<rss version="2.0" xmlns:atom="https://www.w3.org/2005/Atom">
  <channel>
    <title>Transport Research International Documentation (TRID)</title>
    <link>https://trid.trb.org/</link>
    <atom:link href="https://trid.trb.org/Record/RSS?s=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" rel="self" type="application/rss+xml" />
    <description></description>
    <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>
      <url>https://trid.trb.org/Images/PageHeader-wTitle.jpg</url>
      <link>https://trid.trb.org/</link>
    </image>
    <item>
      <title>Australian infrastructure and transport statistics: yearbook 2024</title>
      <link>https://trid.trb.org/View/2569577</link>
      <description><![CDATA[The aim of the Australian Infrastructure Statistics and Transport Yearbook is to provide a single, comprehensive annual source of infrastructure statistics for use by policymakers, industry leaders, transport analysts and the wider Australian community. Chapters can also be downloaded separately.]]></description>
      <pubDate>Thu, 26 Jun 2025 13:31:40 GMT</pubDate>
      <guid>https://trid.trb.org/View/2569577</guid>
    </item>
    <item>
      <title>Meta-analysis and review of valuations of travel time savings in less-developed countries</title>
      <link>https://trid.trb.org/View/2550879</link>
      <description><![CDATA[]]></description>
      <pubDate>Wed, 07 May 2025 13:46:56 GMT</pubDate>
      <guid>https://trid.trb.org/View/2550879</guid>
    </item>
    <item>
      <title>Assessing the impact of multimodal transportation on economic growth: a machine learning and cointegration approach in 28 countries</title>
      <link>https://trid.trb.org/View/2536142</link>
      <description><![CDATA[]]></description>
      <pubDate>Wed, 09 Apr 2025 13:34:52 GMT</pubDate>
      <guid>https://trid.trb.org/View/2536142</guid>
    </item>
    <item>
      <title>The relation between modeled and perceived accessibility</title>
      <link>https://trid.trb.org/View/2534325</link>
      <description><![CDATA[This thesis investigates the relationship between modeled and perceived accessibility across different activities and transport modes. Accessibility is important for people's daily lives as it enables them to reach and carry out various activities. For this reason, researchers and planners have been trying to measure accessibility using mathematical models, mainly focusing on the location of activities and how these locations can be reached using the transport network. However, during the last decade, researchers have started investigating how people perceive their accessibility, giving much more emphasis on individual constraints and preferences. This raises the question of how these two concepts are related. This is important because a lack of association would be problematic for the use of modeled accessibility in planning. This thesis tackles this question by using time geography to understand the similarities and differences between modeled and perceived accessibility and by conducting an in-depth, disaggregated investigation of the relation across four transport modes, five different everyday activities, using three different accessibility models. Focusing on the Gothenburg Region, in western Sweden, perceived accessibility was captured through a web survey (N = 1534. targeting non-retired adults (aged 18-64 years). Modeled accessibility was calculated based on the location of the investigated amenities and the travel time between them and people's approximate residential location.]]></description>
      <pubDate>Fri, 04 Apr 2025 15:16:45 GMT</pubDate>
      <guid>https://trid.trb.org/View/2534325</guid>
    </item>
    <item>
      <title>Rethinking the role of the car in a MaaS framework: insights from a rural context</title>
      <link>https://trid.trb.org/View/2521660</link>
      <description><![CDATA[]]></description>
      <pubDate>Tue, 11 Mar 2025 14:04:27 GMT</pubDate>
      <guid>https://trid.trb.org/View/2521660</guid>
    </item>
    <item>
      <title>The MaaS blueprint for regional towns and rural hinterlands</title>
      <link>https://trid.trb.org/View/2521608</link>
      <description><![CDATA[This Blueprint document presents a vision for how transport services in rural and regional areas in the NSW context could be better organised to meet the needs of residents and visitors. The Blueprint features a mobility framework for Rural and Regional MaaS which is multi-modal (including all modes available, including the private car) and multi-service (e.g., non-mobility services such as parcel deliveries, library services, food and medicine distribution, media streaming). The Blueprint also provides a focus on decarbonising transport and combatting social exclusion.]]></description>
      <pubDate>Tue, 11 Mar 2025 13:45:24 GMT</pubDate>
      <guid>https://trid.trb.org/View/2521608</guid>
    </item>
    <item>
      <title>A spatial-temporal dynamic attention-based Mamba model for multi-type passenger demand prediction in multimodal public transit systems</title>
      <link>https://trid.trb.org/View/2521606</link>
