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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>
    <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>Using wearable measures to infer moments in workload from Electrodermal Activity and individual workload from Heart Rate Variability during a simulated railway signalling task</title>
      <link>https://trid.trb.org/View/2643959</link>
      <description><![CDATA[Physiological measures offer potential for real-time collection of data to inform understanding of the nature of work in safety critical settings. This study collected physiological data from wearable measures to assess the Mental Workload (MWL) of twenty participants whilst they completed a simulated railway signalling task. Electrodermal Activity (EDA) and Heart Rate Variability (HRV) temporal data were compared to task demand (number of trains) and subjective workload. Average HRV showed a strong negative correlation with average subjective workload. EDA peaks indicated moments in workload including moments of realisation, uncertainty, or time pressure during the task in some participants. HRV and EDA results imply individuals vary in their experience of workload and physiological data can detect variation between participants. Results suggest EDA and HRV data could supplement existing measures of MWL during continuous tasks, through detecting both the timing of individuals’ changing experience of workload and underlying physiological state.]]></description>
      <pubDate>Wed, 04 Mar 2026 09:16:08 GMT</pubDate>
      <guid>https://trid.trb.org/View/2643959</guid>
    </item>
    <item>
      <title>Towards In-Vehicle Non-Contact Estimation of EDA-Based Arousal with LiDAR</title>
      <link>https://trid.trb.org/View/2646872</link>
      <description><![CDATA[Driver monitoring systems are increasingly relying on physiological signals to assess cognitive and emotional states for improved safety and user experience. Electrodermal activity (EDA) is a particularly informative biomarker of arousal but is conventionally measured with skin-contact electrodes, limiting its applicability in vehicles. This work explores the feasibility of non-contact EDA estimation using Light Detection and Ranging (LiDAR) as a novel sensing modality. In a controlled laboratory setup, LiDAR reflection intensity from the forehead was recorded simultaneously with conventional finger-based EDA. Both classification and regression tasks were performed as follows: feature-based machine learning models (e.g., Random Forest and Extra Trees) and sequence-based deep learning models (e.g., CNN, LSTM, and TCN) were evaluated. Results demonstrate that LiDAR signals capture arousal-related changes, with the best regression model (Temporal Convolutional Network) achieving a mean absolute error of 14.6 on the normalized arousal factor scale (-50 to +50) and a correlation of r = 0.85 with ground-truth EDA. While random split validations yielded high accuracy, performance under leave-one-subject-out evaluation highlighted challenges in cross-subject generalization. The algorithms themselves were not the primary research focus but served to establish feasibility of the approach. These findings provide the first proof-of-concept that LiDAR can remotely estimate EDA-based arousal without direct skin contact, addressing a central limitation of current driver monitoring systems. Future research should focus on larger datasets, multimodal integration, and real-world driving validation to advance LiDAR towards practical in-vehicle deployment.]]></description>
      <pubDate>Wed, 31 Dec 2025 10:56:40 GMT</pubDate>
      <guid>https://trid.trb.org/View/2646872</guid>
    </item>
    <item>
      <title>Driver Tension Responses and Intersection Illumination</title>
      <link>https://trid.trb.org/View/2543297</link>
      <description><![CDATA[Galvanic skin reflex (GSR), which is a measure of the change in the electrical resistance of the skin due to perspiration, proved to be an adequate means of determining driver reaction to different illumination and geometric conditions at intersections. Nighttime use of different drivers and comparison of their tension patterns as recorded by the GSR instrument showed that increase in illumination brought decrease in tension responses and that greatest tension occurred with no illumination. Also, complexity of the intersection caused increase in tension responses. Although the drivers reacted differently to the various intersection situations, their over-all response patterns were similar. Familiarity with situations brought reduced tension.]]></description>
      <pubDate>Mon, 12 May 2025 12:02:26 GMT</pubDate>
      <guid>https://trid.trb.org/View/2543297</guid>
    </item>
    <item>
      <title>Analysis of urban cyclists’ behaviour using GSR: a scoping review</title>
      <link>https://trid.trb.org/View/2519041</link>
