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    <title>Transport Research International Documentation (TRID)</title>
    <link>https://trid.trb.org/</link>
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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>
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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>The invisible hand of the app: The role of platform operational rules in fostering risky rider behaviour in food delivery</title>
      <link>https://trid.trb.org/View/2687039</link>
      <description><![CDATA[Meal delivery riders are frequently involved in crashes, many of which are linked to dangerous riding behaviors such as running red lights, using mobile phones while riding, and riding on footpaths. This paper investigates how the operational design of meal delivery platforms contributes to these hazardous behaviors. The analysis is based on in-depth interviews with 10 UberEATS riders in Sydney, Australia. Six key platform-related factors were identified as fostering risky riding: handling multiple orders simultaneously, prolonged food preparation times, competition among riders for the best orders, the platform’s incentive system, the customer review system, and the existence of pre-orders. These factors were found to reduce the time till the Estimated Time of Arrival (ETA), intensify the pressure to respond immediately, promote a sense of urgency to deliver quickly, and cause riders to receive customer messages while en route. These outcomes, in turn, contribute to the three dangerous behaviors described above. The paper also offers policy recommendations based on these findings. This study provides an in-depth analysis of how platform design affects rider safety, emphasizing the need to progress beyond safety instructions to implement structural improvements in platform design.]]></description>
      <pubDate>Tue, 28 Apr 2026 11:18:52 GMT</pubDate>
      <guid>https://trid.trb.org/View/2687039</guid>
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    <item>
      <title>Estimating health benefits of a peri-urban railway: A quantitative health impact assessment focused on diabetes</title>
      <link>https://trid.trb.org/View/2663680</link>
      <description><![CDATA[Transport is a significant determinant of health. This study investigates the potential health benefits of introducing a commuter railway line to a rapidly growing peri-urban area in South West Sydney. The authors focus on diabetes, a condition with high regional prevalence. The rationale is to assess how infrastructure development can influence public health outcomes, particularly in areas with low active transport rates and high car dependency. A quantitative Health Impact Assessment (HIA) was conducted using the DYNAMO-HIA modelling tool. This tool utilizes a partial micro-simulation multi-state model to estimate changes in disease burden and life expectancy. Two scenarios were simulated over an 11-year period (2019–2029): a baseline scenario with no intervention and an intervention scenario involving the introduction of a commuter rail line. The intervention was expected to increase walking and reduce diabetes burden. Data inputs included population demographics, walking prevalence, and diabetes incidence and mortality. The intervention scenario showed an annual reduction in diabetes incidence and prevalence of approximately 0.02%. Life expectancy increased by about one month, and a total of 222 Disability-Adjusted Life Years (DALYs) were gained due to reduced diabetes burden. Although these numbers may appear modest, they represent meaningful health gains for a small and rapidly growing community, underscoring the significant impact that even incremental improvements can achieve at this scale. The study demonstrates the feasibility of applying predictive modelling tools in HIA for infrastructure planning. It highlights the importance of integrating health considerations into urban development, especially in peri-urban contexts. Although limited to one disease and intervention, the findings support evidence-based planning for healthier communities.]]></description>
      <pubDate>Wed, 25 Feb 2026 08:53:19 GMT</pubDate>
      <guid>https://trid.trb.org/View/2663680</guid>
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    <item>
      <title>Performance evaluation of geopolymer concrete pavement after five years of service</title>
      <link>https://trid.trb.org/View/2670426</link>
