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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>Emotional Impacts of Manual and Assisted Driving Modes under Varying Time Pressures: A Scenario-based Simulation Study</title>
      <link>https://trid.trb.org/View/2777938</link>
      <description><![CDATA[Emotion as a critical psychological variable in driving behavior has been extensively confirmed to exert a profound influence on traffic safety. With the advancement of autonomous driving technologies, the application value of emotion regulation mechanisms in intelligent transportation systems is gaining importance, given their proven impact on reducing unsafe driving tendencies and improving human-machine interaction quality. However, systematic research on the mechanisms underlying emotional variation across different driving modes remains relatively scarce. This study developed a questionnaire based on the Chinese version of the Driving Anger Scale (DAS), incorporating 19 traffic scenarios and corresponding video stimuli, to examine the impact of manual and assisted driving on driving anger under varying levels of time pressure. In addition, the moderating effects of individual factors such as age and education were analyzed. Descriptive statistics, independent samples t-tests, and one-way ANOVA were applied to 113 valid responses to construct a three-dimensional analytical framework involving driving mode, time pressure, and demographic variables. The results indicate that under high time pressure, assisted driving systems significantly reduce drivers' anger levels in typical delay-related scenarios compared with manual driving. Age is identified as a key moderating factor influencing emotional responses. This research provides theoretical support for the development of emotion-aware driving intervention systems and offers empirical evidence for the formulation of future strategies for driving emotion regulation.]]></description>
      <pubDate>Mon, 12 Oct 2026 19:53:11 GMT</pubDate>
      <guid>https://trid.trb.org/View/2777938</guid>
    </item>
    <item>
      <title>Risk assessment of driver emotion using a data-driven Bayesian Network: Insights from a naturalistic driving study</title>
      <link>https://trid.trb.org/View/2781313</link>
      <description><![CDATA[Driving in a state of high emotions, particularly anger, can impair driving abilities and increase the risk of crashes. Understanding the influencing factors on emotions and how emotions impact safety–critical events (SCEs) is crucial for enhancing traffic safety. This study aims to investigate the relationship between emotions and SCEs on the road considering various contributing factors such as driver demographics, traffic conditions, and road geography. Through a data-driven Bayesian Network (BN), the authors validate the relationships among these factors and assess their joint impact on SCEs. By employing domain expert knowledge and a hybrid structure learning approach, the authors construct a BN structure that effectively captures the dependencies among different variables.  Clear dependency relationships are identified, with traffic flow directly influencing emotion, and factors like traffic density, secondary tasks, emotion, and driving errors significantly affecting SCEs. Through BN inference, the authors explore the marginal and joint effects of various factors on SCEs, highlighting the increased SCE risk associated with emotions, driving errors, high traffic density, and secondary tasks. The identification of high-risk scenarios provides valuable insights for targeted intervention and mitigation efforts. This study contributes to a deeper understanding of the relationship between emotions and SCEs, offering methods for enhancing road safety measures and accident prevention strategies.]]></description>
      <pubDate>Mon, 12 Oct 2026 17:12:15 GMT</pubDate>
      <guid>https://trid.trb.org/View/2781313</guid>
    </item>
    <item>
      <title>Drivers of public engagement in social media discourse on self-driving vehicles: psychological and emotional determinants</title>
      <link>https://trid.trb.org/View/2781338</link>
      <description><![CDATA[Research on self-driving vehicles (SDVs) has largely overlooked the influence of framing strategies on public interests and public engagement on social media discourse regarding SDVs. We address this gap by analysing 102,174 Twitter posts to examine how emotional sentiment and psychological distance shape engagement across private, public, and commercial SDV contexts. Grounded in Construal Level Theory and Appraisal Theory of Emotion, we identify thematic and emotional drivers of interaction. By leveraging large-scale social media analysis, this study advances understanding of the cognitive and emotional mechanisms underlying SDV discourse, offering insights for more effective public communication and consumer engagement strategies. Our analyses show that psychologically proximal topics (e.g., safety or pricing) elicit stronger engagement in private vehicle discourse due to personal relevance. Further, high-arousal negative emotions (fear, anger) spark immediate, intense responses, while low-arousal positive emotions (happiness) encourage sustained, reflective engagement.]]></description>
