<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>The role of government policy in reducing road carnage: Evidence from Zimbabwe’s public passenger transport sector</title>
      <link>https://trid.trb.org/View/2682720</link>
      <description><![CDATA[Road traffic injuries remain a pressing public health and development challenge in Zimbabwe, with public passenger transport accounting for a disproportionate share of fatalities. Despite multiple government-led policy interventions, including driver retesting, vehicle inspections, and speed enforcement technologies, road deaths increased by over 40% between 2020 and 2022. This study critically examines the role and effectiveness of government policy in reducing road carnage in Zimbabwe’s public passenger transport sector between 2000 and 2024. A qualitative systematic literature review was conducted, complemented by a comparative case study analysis of international best practices from Sweden, Singapore, Kenya, and South Africa. The study applies Public Sector Performance Theory and Collaborative Governance Theory to assess policy implementation, institutional capacity, and stakeholder engagement. Findings reveal that Zimbabwe’s policy approach remains reactive, enforcement-heavy, and poorly aligned with long-term safety goals. Key limitations include institutional fragmentation, low technological adoption, underfunding, and minimal stakeholder participation. Comparative insights highlight the potential of decentralizing enforcement, adopting AI-based monitoring, and embedding road safety into broader urban governance frameworks. The study concludes that while government policy is necessary, it is insufficient without structural reforms in implementation, oversight, and collaboration. It recommends a transition toward integrated, evidence-based, and participatory policymaking. The findings have significant implications for policymakers, development partners, and urban planners seeking to improve public transport safety in low-resource contexts.]]></description>
      <pubDate>Wed, 08 Apr 2026 15:32:50 GMT</pubDate>
      <guid>https://trid.trb.org/View/2682720</guid>
    </item>
    <item>
      <title>Wireless Interference and Regulatory Frameworks for Frequency Allocation in V2X Communication Systems</title>
      <link>https://trid.trb.org/View/2579222</link>
      <description><![CDATA[Intelligent Transportation Systems (ITS) and Vehicle-to-Everything (V2X) communication technologies are revolutionizing the transportation sector by enhancing traffic efficiency, safety, and overall user experience. However, the performance of these systems can be significantly hindered by various types of interference. This paper provides a comprehensive overview of the regulatory framework for frequency allocation in V2X communication, identifies the types of interference affecting these systems both co-channel and adjacent channel, and explores strategies for managing and mitigating such interference. It includes an overview of current frequency allocations in the USA, EU/United Kingdom, China, Australia, Japan, South Korea, and Singapore, and discusses the implications of interference on ITS/V2X. The findings underscore the need for robust regulatory frameworks to ensure the successful deployment and operation of ITS and V2X communication systems worldwide.]]></description>
      <pubDate>Tue, 31 Mar 2026 16:34:41 GMT</pubDate>
      <guid>https://trid.trb.org/View/2579222</guid>
    </item>
    <item>
      <title>Crowd-shipping via Train Networks: Optimising Parcel Delivery by Part-timers and Commuters under Varying Occupancy Levels</title>
      <link>https://trid.trb.org/View/2674223</link>
      <description><![CDATA[As urban areas seek sustainable delivery solutions, traditional road-based logistics face scrutiny for inefficiency and environmental impacts. In June 2024, Singapore Post and SMRT launched a trial using trains for postal collection during non-peak hours. This opens opportunities for crowd-shipping, where commuters deliver parcels while traveling. Existing research on crowd-shipping often overlooks variability in train occupancy levels, which can impact delivery feasibility. This study develops an optimisation model considering varying train occupancy levels, parcel capacity, and time window constraints for delivery assignments between crowd-shippers and part-timers. Additionally, it is the first to analyse Singapore Post's model of using part-time staff for mail collection via public transport. Findings show that using the train network reduces operational costs by 37.5% and greenhouse gas emissions by 97.9%, with further reductions achieved by combining part-timers and crowd-shippers. This research demonstrates the potential for a more sustainable, cost-effective urban logistics strategy using train-based crowd-shipping.]]></description>
      <pubDate>Mon, 23 Mar 2026 15:21:00 GMT</pubDate>
      <guid>https://trid.trb.org/View/2674223</guid>
    </item>
    <item>
      <title>Characterizing vehicle–pedestrian interaction behavior in near misses: Insights from three different cities</title>
