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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>
    <image>
      <title>Transport Research International Documentation (TRID)</title>
      <url>https://trid.trb.org/Images/PageHeader-wTitle.jpg</url>
      <link>https://trid.trb.org/</link>
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    <item>
      <title>Hearing loss prediction equation for Iranian truck drivers using neural network algorithm</title>
      <link>https://trid.trb.org/View/2709035</link>
      <description><![CDATA[Background: Given the high prevalence of hearing loss among truck drivers, using artificial neural networks (ANNs) to predict and detect contributing factors early can aid managers significantly. Objective: This study aimed to predict hearing loss using an ANN algorithm and to evaluate the weight and influence of various factors affecting hearing loss among truck drivers. Methods: A total of 692 truck drivers were selected for the study. Their occupational exposure histories were collected to identify factors influencing their hearing loss. The impact and weight of each factor were measured, and an ANN algorithm was used to model and predict the degree of hearing loss. Results: The assessment of hearing loss among truck drivers revealed a prevalence of 59.98% in the right ear and 64.74% in the left ear. The most significant average hearing loss in both ears occurred at frequencies of 6000 and 8000 Hz. According to the ANN model, age and the frequency of 2000 Hz had the greatest impact on hearing loss, while sound pressure level (SPL) had the least impact. Additionally, the relationship between overall hearing loss and the type of heavy truck indicated that drivers of HOWO brand trucks experienced the highest degree of hearing loss compared to other drivers. Conclusions: This study demonstrates that the ANN algorithm is a promising tool for predicting hearing impairments caused by noise exposure among truck drivers.]]></description>
      <pubDate>Tue, 30 Jun 2026 09:45:14 GMT</pubDate>
      <guid>https://trid.trb.org/View/2709035</guid>
    </item>
    <item>
      <title>Parking preferences of delivery drivers in the Paris Region: Understanding the role of anticipation using hybrid choice models</title>
      <link>https://trid.trb.org/View/2606840</link>
      <description><![CDATA[This study explores the determinants of parking choices for commercial vehicles in the Paris Region (France). The analysis is based on data from the 2010 Paris Region Urban Goods Movement Survey (UGMS), which offers insights into the parking preferences of delivery drivers. By examining real-world decision-making, the dataset allows us to consider spatial and temporal characteristics as well as the role of parking decision within the delivery process. An integrated choice and latent variable model is employed, whereby drivers select parking locations based on urban environmental attributes, service type, and a latent variable reflecting anticipated delivery difficulty. This difficulty is inferred from observed delivery times and service characteristics; furthermore, temporal variations are incorporated to assess driver behavior, including fluctuations in parking preferences throughout the day. The model also accounts for parking space availability by the means of latent classes. Our findings contribute to a nuanced understanding of delivery drivers’ behavior, providing valuable insights for policy-making and operational strategies. These results, as well as our modeling approach, can also be incorporated into broader frameworks such as agent-based models.]]></description>
      <pubDate>Wed, 17 Jun 2026 16:14:28 GMT</pubDate>
      <guid>https://trid.trb.org/View/2606840</guid>
    </item>
    <item>
      <title>Whole-body vibration exposure in long-nose 10-wheel dumper trucks and some influencing factors</title>
      <link>https://trid.trb.org/View/2673191</link>
      <description><![CDATA[Long-nose 10-wheel dumper trucks are widely used across North America, yet limited data exist on typical whole-body vibration (WBV) exposure for their drivers. This study evaluated typical WBV exposure in fifteen 10-wheelers in two Canadian cities through 116 measurement trials. WBV at the driver seat level was assessed using frequency-weighted root mean square acceleration (aw) and vibration dose value (VDV), following ISO 2631-1 (1997), using both dominant axis and vector sum approaches. Among 66 typical exposure scenarios, 27 had at least one daily WBV index exceeding the danger limit, indicating likely health risks. Roads travelled, vehicle front suspension characteristics, speed, driver weight, and the seat itself, had a greater influence on WBV exposure than bin loading or pneumatic seat suspension damper count. Moreover, daily VDV indices identified more high-risk cases than acceleration-based indices, highlighting the importance of selecting appropriate WBV metrics as different indices may yield varying interpretations of health risks.]]></description>
