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    <title>Transport Research International Documentation (TRID)</title>
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    <copyright>Copyright © 2026. National Academy of Sciences. All rights reserved.</copyright>
    <docs>http://blogs.law.harvard.edu/tech/rss</docs>
    <managingEditor>tris-trb@nas.edu (Bill McLeod)</managingEditor>
    <webMaster>tris-trb@nas.edu (Bill McLeod)</webMaster>
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      <title>Transport Research International Documentation (TRID)</title>
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      <link>https://trid.trb.org/</link>
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    <item>
      <title>Projected cost competitiveness of zero-emission trucks in Australia</title>
      <link>https://trid.trb.org/View/2686797</link>
      <description><![CDATA[This study compares the Total Cost of Ownership (TCO) of battery-electric trucks (BETs), fuel cell trucks (FCTs), and diesel trucks (DTs) under Australian conditions, using a bottom-up simulation model based on real-world data on fuel consumption, payload, dwell-time, and costs, excluding government incentives.TCO projections, aligned with Australia's decarbonization goals to 2050, indicate that zero-emission trucks (Gross Weight Mass 15.5 t, 22.5 t and 42.5 t) are expected to reach cost competitiveness with diesel by 2050. BETs currently exhibit lower TCO for back-to-base operations, whereas FCTs are more competitive for long-haul applications. By 2050, FCTs are projected to outperform both BETs and DTs across all freight tasks, driven by lower CAPEX and payload capacity gains of up to 1 tonne compared to DTs.Driver wages constitute the largest cost component (30 – 70%), followed by CAPEX in back-to-base and fuel in long-haul operations. Fuel consumption is analyzed across 50 – 100% payload cases. For similar trucks, literature values generally cluster closer to the 50% load case in Australia, suggesting lower payload utilization than is typical in Australia.These findings highlight the need for pilot projects, infrastructure planning, and policy support tailored to Australia's unique freight conditions to accelerate the adoption of zero-emission trucks.]]></description>
      <pubDate>Thu, 09 Jul 2026 13:31:02 GMT</pubDate>
      <guid>https://trid.trb.org/View/2686797</guid>
    </item>
    <item>
      <title>Convergence-triggered reinforcement adaptive relearning control for virtually-coupled trains</title>
      <link>https://trid.trb.org/View/2684788</link>
      <description><![CDATA[This paper proposes a convergence-triggered reinforcement adaptive relearning control (CTRAR) framework for virtual coupling (VC) train formation systems, integrating a novel dual-criteria performance monitor mechanism to ensure optimal and safe operation. Distinct from prior approaches, the proposed mechanism enforces boundary constraints and minimum dwell-time conditions, triggering either convergence to the approximate optimal mode with suspended actor-critic (AC) weight updates or nonconvergence to the nonoptimal mode which indicates a restart of the reinforcement learning (RL) procedure upon violation, termed CTRAR. To enhance learning efficiency visualization, a coefficient-enhanced Gaussian learning rate is developed for AC weight updates, ensuring persistent excitation of tracking error signals without compromising control performance. By leveraging train in-transit data from Beijing South Railway Station (BSRS) to Jinan West Railway Station (JWRS), the simulations successfully demonstrate the execution of reinforcement relearning under convergence-triggered conditions, as well as rigorous validation of the CTRAR algorithm through both error-convergence data illustration and computational resource savings quantification.]]></description>
      <pubDate>Tue, 30 Jun 2026 17:02:35 GMT</pubDate>
      <guid>https://trid.trb.org/View/2684788</guid>
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    <item>
      <title>An Analysis of Dock-Less Bike Sharing Service in Dublin, Ireland</title>
      <link>https://trid.trb.org/View/2670978</link>
      <description><![CDATA[Cycling promotes a healthier lifestyle and alleviates traffic congestion and air pollution. The present study focuses on the dockless bike sharing system in Dublin, Ireland and identifies the socio-economic and built environment factors that affect the dwell time. The origin- destination and timestamp databases obtained from Bleeper Bike are used to estimate the dwell time. The data is analysed using Uber H3 hexagonal zones, and non-spatial and spatial regression models (both spatial lag and spatial error models) are developed. The spatial error model was found to provide a better fit to dwell time. The study identified that key factors such as the presence of public transport stations and car ownership impact dwell time significantly.]]></description>
      <pubDate>Tue, 30 Jun 2026 09:45:08 GMT</pubDate>
      <guid>https://trid.trb.org/View/2670978</guid>
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    <item>
      <title>Container dwell time predictive modelling: an application of ML algorithms</title>
      <link>https://trid.trb.org/View/2709465</link>