      <link>https://trid.trb.org/View/2673145</link>
      <description><![CDATA[Improving the safety of vulnerable road users such as pedestrians requires a good understanding of their interaction behavior and their collision avoidance mechanisms in interactions with other road users. Refining this understanding will become even more important in an automated driving environment, where properly representing road users’ evasive actions is required to develop effective collision avoidance systems, especially in mixed and less organized traffic conditions. This study models vehicle–pedestrian interactions using a multi-agent Markov game modeling framework to measure the degree of cooperation as road users interact with each other (e.g., collectively try to avoid a crash). Data from three cities with different traffic environments were used, including Boston (US), Cairo (Egypt), and Singapore. The model adopts an Inverse Reinforcement Learning framework that captures road users’ utilities from their trajectories while accounting for the equilibrium in their actions. Results demonstrate substantial variations in behavior across different cities. For example, Cairo was shown to be the most cooperative environment, whereas Singapore presented the lowest levels of cooperation. Moreover, the level of cooperation is negatively associated with speed variables, which shows that road users were expected to cooperate more when they reduced their speeds. This paper provides valuable insights into road users’ cooperation levels in different environments. This is useful for accurately modeling road users’ actions and incorporating their behaviors in advanced automated driving systems, which should properly reflect local traffic environment conditions.]]></description>
      <pubDate>Wed, 18 Mar 2026 08:59:57 GMT</pubDate>
      <guid>https://trid.trb.org/View/2673145</guid>
    </item>
    <item>
      <title>Dynamic workforce allocation for train-based crowd-shipping: Multi-period optimization incorporating transit discomfort costs</title>
      <link>https://trid.trb.org/View/2642405</link>
      <description><![CDATA[Last-mile parcel delivery faces challenges such as rising costs, labor shortages, and environmental concerns, especially in urban areas. This paper tackles these issues with a new rolling horizon framework that uses public train networks. We create a dynamic model that assigns delivery tasks to crowd-shippers (commuters) and part-time workers based on real-time data, considering train occupancy and commuter discomfort costs. Our results show that strategic delays in parcel delivery can offer economic benefits, with a delayed end-of-day approach saving 6.3% to 9.1% compared to the faster mixed-workforce approach we studied, which involved both part-timers and crowd-shippers. Additionally, we find that crowdsourced delivery is mainly cost-effective during off-peak transit hours, while part-time staff are more economical during peak times. These findings support adopting a flexible, hybrid workforce strategy that adjusts to changing transit conditions over time.]]></description>
      <pubDate>Tue, 17 Mar 2026 09:47:16 GMT</pubDate>
      <guid>https://trid.trb.org/View/2642405</guid>
    </item>
    <item>
      <title>Navigating haze: Assessing the effect of transboundary haze on households’ choice of transportation mode during the school run</title>
      <link>https://trid.trb.org/View/2633497</link>
      <description><![CDATA[Singapore is affected by transboundary haze events, where adverse health effects from particulate pollutants exposure instigate individuals to take protective measures. Transport policymakers are concerned how individuals might change their commuting behaviours to reduce outdoor air exposure— specifically, switching from “open” transport modes (walking, etc.) where commuters may be required to wait for transit or travel the last mile outdoors, to “closed” transport modes (point-to-point private transport). Such trends make efforts to reduce reliance on private transport more challenging. We assess the effect that knowledge of an impending haze event has on parents' transport mode decisions for their primary school child's school commute. We exploit a randomized experiment embedded within a survey to tease out the incremental effect of varying transboundary haze severity (Band 2: Elevated, Band 3: High, Band 4: Very High) on parents' preference to send their child to school via “closed” transport modes. We find that while haze increases parents' motivation to switch to “closed” modes of transportation, the effect is not linear. Our findings have implications for transport mode utilization beyond haze to all aspects of environmental harms, as households grow increasingly aware and concerned about the interaction of transportation and health outcomes.]]></description>
      <pubDate>Tue, 10 Mar 2026 09:56:53 GMT</pubDate>
      <guid>https://trid.trb.org/View/2633497</guid>
    </item>
    <item>
      <title>Ensemble multimodal fusing learning for geological identification in tunnel construction</title>
