Signal Timing Research Articles

A Novel High-Priority Traffic Light System Based on a Smart Technique

In the world today, traffic congestion in most cities is a severe problem. Recently, the system used is based on a fixed-time system in traffic, which cannot provide incompetence if one or more lane has higher traffic than others. The higher traffic on one side lane needs a longer operation time for a green signal as compared to the fixed time-based systems. This paper proposed a High Priority Traffic Light system HPTL as a technique to compute the period time of green and red lights based on the density of traffic present at that time. To fix this problem an automatic traffic light control system should proposed. The proposed system will be achieved by using PIR proximity Infrared Sensors. When the density is computed, the luminous period of the green and red signal will be selected to help the microcontroller. On each roadside, the sensors can detect the traffic density and calculate the vehicles in a specific lane then forward the density value to the microcontroller. The microcontroller will select the free lane and the high-traffic lane and then assign the priority system. The proposed system is successful in investing time, reducing vehicle density, and increasing reliability.

A Two-Stage Real-Time Optimization Model of Arterial Signal Coordination Based on Reverse Causal-Effect Modeling Approach

The arterial signal coordination is an effective method to improve traffic operational efficiency and reduce vehicle delay. In this paper, a two-stage arterial signal coordination model under dynamic traffic demands is established, and the signal timing and offset are adjusted according to the dynamic traffic demands. The objective is to minimize the expected intersection delay and the overflow of the coordinated direction. In the first stage, a calculation model for intersection signal timing based on phase clearing reliability is proposed by the reverse causal-effect modeling approach, which can calculate the signal timing of each intersection in real time. In the second stage, an offset calculation model is established to achieve the goal of minimizing delay in the coordinated direction, which can calculate the offset of trunk coordination in real time. The concept of phase clearance reliability is introduced in the model, which can dynamically adjust the balance between the coordinated phase and the non-coordinated phase, thus taking the overall control efficiency of intersections into account. We then develop an algorithm to solve the problem and then apply the model and the solution algorithm to an arterial road with three intersections to investigate and compare its performance with the Allsop’s method and the Webster’s method. A comparison between the proposed coordinated two-stage logic and a coordinated actuated logic is also conducted in the case study to show the advantages and disadvantages.

Eco-driving strategy for connected vehicles at signalized intersections considering human driver error

In recent years, eco-driving strategies based on connected vehicle (CV) technologies have been studied to assist human drivers to reduce fuel consumption and pollutant emissions. In this paper, a real-time eco-driving strategy for CVs that considers human driver error is proposed to improve both traffic and fuel efficiency at signalized intersections where CVs and human-driven vehicles (HDVs) coexist. Firstly, a human driver error estimation model is established using real-world driving data. Then, based on the signal phase and timing information, vehicle state information, and the estimated human driver errors, a constrained nonlinear optimal control problem (OCP) is proposed to calculate the optimal advisory speed of each CV. The trajectory of HDV is estimated by utilizing the Gipps’ car-following model. Fast stochastic model predictive control (SMPC) is employed to solve the proposed OCP effectively. At last, simulation studies and real-vehicle experiments are conducted in various scenarios to verify the performance of the proposed strategy. Simulation and experiment results indicate that compared with the baseline strategies, the proposed eco-driving strategy can significantly reduce travel time and fuel consumption while ensuring the real-time performance.

A critical evaluation of decadal solar cycle imprints in the MiKlip historical ensemble simulations