      <description><![CDATA[This study presents a five-year performance evaluation of a geopolymer concrete (GPC) pavement installed in 2019 in Alexandria, City of Sydney, as part of an Australian Government initiative supporting low-carbon infrastructure. The condition of the in-service GPC slab was compared with that of a companion blended cement concrete (BCC) slab, both subjected to identical traffic loads and environmental exposure. Visual inspection and testing reveal that while both slabs developed flexural and shrinkage cracks, the GPC exhibited only one flexural crack over five years, compared to three in the BCC slab. Surface abrasion was more pronounced in the GPC, resulting in greater surface loss. Cores showed that the GPC met the required design compressive strength but was 18 % lower than the BCC. However, the performance difference in terms of abrasion is primarily attributed to aggregate grading and hardness – the GPC mix incorporated a gap-graded 14 mm olivine basalt. In comparison, BCC used a well-graded 8 mm granite aggregate. Image analysis confirms a higher aggregate fraction on a typical GPC surface than on a BCC surface. Petrographic analysis, along with on-site abrasion testing, indicates that aggregate hardness and grading significantly influence abrasion resistance. Both pavements have maintained their structural integrity under service conditions. The findings highlight the importance of selecting high-quality aggregates, their relative volume, and grading for pavement applications, and identify areas for improvement, including subgrade preparation and curing methods. With these enhancements, GPC has strong potential as a sustainable alternative for pavement applications and in infrastructure projects.]]></description>
      <pubDate>Mon, 23 Feb 2026 11:24:46 GMT</pubDate>
      <guid>https://trid.trb.org/View/2670426</guid>
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      <title>Reallocating Street Space for People Walking and Cycling – A Case Study of Castlereagh Street, Sydney</title>
      <link>https://trid.trb.org/View/2643437</link>
      <description><![CDATA[The City of Sydney has been re-imagining its streets as places for people, transforming streets from car-dominated to ones that favor walking and cycling. A recent project to create more space for people walking and cycling through footpath widening and implementing a two-way cycleway on Castlereagh Street highlights the City’s pragmatic approach to designing, engaging, and reallocating road space. The Castlereagh Street case study shows lessons learned working in a car-dominated and multifaceted urban regulatory environment.]]></description>
      <pubDate>Wed, 18 Feb 2026 11:59:29 GMT</pubDate>
      <guid>https://trid.trb.org/View/2643437</guid>
    </item>
    <item>
      <title>Assessing the economic impacts of labour time in autonomous vehicles</title>
      <link>https://trid.trb.org/View/2618636</link>
      <description><![CDATA[Previous studies have analysed the impacts of the introduction of autonomous vehicles on transport networks and estimated the safety, congestion, freight, parking and vehicle ownership impacts to social welfare and the economy. However, there appears to be a gap in the literature on the economic impacts of individuals allocating travel time in an autonomous vehicle to labour activities. This additional labour time could then have resulting impacts to productivity and the broader economy. This paper addresses this gap through the development of a novel microeconomic model incorporating time use in autonomous vehicles. The model captures an individual’s consumption behaviour, demand for leisure and supply of labour while accounting for the allocation of travel time to labour and leisure. It is an extension of existing microeconomic models of time use for two features: (1) travel utility, and (2) labour while travelling. This model is then implemented in an integrated computable general equilibrium and transport model for Sydney, Australia, and is tested to understand the order of magnitude of impacts. From this model, the increase in autonomous vehicle penetration rate and the resultant increases to household budget from travelling wages will result in a total welfare increase but with a decreasing rate, which are mainly due to congestion effects. Interestingly, the congestion effects also result in the production and value of time first increase, followed by a decrease, which are counter intuitive.]]></description>
      <pubDate>Wed, 11 Feb 2026 09:17:42 GMT</pubDate>
      <guid>https://trid.trb.org/View/2618636</guid>
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    <item>
      <title>Decomposing Urban Traffic Flows: A Multistage Approach to Model Heavy Vehicle Movements in Greater Sydney</title>
      <link>https://trid.trb.org/View/2658637</link>
      <description><![CDATA[This study presents an innovative multistage methodology for decomposing urban traffic flows into light vehicle (LV) and heavy vehicle (HV) categories, addressing a critical gap in transportation network analysis. Utilizing data from Greater Sydney’s road network, we develop a comprehensive approach comprising three main stages: origin–destination (OD) matrix estimation using RapidEx, quadratic programming optimization for HV/LV proportion estimation, and XGBoost regression for generalization. Our analysis examines associations between HV proportions and urban characteristics, including points of interest (POI), nightlight intensity, and zonal attributes. The XGBoost model achieves a test R² of 0.637, demonstrating strong predictive power for real-world applications. Through SHAP (SHapley Additive exPlanations) analysis, we uncover complex nonlinear relationships between nightlight intensity and HV proportions, with significant interaction effects between urban features. The model performs particularly well in predicting common urban HV proportion ranges (0.2–0.6), reflecting typical urban traffic compositions. These findings provide valuable insights for urban planning and policy development, especially in contexts where detailed vehicle classification data are limited.]]></description>