      <link>https://trid.trb.org/View/2639228</link>
      <description><![CDATA[This paper proposes an ensemble multimodal deep learning approach to identify the geological conditions in shield tunnel engineering. The proposed approach improves the Swin Transformer architecture, building a multimodal model with soil images and tunnel boring machine (TBM) parameters as input. The Dempster Shafer theory is used for information fusion to develop the Ensemble Swin-Transformer-MLP (ESwin-T-M), which accurately identifies geological conditions for tunnel shield projects. In addition, the Gradient-weighted Class Activation Mapping (Grad-Cam) and the Model-Agnostic Permutation Feature Importance (MAPFI) are used to perform model interpretation analysis. The Circle Line Phase 6 C885 Tunnel Project in Singapore is used to validate the effectiveness of the proposed approach. The results indicate that: (1) The proposed ensemble multimodal fusing network identifies geological conditions with a high F1-Score of 0.9746 in 5-fold cross-validation. (2) The recognition accuracy of the ensemble multimodal fusion network is superior to that of the single modality models and the basic models. (3) The ensemble fusing approach outperforms the approaches utilizing soil images or TBM parameters separately in terms of robustness and anti-interference ability. The novelty of this research lies in the development of an integrated detection method for multimodal information, which overcomes the trustworthy limitations of traditional single-data method and provides technical support for the precise and reliable identification of geological conditions in tunnel engineering.]]></description>
      <pubDate>Thu, 26 Feb 2026 17:01:02 GMT</pubDate>
      <guid>https://trid.trb.org/View/2639228</guid>
    </item>
    <item>
      <title>Jet Grouting Soft Clays for Tunnelling and Deep Excavations—Design and Construction Issues</title>
      <link>https://trid.trb.org/View/2200154</link>
      <description><![CDATA[Jet grouting is widely used to enhance the stability of tunnels and excavations in soft clay in Singapore. Some of the design and construction issues associated with the grouting are discussed.]]></description>
      <pubDate>Fri, 06 Feb 2026 13:53:33 GMT</pubDate>
      <guid>https://trid.trb.org/View/2200154</guid>
    </item>
    <item>
      <title>DMODT: Dynamic maritime environment object detection and tracking model for MASS</title>
      <link>https://trid.trb.org/View/2627188</link>
      <description><![CDATA[Accurate visual detection and tracking of maritime objects are vital for safe navigation in Maritime Autonomous Surface Ships (MASS). However, occlusion, nonlinear motion, camera blur, and complex lighting such as strong water reflections pose significant challenges. To address these issues, this work proposes the Dynamic Maritime Object Detection and Tracking (DMODT) model, enhancing detection accuracy and enabling robust tracking in dynamic environments. In detection, a Maritime Multi-scale Edge Enhancement (MMEE) module is integrated into Real-Time DEtection TRansformer (RT-DETR), emphasizing occluded object edges through attention mechanisms. In tracking, an Observation-centric Kalman Filter (OCKF) reduces errors from nonlinear motion. Additionally, the Robust Horizontal Observation-Centric Momentum Estimation (RHOCME) strategy enhances tracking stability by incorporating horizontal motion information, reducing identity losses caused by camera instability. A Height-Modulated Intersection-over-Union (HMIoU) metric further improves tracking reliability under vertical disturbances. Experiments on the Singapore Maritime Dataset demonstrate that, in highly dynamic maritime environments with significant occlusions, the proposed DMODT model substantially outperforms the baseline model, achieving improvements of approximately 33.8 % in Higher-Order Tracking Accuracy (HOTA), 10.3 % in Multiple Object Tracking Accuracy (MOTA), and 43.4 % in Identity F1 score (IDF1). Additionally, the DMODT model effectively enhances environmental perception capabilities and navigational safety for MASS.]]></description>
      <pubDate>Thu, 29 Jan 2026 17:01:37 GMT</pubDate>
      <guid>https://trid.trb.org/View/2627188</guid>
    </item>
    <item>
      <title>Distributed optimal energy management strategy for S-IES with IMO’s regulation on CII</title>
      <link>https://trid.trb.org/View/2627127</link>