      <pubDate>Wed, 17 Jun 2026 12:23:20 GMT</pubDate>
      <guid>https://trid.trb.org/View/2673191</guid>
    </item>
    <item>
      <title>Excessive Daytime Sleepiness and Its Associated Factors among Male Road Transport Workers in Brazil</title>
      <link>https://trid.trb.org/View/2691587</link>
      <description><![CDATA[This study aimed to estimate the occurrence of excessive daytime sleepiness (EDS) and its associated factors among male road transport workers. A cross-sectional study was conducted with a non-probabilistic sample of 414 drivers recruited at gas stations and parking lots in Formosa and Rio Verde, Goias, Brazil, in 2024. The presence of EDS was evaluated using the Epworth Sleepiness Scale, and the investigated associated factors included demographic, socioeconomic, behavioral, health and professional characteristics. Logistic regression was used to explore the factors associated with EDS. The prevalence of EDS in the sample was 39.9% (95% CI: 35.1-44.6). After adjustment, a higher probability of EDS was observed among drivers aged between 41 and 60 years, with non-white skin color, and those who were married. The analysis also indicated that drivers with high levels of anxiety and a high risk of obstructive sleep apnea were more susceptible to EDS whereas drivers with good sleep quality and adequate rest practices had a lower probability of EDS. Additionally, long working hours significantly increased the chance of EDS. In conclusion, the findings of this study revealed a high occurrence of EDS among male road transport drivers and its association with demographic characteristics, working conditions, mental health and sleep quality. Therefore, strategies addressing these factors are essential to reducing the occurrence of EDS and contributing to a safer road environment.]]></description>
      <pubDate>Thu, 04 Jun 2026 15:13:33 GMT</pubDate>
      <guid>https://trid.trb.org/View/2691587</guid>
    </item>
    <item>
      <title>Investigating container-truck drivers’ choice preferences toward different parking modes in port cities: A stated preference case study</title>
      <link>https://trid.trb.org/View/2659534</link>
      <description><![CDATA[This paper aims to investigate the preferences of container truck drivers to choose different parking modes (i.e., exclusive parking, shared parking and nearby parking) in port cities. Stated choice data of container truck drivers' parking choice are collected based on an online survey carried out in Beilun, a part of the Ningbo-Zhoushan port of China. Discrete choice models are applied and estimated based on the collected data. In addition, the analyses of willingness-to-pay and marginal effect are carried out as well. The final results show the following conclusions. First, in terms of alternative-specific attributes, the attributes “distance from parking space to the residence”, “distance from parking space to the nearest port”, “parking fee”, “fine for illegal parking”, “number of stock dumps within 5 km of the parking space”, “whether the parking space has a monitoring system” and “whether there are shared bikes near the parking space” have significant heterogeneous influences on drivers' choice behavior. Second, in terms of personal attributes, drivers' age and education level have significant effects. Third, container truck drivers would like to pay ¥14.44 per month in average to make 1 km shorter of the distance from the parking space to drivers’ residential locations, and would like to pay ¥44.80 per month in average to make 1 km shorter of the distance from the parking space to the nearest port. Fourth, decreasing the distance from the parking space to the residence and improving the education level of container truck drivers show significant marginal effects toward the market share of shared parking. Based on these conclusions, relevant policy recommendations are proposed to related stakeholders.]]></description>
      <pubDate>Thu, 28 May 2026 09:06:01 GMT</pubDate>
      <guid>https://trid.trb.org/View/2659534</guid>
    </item>
    <item>
      <title>Relay transport network design for sustainable trucking industry considering carbon emissions and driver working conditions</title>
      <link>https://trid.trb.org/View/2659838</link>