      <pubDate>Wed, 28 Jan 2026 16:59:34 GMT</pubDate>
      <guid>https://trid.trb.org/View/2658637</guid>
    </item>
    <item>
      <title>The impact of train accessibility on housing affordability: evidence from COVID-19 and the work-from-home shift</title>
      <link>https://trid.trb.org/View/2612457</link>
      <description><![CDATA[This study examines the impact of train accessibility on housing affordability in Sydney, Australia, before, during, and after COVID‑19. Using panel regression and propensity score matching (PSM) to mitigate bias from unobserved neighbourhood heterogeneity and sample selection, we compare housing affordability between train-stop and non-stop suburbs for both house and unit (apartment) markets. Our findings indicate that before COVID‑19, housing affordability was worse in train-stop suburbs relative to non-stop suburbs due to an accessibility premium, particularly for houses. However, the pandemic and the shift towards remote work significantly reduced this premium, resulting in improved purchase affordability in train-stop suburbs for homeowners and investors, particularly for units. Meanwhile, rental affordability for houses in train-stop suburbs improved during and after the pandemic, but not for units. Our findings may influence the effectiveness and design of the government’s Transport‑Oriented Development (TOD) initiatives aimed at increasing unit/apartment supply around major train stations under changing commuting patterns and post-pandemic housing demand.]]></description>
      <pubDate>Wed, 14 Jan 2026 17:40:22 GMT</pubDate>
      <guid>https://trid.trb.org/View/2612457</guid>
    </item>
    <item>
      <title>Car-free not care-free – the social practices of parents without cars</title>
      <link>https://trid.trb.org/View/2603405</link>
      <description><![CDATA[Using data from interviews with 30 car-free parents in Sydney, Australia, this paper details the way parents confront social norms by raising children without private car ownership. Social practice theory is applied to fuse the influence of transport material structures with cultural expectations and emotions in a detailed examination of how parents live without cars. The paper exposes the way cultural scripts of good parenting are re-written by car-free parents, who have developed skills to take advantage of mixed-use and transit rich urban form, yet also accept having access to less. In doing so, a series of emotions are stirred, which parents absorb, as they forge an alternative transport lifestyle through a notoriously car dependent life-stage in a car-dependent city. This story sheds light on the barriers and enablers to less car-dependent parenting in the hope of informing realistic understandings of the influence of material transport structures on sustainable transport transitions. While alternative transport infrastructure is necessary for car-free living, a series of cultural and psycho-social elements must also be factored into our aspirations for less car dependent cities.]]></description>
      <pubDate>Mon, 15 Dec 2025 10:34:10 GMT</pubDate>
      <guid>https://trid.trb.org/View/2603405</guid>
    </item>
    <item>
      <title>Exploring and bridging the gap between recreational and school commute cycling</title>
      <link>https://trid.trb.org/View/2606846</link>
      <description><![CDATA[This study explores the cycling behaviours of high school students, with a particular emphasis on the distinctions between recreational and school commuter cyclists. While much of the existing research has predominantly addressed cycling to school, this study broadens the scope to identify insights that could encourage the transition from recreational cycling to school commuting. We surveyed 325 high-school students in Sydney, Australia, exploring their demographics, cycling attitudes, perceptions of safety, and environmental factors that influence their bicycle use. The data is analysed through two logistic regression models, one for recreational cycling and the other for school commuting, both reinforced by the synthetic minority oversampling technique to address class imbalances. Recreation emerged as the primary motivation for cycling, with students more likely to ride when accompanied by friends or family. There are more recreational cyclists than commuters, likely due to the lack of safe cycling routes, heavy loads during commuting, and insufficient bike parking at schools. The smaller group of commuting cyclists tends to use bicycles more consistently and frequently and exhibit greater risk acceptance towards cycling infrastructure. To encourage cycling to school, we recommend the establishment of dedicated bike lanes to schools completely separated from vehicular traffic and increased bike parking facilities at schools, as well as government subsidies and incentives for cargo racks and e-bike promotion.]]></description>
      <pubDate>Mon, 08 Dec 2025 11:43:47 GMT</pubDate>
      <guid>https://trid.trb.org/View/2606846</guid>
    </item>
    <item>
      <title>Ready for road pricing reform? Identifying segments of support in Australia and uncovering (Hidden) attitudes</title>