      <description><![CDATA[There is a global consensus on the urgent need to promote sustainable modes of mobility such as the bicycle. This realisation is accompanied by an increased promotion of the research on bicyclists’ behaviour to plan bicycle-friendly road infrastructure effectively. Some of the commonly used methods of data collection in this research include surveys and interviews, which help in capturing the cyclists’ perceptions and conscious behaviour. However, these data collection methods are susceptible to reporting and hypothetical biases. Hence, recent studies have started exploring the use of physiological measures, such as the Galvanic Skin Response (GSR), as the objective measures of cyclists’ subconscious behaviour. The GSR potentially captures the perceived stress of the cyclists and can be easily recorded using sensor-based wearables. For the processing and analysis of the data thus collected, it is necessary to identify the appropriate statistical tools. Previous studies have discussed the merits, demerits, scope, and limitations of using GSR in urban cyclists’ behaviour analysis at length. However, methodological advancements expected in future studies in this domain need a comprehensive understanding of the currently used statistical methods and tools to build upon. This review facilitates them by providing an overview of these methods and tools, and by categorising existing studies based on their purpose (Methodological Exploration or Stimuli Response Analysis), methodological approach (Inductive or Deductive), method of data processing (splitting GSR signal, counting GSR peaks, measuring GSR peaks, taking Log of GSR, or curve fitting), and method of data analysis (map matching, descriptive analysis, hypothesis testing or Generalized Linear Modelling). The review further concludes that future studies need to find the correct balance between the granularity of GSR data to be preserved (given higher granularity ensures a higher level of detail in the analysis) and the resource consumption owing to its procedural and computational complexity.]]></description>
      <pubDate>Thu, 20 Mar 2025 13:25:00 GMT</pubDate>
      <guid>https://trid.trb.org/View/2519041</guid>
    </item>
    <item>
      <title>Assessment of Driver Situation for Control Authority Transition from Conditionally Automated Vehicles using Chassis and Galvanic Skin Response Sensors</title>
      <link>https://trid.trb.org/View/2519815</link>
      <description><![CDATA[The authority transitions are important when humans interact with automated driving. The monitoring systems should be able to smartly adapt to the detected driver state, adjusting the time given for take-over requests (TOR). The proposed system in the study obtains drivers’ ideal driving authority takeover times by analyzing wearable sensor and other sensor data. The driver’s authority during the transition is evaluated in this study using the sensors including the chassis velocity sensor, galvanic skin response (GSR) sensor, and current (torque) sensor subjected to longitudinal quality metrics. Three different traffic situations are analyzed to compare four different takeover times (0s, 2s, 4s, and 6s) and the ideal TOR times of drivers are detected for the authority transition. Here, TOR 6s is close to a critical/dangerous situation and TOR 0s time is a sudden transition. According to the results, ideal TOR times are mostly TOR 2s and TOR 4s. TOR 6s is not considered as an ideal TOR time for any driver in the study.]]></description>
      <pubDate>Sun, 16 Mar 2025 18:12:05 GMT</pubDate>
      <guid>https://trid.trb.org/View/2519815</guid>
    </item>
    <item>
      <title>Decoding pedestrian stress on urban streets using electrodermal activity monitoring in virtual immersive reality</title>
      <link>https://trid.trb.org/View/2476420</link>
      <description><![CDATA[The pedestrian stress level is shown to significantly influence human cognitive processes and, subsequently, decision-making, e.g., the decision to select a gap and cross a street. This paper systematically studies the stress experienced by a pedestrian when crossing a street under different experimental manipulations by monitoring the Electrodermal Activity (EDA) using the Galvanic Skin Response (GSR) sensor. To fulfil the research objectives, a dynamic and immersive virtual reality (VR) platform was used, which is suitable for eliciting and capturing pedestrian’s emotional responses in conjunction with monitoring their EDA. A total of 171 individuals participated in the experiment, tasked to cross a two-way street at mid-block with no signal control. Mixed effects models were employed to compare the influence of socio-demographics, social influence, vehicle technology, environment, road design, and traffic variables on the stress levels of the participants. The results indicated that having a street median in the middle of the road operates as a refuge and significantly reduced stress. Younger participants (18–24 years) were calmer than the relatively older participants (55–65 years). Arousal levels were higher when it came to the characteristics of the avatar (virtual pedestrian) in the simulation, especially for those avatars with adventurous traits. The pedestrian location influenced stress since the stress was higher on the street while crossing than waiting on the sidewalk. Significant causes of arousal were fear of accidents and an actual accident for pedestrians. The estimated random effects show a high degree of physical and mental learning by the participants while going through the scenarios.]]></description>
