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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>
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    <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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      <title>Comparative analysis of condition assessment standards for roads and bridges based on fuzzy comprehensive evaluation</title>
      <link>https://trid.trb.org/View/2569582</link>
      <description><![CDATA[]]></description>
      <pubDate>Thu, 26 Jun 2025 13:31:44 GMT</pubDate>
      <guid>https://trid.trb.org/View/2569582</guid>
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      <title>Network Safety Plan process: Bundaberg Regional Council</title>
      <link>https://trid.trb.org/View/2509264</link>
      <description><![CDATA[NTRO and Bundaberg Regional Council have developed a Network Safety Plan (NSP) to prioritise road safety actions on Council’s road network. Since the process of developing an NSP is new and largely untested in Australia, this paper seeks to share the experience gained in developing the NSP and serve as a potential model for other councils to apply. The project started with a Movement and Place assessment of Council roads and a review of Council’s road hierarchy, with the purpose of integrating these complimentary frameworks to establish a holistic reimagining of road infrastructure to deliver 3-Star or better outcomes for all road users. An assessment of existing network road safety risk and review of Council’s standard road cross sections provided a Star Rating Score to compare and identify gaps between current infrastructure standards and the desired end state. Infrastructure investment scenarios included practical and financial constraints, as well as a challenge to achieve a 3-Star or better outcome across the network.]]></description>
      <pubDate>Thu, 13 Feb 2025 09:06:41 GMT</pubDate>
      <guid>https://trid.trb.org/View/2509264</guid>
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      <title>Development of Road Safety Assessment Framework in Nepal</title>
      <link>https://trid.trb.org/View/2509210</link>
      <description><![CDATA[It is crucial to effectively utilize Nepal's limited resources to address the increasingly concerning road safety issues. This study aimed to develop a Nepal Road Safety Assessment Program (NeRSAP) for evaluating relative risks on roads or road section in the country which uses limited road risk attributes than iRAP. Data pertaining to road attribute data (land use, carriageway, presence of medians, road type, number of lanes in each direction, length of homogenous section of road), road risk attributes (horizontal alignment, vertical alignment, lane width, roadside hazards, pavement surface, private access, number of intersections, number of interchanges, on street parking and traffic data (Average Daily Traffic (ADT), pedestrian exposure, cyclist’s exposure) were collected from either primary or secondary sources wherever available. The results of NeRSAP were validated through a case study conducted on the Kamala Dhalkebar Pathalaiya and Naghdhunga Naubise Mugling sections of the country’s two national highways. NeRSAP star rating  based on limited risk attributes has been found to be well aligned with the iRAP star rating which requires extensive input data which demonstrates that NeRSAP can offer a promising “quick and dirty” risk assessment tool in a constrained resource environment of LMICs like Nepal.]]></description>
      <pubDate>Thu, 13 Feb 2025 09:06:08 GMT</pubDate>
      <guid>https://trid.trb.org/View/2509210</guid>
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    <item>
      <title>Automated iRAP encoding using computer vision and deep learning</title>
      <link>https://trid.trb.org/View/2509153</link>
      <description><![CDATA[Addressing the issue of manual encoding inefficiencies in International Road Assessment Program (iRAP) ratings, SmartRAP was conceived as a cost-effective, automated solution. AI-powered automation avoids labour-intensive and error-prone manual encoding, particularly impacting developing countries. Leveraging advanced computer vision techniques, SmartRAP is designed around the U-Net semantic segmentation model, specifically adapted for iRAP's complex encoding criteria. The prototype uses a parallelized convolutional neural network (CNN), effectively automating attribute encodings. It successfully encoded four attributes, Upgrade Cost, Land Use, Area Type, and Road Condition, using low-cost dashcam footage. The results showcase the prototype's success in significantly reducing the cost and time associated with iRAP encoding. This innovative approach offers a scalable and low-cost solution, particularly for low and middle-income countries. The feasibility of SmartRAP shows potential for a solution aligning with the UN's road safety goals for 2030 that enables standardized and cost-effective iRAP assessments worldwide.]]></description>