      <link>https://trid.trb.org/View/2597016</link>
      <description><![CDATA[Despite decades of research and broad consensus in the academic literature regarding its efficiency, road pricing reform remains a controversial topic that many policymakers are loath to debate, and the public strongly resists. Nevertheless, as traditional sources of road funding decline and the costs of maintaining road infrastructure continue to rise, some form of reform will be necessary. Using a mixed-methods approach, this paper revisits the foundations of the debate and presents an in-depth analysis of public attitudes towards the need for road funding reform, specifically examining the acceptability of a simple road user charge (cents per kilometre), independent of specific pricing levels or policy design features. We segment respondents into five distinct clusters based on their underlying support for, or resistance to, reform, and further contextualise these segments by exploring their perceptions of a road user charge. Overall, we identify that the time to discuss reform is now, as there is growing recognition that funding change will be required. We provide recommendations on how to navigate these initial discussions, particularly as our analysis reveals potentially hidden ulterior motivations that may ultimately shape how reform proposals are received and, in turn, their viability.]]></description>
      <pubDate>Mon, 24 Nov 2025 15:30:52 GMT</pubDate>
      <guid>https://trid.trb.org/View/2597016</guid>
    </item>
    <item>
      <title>Valuing travel time and reliability from emerging connected vehicle data</title>
      <link>https://trid.trb.org/View/2596738</link>
      <description><![CDATA[The value of travel time and reliability are significant economic parameters in canonical transport Cost–Benefit Analysis. Our study employs connected vehicle data paired with Sydney, Australia’s, extensive toll road network to introduce a novel approach to valuing these metrics. Toll uptake makes the time–money trade-off explicit: travellers pay to avoid congestion. While toll choices have long been used to infer time valuation, a network-wide approach incorporating passive revealed preferences has not yet been explored. We design choice sets using methods termed route ‘observation’ and ‘generation’, and estimate time and reliability valuations using mixed-path size logit. Our findings align closely with official estimates used in project appraisal, and set the stage for panel revealed preference studies as connected vehicles occupy more of the vehicle fleet.]]></description>
      <pubDate>Mon, 24 Nov 2025 10:22:53 GMT</pubDate>
      <guid>https://trid.trb.org/View/2596738</guid>
    </item>
    <item>
      <title>Spatial transferability of pedestrian trip generation models</title>
      <link>https://trid.trb.org/View/2593810</link>
      <description><![CDATA[The availability and consistency of pedestrian travel data vary across different locations, often requiring the transfer of estimated models in the absence of comprehensive local data. However, the extent to which pedestrian demand models are spatially transferable is not well understood. This study explores the spatial transferability of both aggregate and disaggregate pedestrian trip generation models using data from the Household Travel Surveys of Sydney, Melbourne, and Brisbane, Australia and two cities in the United States, Seattle and Chicago. We estimate Negative Binomial regression, Bayesian regression, and Random Forest models as aggregate approaches, while for disaggregate individual walking trip generation, we estimate a Poisson zero-inflated model, a two-step Logit-Bayesian approach, and a two-step Random Forest model. Results suggest that aggregate models exhibit reasonable transferability under certain conditions, while disaggregate models show greater limitations. The study demonstrates that while Random Forest generally outperforms other models in estimating the number of walking trips and shows strong transferability between cities, Negative Binomial Regression is effective at handling data with high variability, often surpassing machine learning models. The results highlight that both traditional and machine learning approaches have distinct advantages depending on data characteristics and under some data conditions such as sample size, the distribution of variables, and the heterogeneity of input variables. The combined use of these models can effectively capture the behavior of walking trip generation at different scales and provide valuable insights for policymakers and urban planners at both city-wide and localized levels, especially in areas where data might be lacking.]]></description>
      <pubDate>Tue, 18 Nov 2025 11:04:19 GMT</pubDate>
      <guid>https://trid.trb.org/View/2593810</guid>
    </item>
    <item>
      <title>Exploring aspects of transport-induced gentrification across the project lifecycle: An NLP approach</title>
      <link>https://trid.trb.org/View/2589262</link>