      <pubDate>Mon, 27 Jan 2025 15:39:52 GMT</pubDate>
      <guid>https://trid.trb.org/View/2476420</guid>
    </item>
    <item>
      <title>Driver Drowsiness Detection: A Machine Learning Approach on Skin Conductance</title>
      <link>https://trid.trb.org/View/2170131</link>
      <description><![CDATA[The majority of car accidents worldwide are caused by drowsy drivers. Therefore, it is important to be able to detect when a driver is starting to feel drowsy in order to warn them before a serious accident occurs. Sometimes, drivers are not aware of their own drowsiness, but changes in their body signals can indicate that they are getting tired. Previous studies have used large and intrusive sensor systems that can be worn by the driver or placed in the vehicle to collect information about the driver's physical status from a variety of signals that are either physiological or vehicle-related. This study focuses on the use of a single wrist device that is comfortable for the driver to wear and appropriate signal processing to detect drowsiness by analyzing only the physiological skin conductance (SC) signal. To determine whether the driver is drowsy, the study tests three ensemble algorithms and finds that the Boosting algorithm is the most effective in detecting drowsiness with an accuracy of 89.4%. The results of this study show that it is possible to identify when a driver is drowsy using only signals from the skin on the wrist, and this encourages further research to develop a real-time warning system for early detection of drowsiness.]]></description>
      <pubDate>Tue, 23 May 2023 10:09:12 GMT</pubDate>
      <guid>https://trid.trb.org/View/2170131</guid>
    </item>
    <item>
      <title>The effect of flight phase on electrodermal activity and gaze behavior: A simulator study</title>
      <link>https://trid.trb.org/View/2115878</link>
      <description><![CDATA[Current advances in airplane cockpit design and layout are often driven by a need to improve the pilot's awareness of the aircraft's state. This involves an improvement in the flow of information from aircraft to pilot. However, providing the aircraft with information on the pilot's state remains an open challenge. This work takes a first step towards determining the pilot's state based on biosensor data. The authors conducted a simulator study to record participants' electrodermal activity and gaze behavior, indicating pilot state changes during three distinct flight phases in an instrument failure scenario. The results show a significant difference in these psychophysiological measures between a phase of regular flight, the incident phase, and a phase with an additional troubleshooting task after the failure. The differences in the observed measures suggest great potential for a pilot-aware cockpit that can provide assistance based on the sensed pilot state.]]></description>
      <pubDate>Tue, 28 Mar 2023 09:56:56 GMT</pubDate>
      <guid>https://trid.trb.org/View/2115878</guid>
    </item>
    <item>
      <title>Effect of Two-Lane Rural Highway Horizontal Curves on Driver’s Galvanic Skin Resistance</title>
      <link>https://trid.trb.org/View/2113400</link>
      <description><![CDATA[Galvanic skin resistance can be used as a measure for emotional arousal, or workload of a driver that geometry of a rural two-lane highway horizontal curve imparts on him or her. The study engaged more than 90 car and 30 bus drivers to drive along identified highway stretches, and the GSR data along 114 study curves were collected. Cross-sectional and geometrical details of curves were measured. The study tested the driver characteristic to assess the physical characteristics of the driver, but ANOVA test invalidated the influence of physical characteristics on GSR. It was found that GSR of car and bus drivers is significantly different. Further studies showed that sight distance is the most influencing parameter. Increase in sight distance increases the workload of a car driver whereas decreases the workload of a bus driver.]]></description>
      <pubDate>Tue, 28 Feb 2023 09:18:01 GMT</pubDate>
      <guid>https://trid.trb.org/View/2113400</guid>
    </item>
    <item>
      <title>Effect of emotion on galvanic skin response and vehicle control data during simulated driving</title>
      <link>https://trid.trb.org/View/2098111</link>