      <pubDate>Mon, 28 Sep 2026 16:46:29 GMT</pubDate>
      <guid>https://trid.trb.org/View/2781338</guid>
    </item>
    <item>
      <title>Identifying instant utility using psychophysiological indicators in a transport experiment with ecological validity</title>
      <link>https://trid.trb.org/View/2780379</link>
      <description><![CDATA[In this article, we formulate a hybrid model that allows to identify the triggers of instant utilities using psychophysiological indicators (PPIs). Instant utilities are understood as momentary emotions perceived in every instant of an experience. We build the model using transport and environmental variables associated with the experience to explain instant utilities, which are measured by PPIs and stated emotions. The model is estimated with data from a real-life travel experiment, in which SKT (skin temperature), HR (heart rate), HRV (heart rate variation), and EDA (electrodermal activity) were measured with a wristband. In addition, environmental variables such as CO2, noise, brightness, and temperature were collected and used to explain instant utility and to control for variation of PPIs. As emotions can be discomposed into at least two dimensions (valence and activation) we capture this multidimensionality estimating two independent models that explain the valence and activation of stated emotions. To analyse what is gained by including physiological data, these models are compared with baseline models without PPIs. Our main findings are: (1) instant utilities are sensible, for instance, to the travel mode; velocity; crowding; brightness; temperature; and humidity; (2) PPIs help to identify the effect of stimuli that cause small variations in the underlying emotions; and (3) instant utility has heterogeneous effects on PPIs across individuals, implying that it is necessary individuals-specific considerations to infer instant utility from PPIs. We discuss the potential applications of this framework in the evaluation of travel satisfaction and demand estimation.]]></description>
      <pubDate>Mon, 28 Sep 2026 16:46:18 GMT</pubDate>
      <guid>https://trid.trb.org/View/2780379</guid>
    </item>
    <item>
      <title>Empathy: A participatory tool for enhancing walkability in cities — the case of Istanbul</title>
      <link>https://trid.trb.org/View/2742286</link>
      <description><![CDATA[In urban planning, the ability to lead with empathy is becoming increasingly crucial as decisions significantly impact individuals’ lives. Successful leadership relies on the capacity to understand and respond to others’ emotions and experiences. Jahn Gehl’s statement that ‘cities must be given back to the people’ highlights the necessity of prioritizing human needs in urban development. Walking, as the most essential form of mobility, emphasizes these needs and facilitates interaction between pedestrians and their built environment. Walkability goes beyond foot traffic; it serves as a critical foundation for fostering positive engagement between walking and daily activities. This short paper based on a project titled ‘A Promise to Istanbul: A Mutual Vision for a Metropol’, explores the question: How does the application of empathy as a tool in urban governance influence stakeholder collaboration and the prioritization of human-centric urban development, such as walkability, in Istanbul? By the project’s conclusion, a cohesive structure emerged that fostered understanding and collaboration among diverse stakeholders, enabling them to think and act collectively for the benefit of the city.]]></description>
      <pubDate>Fri, 28 Aug 2026 10:59:32 GMT</pubDate>
      <guid>https://trid.trb.org/View/2742286</guid>
    </item>
    <item>
      <title>Music behind the wheel: Effects of tempo, style, and preference on driving safety</title>
      <link>https://trid.trb.org/View/2731054</link>