      <description><![CDATA[This study analyses factors affecting container dwell time (CDT) at the Mombasa Port using machine learning (ML) algorithms. The study employs real-time container movement data to evaluate several ML models. It finds that CDT varies significantly across different periods in the year and even in the days and weeks. For example, it peaks in the afternoons and during November/December. Although models like Artificial Neural Networks and Random Forest outperform others, the Decision Tree model was chosen for its interpretability, despite a slightly higher error rate. It identifies transportation modes as the key predictor, with truck-based movements leading to longer dwell times than rail transport. The study highlights the impact of specific locations and times of the week/year on CDT. Its originality lies in using real-time data from the Global South and its application of ML to improve operational efficiency and strategic decision-making. Unlike typical studies focused on terminal operations, this research also considers broader exogenous factors. The findings provide valuable insights for optimizing port operations and reducing CDT.]]></description>
      <pubDate>Tue, 30 Jun 2026 08:51:20 GMT</pubDate>
      <guid>https://trid.trb.org/View/2709465</guid>
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    <item>
      <title>Recent Research on Bus Dwell Time Factors</title>
      <link>https://trid.trb.org/View/2714999</link>
      <description><![CDATA[As part of the Transit Cooperative Research Program (TCRP) Project A-47, “Transit Capacity and Quality of Service Manual, Fourth Edition,” the research team conducted a series of small research tasks to support development of new content for the manual. This TCRP research results digest (RRD) is one of 12 presenting the results of these research tasks. Dwell time—the time required at a bus stop to serve passengers and open and close the bus doors—is a key factor affecting bus operating speeds and the capacities of bus facilities. This RRD synthesizes the findings of bus dwell time research that occurred after the Transit Capacity and Quality of Service Manual (TCQSM), 3rd Edition, was published and presents methods for estimating average dwell times from archived, automatically collected data.]]></description>
      <pubDate>Thu, 18 Jun 2026 16:35:20 GMT</pubDate>
      <guid>https://trid.trb.org/View/2714999</guid>
    </item>
    <item>
      <title>Max-Pressure Signal Control with Transit Priority and Lane Blockage Mitigation: Considering Dwelling Buses and Permitted Left Turns</title>
      <link>https://trid.trb.org/View/2706338</link>
      <description><![CDATA[Urban traffic congestion remains a critical issue, intensifying with ongoing urbanization and increasing traffic volumes. While “max-pressure” (MP) control has emerged as a robust, decentralized strategy for optimizing intersection signals based on real-time queue dynamics, it traditionally overlooks transit vehicles and real-world complexities such as lane blockages (LB) because of buses dwelling at stops and permitted left-turn movements. This paper introduces an innovative extension of the MP paradigm for right-hand traffic systems, termed “MP-TSP-LB,” which explicitly integrates transit signal priority (TSP) through weighted priority schemes and accounts for LB caused by both dwelling buses and permitted left turns. The proposed approach modifies the conventional MP framework by introducing weight-based prioritization of buses without disrupting overall network stability. Additionally, the model incorporates effective lane capacities by estimating blockage probabilities, applying critical gap acceptance theory for permitted left turns and probabilistic dwell-time distributions for buses. The MP-TSP-LB model was rigorously tested through simulation on a 3 × 3 grid network using the Simulation of Urban Mobility simulation environment. Results indicate that the MP-TSP-LB model significantly enhances network performance across multiple metrics compared with baseline MP formulations. Sensitivity analysis demonstrates that the model reduces total waiting times and increases average travel speeds for both private vehicles and transit across various demand levels. Incorporating permitted left turns effects significantly improves performance under low-to-moderate demand levels, while modeling dwelling bus blockages becomes increasingly effective as transit service frequency intensifies.]]></description>
      <pubDate>Thu, 28 May 2026 10:47:37 GMT</pubDate>
      <guid>https://trid.trb.org/View/2706338</guid>
    </item>
    <item>
      <title>Influence of the Rail Vehicle Layout on Efficiency and Railway Operation</title>
      <link>https://trid.trb.org/View/2666465</link>