      <description><![CDATA[Public transport projects reshape neighbourhoods, creating opportunities for urban revitalisation, place-based and transit-oriented development. However, project planning processes may create social disbenefits, enacting gentrification and community displacement. This paper explores the relationship between Light Rail Transit (LRT) and economic and socio-demographic aspects that provide early indications of gentrification processes, by analysing public engagement sources. We adopt a mixed-method case study approach using Natural Language Processing (NLP) and document review to analyse public engagement data during the design, construction and operation phases of the project. A case study of the CBD and South-East Light Rail Project in Sydney, Australia, is employed. Our results indicate that while early indications show gentrification processes may be occurring, they are not reflected in the project-led public engagement or project documentation. Identifying aspects of gentrification processes earlier through public engagement during the design and construction phases could facilitate timely intervention to mitigate adverse impacts on local communities. We conclude that metropolitan governments and project leaders must improve engagement practices about the potential effects of gentrification processes and work with local authorities and communities to be transparent about the significance of effects over time.]]></description>
      <pubDate>Thu, 13 Nov 2025 13:32:30 GMT</pubDate>
      <guid>https://trid.trb.org/View/2589262</guid>
    </item>
    <item>
      <title>Effectiveness of vaccination, travel load, and facemask use control strategies for controlling COVID Delta variant: the case of Sydney Metropolitan Area</title>
      <link>https://trid.trb.org/View/2561642</link>
      <description><![CDATA[The Delta variant of SARS-CoV-2, specifically identified as B.1.617.2, is responsible for the severe outbreaks witnessed globally, including in various countries and cities, with Sydney Greater Metropolitan Area (Sydney GMA) being no exception. According to scientific studies, the Delta strain exhibits increased contagion and leads to a higher incidence of vaccine breakthrough cases, posing significant challenges to pandemic control efforts. In this study, we explore the efficacy of three fundamental control strategies—namely, vaccination rates, adherence to facemask usage, and the management of travel loads—in mitigating the spread of the disease and, consequently, eliminating the Delta variant pandemic in Sydney GMA. We employ an agent-based disease spread model to thoroughly investigate these strategies. Moreover, factorial MANOVA is utilised to assess the significance of variations in the impact of diverse compliance levels with the aforementioned control strategies on various attributes of the pandemic. As complete lockdowns and stringent travel regulations have the potential to induce physical and mental distress in individuals and economic crises for countries, our study examines the interactive effects of implementing control strategies to mitigate the necessity for a full lockdown. The simulation results suggest that suppressing a pandemic with similar characteristics to Delta variant of COVID is feasible with a vaccination rate of 80% or higher, as long as travel load and activity participation are maintained at pre-COVID levels. Alternatively, a more realistic and attainable combination of control measures—a vaccination rate of 60%, a facemask usage level of 60%, and a 50% compliance level for social distancing—demonstrates comparable efficacy, leading to effective pandemic control. Notably, the vaccination rate emerges as a more potent control strategy compared to others in the elimination of the disease within society.]]></description>
      <pubDate>Wed, 17 Sep 2025 09:01:33 GMT</pubDate>
      <guid>https://trid.trb.org/View/2561642</guid>
    </item>
    <item>
      <title>Predicting and analyzing ferry transit delays using open data and machine learning</title>
      <link>https://trid.trb.org/View/2569939</link>
      <description><![CDATA[The utilization of public transport data has evolved rapidly in recent decades. Ferries, with their unique characteristics and sensitivity to weather conditions, pose significant challenges for delay prediction. Given their pivotal role in the transportation systems of numerous cities, accurately predicting ferry delays is crucial for synchronizing transit services.This paper demonstrates the value of open data for improving ferry delay predictions through machine learning, focusing on two case studies. The authors' approach leverages General Transit Feed Specification (GTFS) data, ridership and vessel information, and hourly weather data, combined with SHAP explainable artificial intelligence analysis to assess key delay determinants. While support vector regression and deep neural networks showed high accuracy in individual case studies, gradient boosting consistently offered the best balance between prediction accuracy and computational efficiency. Moreover, SHAP analysis reveals that operational and temporal features – such as stop sequence, trip start time, headway, and vehicle label – are the dominant drivers of delays, with weather-related factors exerting only a modest influence.]]></description>
      <pubDate>Fri, 18 Jul 2025 09:06:53 GMT</pubDate>
      <guid>https://trid.trb.org/View/2569939</guid>
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