      <description><![CDATA[Listening to music while driving is a common in-vehicle behavior, yet existing studies have reported conflicting findings regarding its impact on driving safety. This study aims to comprehensively investigate how listening to music affects driving safety, with particular attention to music preference and the musical attributes of style and tempo. A mixed experimental design was adopted, and data were collected from 32 drivers with different music preferences during simulated driving tasks under multiple musical conditions. Both objective driving measures and subjective emotional ratings were analyzed. The results showed that, under rock music, drivers traveled significantly faster, demonstrated greater car-following coherence, and reported higher arousal. Music tempo significantly differentiated driving behavior, with medium- and fast-tempo conditions associated with higher arousal and faster driving speed than slow-tempo conditions. In addition, among rock-preferring drivers, speed variability was significantly greater when they listened to their preferred rock music, indicating poorer driving stability. However, compared with the no-music condition, these drivers showed less speeding under the slow-rock and fast-rock conditions. Overall, the findings suggest that music tempo mainly influences emotional experience, whereas music style more strongly affects vehicle control. Music preference also played an important moderating role, highlighting the importance of individual differences in understanding music-related driving effects.]]></description>
      <pubDate>Fri, 21 Aug 2026 14:01:30 GMT</pubDate>
      <guid>https://trid.trb.org/View/2731054</guid>
    </item>
    <item>
      <title>Driving under threat: Navigating emotion regulation during wartime</title>
      <link>https://trid.trb.org/View/2721837</link>
      <description><![CDATA[Armed conflicts expose civilians to sustained existential threats while requiring the continuation of everyday activities such as driving. Despite growing research on mobility during crises, little is known about drivers' subjective emotional and regulatory processes under direct missile threat. The current study explored Israeli civilians' experiences of driving during the 12-day Israel–Iran war beginning on June 12, 2025. A total of 247 licensed drivers were recruited using convenience and snowball sampling through social media platforms and WhatsApp groups. They completed an online survey including two open-ended questions regarding differences between routine and wartime driving and descriptions of siren events while driving. Data was analyzed using an inductive thematic analysis. Findings yielded three interrelated themes: Theme 1, titled Recognizing the Emergency Situation, relates to driving in an atmosphere of heightened anxiety, tension, irritability, and existential fear despite reduced traffic volume. Theme 2, titled The Cognitive Aspect of Driving in an Emergency reflects intensified appraisal processes, including advanced route planning, constant alertness to sirens, and consideration of other drivers' emotional states. Theme 3, titled Coping with the Situation in Practice – The Behavioral Aspect of Driving in an Emergency, focuses on both adaptive adherence to official safety guidelines and emotion-driven risk behaviors such as speeding, traffic violations, and even avoidance of driving. It is evident that driving under missile threat constitutes a routine-in-motion under existential threat in which cognitive, emotional, and behavioral mechanisms operate simultaneously. The findings illustrate the applicability of the Transactional Stress and Coping Theory in the context of driving under missile threat by demonstrating how survival-oriented meaning reshapes everyday mobility behavior. Practical implications highlight the need for emergency traffic guidelines that address not only behavioral instructions but also drivers' emotional regulation under sustained armed conflict.]]></description>
      <pubDate>Fri, 21 Aug 2026 14:01:30 GMT</pubDate>
      <guid>https://trid.trb.org/View/2721837</guid>
    </item>
    <item>
      <title>How do emotional cues influence drivers' pre-takeover readiness in automated driving? Evidence from olfaction-involved affective multimodal interaction</title>
      <link>https://trid.trb.org/View/2739349</link>
      <description><![CDATA[In highly automated driving, drivers' pre-takeover readiness depends not only on system alerts but also on how emotional cues across sensory modalities are integrated. Although olfactory cues may influence emotional experience and autonomic processes during this transition, their role in takeover preparation remains underexplored. To address this gap, this study examined how emotional resonance in an affective interaction engine, together with visual–olfactory emotional consistency, relates to drivers' pre-takeover readiness. A simulated takeover experiment was conducted with 60 licensed drivers. Behavioral performance, takeover quality, autonomic activity indexed by HRV, EEG measures, and subjective evaluations were collected following short olfactory and emotional cues. The results showed that emotionally consistent visual–olfactory cues were associated with higher RMSSD over a 35-s analysis window spanning the pre-odor stabilization and odor presentation periods, as well as more coherent subjective evaluations. In contrast, emotionally inconsistent cue combinations were associated with faster takeover responses. Sustained EEG spectral power did not show reliable condition differences, whereas event-related components showed condition-dependent modulation. Overall, faster takeover responses may not necessarily reflect more stable readiness, but rather transient response facilitation under certain cue conditions. From a traffic safety perspective, supporting a stable psychological and physiological transition before takeover may be as important as minimizing reaction time. In this context, olfaction-supported emotional cue coordination may provide a complementary design approach for promoting coherent and regulated transitions in affective multimodal interfaces.]]></description>