      <description><![CDATA[Railway efficiency is increasingly significant given the competition with road and air transport, where usability of travel time, passenger comfort and operational reliability play key roles. This study investigates the influence of passenger rail vehicle layout on efficiency, focusing on aspects such as passenger changeover time, luggage storage, seating usability and overall comfort. Since 2001, empirical research at TU Wien has combined extensive passenger observations, surveys of more than 60,000 travellers, detailed luggage measurements and video analyses of over 20,000 boarding and alighting processes, complemented by controlled changeover tests in multiple vehicle layouts. These data informed the development of a calculation model and the TrainOptimizer software to evaluate and optimise layouts. Findings show that maximising seating capacity often reduces efficiency by limiting luggage storage, blocking seats and extending changeover times, whereas layouts with fewer but better-positioned seats and distributed luggage racks increase usable capacity and passenger satisfaction. Results also indicate that interior design directly affects time use, stress and well-being during travel. The study concludes that vehicle design has a significant impact on operational performance, energy use and the competitive position of railways, with well-balanced layouts offering both higher efficiency and an enhanced passenger experience.]]></description>
      <pubDate>Tue, 26 May 2026 09:41:00 GMT</pubDate>
      <guid>https://trid.trb.org/View/2666465</guid>
    </item>
    <item>
      <title>Airport regulation, terminal congestion, and capacity expansion</title>
      <link>https://trid.trb.org/View/2669967</link>
      <description><![CDATA[This paper investigates the issues of airport regulation and terminal capacity expansion in the presence of terminal congestion. An analytical bottleneck model is first proposed for simulating the passenger arrival behavior at the terminal, which captures the dynamic formation and dissipation of passenger queues there. Using the proposed model, the interactions among passenger arrival distribution, terminal congestion, non-aeronautical services, and terminal capacity are revealed. A vertical-structure game-theoretical model is then developed to determine the optimal airport charge and the optimal terminal capacity under different scenarios, including profit maximization without regulation, single-till regulation, dual-till regulation, and welfare maximization. Our findings show that the single-till regulation leads to a higher social welfare compared to the dual-till regulation. When the marginal benefit from non-aeronautical services is relatively high, both welfare-maximizing and profit-maximizing airports under-invest in the terminal capacity, aiming to prolong passenger dwell time and thus increase non-aeronautical profit. Particularly, the welfare-maximizing airport suffers a longer total queuing time than the profit-maximizing airport, reflecting heavier underinvestment in its terminal capacity. The airport regulation would distort the terminal capacity investment, and the profit-maximizing airport under the dual-till regulation always under-invests in the terminal capacity, causing terminal congestion delay.]]></description>
      <pubDate>Tue, 26 May 2026 09:40:58 GMT</pubDate>
      <guid>https://trid.trb.org/View/2669967</guid>
    </item>
    <item>
      <title>A foundational dwell time model for regional railways</title>
      <link>https://trid.trb.org/View/2668531</link>
      <description><![CDATA[This study presents a novel foundational Regional Dwell Time (RDT) Model tailored for regional railway networks, addressing the gap of existing dwell time models specifically designed for this type of railway. Using video-based observations from Victoria, Australia, empirical data were collected and analysed to develop a statistical regression model integrating passenger flow dynamics and operational constants such as door operation and train dispatch procedures. The proposed RDT Model was calibrated and validated against 398 regional train services at two stations. The model was compared with established statistical dwell time models in the literature. The RDT Model demonstrated superior predictive accuracy, with significantly lower error metrics (RMSE, MAPE, MAE) compared to alternative models. The study emphasizes the significance of operational time in dwell time estimations and highlights opportunities for reducing dwell time through improved operational procedures. Findings suggest the RDT Model's adaptability to various other regional railways, provided operational constants are recalibrated. This model serves as a proof-of-concept and a novel framework for dwell time modelling in peri‑urban regional railways, with broader applicability contingent on future multi-corridor validation. Future research on regional railways should aim to build on this type of model and explore real-time data integration and machine learning techniques to enhance predictive capabilities and network efficiency.]]></description>
      <pubDate>Tue, 26 May 2026 09:40:58 GMT</pubDate>
      <guid>https://trid.trb.org/View/2668531</guid>
    </item>
    <item>
      <title>Analysis of bus dwell times from automated passenger count data and the impact of dwell-time variability on the performance of transit signal priority</title>
      <link>https://trid.trb.org/View/2692331</link>