      <description><![CDATA[Relay transport is an emerging collaborative approach that divides traditional long-haul shipments into multiple shorter segments, each handled by a different truck driver. By jointly completing shipments and enabling the shared use of transportation resources, this approach has the potential to alleviate operational inefficiencies, environmental pollution, and poor driver working conditions in the trucking industry. However, existing studies on relay transport network design primarily concentrate on the operational perspective of private companies. To address environmental and social issues in the trucking sector, this study proposes a bi-level programming model to develop a sustainable relay transport network that accounts for carbon reduction targets, carbon tax policies, driver work regulations, relay point capacity limits, and investment budgets. The upper-level government agency aims to minimize carbon emissions, driver overtime hours, driver overnight stays, and infrastructure construction costs by determining the number, location, and capacity of relay points. The lower-level trucking companies select freight transport routes based on the given network configuration, influenced by transport costs, cargo transit times, and carbon taxes. The model is solved using a genetic algorithm integrated with the method of successive averages. A case study in Japan identifies 22 relay points (14 small, 7 medium, 1 large). The optimized relay transport network offers substantial improvements compared to the traditional direct transport network, achieving reductions of 40.48% in carbon emissions, 53.21% in driver overtime hours, 70.38% in driver overnight stays, 20.07% in operational costs, and 5.13% in total cargo transit times. Sensitivity analyses highlight the positive impact of appropriate relay point capacity and higher carbon tax on the network’s sustainability performance. These findings offer valuable insights for government agencies to configure relay transport networks and support policymaking for sustainable freight transport planning.]]></description>
      <pubDate>Thu, 28 May 2026 09:06:01 GMT</pubDate>
      <guid>https://trid.trb.org/View/2659838</guid>
    </item>
    <item>
      <title>From road waiting to road leading: can toll-road marketing strategies make a difference for truck drivers under uncertain travel times?</title>
      <link>https://trid.trb.org/View/2664083</link>
      <description><![CDATA[Understanding truck drivers’ route choice behavior amid the rapid expansion of freight transport is crucial for road operators to improve their services and secure stable revenue. In addition to the strict delivery schedules, truck drivers’ route choice decisions are highly sensitive to uncertain traffic conditions and policy constraints. However, to what extent operator-led marketing strategies shape their route choice decisions remains insufficiently addressed in the literature. Therefore, this study aims to investigate truck drivers’ route decisions under travel time uncertainty with a particular focus on the effects of promotional marketing strategies. A stated choice experiment was designed to collect truck drivers’ responses under different cargo-delivery contexts to toll and parallel toll-free route alternatives. A hybrid prospect-theoretic Probit model (HPTPM), incorporating interaction effects, is further developed to account for risk attitudes in decision-making under uncertainty. The findings reveal that cargo-delivery context variables, marketing strategies, and risk preferences all significantly shape truck drivers’ route choices, with notable heterogeneity observed across driver groups. Based on these insights, this study provides practical recommendations for road operators, supporting the development of tailored marketing strategies to improve the effectiveness of toll-road management.]]></description>
      <pubDate>Wed, 29 Apr 2026 16:34:58 GMT</pubDate>
      <guid>https://trid.trb.org/View/2664083</guid>
    </item>
    <item>
      <title>Assessing urban curbside parking for commercial vehicles: Simulation and policy insights</title>
      <link>https://trid.trb.org/View/2686816</link>