      <pubDate>Fri, 21 Aug 2026 14:01:29 GMT</pubDate>
      <guid>https://trid.trb.org/View/2739349</guid>
    </item>
    <item>
      <title>SAFE-Drive: A synergistic adaptive fusion framework for multimodal driver emotion recognition in real driving scenarios</title>
      <link>https://trid.trb.org/View/2737127</link>
      <description><![CDATA[Driver emotion recognition is an important component of driver-state monitoring and has potential value for improving traffic safety. In recent years, multimodal driver emotion recognition has attracted increasing attention. However, many existing studies are conducted in simulated environments or rely on limited modality combinations, which restricts their applicability to complex real-driving conditions. Moreover, physiological and behavioral signals collected during driving are susceptible to environmental noise, and substantial between-subject variability degrades cross-subject recognition performance. To address these challenges, the authors propose SAFE-Drive, an adaptive multimodal fusion framework for driver emotion recognition in real-driving scenarios. The framework uses collaborative multimodal representation learning and a cross-modal attention mechanism to model interactions among EEG signals, facial video, and EDA signals, allowing complementary representations to be learned in a unified feature space. Meanwhile, an attention-based adaptive fusion strategy dynamically adjusts modality contributions across time windows, reducing the influence of noisy or low-quality modality representations. SAFE-Drive also combines source-domain selection with subject-adaptive training through pretraining and few-shot fine-tuning to mitigate individual differences and improve calibration-assisted cross-subject adaptation. The authors conduct experiments on a self-collected real-driving dataset and further evaluate its transferability on the public PPB-Emo dataset using the available modalities. SAFE-Drive achieves 80.25% average accuracy and 79.91% macro-averaged F1-score under the few-shot adapted LOSO protocol, supporting the effectiveness of multimodal fusion and limited target-subject calibration for driver emotion recognition in real-driving scenarios.]]></description>
      <pubDate>Thu, 13 Aug 2026 17:02:49 GMT</pubDate>
      <guid>https://trid.trb.org/View/2737127</guid>
    </item>
    <item>
      <title>Material incentive or emotional incentive-A study on the effect of incentive mechanism on passengers' willingness to pay for carbon offset in aviation</title>
      <link>https://trid.trb.org/View/2673119</link>
      <description><![CDATA[The rapid development of the aviation transport industry has significantly increased carbon emissions. Due to limitations in research, development time, and production costs, new energy aircraft and sustainable aviation fuel cannot be widely adopted in the short term. As a result, carbon offset is currently the primary method to balance aviation carbon emissions. Despite the introduction of carbon offset programs by most airlines, passengers' willingness to pay (WTP) remains low. This study aims to explore what incentive mechanism is conducive to enhancing passengers' WTP for aviation carbon offset. We propose three categories of incentive mechanisms: material incentive mechanism (MIM), emotional incentive mechanism (EIM), and hybrid incentive mechanism (HIM), with the latter further subdivided into four types based on incentive form and seasonal dimensions. A questionnaire survey is conducted to collect decision-making data on passengers' participation in aviation carbon offset. Based on this data, a public goods game (PGG) model is built to explore the impact of different incentive mechanisms on passengers' WTP. The simulation results indicate that: (i) Incentive mechanisms increase passengers' WTP compared to no incentive. (ii) MIM and first material incentive followed by emotional incentive (HIM₂) are the most effective. (iii) The use of purely material incentives has a limited effect on improving cooperation frequency (ƒ). These findings provide theoretical guidance and strategic recommendations for airlines in implementing carbon offset programs.]]></description>