      <description><![CDATA[The design of transit signal priority (TSP) systems requires knowledge of dwell-time distributions at bus stops within the block. Dwell-time trends are not well established in the literature despite the ubiquity of large sets of automated passenger count (APC) data. Additionally, the impact of dwell-time variability on TSP performance is not well studied, particularly with field-collected dwell-time data. This study first analyzes trends and distributions inherent in dwell-time data deduced from APC data. Dwell times vary from stop to stop and for each stop by time of day (TOD). Stops with the highest proportions of non-zero dwell time also had the highest dwell-time magnitudes and variability. For most stops, dwell-time data was closely fitted by inverse Gaussian, log-normal, power log-normal, Fisk (log-logistic), and Johnson’s SU distributions. The second part of the study used a simulation environment to evaluate the impact of dwell-time magnitude and variability on TSP performance at both far-side and near-side bus stops. For far-side bus stops, dwell time significantly impacted the bus arrival profile at the check-in detector and thus the selected TSP strategy. Higher dwell-time magnitudes and variability led to a higher share of an Early Green Phase (EG) which is not as effective as a Green Phase Extension (GE). At near-side bus stops, dwell-time variability induced more uncertainty in an estimated time of arrival (ETA) and significantly reduced TSP effectiveness especially for GE. TSP performance in terms of GE success, bus travel time, and side-street traffic delay was significantly better at far-side stops compared to near-side stops.]]></description>
      <pubDate>Wed, 20 May 2026 10:20:54 GMT</pubDate>
      <guid>https://trid.trb.org/View/2692331</guid>
    </item>
    <item>
      <title>Connecting to Compete 2025: The New Logistics Performance Indicators 2.0.</title>
      <link>https://trid.trb.org/View/2696150</link>
      <description><![CDATA[Connecting to Compete 2025 introduces the Logistics Performance Indicators 2.0 (LPI 2.0), a fundamental redesign of the World Bank’s former survey-based Logistics Performance Index toward a system grounded in shipment level data. Drawing on large-scale tracking data from maritime, aviation, and postal operators —together accounting for over 80 percent of global goods trade—the LPI 2.0 provide standardized and comparable measures of supply chain connectivity, speed, and reliability across economies and over time. The framework comprises 21 indicators, including 6 core indicators capturing direct connectivity and import time performance in each mode and 15 supplementary indicators that shed light on maritime competition, transshipment dependence, corridor performance for landlocked developing countries, and postal logistics for e-commerce. Data for 2023–24 reveal large variations in connectivity across regions, substantial time penalties associated with transshipment and border procedures, and high unpredictability concentrated at ports, transshipment hubs, and inland checkpoints—particularly for imports and for landlocked countries. By shifting the focus from perceptions to observed outcomes, the LPI 2.0 significantly enhance diagnostic precision and policy relevance, enabling governments to better prioritize reforms with the highest potential impact on supply chain connectivity, speed, reliability, and resilience.]]></description>
      <pubDate>Mon, 18 May 2026 10:59:38 GMT</pubDate>
      <guid>https://trid.trb.org/View/2696150</guid>
    </item>
    <item>
      <title>The New Logistics Performance Indicators 2.0 (LPI 2.0): Methodology and User Guide</title>
      <link>https://trid.trb.org/View/2696151</link>
      <description><![CDATA[This report presents the methodology for the Logistics Performance Indicators 2.0 (LPI 2.0), a data‑driven framework developed by the World Bank to measure countries’ trade logistics performance, as well as a user guide. The LPI 2.0 replace the original perception‑based Logistics Performance Index used through 2023 with objective indicators derived from large‑scale shipment‑ and vessel‑level tracking data. The framework measures logistics performance along two core dimensions—connectivity and time—across maritime, aviation, and postal logistics. Six core indicators are complemented by supplementary indicators that provide greater granularity on maritime operations, postal flows, and transit logistics, with specific attention to the challenges faced by landlocked developing countries. The indicators are constructed from harmonized global datasets obtained from multiple data partners, using standardized definitions and transparent statistical methods to ensure cross‑country comparability. The LPI 2.0 are intended as a high‑level diagnostic and benchmarking tool to support policy dialogue, comparisons, and analysis of structural logistics performance. While not a substitute for detailed country studies, the LPI 2.0 provide an evidence-based entry point for policy dialogue and reform prioritization in trade facilitation, transport, and logistics.]]></description>
      <pubDate>Mon, 18 May 2026 10:59:38 GMT</pubDate>
      <guid>https://trid.trb.org/View/2696151</guid>
    </item>
    <item>
      <title>Modelling railway dwell time: A structured review and lifecycle framework for real-world integration</title>
      <link>https://trid.trb.org/View/2665698</link>