      <description><![CDATA[This study aims to investigate the effect of driver emotion on the physiological and vehicle control data and the possibility of predicting the driver emotion to enhance the driving experience. The driver emotions in the driving context were classified into eight categories depending on high and low levels of arousal and valence: happiness, surprise, fear, anger, depression, boredom, relief, and neutrality. Fourteen male and female volunteers with ages between 22 and 34 y participated in the test, and approximately 540 min of image, physiological, and vehicle data were collected. After inducing the participants’ target emotion through the viewing of a film and writing of passages, the authors asked the participants to drive on a highway through a driving simulator and self-evaluate their emotion. After the test, the participants were allowed to go home after emotion neutralization. The participants’ self-evaluated emotions correlated highly with the intended induced emotions. High arousal and negative valence emotions such as anger and low arousal and positive valence emotions such as relief exhibited a statistically significant elevation for the following indicators: galvanic skin response amplitude, longitudinal vehicle control data such as throttling and braking, and lateral vehicle control data. The test results confirmed that a driver’s emotional state could be reflected in the differences between the biometric data or vehicle control data. In particular, the emotion associated with a high arousal and negative valence could be clearly distinguished from that associated with a low arousal and positive valence. Therefore, the driver's emotional state affects the traffic condition, and the detection of potentially risky emotions such as those associated with road rage and development of a suitable driving mode can help enhance the driving safety. Drivers’ emotions can be identified based on physiological data and vehicle control data and integrated into the system to formulate appropriate responses. For instance, anger anticipated from a driver can be alleviated by a preemptive measure, thereby enhancing the traffic safety.]]></description>
      <pubDate>Mon, 13 Feb 2023 09:32:11 GMT</pubDate>
      <guid>https://trid.trb.org/View/2098111</guid>
    </item>
    <item>
      <title>Evaluating Effects of Urgent Takeover Requests on Driver Stress through Galvanic Skin Response Analysis</title>
      <link>https://trid.trb.org/View/2019027</link>
      <description><![CDATA[In conditionally autonomous vehicles, drivers should take over vehicle control when the automated system fails, which is a challenge to perform safely in urgent situations due to critical time constraints and great stress increment. To identify how driver stress changes during urgent takeovers and the association with driver performance, this study conducted a human-in-the-loop driving simulator experiment. The urgent takeover request was designed with a time budget of 3s. Stress levels were evaluated through galvanic skin responses (GSR). A moderating effect model was then built between stress levels, minimum time-to-collision and brake reaction time. Results showed driver stress was significantly high and peaked from 5 s to 10 s after the takeover was triggered. Besides, too much stress indicated by GSR data (0.059–0.096 mμS) showed significant moderating effects on extending brake reaction time. This study could contribute to improving the takeover strategy with stress traits considered.]]></description>
      <pubDate>Thu, 17 Nov 2022 10:15:19 GMT</pubDate>
      <guid>https://trid.trb.org/View/2019027</guid>
    </item>
    <item>
      <title>Robust Prediction of Lane Departure Based on Driver Physiological Signals</title>
      <link>https://trid.trb.org/View/1834104</link>
      <description><![CDATA[Lane change events can be a source of traffic accidents; drivers can make improper lane changes for many reasons. In this paper the authors present a comprehensive study of a passive method of predicting lane changes based on three physiological signals: electrocardiogram (ECG), respiration signals, and galvanic skin response (GSR). Specifically, the authors discuss methods for feature selection, feature reduction, classification, and post processing techniques for reliable lane change prediction. Data were recorded for on-road driving for several drivers. Results show that the average accuracy of a single driver test was approx. 70%. It was greater than the accuracy for each cross-driver test. Also, prediction for younger drivers was better.]]></description>
      <pubDate>Wed, 23 Feb 2022 16:16:10 GMT</pubDate>
      <guid>https://trid.trb.org/View/1834104</guid>
    </item>
    <item>
      <title>Driver Lane Change Prediction Using Physiological Measures</title>
      <link>https://trid.trb.org/View/1779035</link>