Abstract. Studies concerning solar–terrestrial connections over the last decades claim to have found evidence that the quasi-decadal solar cycle can have an influence on the dynamics in the middle atmosphere in the Northern Hemisphere (NH) during the winter season. It has been argued that feedbacks between the intensity of the UV part of the solar spectrum and low-latitude stratospheric ozone may produce anomalies in meridional temperature gradients which have the potential to alter the zonal-mean flow in middle to high latitudes. Interactions between the zonal wind and planetary waves can lead to a downward propagation of the anomalies, produced in the middle atmosphere, down to the troposphere. More recently, it has been proposed that top-down-initiated decadal solar signals might modulate surface climate and synchronize the North Atlantic Oscillation. A realistic representation of the solar cycle in climate models was suggested to significantly enhance decadal prediction skill. These conclusions have been debated controversial since then due to the lack of realistic decadal prediction model setups and more extensive analysis. In this paper we aim for an objective and improved evaluation of possible solar imprints from the middle atmosphere to the surface and with that from head to toe. Thus, we analyze model output from historical ensemble simulations conducted with the state-of-the-art Max Planck Institute for Meteorology Earth System Model in high-resolution configuration (MPI-ESM-HR). The target of these simulations was to isolate the most crucial model physics to foster basic research on decadal climate prediction and to develop an operational ensemble decadal prediction system within the “Mittelfristige Klimaprognose” (MiKlip) framework. Based on correlations and multiple linear regression analysis we show that the MPI-ESM-HR simulates a realistic, statistically significant and robust shortwave heating rate and temperature response at the tropical stratopause, in good agreement with existing studies. However, the dynamical response to this initial radiative signal in the NH during the boreal winter season is weak. We find a slight strengthening of the polar vortex in midwinter during solar maximum conditions in the ensemble mean, which is consistent with the so-called “top-down” mechanism. The individual ensemble members, however, show a large spread in the dynamical response with opposite signs in response to the solar cycle, which might be a result of the large overall internal variability compensating for rather small solar imprints. We also analyze the possible surface responses to the 11-year solar cycle and review the proposed synchronization between the solar forcing and the North Atlantic Oscillation. We find that the simulated westerly wind anomalies in the lower troposphere, as well as the anomalies in the mean sea level pressure, are most likely independent from the timing of the solar signal in the middle atmosphere and the alleged top-down influences. The pattern rather reflects the decadal internal variability in the troposphere, mimicking positive and negative phases of the Arctic and North Atlantic oscillations throughout the year sporadically, which is then assigned to the solar predictor time series without any plausible physical connection and sound solar contribution. Finally, by applying lead–lag correlations, we find that the proposed synchronization between the solar cycle and the decadal component of the North Atlantic Oscillation might rather be a statistical artifact, affected for example by the internal decadal variability in the ocean, than a plausible physical connection between the UV solar forcing and quasi-decadal variations in the troposphere.

Use of High-Resolution Signal Controller Data to Measure Transit Signal Priority Performance: A Case Study in the Boston Region

Automated traffic signal performance measures (ATSPMs) have been developed to organize data within a traffic signal, which will then be used to evaluate and monitor signal timing strategies. Past research has summarized the use of ATSPMs for performance-based management of a traffic signal system. The National Cooperative Highway Research Program Research Report 954 focused on many measures useful for general traffic, but did not consider transit agencies as specific stakeholders. ATSPMs do provide insights into measuring the number of preemption events, and the percentage of transit vehicles arriving on green, but additional information is needed to determine the effectiveness of transit signal priority or other transit preferential treatment strategies. This paper proposes a method for evaluating transit performance at signalized intersections more fully using the high-resolution data available within modern traffic signal controllers. The addition of transit-specific inputs within the ATSPM enumerations is used to produce transit-specific signal performance measures designed to be useful to practitioners. The methodology can be followed by other agencies seeking to enhance the capabilities of existing automatic vehicle location systems to further describe the performance at each signalized intersection. By incorporating transit into the ATSPM system, an agency can better assess the delay of buses, which can contribute to schedule adherence, on-time performance, and overall bus service. The methodology is presented as a prototype for further development and adaptation to individual agency objectives and data sources.

Minimizing Intersection Waiting Time: Proposal of a Queue Network Model Using Kendall’s Notation in Panama City

The paper presents a proposed queuing model based on Kendall’s notation for the intersection of two streets in Panama City (53 East and 56 East). The proposed model is based on a set of traffic lights that controls the flow of vehicles at the intersection according to a predetermined schedule. The model analyzes the stability of the system and simulations are performed to evaluate its performance. The main objective of the paper is to optimize the vehicle flow by minimizing the waiting time for passage. In the study, it was observed that the current traffic light system on Calle 50 (50th Street) is unstable and oversaturated during weekdays, which generates large vehicle queues with no estimated exit times. It was proposed to increase the system capacity to 1300 vehicles per hour to achieve reasonable stability and provide a solution to improve traffic signal timing on 50th Street. The need to increase the system capacity has been demonstrated and an optimal value has been suggested. The evaluation of other models and the use of AI can further strengthen the system and improve the prediction accuracy in different traffic scenarios.

