigate pedestrian injury severity at signalized intersections, a survival analysis-based approach is proposed to analyze injury severity varying with time to identify the influencing factors, while addressing the heterogeneity of unobserved factors at different signalized intersections. The crash data of Las Vegas metropolitan area from 2004 to 2008 is applied, involving 450 signalized intersections with 550 pedestrian crashes. To address the heterogeneity issue due to unobserved factors at different signalized intersections, the Weibull model with gamma heterogeneity is employed and compared with the Weibull model. The comparison indicates that heterogeneity is present in the Weibull survival analysis. The results show that pedestrian crashes, time of day, light conditions, and annual average daily traffic (AADT) on minor arterials are potentially significant factors of increasing the pedestrian injury severity probability. The findings provide useful insights for practitioners and policy makers to improve pedestrian safety at signalized intersections.]]></description>
      <pubDate>Wed, 28 Dec 2022 16:14:36 GMT</pubDate>
      <guid>https://trid.trb.org/View/1990361</guid>
    </item>
    <item>
      <title>Analysis and O-D demand estimation of a public bike-sharing program in Las Vegas</title>
      <link>https://trid.trb.org/View/1948036</link>
      <description><![CDATA[]]></description>
      <pubDate>Wed, 04 May 2022 15:41:56 GMT</pubDate>
      <guid>https://trid.trb.org/View/1948036</guid>
    </item>
    <item>
      <title>Exploring traffic safety climate with driving condition and driving behaviour: A random parameter structural equation model approach</title>
      <link>https://trid.trb.org/View/1902913</link>
      <description><![CDATA[This study aimed to explore traffic safety climate by quantifying driving conditions and driving behaviour. To achieve the objective, the random parameter structural equation model was proposed so that driver action and driving condition can address the safety climate by integrating crash features, vehicle profiles, roadway conditions and environment conditions. The geo-localized crash open data of Las Vegas metropolitan area were collected from 2014 to 2016, including 27 arterials with 16 827 injury samples. By quantifying the driving conditions and driving actions, the random parameter structural equation model was built up with measurement variables and latent variables. Results revealed that the random parameter structural equation model can address traffic safety climate quantitatively, while driving conditions and driving actions were quantified and reflected by vehicles, road environment and crash features correspondingly. The findings provide potential insights for practitioners and policy makers to improve the driving environment and traffic safety culture.]]></description>
      <pubDate>Mon, 28 Feb 2022 09:42:16 GMT</pubDate>
      <guid>https://trid.trb.org/View/1902913</guid>
    </item>
    <item>
      <title>Comparison of injuries among motorcycle, moped and bicycle traffic accident victims</title>
      <link>https://trid.trb.org/View/1906747</link>
      <description><![CDATA[Motorcycles, moped scooters and bicycles are commonly involved in traffic accidents and riders often suffer significant morbidity and mortality. The aim of this study is to compare and categorize the different injury patterns and fractures suffered by riders of each vehicle type after a traffic accident.  Data from a level 1 trauma center in Las Vegas, Nevada were analyzed. Traffic accident victims riding a motorcycle, moped, or bicycle from 2013 to 2017 were included. Injury location and fracture location were assigned to six and sixteen categorical locations, respectively. Descriptive statistics, including frequency counts for categorical data and mean for continuous data, were calculated for the full sample and for each of the vehicle types. Logistic regression was performed on race, categorized age, vehicle type and helmet use to calculate adjusted odds ratios for injury type between the three groups.  Of the 2115 patients, 1372 were motorcyclists, 356 were moped scooter riders and 387 were bicyclists. Overall, the vast majority of injuries reported were of the extremities or pelvic girdle (62.2%), and this was true regardless of vehicle type. Head and neck injuries were significantly more common in bicyclists (39.5%) and moped riders (34.6%), than in motorcyclists (22.7%). Helmet use was substantially lower in the moped (34%) and bicycle (20%) groups compared to the motorcycle group (85%). The most common fractures regardless of vehicle type were of the skull/face, rib, vertebral, and tibia/fibula with slight variations between vehicle groups.  Similarities were seen in the most common fracture and injury patterns between the three groups. Head and neck injuries were much more common in moped and bicycle riders compared to motorcyclists. This is most likely due to the significantly higher percentage of motorcycle riders who wore a helmet. Counseling regarding helmet and protective equipment use, especially among moped and bicycle riders is essential to reduce head injuries.]]></description>
      <pubDate>Tue, 22 Feb 2022 10:28:26 GMT</pubDate>
      <guid>https://trid.trb.org/View/1906747</guid>
    </item>
    <item>
      <title>Perceptions and Attitudes Towards the Deployment of Autonomous and Connected Vehicles: Insights from Las Vegas, Nevada</title>
      <link>https://trid.trb.org/View/1888848</link>
      <description><![CDATA[Connected and autonomous vehicles (CAVs) are quickly becoming part of the transportation systems, and their use is largely dependent on public perceptions. The objective of this study was to evaluate perceptions of CAVs. Specifically, understanding the differences between people who have ridden a CAV in downtown Las Vegas (shuttle-rider survey) versus those who have not (general survey) yet. Two different survey questionnaires were used to collect data that was analyzed by using penalized logistic regression. Results suggest that people who had exposure to CAVs feel more positively about CAVs. Similarly, young, highly educated, males feel more positively about CAVs than their respective counterparts.]]></description>
      <pubDate>Mon, 20 Dec 2021 09:14:29 GMT</pubDate>
      <guid>https://trid.trb.org/View/1888848</guid>
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