      <description><![CDATA[As e-commerce and urban deliveries spike, there is an increasing demand for curbside loading/unloading space. However, commercial vehicle drivers face numerous challenges while navigating dense urban road networks. These challenges can lead to conflicts with other road users, congestion, illegal parking, and parking time violations. While existing research often highlights pedestrian and bicyclist safety in urban environments, far less attention has been given to the experience and perspective of the truck drivers themselves, who are central to urban goods movement. Moreover, previous research on how commercial vehicle drivers make choices about when and where to park is limited. Available data often comes from field studies where only limited situations can be observed, with no experimental controls and a lack of known drivers’ characteristics. To address this gap, this study used the Oregon State University heavy vehicle driving simulator to examine the behavior of commercial vehicle drivers in various parking and delivery situations while accounting for key variables. A fully counterbalanced, partially randomized, factorial design was chosen to explore four independent variables: number of lanes (2-lane and 4-lane roads), with/without bike lane, available/unavailable passenger vehicle parking, and commercial vehicle loading zone (none, occupied, and unoccupied CVLZ). Driver speed, eye tracking, and parking behavior were used as performance measures. Data from 33 commercial driver’s license (CDL) holders yielded 792 observations across 24 scenarios. The findings from speed, eye movement, and parking behavior support more effective curb management strategies that improve delivery efficiency while recognizing the operational problems faced by truck drivers.]]></description>
      <pubDate>Tue, 28 Apr 2026 11:18:52 GMT</pubDate>
      <guid>https://trid.trb.org/View/2686816</guid>
    </item>
    <item>
      <title>Driving style characteristics based lane-changing intention recognition research for truck drivers near highway ramps</title>
      <link>https://trid.trb.org/View/2680645</link>
      <description><![CDATA[ObjectiveThe research aims to analyze the driving styles and lane-changing intentions of truck drivers near the highway ramps.MethodsUsing principal component analysis (PCA), three principal components were selected for cluster analysis, examining driving styles from the perspectives of risk tolerance, longitudinal, and lateral driving characteristics. An intention recognition model for lane-changing was developed and trained, and its validity was verified with High-D dataset.ResultsThe proposed model in this study achieves an accuracy of 93.7% and an F1 score of 0.891, demonstrating its excellent performance in precision-related metrics. Moreover, the study compares the differences in driving styles and lane-changing intentions between truck drivers and sedan drivers.ConclusionsThe main conclusions are as follows: the lane-changing process consists of two stages: intention and execution. Driving style is a critical factor in the establishment of lane-changing intention models. Four seconds is a proper time window for lane-changing intention prediction. The lane-changing behavior characteristics of truck drivers differ significantly from those of sedan drivers. The study results improve the understanding of truck lane-changing behavior near highway ramps, and they also help to figure out the safety mechanisms in the future human-vehicle cooperative traffic scenarios.]]></description>
      <pubDate>Wed, 15 Apr 2026 10:29:29 GMT</pubDate>
      <guid>https://trid.trb.org/View/2680645</guid>
    </item>
    <item>
      <title>Evaluating User Acceptance and Effectiveness of Cognitive Measurements and Intervention for Shared Autonomy</title>
      <link>https://trid.trb.org/View/2690985</link>
      <description><![CDATA[Vehicles equipped with automated driving systems (ADS) have become more widespread in the trucking industry. On the one hand, ADS are known to be susceptible to occasional errors in environment perception, but on the other, ADS can demonstrate safer and more efficient behavior in situations where the driver is cognitively impaired. Shared autonomy systems thus have the potential to combine the best of both paradigms. Some early instantiations of such shared autonomy ADS use measurements of the human cognitive state to perform interventions, either in the form of sensory feedback, and/or by actively taking over the driving task. The main objective of this project is to address the gap in research on the effectiveness and acceptance of cognition-aware shared-autonomy methods with respect to the overall system safety. Qualitative data will be collected through semi-structured interviews with truck drivers and systematically encoded into operational design requirements and hypothesis-driven performance metrics that directly inform the design of cognition-aware shared autonomy systems. The research team will perform a driving simulator study that enables a controlled evaluation of adaptive cognition-aware intervention