How does temporal blurring alter movement timing?

Subjective uncertainty arises because the estimation of the timing of an event into the future is error prone. This impact of stimulus-bound uncertainty on movement preparation has often been investigated using reaction time tasks where a warning stimulus (WS) predicts the occurrence of a 'go' signal. The timing of the 'go' signal can be chosen from a particular probability distribution with a given variance or uncertainty. It has been repeatedly shown that reaction times covary with the shape of the used 'go' signal distribution. This is interpreted as evidence for temporal preparation. Moreover, the variance of the response time should always increase with the duration of the delay between the WS and the 'go' signal. This increasing variance has been interpreted as a consequence of the temporal 'blurring' of future events (scalar expectancy).The present paper tested the validity of the temporal 'blurring' hypothesis in humans with a simple oculomotor reaction time task where subjective and stimulus-bound uncertainties were increased. Subjective uncertainty about the timing of a 'go' signal was increased by lengthening the delay between the WS and the 'go' signal. Objective uncertainty was altered by increasing the variance of 'go' signal timing. Contrary to temporal blurring hypotheses, the study has shown that increasing the delay between events did not significantly increase movement timing variability. These results suggest that temporal blurring could not be a property of movement timing in an implicit timing context.Significance StatementIt is often assumed that the estimation of the timing of a distant future event is more error prone (or variable) than the estimation of a closer one. This phenomenon is referred to as temporal 'blurring'. Metaphorically, this could be compared in the spatial domain with the blurring of a visual stimulus when perceived at an increasing distance. Surprisingly, in the current oculomotor implicit timing task, movement variability actually decreased with the increasing temporal delay between a warning and an imperative 'go' signal. Therefore, temporal blurring is not a general property but depends on the precise context of timing behaviour.

Negative Impact of Traffic Congestion on Air Pollution and Estimation Delay Mode for Signalized Intersections in Babil City Utilizing SIDRA8.0 Plus Software

Traffic congestion is one of the most important factors contributing to air pollution. Vehicle emissions account for a significant portion of this pollution. Estimate the negative impact of traffic congestion by empirical model for predicting total delay time as a function of traffic conditions and geometric features and calculating the quantities of gases emitted during the traffic congestion at the signalled intersection is the goal of this paper. Two signalized intersections in Babil City with different characteristics are select. video recording is used to measure traffic volumes during peak and off-peak times. Afield survey is used to measure geometric design elements, signal timings, phasing, and selected emissions of HC, CO, and CO2 in (kg/hr) are based on delay. with 95% confidence, a linear regression model predicted the delay model. according to the statistical analysis, both geometry and traffic characteristics have a substantial influence on delay time. The constructed regression model for estimating delay has a high degree of correlation with actual values. The proportion of the total width of the exit route of an intersection of straight-forward departing traffic to the total width of groups of lanes departing on the same exit route at the same phase at the stop line it is observed that although the SIDRA 8.0 PLUS delay overestimates the delay at the high (v/c) range, there is no noticeable difference with the field delay and the study will offer suggestions to transportation and public health officials on how to try eco-routing and deco-signal timing to lower the risk of air pollutants.