      <description><![CDATA[Predicting multi-type passenger demand, such as for adults, seniors, pensioners, and students, is essential for improving the operational efficiency, equity, and sustainability of multimodal public transit (PT) systems. However, traditional demand prediction models often struggle to capture the complex spatial-temporal variability inherent in diverse socio-demographic groups. To address this gap, we propose a novel spatial-temporal dynamic attention-based state-space model, i.e., STDAtt-Mamba, tailored for multi-type passenger demand prediction in multimodal PT systems. The STDAtt-Mamba model comprises three key components: an adaptive embedding layer that integrates station-level, passenger-type-specific, and temporal embeddings into a unified representation for efficient data processing; a spatial-temporal dynamic attention (STDAtt) module that employs sparse attention mechanisms to selectively capture crucial global spatial-temporal dynamics; and a spatial-temporal dynamic Mamba (STDMamba) module that extends state-space modeling to fuse spatial and temporal dependencies dynamically. We reformulate STDAtt-Mamba as a spatial-temporal dual-path attention mechanism and theoretically prove the complementarity of STDMamba and STDAtt in capturing local and global dependencies, thereby improving the interpretability of the STDAtt-Mamba. We conduct extensive experiments on a large-scale multimodal dataset of over 1.58 million smart card users of 9 passenger types from Queensland, Australia, from 01/2021 to 01/2023. Experimental results demonstrate that STDAtt-Mamba outperforms state-of-the-art baseline models regarding the prediction accuracy across all passenger types and travel modes. By addressing the challenges of heterogeneity in spatial-temporal travel patterns and socio-demographic groups, this study offers an adaptive, robust, scalable, and data-driven tool for managing the heterogeneous passenger demand in multimodal PT systems.]]></description>
      <pubDate>Tue, 11 Mar 2025 13:45:23 GMT</pubDate>
      <guid>https://trid.trb.org/View/2521606</guid>
    </item>
    <item>
      <title>Injury and fatality risk by transport mode: a systematic review</title>
      <link>https://trid.trb.org/View/2509234</link>
      <description><![CDATA[Safety concerns can deter people from using active modes of transport, however, differences in data collection and reporting of transport injuries across modes makes quantifying safety by mode difficult. We undertook a systematic review to investigate injury and fatality rates across different land-based transportation modes. We included studies that used travel related denominators to examine how travel injury and fatality rates vary by mode of transport. Findings indicated disparities in injury and fatality rates, dependent on the combination of mode and exposure measures - including kilometres travelled, hours spent commuting or number of trips made. The research provides valuable insights into injury and fatality rates associated with various transportation modes. It offers a foundation for evidence-based decision-making, enabling policymakers, researchers, and practitioners to design contextually relevant interventions, including infrastructure investment; ultimately fostering safer transportation environments and saving lives.]]></description>
      <pubDate>Thu, 13 Feb 2025 09:06:23 GMT</pubDate>
      <guid>https://trid.trb.org/View/2509234</guid>
    </item>
    <item>
      <title>Managing competing sectoral demands for energy resources: transitioning to sustainable transport</title>
      <link>https://trid.trb.org/View/2509083</link>
      <description><![CDATA[Decarbonising the transport sector will require rapidly adopting new renewable energy sources. The increasing demand for renewable energy must be balanced with available supply, which needs to be scaled up at an unprecedented speed to achieve climate goals. At the same time, countries seek to decarbonise their entire economy with tremendous needs for renewable energy resources in very short timescales. Potential bottlenecks in renewable energy supply could place the transport sector in direct competition for renewable energy resources with other sectors of the economy, such as buildings and industry. There may also be competition for renewable energy supplies between different modes within the transport sector, affecting the mode or technology choice or between different global regions, each with different purchasing power and resource availability. This report assesses the constraints to the transport sector arising from the potential scarcity of renewable energy supply over the coming decades. In doing so, it aims to support governments in long-term planning to decarbonise the transport sector, accounting for energy system constraints and supply bottlenecks and facilitating international co-operation.]]></description>
      <pubDate>Thu, 13 Feb 2025 09:02:21 GMT</pubDate>
      <guid>https://trid.trb.org/View/2509083</guid>
    </item>
    <item>
      <title>Networking access, substitution effects and design issues surrounding e-scooter use</title>
      <link>https://trid.trb.org/View/2509079</link>