      <description><![CDATA[Side swipe accidents occur primarily when drivers attempt an improper lane change, drift out of lane, or the vehicle loses lateral traction. Past studies of lane change detection have relied on vehicular data, such as steering angle, velocity, and acceleration. In this paper, the authors use three physiological signals from the driver to detect lane changes before the event actually occurs. These are the electrocardiogram (ECG), galvanic skin response (GSR), and respiration rate (RR) and were determined, in prior studies, to best reflect a driver's response to the driving environment. A novel system is proposed which uses a Granger causality test for feature selection and a neural network for classification. Test results showed that for 30 lane change events and 60 non lane change events in on-the-road driving, a true positive rate of 70% and a false positive rate of 10% was obtained.]]></description>
      <pubDate>Mon, 03 May 2021 11:49:50 GMT</pubDate>
      <guid>https://trid.trb.org/View/1779035</guid>
    </item>
    <item>
      <title>Integrating Driving Simulator Experiment Data With a Multi-Agent Connected Automated Vehicles Simulation (MA-CAVs) Platform to Quantify Improved Capacity</title>
      <link>https://trid.trb.org/View/1767047</link>
      <description><![CDATA[Autonomous vehicles (AVs) at varying market penetration rates will change traffic flow and highway performance. At AV market penetration rates of between 0 percent and 100 percent, human-driven vehicles (HVs) will be interacting with AVs. However, little is known about how HVs interact with AVs. Using the Oregon State University Driving Simulator, this study measured HV headways when drivers followed an AV and integrated those data into a multi-agent simulation to quantify new highway travel time and flow predictions at varying AV market penetration levels. This study also collected galvanic skin response data to quantify drivers’ levels of stress when presented with a hard-braking AV and HV. The driving simulator experiment was successfully completed by 36 participants. The results of this study showed that drivers’ levels of stress were 70 percent higher in hard braking scenarios involving HVs versus AVs. Additionally, drivers over the age of 34.5 were found to give AVs 2 percent more headway than HVs, while younger drivers gave AVs 18 percent less headway than HVs. Thirty-six scenarios were tested in the multi-agent simulation using results from the driving simulator. Given the driving simulator results, average travel times were found to increase at most by 2.3 percent, while flow was found to decrease at most by 1.3 percent.]]></description>
      <pubDate>Wed, 17 Feb 2021 10:44:09 GMT</pubDate>
      <guid>https://trid.trb.org/View/1767047</guid>
    </item>
    <item>
      <title>Reducing uncertainty by anticipation in traffic – The effect of situational characteristics and criticality on behavioral, subjective, and psychophysiological parameters</title>
      <link>https://trid.trb.org/View/1752233</link>
      <description><![CDATA[Uncertainty in traffic may have fatal consequences when operating a vehicle. Enabling drivers to anticipate the behavior of other traffic participants can help reduce uncertainty and thus increase traffic safety. A multi-method approach using behavioral, subjective, and psychophysiological measures was applied to investigate the relation of uncertainty and anticipation. Participants (N = 30) observed simulated, video-based urban traffic scenarios while skin conductance responses were recorded. They had to indicate, by pressing a button, (1) the moment they first thought another vehicle might merge into their lane (low certainty anticipation) and (2) the moment they were sure another vehicle would merge (high certainty anticipation). Situational characteristics served as anticipatory cues that helped predict the other vehicle’s action. On the one hand, in this study, target cues (which are clearly related to the target’s activity) served to indicate an imminent lane change. On the other hand, context cues represented visible precursors in the traffic environment, e.g. a traffic sign pointing to upcoming road work. In addition, causal cues were used to determine a reason for the other vehicle to change lanes (in this instance, a construction site blocking the lane). These situational characteristics, as well as the situational criticality, were manipulated to gain insights into factors influencing the process of anticipation. Results offered an effect of target cue moderated by criticality: especially in more critical situations, the anticipation rate and subjective certainty increased, and physiological activation was reduced with target cues. Overall, the anticipatory performance was found to be a predictor of subjective certainty (through its impact on skin conductance responses). The findings are discussed in the context of the methodological approach for applications in traffic.]]></description>
      <pubDate>Thu, 10 Dec 2020 12:20:11 GMT</pubDate>
      <guid>https://trid.trb.org/View/1752233</guid>
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