Utilizing speech analysis to differentiate progressive supranuclear palsy from Parkinson's disease

IntroductionDistinguishing Parkinson's disease (PD) from Progressive supranuclear palsy (PSP) at early disease stages is important for clinical trial enrollment and clinical care/prognostication. MethodsWe recruited 21 participants with PSP(n = 11) or PD(n = 10) with reliable caregivers. Standardized passage reading, counting, and sustained phonation were recorded on the BioDigit Home tablet (BioSensics LLC, Newton, MA USA), and speech features from the assessments were analyzed using the BioDigit Speech platform (BioSensics LLC, Newton, MA USA). An independent t-test was performed to compare each speech feature between PSP and PD participants. We also performed Spearman's correlations to evaluate associations between speech measures and clinical scores (e.g., PSP rating scales and MoCA). In addition, the model's performance in classifying PSP and PD was evaluated using Rainbow passage reading analysis. ResultsDuring Rainbow passage reading, PSP participants had a significantly slower articulation rate (2.45(0.49) vs 3.60(0.47) words/minute), lower speech-to-pause ratio (2.33(1.08) vs 3.67(1.18)), intelligibility dynamic time warping (DTW, 0.26(0.19) vs 0.53(0.26)), and similarity DTW (0.43(0.27) vs 0.67(0.13)) compared to PD participants. PSP participants also had longer pause times (17.24(5.47) vs 8.45(3.13) sec) and longer total signal times (52.44(6.67) vs (36.67(6.73) sec) when reading the passage. In terms of the phonation ‘a’, PSP participants showed a significant higher spectral entropy, spectral centroid, and spectral spread compared to PD participants and no differences were found for phonation ‘e’. PD participants had more accurate reverse number counts than PSP participants (14.89(3.86) vs 7.36(4.67)). PSP Rating Scale (PSPRS) dysarthria (r = 0.79, p = 0.004) and bulbar item scores (r = 0.803, p = 0.005) were positively correlated with articulation rate in reverse number counts. Correct reverse number counts were positively correlated with total Montreal Cognitive Assessment scores (r = 0.703, p = 0.016). Machine learning models using passage reading-derived measures obtained an AUC of 0.93, and the sensitivity/specificity in correctly classifying PSP and PD participants were 0.95 and 0.90, respectively. ConclusionOur study demonstrates the feasibility of differentiating PSP from PD using a digital health technology platform. Further multi-center studies are needed to expand and validate our initial findings.

Driving Risk Field and Control Strategies for Autonomous Vehicles at a Signalized Intersection

Driving pattern has been increasingly researched to improve driving safety and develop autonomous vehicles. Oriented towards the complex infrastructures at signalized intersections, this research digs into the risk sources brought by different kinds of road elements, including road lane markings, road curbs, median separators, signal timing, and neighboring vehicles around the ego car. Referring to vehicle speed both in the longitudinal and latitudinal dimensions, risk scope and distribution are quantified with the vehicle position of a torus with a Gaussian cross-section. Then, the risk is summed over all the road elements across all the points involved by the ego car, the level of which should be controlled within the threshold value when the ego vehicle explores to minimize trip delay. Thus, autonomous driving strategies are developed with respect to vehicle speed and steering angle. The proposed model is validated with NGSIM data, where a signalized intersection on Peachtree Street is selected and vehicles moving in different directions are analyzed. It is found that the proposed model manages to control vehicles with risk at the accepted level and to enhance the speed level as well as reduce acceleration fluctuations. This research contributes to improving autonomous driving against complex driving conditions for driving safety and efficiency.

Pedestrian delay models for compliant & non-compliant behaviour at signalized midblock crosswalks under mixed traffic conditions

The present study aimed to propose new pedestrian delay models for Signalized Midblock Crosswalks (SMC) for mixed traffic conditions. A detailed study of the literature revealed that most of the existing pedestrian delay models were developed for signalized intersections. Thus, the need for the study was established and data were collected at eight SMC in Hyderabad, one of the most densely populated metropolitan cities in India, using video-graphic technique. Two delay models were developed based on the compliance behaviour and non-compliance behaviour of pedestrians. Both models have two components i.e., waiting delay and crossing delay where the latter has two subset components i.e., frictional delay and pedestrian-vehicle interaction delay. The bidirectional effect (pedestrian-pedestrian interaction while crossing the road) of pedestrians was addressed as frictional delay while the non-compliance behaviour by pedestrians and vehicles was addressed as pedestrian-vehicle interaction delay in the present models. The waiting delay component was defined by modifying the Webster delay model for non-uniform pedestrian arrivals. The proposed delay models yielded an error of 5% and 7% for compliance behaviour model and non-compliance behaviour model respectively. The proposed models can be used for optimizing the signal timings and defining Level of Service (LOS) of facilities.