      <pubDate>Thu, 13 Feb 2025 09:05:30 GMT</pubDate>
      <guid>https://trid.trb.org/View/2509153</guid>
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    <item>
      <title>AusRAP Star Rating using aiRAP methodology</title>
      <link>https://trid.trb.org/View/2509127</link>
      <description><![CDATA[Following the announcement of aiRAP by iRAP in 2019, and a pilot study to investigate the potential use of LiDAR and 360° imagery for autonomous extraction of AusRAP features, Main Roads Western Australia partnered with Anditi in 2023 to deliver the world’s largest aiRAP project to date. Main Roads has collected LiDAR using mobile laser scanning for the entire, ~20,000km, of Western Australia’s state road network. Using artificial intelligence and machine learning we extracted and coded features in the road corridor according to the emerging aiRAP framework. The extracted features were used to obtain AusRAP Star Ratings for all state roads. Star Ratings form an important basis to support improvements and treatment decisions; the results will help us find the best pathway forward to reduce trauma on our state road network. Here we share the learnings from this project including data collection, feature extraction, assumptions, estimations, and resulting Star Ratings.]]></description>
      <pubDate>Thu, 13 Feb 2025 09:05:12 GMT</pubDate>
      <guid>https://trid.trb.org/View/2509127</guid>
    </item>
    <item>
      <title>Framework to incorporate bushfire resilience into road infrastructure</title>
      <link>https://trid.trb.org/View/2134785</link>
      <description><![CDATA[Unprecedented bushfires across Australia have highlighted the fact that roads and associated infrastructure are critical enablers of bushfire prevention, preparation, response and recovery activities. However, these bushfires have also highlighted the vulnerability of road infrastructure and the travelling public during and after a bushfire. Severe bushfires have the potential to produce numerous economic, social and environmental impacts, which can range from short-term inconveniences to long-term life-changing effects. This framework document has been developed to assist road agencies with managing the potential risk to road infrastructure caused by the impacts of bushfires.]]></description>
      <pubDate>Mon, 06 Mar 2023 16:18:58 GMT</pubDate>
      <guid>https://trid.trb.org/View/2134785</guid>
    </item>
    <item>
      <title>Automated collection of AusRAP road attributes using DVR and pattern recognition techniques: Y3 2019/20</title>
      <link>https://trid.trb.org/View/2035902</link>
      <description><![CDATA[For improved road safety, Queensland Department of Transport and Main Roads (TMR) routinely undertakes proactive risk assessment of the road network for the identification and treatment of high-risk sections, thereby eliminating the crash risk on the network. The risk assessment models used include Australian Road Assessment Program (AusRAP) and Australian National Risk Assessment Model (ANRAM). In Australia and around the world, the current systems for collecting road condition data for the above purposes are labour intensive (manual), expensive and prone to many errors. Furthermore, much of the available data is inaccurate due to changing conditions, requiring regular updates. Automating the data collection process is essential for improving road infrastructure and reducing fatalities on the roads, by providing up-to-date and reliable datasets needed for the timely assessment of the road network. The main aim of this project was to develop a process for automating the extraction of road attributes from DVR video using advanced image analysis. The automatic collection of road attributes from video data using machine learning techniques and cross-validation with other data sources has the potential to provide a range of value-added products for road condition, road safety, environmental and improved obstacle clearance estimates consistently and inexpensively. Specific objectives of the project included the following: Review video (DVR) and MLS data sources to determine their usefulness and applicability; Develop deep learning techniques for automatically identifying road infrastructure features and roadside hazards for AusRAP and ANRAM models; Incorporate the techniques into an automatic system (software program) for the assessment of road safety and road rating; Undertake a case study to demonstrate the application of the program to collect road attributes from selected state-controlled roads.]]></description>
      <pubDate>Thu, 06 Oct 2022 13:48:36 GMT</pubDate>
      <guid>https://trid.trb.org/View/2035902</guid>
    </item>
    <item>
      <title>Calibrating Infrastructure Risk Rating (IRR) for Victorian Roads</title>
      <link>https://trid.trb.org/View/1503466</link>