      <description><![CDATA[E-scooters are an efficient and economical form of micro mobility that provide benefits to individual users for transportation needs as well as economic benefits through the gig economy. This form of micro mobility has the potential to improve public transport usage by facilitating first and last mile transport, replacing car trips and reducing congestion on the road transport network. The consequences of a fast-growing new transportation mode can also produce safety issues such as collisions involving vulnerable road users, as well exposing e-scooter riders to injury by road traffic in a transport system that has not matured to a level where micro mobility has been integrated effectively. This report considers the different legislative, policy frame works, benefits and issues that jurisdictions both overseas and within Australia have experienced with both private and shared e-scooters. Additionally, the report provides some insight into e-scooter safety performance, particularly braking considerations and emergency stopping distances, kinetic energy exposure and potential injury consequences with increasing vehicle impact speed for existing implementation scenarios.]]></description>
      <pubDate>Thu, 13 Feb 2025 09:02:19 GMT</pubDate>
      <guid>https://trid.trb.org/View/2509079</guid>
    </item>
    <item>
      <title>Dealing with logistics and heterogeneity in freight transport demand. A discrete mixture model for joint mode and shipment size choice</title>
      <link>https://trid.trb.org/View/2449046</link>
      <description><![CDATA[A discrete mixture model for joint mode and shipment size choice is estimated on the basis of the total logistics costs framework. This approach allows to account for unobserved dependencies among alternatives on the one hand and on the other hand enables to deal efficiently with the tremendous heterogeneity existing in freight transportation. Heterogeneity is accounted for by endogenously segmenting flows of goods according to their logistics characteristics. For each segment a separate model of mode and shipment size choice is estimated. It is shown that an endogenous segmentation with three classes outperforms the segmentation according to the standard classification of goods applied in official statistics with respect to various prediction accuracy measures. (A)]]></description>
      <pubDate>Mon, 27 Jan 2025 09:02:26 GMT</pubDate>
      <guid>https://trid.trb.org/View/2449046</guid>
    </item>
    <item>
      <title>A multi-dimensional approach to human mobility and transportation mode detection using GPS data</title>
      <link>https://trid.trb.org/View/2491260</link>
      <description><![CDATA[GPS tracking data is an essential resource for analyzing human travel patterns and evaluating the effects on transportation systems. The primary challenge, however, is to accurately identify the modes of transportation within unlabeled GPS data. These approaches range from simple rule-based systems to advanced machine-learning techniques. This dissertation aims to bridge this gap by examining the critical features and techniques of these methods and proposing a novel approach for detecting transportation modes in GPS tracking data. To achieve this goal, a comprehensive understanding of individual journeys is crucial. Thus, this research adopts a microdata analytic approach, encompassing data collection, processing, analysis, and decision-making stages. Doing so contributes to advancing human mobility research and transportation mode detection. Paper I undertook a systematic review of transport mode detection methodologies to fill the research gap, emphasizing the predominance of supervised learning algorithms and highlighting the need for further research to address the limitations of small datasets. Paper II introduced a stepwise methodology, integrating unsupervised learning, GIS, and supervised algorithms to detect transport modes while minimizing reliance on labelled data. The Random Forest algorithm emerged as a precise but time-intensive solution. Paper III showcased a novel approach to transport mode detection using deep learning models, outperforming traditional machine learning methods. This paper signals the potential of deep learning in the field and demonstrates the importance of raw GPS data in enhancing accuracy. Paper V addressed the challenge of predicting human mobility patterns under the Hidden Markov Model (HMM) framework, highlighting the applicability of HMMs to understanding and predicting complex mobility behaviour. This paper emphasized the need for GPS tracking data in developing advanced mobility models. Paper IV ventured into hybrid methodology by combining K-means clustering with the ANP-PSO algorithm to enhance transportation mode classification. This pioneering approach improved classification accuracy while reducing dependence on labelled datasets.]]></description>
      <pubDate>Fri, 17 Jan 2025 15:17:24 GMT</pubDate>
      <guid>https://trid.trb.org/View/2491260</guid>
    </item>
    <item>
      <title>Driving as an employment qualification</title>
      <link>https://trid.trb.org/View/2475131</link>