      <description><![CDATA[The transformation of ship energy systems has become imperative, driven by substantial carbon emissions and the depletion of fossil fuel reserves. The ship-integrated energy system (S-IES), an emerging energy structure, enables efficient coupling of heterogeneous energy sources. To reconcile the conflict between the shipping industry and marine environmental pollution, this paper overcomes challenges in minimizing carbon reduction throughout the energy management of S-IES by decarbonization policies established by the International Maritime Organization (IMO). Firstly, We propose a fuzzy logic-based grading mechanism to quantify carbon emission levels and assess intensity for the ships. It ensured that shipowners could not resort to opportunistic practices-such as manipulating ratings, falsely claiming subsidies, or evading carbon emission penalties. Secondly, we develop an economic-environmental integrated energy management model for S-IES, along with a distributed energy allocation algorithm using prescribed-time consensus theory. This framework enables rapid and accurate responses to load fluctuations resulting from ship navigation characteristics. Finally, carbon emission data from four vessels operating in the Bohai Sea region confirmed the efficacy of the proposed grading mechanism. The simulated voyage records on the Singapore-Penang route demonstrated the operational validity of the energy management model and its distributed algorithm. According to the simulation results, the carbon emissions of the tested ships can be reduced by approximately 16.26 %, and the carbon emission intensity grade can be upgraded from grade C to grade B. Additionally, fluctuating load demands can be rapidly addressed within the prescribed time, and the response can be completed approximately 20 % ahead of schedule.]]></description>
      <pubDate>Wed, 28 Jan 2026 09:48:33 GMT</pubDate>
      <guid>https://trid.trb.org/View/2627127</guid>
    </item>
    <item>
      <title>Trajectory-based anomaly detection of vessel motion patterns using profile monitoring</title>
      <link>https://trid.trb.org/View/2623423</link>
      <description><![CDATA[Marine anomaly detection is critical for sea traffic safety, and the detection is mostly based on regularly transmitted data from the Automatic Identification System (AIS) installed in the vessel, which include location, velocity, course, and safety-related information. Most existing detection methods rely solely on the sailing vessel’s most recent AIS information without fully utilizing the historical AIS data from the same voyage, and they fall short of providing a comprehensive assessment of the vessel’s sailing state, particularly in terms of identifying deviations in its course or unusual accelerations. To fill the gap, we propose a novel two-stage profile monitoring framework for real-time anomaly detection to make full use of historical sailing information. The first stage aims to extract distinct routes from all the trajectories in the historical data, where a novel trajectory-based route extraction method is developed. The second stage then proposes an anomaly detection algorithm by treating each trajectory as a profile and carefully constructing the monitoring statistics and control charts. We compare the proposed trajectory-based method with existing approaches that rely solely on the most recent vessel information, using comprehensive simulations. The results reveal the superiority of our proposal in detecting anomalies, with fewer false alarms and the ability to detect a wider range of anomalous behaviors. An AIS dataset covering the Singapore Strait is used for illustration throughout the study.]]></description>
      <pubDate>Mon, 26 Jan 2026 14:44:16 GMT</pubDate>
      <guid>https://trid.trb.org/View/2623423</guid>
    </item>
    <item>
      <title>Incorporating Credit Mechanism for Joint Pricing and Charging Optimization for an Electric Taxi Charging System</title>
      <link>https://trid.trb.org/View/2651972</link>
      <description><![CDATA[In recent years, research on electric vehicles has gained significant momentum driven by the growing emphasis on low-carbon mobility. Despite this progress, the specific challenges faced by electric taxis (ETs) as a vital component of sustainable urban transport have received limited attention. This study formulates a joint optimization problem to determine optimal pricing for charging stations (CSs) while guiding plug-in pure electric taxi (PPET) drivers toward the most profitable charging options. The proposed model aims to maximize the overall utility of CSs under cooperative relationships and provide the optimal charging strategy for ETs. To guarantee that the behaviors of both CS and ET drivers do not interfere with the smooth operation of the system, we incorporate a credit mechanism. We propose an algorithm to simultaneously optimize charging prices at each CS and charging selections for ET drivers. Additionally, we present a comprehensive framework for the credit system, detailing its reward and penalty effect. To validate the efficacy of our pricing optimization system and credit mechanisms, comparative experiments are conducted. The results demonstrate the system's advantages and confirm the credit mechanisms' crucial role in maintaining system integrity and promoting good behavior. As a further testament to our approach's practicality, we conducted a case study in Singapore, verifying the system's performance in a real-world context.]]></description>
      <pubDate>Mon, 26 Jan 2026 08:41:45 GMT</pubDate>
      <guid>https://trid.trb.org/View/2651972</guid>
    </item>
    <item>