      <description><![CDATA[Dwell time is a critical component of railway operations, influencing network capacity, service reliability, and passenger experience. Despite extensive methodological development, discussion of how dwell time models are operationalised in practice remains limited in the publicly available literature, a situation that may partly reflect commercial confidentiality in real-world applications. To address this, the paper proposes a novel lifecycle-oriented, systems-theoretic framework to support the selection, calibration, and operationalisation of dwell time models in alignment with institutional capabilities, data environments, and planning objectives. The framework is informed by a structured, non-exhaustive review of railway dwell time modelling approaches, synthesizing statistical-based, simulation-based, and advanced models to examine how passenger behaviour, operational constraints, and uncertainty are represented across different operational contexts. Unlike prior reviews that focus on individual modelling paradigms in isolation, this study integrates insights across major approaches and aligns them with practical deployment considerations. By introducing a six-part lifecycle framework, this work provides a structured, actionable pathway for translating dwell time models into real-world applications. By bridging academic rigor with real-world applicability, the proposed framework offers a pragmatic pathway for agencies to leverage data-driven modelling for improved dwell time management by advancing the operational maturity and responsiveness of railway systems.]]></description>
      <pubDate>Thu, 14 May 2026 17:04:32 GMT</pubDate>
      <guid>https://trid.trb.org/View/2665698</guid>
    </item>
    <item>
      <title>Effect of Land Use Pattern on Bus Blockage Duration at Curb Side Bus Stops</title>
      <link>https://trid.trb.org/View/2581535</link>
      <description><![CDATA[A bus stop is a strategic location considering congestion and delay in an urban road network. The influence of a bus stop on the traffic stream is generally characterised by the average dwell time. However, dwell time alone cannot be a complete measure of the influence of a stopping bus on the traffic stream. The duration of the bus to decelerate and accelerate to the required stream speed needs to be taken into consideration. The bus blockage duration that takes these factors into consideration gives a better picture of the effect of a bus on the stream. Again, the dwell time is usually correlated to the number of boarding/alighting. However, the other factor that influence passenger behaviour is the activity around the vicinity of the bus stop. These activities influence the passenger behaviour and the bus frequency and are a good measure of a combination of these various factors. Thus, in this study, the various factors influencing bus stops are studied in detail based on bus arrivals, departures and dwell time data collected from 1651 buses along 11 bus stops in the Mumbai region. The land use activity near the bus stop and its influence on the bus blockage duration is the prime contribution of this paper.]]></description>
      <pubDate>Wed, 29 Apr 2026 16:47:26 GMT</pubDate>
      <guid>https://trid.trb.org/View/2581535</guid>
    </item>
    <item>
      <title>Person-based aggregate space-time accessibility (PASTA): Bridging the gap between place- and person-based accessibility</title>
      <link>https://trid.trb.org/View/2676640</link>
      <description><![CDATA[Accessibility is the potential to interact with opportunities. Place-based measures of accessibility, such as gravity-type measures, have been widely applied to compute the opportunities accessible when traveling from a given origin and reflect aggregate geographic patterns of access across a city or region. Despite this, place-based measures are criticized for lacking a consideration of individual-level space-time constraints, which can result in inaccurate estimates of the potential for interaction at the destination, particularly due to the insufficient consideration of available time. In contrast, people-based measures rooted in time geography, such as space-time prisms (STPs), can compute both accessible opportunities and potential time to spend at each opportunity for individuals, given their travel modes and travel budgets. However, it can be difficult to represent person-based accessibility at spatially aggregate levels, limiting their application in planning practice. This study bridges place- and people-based accessibility by proposing person-based aggregate space-time accessibility (PASTA), a novel measure that starts by calculating voxel-based STPs. The discretized spatial and temporal dimensions enable further aggregations of the multidimensional arrays to return place-based measures of access whose units are measured in minutes of potential dwell time. From this, the PASTA-based approach can map potential dwell time across a city or region for many individuals or, with the incorporation of person weights, the entire population. Moreover, the aggregation can be conducted along different dimensions, making it easy to compare PASTA across space and time, as well as across travelers with different mode choices and socioeconomic characteristics. Comparisons with traditional place-based measures of market potential accessibility find positive and non-linear relationships between PASTA and gravity-type accessibility for transit and car, but more significant disparities between potential dwell time and walking and cycling access. Overall, PASTA bridges the concepts of population time and interaction potential. When paired with the underlying computational advances, this approach facilitates bringing people back into place-based accessibility analysis.]]></description>
      <pubDate>Wed, 29 Apr 2026 09:17:13 GMT</pubDate>
      <guid>https://trid.trb.org/View/2676640</guid>
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