policies, including rule-based and data-driven triggering mechanisms that dynamically adjust system behavior based on real-time cognitive interventions. Researchers will study how specific design choices in cognition-aware intervention policies (e.g., trigger thresholds, modality selection, and intervention persistence) influence system acceptance, misuse, and compliance, enabling actionable design guidance beyond descriptive acceptance analysis. The datasets collected inform policy on the use of ADS in both drayage and long-haul trucking. This project will develop a methodology for designing and evaluating cognition-aware behavioral interventions that couple driver monitoring outputs with explicit control and feedback policies, enabling reproducible comparison across intervention strategies and deployment contexts.]]></description>
      <pubDate>Thu, 09 Apr 2026 14:23:07 GMT</pubDate>
      <guid>https://trid.trb.org/View/2690985</guid>
    </item>
    <item>
      <title>The impact of acute stress on simulated driving performance of occupational road freight drivers</title>
      <link>https://trid.trb.org/View/2681663</link>
      <description><![CDATA[Stress is common among occupational road freight drivers due to the demanding nature of their profession. Stress has been shown to negatively influence driving behavior among car drivers, thus raising concerns about its impact on the safety of professional drivers. This study examines how stressful events affect the driving behavior of occupational road freight drivers in three different types of situations: routine road sections, transitions in speed limit, and hazardous events. Driving simulator data from 27 participants was analyzed. They drove through a neutral driving scenario in a TruckSim driving simulator, followed by a stressful scenario with stressful driving events and simulated time pressure. The results showed that under stressful circumstances, drivers exhibited riskier driving behavior, including increased speed and harsher acceleration and braking. On the other hand, the standard deviation of the lateral position (SDLP) decreased. These findings highlight that action should be taken to monitor and reduce occupational road freight driver stress during driving. Future research may involve measuring stress and driving behavior in a naturalistic driving study, allowing for a deeper understanding of the real-world implications of stress. Additionally, studies could investigate the development of interventions that can reduce stress of occupational drivers during driving.]]></description>
      <pubDate>Wed, 08 Apr 2026 15:32:50 GMT</pubDate>
      <guid>https://trid.trb.org/View/2681663</guid>
    </item>
    <item>
      <title>Communication of Fixed and Mobile Warnings to Commercial Trucks Using In-Cab Notification</title>
      <link>https://trid.trb.org/View/2681391</link>
      <description><![CDATA[Improving commercial vehicle safety continues to be an important priority for all stakeholders. There has been considerable focus in Indiana on reducing work zone related crashes, particularly those involving commercial motor vehicles encountering unexpected slowdowns or stopped traffic on the Interstate. Connected vehicle data have the potential to warn motorists of impending slowdowns and congestion in real-time. Multiple data providers have recently begun providing in-cab alerts to commercial vehicle drivers in areas of congestion, dangerous slowdowns, and work zone construction to increase driver awareness of potential hazards. This research utilized 1-second frequency data from trucks receiving in-cab alerts for Congestion or Dangerous Slowdown incidents on limited access roadways in Indiana to analyze the impact of these alerts on commercial vehicle driver behavior from about 30 seconds prior up to 5 minutes after an alert was received. Analysis of approximately 20,000 in-cab alerts sent to commercial vehicle drivers along 44 limited access corridors in Indiana for the months of April–June 2024 showed that 21.2% of drivers receiving a Dangerous Slowdown alert and 15% of drivers receiving a Congestion alert had reduced their speeds by at least 5 mph within 30 s of receiving an alert. As this area of in-cab alerts continues to evolve, it will be important to converge on a shared vision and common targets for these safety and mobility performance measures so that public agencies, in-cab alert providers, and trucking companies can work closely together to agilely improve these systems and increase driver confidence.]]></description>
      <pubDate>Mon, 30 Mar 2026 08:55:11 GMT</pubDate>
      <guid>https://trid.trb.org/View/2681391</guid>