      <title>Integrating freight transport into first-and-last-mile ridesharing services with modular autonomous vehicles</title>
      <link>https://trid.trb.org/View/2611723</link>
      <description><![CDATA[Modular autonomous vehicles (MAVs), which can be physically connected as a platooned MAV (PMAV) with flexible capacities and cost savings, are widely recognized as effective solutions for shared mobility services. Motivated by this potential, this study investigates a novel first-and-last-mile ridesharing problem with integrated freight requests to address imbalanced demands and reduce overall operational costs simultaneously. To preserve service quality and passenger acceptance, we incorporate mechanisms for passenger requests to specify their willingness to share rides with freight. Due to the separable advantage of a PMAV fleet, passengers and freight can be appropriately allocated to adaptable MAVs in a PMAV by respecting passengers’ travel requirements. To address the problem mathematically, we adopt it as a route-based set-covering (RSC) model. A column generation algorithm is applied to solve the linear relaxation of RSC, and an improved bidirectional label setting algorithm is employed to efficiently tackle multiple pricing subproblems for finding boosted columns. To obtain high-quality integer solutions, an exact branch-and-price (BP) method is finally utilized. To accelerate the labeling process, we design a tailored ranking technique to eliminate symmetrical labels and an enhanced dominance rule to prevent redundant computations. Furthermore, to handle large-scale cases, we develop an adaptive large neighborhood search (ALNS) metaheuristic, strengthened by a problem-specific MAV matching strategy. Extensive computational experiments demonstrate the effectiveness of the methods for finding high-quality solutions in realistic instances from Singapore. The results reveal that the implemented solution can reduce total operational costs by 53.67% in comparison with separate passenger and freight transport.]]></description>
      <pubDate>Wed, 14 Jan 2026 17:40:22 GMT</pubDate>
      <guid>https://trid.trb.org/View/2611723</guid>
    </item>
    <item>
      <title>Semi-supervised learning for multi-target regression in ship risk prediction</title>
      <link>https://trid.trb.org/View/2633383</link>
      <description><![CDATA[Limited port state control (PSC) inspection resources pose an urgent need to enhance PSC inspection efficiency. While existing PSC officers (PSCOs) assignment randomly assign ships with PSCOs, this study aims to predict ship deficiency numbers under various deficiency categories (types of non-compliance) using machine learning (ML) models. Moreover, not all foreign visiting ships to a port are inspected by PSC, which leads to a large amount of unlabeled data remaining unexplored. Using the port of Singapore as a case study, this paper utilizes both labeled and unlabeled data to predict ship deficiency numbers under the six deficiency categories of individual ships. A semi-supervised multi-target regression (SSMTR) framework is developed, which innovates in using prediction performance on the validation dataset to judge the reliability of unlabeled data. The SSMTR framework is extended to various ML regression methods, such as decision tree (DT), support vector regression (SVR), extreme gradient boosting (XGBoost), and multilayer perceptron (MLP), resulting in DT-SSMTR, SVR-SSMTR, XGBoost-SSMTR, and MLP-SSMTR. Across four experiment groups with different numbers of labeled data samples, the mean squared error improves on average by 13.65% for DT-SSMTR, 1.62% for SVR-SSMTR, 4.39% for XGBoost-SSMTR, and 2.65% for MLP-SSMTR compared to models that only use labeled data.]]></description>
      <pubDate>Tue, 13 Jan 2026 09:21:00 GMT</pubDate>
      <guid>https://trid.trb.org/View/2633383</guid>
    </item>
    <item>
      <title>A Study on Events Held in the Vicinity of the Port Limit of Specified Ports - Considerations for Holding Events Safely, Using Maritime Traffic Regulations-</title>
      <link>https://trid.trb.org/View/2598684</link>
      <description><![CDATA[Based on the Port Regulations Act, various powers are granted to the captain of the port, such as the requirement that event organizers must obtain the permission from the captain of the port when holding an event in the specified port. However, for the event, marine fireworks festivals and drone shows, held in the vicinity of port limit of specified ports, the current Port Regulations Act restricts the measures that the captain of the port can take to ensure the view of navigational safety, despite the possibility that an accident occurred during the event could have a negative impact on port traffic. In this paper, the authors point out the 6 problems with the Port Regulations Act, such as article 32, and propose improvements, referring to the foreign example of fireworks festivals that was held at Singapore in 2022.]]></description>
      <pubDate>Mon, 29 Dec 2025 09:35:56 GMT</pubDate>
      <guid>https://trid.trb.org/View/2598684</guid>
    </item>
  </channel>
</rss>