      <pubDate>Fri, 24 Jul 2026 17:04:26 GMT</pubDate>
      <guid>https://trid.trb.org/View/2673119</guid>
    </item>
    <item>
      <title>Which method do you prefer to calm down? Evaluating multimodal anger regulation in autonomous driving</title>
      <link>https://trid.trb.org/View/2683183</link>
      <description><![CDATA[Anger while driving negatively affects performance in both manual and supervised driving scenarios. While numerous studies have focused on detecting anger behind the wheel, research on its regulation remains limited and does not extend to automated driving. Moreover, previous work used alone emotion regulation strategies while combined methods work best in emotion modulation. This study explores a multimodal approach to regulate anger in autonomous driving by integrating auditory and visual modalities. Specifically, we combined low-arousal music, unconscious biofeedback via green ambient lighting, and participants’ photos. In our experiment, 36 participants were split into three groups. Anger was induced in two groups with only one benefiting from the regulation. The third is a control group with neutral induction and no regulation. Our findings highlight that the multimodal intervention effectively improves subjective and physiological emotional responses without specific impact on driving performances. Additionally, participants reported high acceptance and perceived relaxation, highlighting the potential of this approach for enhancing driver well-being in autonomous vehicles.]]></description>
      <pubDate>Mon, 13 Jul 2026 13:58:15 GMT</pubDate>
      <guid>https://trid.trb.org/View/2683183</guid>
    </item>
    <item>
      <title>From Mothers to Daughters: Examining Intergenerational Safety Work of Young Women in Public Spaces and Public Transportation</title>
      <link>https://trid.trb.org/View/2721768</link>
      <description><![CDATA[Safety work, known as the invisible burden, is the work women undertake in public spaces, including on public transport, to ensure their personal safety. The present study examines safety work from an intergenerational perspective and the influence of parental advice on young women’s perceptions of risk and safety. It places special emphasis on knowledge transfer from mothers to daughters. Twenty-nine young women, mostly university students and regular public transport users, were interviewed in Auckland, New Zealand. The findings revealed that safety work is not only a behavioral adaptation but also an emotional and relational practice embedded within the mother–daughter relationship. Acts of care, such as travel check-ins, avoidance strategies, and self-monitoring, reflected intergenerational patterns of protection that simultaneously preserved gendered inequity. Although mothers’ advice gave daughters a sense of preparedness, it also reinforced a culture in which women carry the primary responsibility for their own safety. Participants described the mental and emotional burden of being constantly alert, as well as feeling frustrated at the normalization of fear as a condition of mobility. For some, safety work has become reflexive and accepted, whereas for others it provokes resistance and a desire for autonomy. These tensions illustrate how safety work functions as both a coping mechanism and a symptom of systemic gendered inequity. This study is a call for action. For change to occur, transport professionals need to recognize the intergenerational dimension of this invisible burden, which is predominantly emotional and physical.]]></description>
      <pubDate>Thu, 09 Jul 2026 14:05:01 GMT</pubDate>
      <guid>https://trid.trb.org/View/2721768</guid>
    </item>
    <item>
      <title>KYA: Vision–Language Assistant for Emotional Reactions to Risky Driving</title>
      <link>https://trid.trb.org/View/2720345</link>
      <description><![CDATA[This study introduces a vision–language pipeline that detects risky driving behaviors and generates emotionally expressive responses to support driver awareness and comfort. Although vision–language models have advanced perception and reasoning in autonomous driving, existing systems rarely consider the emotional dimension or real-world user experience. Keep Yelling Assistant (KYA) detects high-risk driving maneuvers in real time, such as sudden cut-ins. It then produces emotional responses through a large language model tailored to driver preferences. The framework comprises two core modules. The vision module uses You Only Look Once (YOLO) v8 variants to detect nearby vehicles and identify risky behaviors such as sudden cut-ins. Key driving metrics, including relative distance, speed, and projected reach time, are extracted and normalized to produce a structured behavior log. The language module processes this log with user-defined emotional tone settings (e.g., neutral, humorous, analytical) and generates verbal reactions using state-of-the-art