    </item>
    <item>
      <title>Truck drivers and autonomous trucks: A topic modeling analysis of truck driver posts</title>
      <link>https://trid.trb.org/View/2636368</link>
      <description><![CDATA[Social media provides a rich, alternative data source to interviews or survey-based research to study hard-to-reach populations (e.g., truck drivers, because of their transient work structure and unique subculture). This study uses public social media posts from the largest trucking forum in the United States to examine truck drivers’ views on autonomous trucks (ATs), which are poised to transform the trucking industry. The authors expand on traditional qualitative strategies of analyzing social media data by combining newer methods, including BERT-based topic modeling, sentiment analysis, stance detection, emotion analysis, topic similarity, and location analysis through a social interaction network, to analyze a large qualitative sample of social media posts (N = 4,245 posts from 1,319 users). The BERT-based topic modeling results corroborated with research using traditional qualitative analytic approaches that drivers expressed a generally unfavorable view towards ATs, driven by a lack of trust in their feasibility and effective implementation, and concern for displacement. This study advances the current knowledge of truck drivers views of ATs by offering more comprehensive and nuanced insights enabled by novel combination of emerging methods for analyzing passive and active use of social media data, including sentiment analysis, stance detection, emotion analysis, and location analysis.]]></description>
      <pubDate>Fri, 27 Mar 2026 10:20:35 GMT</pubDate>
      <guid>https://trid.trb.org/View/2636368</guid>
    </item>
    <item>
      <title>Heavy Goods Vehicle Drivers’ Insights on Crash Contributing Factors in Rigid and Articulated Lorries</title>
      <link>https://trid.trb.org/View/2669870</link>
      <description><![CDATA[Heavy goods vehicles (HGVs) are vital to the logistics industry, but their involvement in road traffic crashes in Malaysia raises concerns. Differences in design and operation between rigid and articulated lorries lead to varying crash outcomes. This study examines crash-contributing factors from the driver’s perspective through an online survey of 424 HGV drivers. Contingency table analysis found that older drivers (>59 years), those driving articulated lorries with extensive experience (≥10 years), and extended driving durations (>12 hours) significantly increased crash likelihood. Factors contributing to rigid lorry crashes include lack of sleep, reckless driving, fatigue, brake deficiency, and tyre bursts, while articulated lorries are influenced by reckless driving, speeding, traffic violations, adverse weather conditions and tyre bursts. Inadequate road signage and poor street lighting were significant factors for both types. These findings provide valuable insights for developing targeted safety measures specific to each HGV type to improve overall road safety.]]></description>
      <pubDate>Mon, 23 Mar 2026 15:21:00 GMT</pubDate>
      <guid>https://trid.trb.org/View/2669870</guid>
    </item>
    <item>
      <title>A pre/post evaluation of fatigue, stress and vigilance amongst commercially licensed truck drivers performing a prolonged driving task</title>
      <link>https://trid.trb.org/View/2661767</link>
      <description><![CDATA[The main purpose of this research study was to evaluate changes in fatigue, stress and vigilance amongst commercially licensed truck drivers involved in a prolonged driving task. The secondary purpose was to determine whether a new ergonomic seat could help reduce both physical and cognitive fatigue during a prolonged driving task. Two different truck seats were evaluated: an industrial standard seat and a new truck seat prototype. Twenty male truck drivers were recruited to attend two testing sessions, on two separate days, with each session randomized for seat design. During each session, participants performed two 10-min simulated driving tasks. Between simulated sessions, participants drove a long-haul truck for 90 min. Fatigue and stress were quantified using a series of questionnaires whereas vigilance was measured using a standardized computer test.  Seat interactions had a significant effect on fatigue patterns. The new ergonomic seat design holds potential in improving road safety and vehicle accidents due to fatigue-related accidents.]]></description>
      <pubDate>Wed, 18 Mar 2026 09:00:17 GMT</pubDate>
      <guid>https://trid.trb.org/View/2661767</guid>
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