      <description><![CDATA[IRR, developed in New Zealand, is a simplified-risk based road assessment methodology, based on fewer features than other road risk tools – requiring the input of only ten key road variables.  Although early days, IRR seems to perform as well as more complicated, proprietary, road risk tools. Safe System Solutions Pty Ltd is undertaking a project that involves calibrating New Zealand’s IRR by correlating the IRR rating for Victorian roads against real world crashes so as to understand and quantify the strength of the relationship between crash rates and risk as assessed by IRR.]]></description>
      <pubDate>Thu, 01 Mar 2018 10:01:31 GMT</pubDate>
      <guid>https://trid.trb.org/View/1503466</guid>
    </item>
    <item>
      <title>Estimating levels of service (LOS) for freight on rural roads</title>
      <link>https://trid.trb.org/View/1471198</link>
      <description><![CDATA[This paper presents: (i) the analysis and outcomes of a large interview survey for three groups of transport stakeholders (road freight drivers, operators and road infrastructure managers); and (ii) analysis and outcomes of a rural arterial road driver test based circuit survey using both drivers of heavy vehicles and cars to rate variations in three major factors impacting on levels of service (LOS) in order to define the comparative requirements for rural freight. The top three major factors, or road attributes, impacting on LOS for heavy vehicle drivers and freight operators subsequently ranked in descending order of importance by the interview survey were: (i) ride comfort (road roughness); (ii) road shoulder width and condition; and (iii) road and bridge geometry and general access. The follow-up driver test survey investigated the responses of truck and car drivers to variations to the above identified three key road inventory attributes. Analysis of sample rating data indicated that LOS ratings provided by car and truck drivers closely followed changes in LOS for roughness,  shoulder width and lane width, but truck drivers on average rated LOS below that rated by car drivers. Results also indicated that the use of road surface measures linked to truck ride characteristics, as opposed to currently used roughness measures such as IRI which heavily reflect car ride response, would improve the capability of asset managers to deliver LOS better tailored to the needs of freight vehicles.]]></description>
      <pubDate>Mon, 19 Jun 2017 10:56:34 GMT</pubDate>
      <guid>https://trid.trb.org/View/1471198</guid>
    </item>
    <item>
      <title>Regional first and last mile pilot project</title>
      <link>https://trid.trb.org/View/1457907</link>
      <description><![CDATA[This project used infrastructure and road use data analyses to inform road upgrade projects in two local government areas in Queensland assessing the benefits that would be gained by the road freight industry and their customers. Road upgrade projects were identified to enable larger, heavier and more productive freight vehicles permitted on adjoining state highways to use these local roads. Benefits come from the lower operating costs per freight tonne-kilometre of High Productivity Vehicles (HPVs), plus from time savings, reductions in accident costs and other externalities. The basis of the analysis focused on the quantity of freight carried on local roads in smaller vehicles, and the proportion of this freight that was estimated to transfer to higher productivity freight vehicles. It also considered existing infrastructure deficiencies that prevented the use of HPVs and the cost of upgrades to meet the agreed vehicle class appropriate for the road. The agreed vehicle class was determined by classes currently approved on adjoining roads, industry preferences and an assessment of the expected economic benefits compared to the upgrade costs. The results from this pilot assessment are presented, together with suggestions on how the program could be applied more broadly across local government areas and in other jurisdictions. The study encountered a number of methodological and data issues related to matters such as data concerning road usage, industry engagement, estimating the proportion of freight that would be carried in HPVs if upgrades proceeded and the economic activity generated by businesses located on local roads that would benefit from road upgrades. The approaches used, resulting outcomes and suggestions for future investigation are presented.]]></description>
      <pubDate>Mon, 27 Feb 2017 10:06:27 GMT</pubDate>
      <guid>https://trid.trb.org/View/1457907</guid>
    </item>
    <item>
      <title>Estimating levels of service (LOS) for freight on rural roads</title>
      <link>https://trid.trb.org/View/1457869</link>