large language models (LLMs) (ChatGPT-4o, Claude 3, Gemini 2.5, and Copilot). We evaluated the proposed system using dashcam videos containing risky driving behaviors and a user study involving 108 participants. Participants selected preferred response styles, and LLMs were evaluated based on emotional alignment. All models received favorable ratings, though preferences varied across personas. Notably, the combination of YOLOv8s and ChatGPT-4o achieved the highest score, 4.29 out of 5.00. By integrating real-world perception with emotionally adaptive dialogue, KYA advances emotionally intelligent in-vehicle artificial intelligence. It highlights new opportunities to improve safety, trust, and driver comfort in conventional and autonomous vehicles.]]></description>
      <pubDate>Thu, 02 Jul 2026 16:00:31 GMT</pubDate>
      <guid>https://trid.trb.org/View/2720345</guid>
    </item>
    <item>
      <title>Distributed Driver Emotion Prediction for Intelligent 6G Internet of Vehicles</title>
      <link>https://trid.trb.org/View/2617911</link>
      <description><![CDATA[Accurate driver emotion prediction is critical for enhancing safety and efficiency in 6G-enabled Intelligent Transportation Systems (ITS). In real-world ITS, drivers may face complex situations, and as a results they may express complex emotion. However, traditional driver emotion analysis struggles to capture the complexity of such blended emotions. To address this challenge, we apply emotion distributions to represent complex driver emotions. Besides, we apply the Label Enhancement (LE) to transform logical emotion labels into more informative emotion distributions, which reflect the varying relevance of multiple emotions. We propose in this paper a novel distributed LE method, called Distributed Label Enhancement with Label Manifold (DLEM). It leverages the manifold structure of logical emotions to enhance them into emotion distributions. DLEM is well-suited for edge and large-scale Internet-of-Vehicles (IoV) systems. Additionally, we design the Distributed Emotion Distribution Learning (DEDL) to learn and predict driver emotions from such enhanced emotion distributions. Finally, we conduct experiments on three large-scale emotion datasets. The experimental results show that DLEM significantly outperforms several state-of-the-art approaches and achieves the best performance.]]></description>
      <pubDate>Tue, 30 Jun 2026 09:45:14 GMT</pubDate>
      <guid>https://trid.trb.org/View/2617911</guid>
    </item>
    <item>
      <title>Chinese adaptation of the pedestrian anger expression inventory and its associations with trait anger and crash involvement</title>
      <link>https://trid.trb.org/View/2707993</link>
      <description><![CDATA[The association between pedestrians' anger expression and traffic crashes has rarely been investigated in China because of the lack of a validated instrument. The purpose of this study was to translate and adapt the Pedestrian Anger Expression Inventory (PAX) for Chinese pedestrians and to examine its reliability and validity. Seven hundred thirty pedestrians aged 12–50 years agreed to participate in this study. The measurements included the PAX, the Anger Rumination Scale (ARS), and the Trait Anger Scale (TAS). The factorial structure of the PAX was examined with exploratory factor analysis (n = 365) and confirmatory factor analysis (n = 365).The PAX consists of 23 items that are divided into 3 factors: adaptive/ constructive expression, anger expression-in, and anger expression-out. The reliability of the PAX factors ranged from 0.80 to 0.94. The significant associations among the factors of the PAX and TAS suggested that the convergent validity of the PAX was acceptable. The known-group validity of the PAX was also satisfactory, with pedestrians who had experienced previous traffic crashes scoring higher on anger expression-in and anger expression-out than those who did not experience crashes. More importantly, trait anger did not directly affect the crash involvement of pedestrians, but the paths through which trait anger affects crash involvement through the mediating role of anger expression-in and through the joint mediating roles of anger rumination and anger expression-in were confirmed. The present study addresses a critical research gap within the Chinese context; the confirmed structural stability of the three-factor PAX model supports its preliminary applicability in China. Moreover, the identified mediating role of anger expression-in in the pathway from trait anger and rumination to crash involvement provides empirical support for emotion-focused interventions in developing countries where motor vehicle–pedestrian conflicts remain prevalent.]]></description>
      <pubDate>Thu, 18 Jun 2026 16:34:46 GMT</pubDate>
      <guid>https://trid.trb.org/View/2707993</guid>
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