      <description><![CDATA[More than one in six jobs being advertised in the UK requires applicants to be able to drive, analysis suggests. This research shows that in the first week of October 2023 a total of 1,092,172 recruitment ads were posted on the job vacancy aggregator site Adzuna. Of the 1,092,172 advertisements, 189,608 (17.4%) explicitly or implicitly required those applying to have at least a standard driving licence because the job was either: 1. specifically for a driver, 2. or required driving during the course of work, 3. or a car was needed to reach work (due to reduced accessibility by public transport). The analysis looked at data for the same week in October in the eight years from 2016 to 2023, inclusive. Whilst the highest proportion of jobs requiring the ability to drive was seen in 2020 at the height of the pandemic (120,190 out of 611,702, or 19.6%) the proportion for 2023 was still higher than in any of the four years pre-Covid.]]></description>
      <pubDate>Mon, 09 Dec 2024 19:05:09 GMT</pubDate>
      <guid>https://trid.trb.org/View/2475131</guid>
    </item>
    <item>
      <title>Mode-shift impacts on safety</title>
      <link>https://trid.trb.org/View/2452440</link>
      <description><![CDATA[This study examined the available research on the impact that shifting travel modes – from private motor vehicles to public transport, active travel modes and micro-mobility– can have on crash rates, and developed a model to enable different mode-shift scenarios to be tested. The study found that, while the majority of the risk factors associated with the various travel modes had been studied individually, most of these studies had only looked at a few modes and did not explore the multiple interactive relationships that can exist between them. The Excel-based model developed enables the impact of changes in the overall vehicle-kilometres travelled, mode share, and different walking and cycling network levels of service to be tested. However, the authors caution that the model only predicts changes in distance-based risks and casualty rates. Because travellers often reduce how far they travel in total when they shift modes – for example, walking or cycling to the local shops rather than driving across town to a shopping centre – the model is likely to significantly underestimate the reductions in crash casualties per capita that shifting modes will have. In practice, policies and programmes that encourage shifts from driving to active transport modes are likely to reduce total crash injuries and deaths much more than the results of the model indicate. In developing the model, the study collated recent New Zealand Police data about road transport crashes, Ministry of Health hospital admission and ACC data. However, the authors also recognised that this data was inadequate, for example in significantly under-reporting crashes that cause less serious injuries or that don’t involve motor vehicles, and the report’s recommendations include several aimed at improving data collection and analysis.]]></description>
      <pubDate>Mon, 11 Nov 2024 14:17:46 GMT</pubDate>
      <guid>https://trid.trb.org/View/2452440</guid>
    </item>
    <item>
      <title>The benefit of rail to New Zealand</title>
      <link>https://trid.trb.org/View/2438003</link>
      <description><![CDATA[Since the 1880s, rail has played an important role in the social and economic life of New Zealand. Over time, that role has changed, developed, and evolved. The dynamic and integrated nature of rail services means that the full breadth of benefits is difficult to fully conceptualise. This report brings together a range of economic methodologies for estimating the benefit that rail generates for Aotearoa, such as the reduced congestion resulting from fewer truck movements, or the additional employment generated from cost savings for freight. Rail makes a critical contribution to the New Zealand economy, acting as a foundation for regional supply chains, boosting productivity, and reinforcing domestic output. This research estimates that rail provides almost $3.3 billion in value to New Zealand each year, when effects on individuals and the national economy are considered. “Value” is defined using Treasury guidance and represents economic benefits in the form of real resource use or other observable impacts on New Zealanders. This value is considered additive to the direct economic contribution of rail through its baseline activities (such as GDP impacts from the operation of KiwiRail). Applying a dynamic model of the New Zealand economy, in addition to best-practice appraisal tools, permits the computation of rail benefits in monetised (dollar) terms. These benefits not only estimate real benefits for New Zealanders through factors such as avoided health impacts of pollution, but also measure the impact of the rail sector on New Zealand freight services, supply chains, and other sectors and industries. In aggregate, we estimate that the existence of rail services contributes $3.3 billion to New Zealand each year, comprising approximately $1 billion in Gross Domestic Product (GDP) benefits as well as $2.3 billion of economic externality impacts. All values are in net terms, such that results represent the difference between rail and road impacts.]]></description>
      <pubDate>Wed, 09 Oct 2024 14:27:21 GMT</pubDate>
      <guid>https://trid.trb.org/View/2438003</guid>
    </item>
  </channel>
</rss>