      <description><![CDATA[This paper presents: (i) the analysis and outcomes of a large interview survey for three groups of transport stakeholders (road freight drivers, operators and road infrastructure managers); and (ii) analysis and outcomes of a rural arterial road driver test based circuit survey using both drivers of heavy vehicles and cars to rate variations in three major factors impacting on LOS in order to define the comparative requirements for rural freight. The top three major factors, or road attributes, impacting on LOS for heavy vehicle drivers and freight operators subsequently ranked in descending order of importance by the interview survey were: (i) ride comfort (road roughness); (ii) road shoulder width and condition; and (iii) road and bridge geometry and general access. The follow-up driver test survey investigated the responses of truck and car drivers to variations to the above identified three key road inventory attributes. Analysis of sample rating data indicated that LOS ratings provided by car and truck drivers closely followed changes in LOS for roughness, shoulder width and lane width, but truck drivers on average rated LOS below that rated by car drivers. Results also indicated that the use of road surface measures linked to truck ride characteristics, as opposed to currently used roughness measures such as IRI which heavily reflect car ride response, would improve the capability of asset managers to deliver LOS better tailored to the needs of freight vehicles.]]></description>
      <pubDate>Mon, 27 Feb 2017 10:02:16 GMT</pubDate>
      <guid>https://trid.trb.org/View/1457869</guid>
    </item>
    <item>
      <title>What are stars made of? The process of “star rating” the state controlled road network in Queensland</title>
      <link>https://trid.trb.org/View/1435981</link>
      <description><![CDATA[In 2014, the Queensland Department of Transport and Main Roads embarked upon the epic journey of coding and star rating the entire state controlled road network, a length of over 33,000km. The objective of the project was to capture and code 72 road-related attributes that would allow the Department of Transport and Main Roads to analyse the safety indicators on the network, using the AusRAP and ANRAM methodologies. It would also allow for the captured data to inform other road infrastructure business program development for the department, including asset management and maintenance.]]></description>
      <pubDate>Mon, 28 Nov 2016 14:33:38 GMT</pubDate>
      <guid>https://trid.trb.org/View/1435981</guid>
    </item>
    <item>
      <title>A road less travelled: development of a localised rating system for roads in South Africa</title>
      <link>https://trid.trb.org/View/1404774</link>
      <description><![CDATA[During the September 2011 CAPSA, Dr. Steve Muench of the Greenroads Foundation in the United States introduced attendees to the Greenroads Rating System and its potential role in South Africa's road infrastructure and green economy. Shortly after the conference, a memorandum of understanding was signed between the Greenroads Foundation and SSI Engineers & Environmental Consultants providing for the "localisation" of the Greenroads Rating System to suit the unique local infrastructure development challenges in South Africa. This paper serves to summarise the developments and localisation process that have taken place in the years following the 2011 CAPSA.]]></description>
      <pubDate>Fri, 22 Apr 2016 11:11:52 GMT</pubDate>
      <guid>https://trid.trb.org/View/1404774</guid>
    </item>
    <item>
      <title>Satellite imagery for establishing inventory and condition of unsurfaced roads.</title>
      <link>https://trid.trb.org/View/1331915</link>
      <description><![CDATA[Many low-income countries have no inventory of roads or their condition, particularly their rural networks. In most countries it would be a huge task to establish this information, especially where access is difficult due to geography or conflict. High resolution Satellite imagery is now available worldwide and covers many of these inaccessible areas. Therefore it has the potential to provide inventory data and condition assessments of entire networks. This approach has been trialled in northern Nigeria on earth and gravel roads with some success; but there were some issues that would have to be resolved if the technique is to have a wider application. Northern Nigeria is sparsely vegetated, which makes it ideal for satellite imagery, but this approach may not be feasible for tropical areas where the tree cover would make locating roads difficult. The age and cost of the imagery can also be a restricting factor. However, the results of this trial demonstrate that inventory and condition of roads can be established relatively accurately using satellite imagery. Indicators have been developed to determine condition by manual assessment.]]></description>
      <pubDate>Fri, 21 Nov 2014 10:40:23 GMT</pubDate>
      <guid>https://trid.trb.org/View/1331915</guid>
    </item>
    <item>
      <title>Metropolitan street service study 1961, part 1 report</title>
      <link>https://trid.trb.org/View/1211903</link>
      <description><![CDATA[]]></description>
      <pubDate>Mon, 27 Aug 2012 12:22:34 GMT</pubDate>
      <guid>https://trid.trb.org/View/1211903</guid>
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