Enhance traffic flow prediction with Real-Time Vehicle Data Integration

<p>This study examines how sophisticated traffic control systems affect traffic flow. These cutting-edge solutions use real-time traffic data to increase road networks’ intelligence. These technologies enable the creation of a smoother and more efficient traffic flow by enhancing traffic signal timings and automatically rerouting cars towards less crowded routes. Notably, these innovations significantly lower air pollution, greenhouse gas emissions, and fuel consumption while also minimizing the financial and time expenses related to traffic congestion. Our unique Real-Time Vehicle Data Integration (RTVDI) algorithm is being used to portray the potential of intelligent traffic control systems. These technologies have the potential to revolutionize traffic management procedures by using real-time data and complex processes. They have the potential to improve commuter safety, increase road efficiency, and improve traffic flow.</p>

Optimization of Signal Timing for Urban Expressway Exit Ramp Connecting Intersection.

To alleviate the traffic problems of congestion and queue overflow on a mainline at the intersection of an urban expressway exit ramp articulation during peak hours, a bi-level programming optimization model of signal timing is proposed. The lower-level optimization objective is to maximize the capacity of the expressway exit ramp that articulates with the entrance road, while the upper-level optimization objective is to minimize the average vehicle delay and the number of stops per vehicle, taking into account the queue length in the direction of the ramp and other directions. The particle swarm optimization algorithm is selected to solve the proposed model, applied to a real case, and is validated using MATLAB and VISSIM simulation platforms. The simulation results show that the average vehicle delay and the number of stops per vehicle in the exit ramp on the expressway are reduced by 22.09% and 18.60%, while those in the intersection area are reduced by 20.96% and 17.19%, respectively. The conclusion indicates that the signal timing scheme obtained by this method can effectively improve the traffic efficiency at the intersection of the exit ramp on the expressway and alleviate the problem of congestion and the overflow of the exit ramp back to the mainline.

Deep Domain-Invariant Long Short-Term Memory Network for Partial Discharge Localization in Gas-Insulated Switchgear

Localization based on differences in the timing of an ultrahigh-frequency signal is difficult to apply in actual settings because of the strict requirement for accurate calculation of the difference in arrival time between discharge pulses. With the development of digital twinning, the operational state of equipment is mapped onto a digital virtual model, and large-scale simulation data can be obtained to assist in localization of partial discharge (PD) in gas-insulated switchgear (GIS). With this aim, this paper proposes a novel deep domain-invariant long short-term memory network (LSTM) for GIS PD localization. Firstly, an attention residual LSTM is constructed to automatically extract discriminative features and effectively obtain the temporal and spatial dependences between features. Collaborative domain adaptation is proposed to transfer the diagnostic knowledge learned from large-scale simulation data to GIS PD localization. Via maximum mean discrepancy-based domain adaptation and domain adversarial training, discrepancies between samples in the two domains are reduced so as to achieve a more perfect match. The domain-invariant LSTM achieved an average localization accuracy of less than 11 cm, and the standard deviation of the error reached 8.15. The domain-invariant LSTM thus provides a novel solution for the high-precision and robust localization of PD in GIS.

Sub-nanosecond time-resolved radiation measurement using x-ray focusing crystal spectrometers

In this paper, we describe a technique using a crystal spectrometer, a silicon-diode detector, and a filtered photoconductive detector to monitor photon energies in the L-shell (0.9–1 keV) and K-shell regimes for nickel and copper hybrid X-pinch x-ray sources. The detectors, system cabling, and an 8 GHz digital oscilloscope in combination enable time resolution better than 200 ps for photoconductive detectors and 700 ps for silicon-diode detectors of the K- and L-shell radiation signals, respectively. We substantially improve the relative timing of signals obtained using the oscilloscope by using an x-ray streak camera with a crystal spectrometer to monitor the L-shell line spectra and, separately, the K-shell line spectra relative to the continuum burst to better than 17 ps time resolution. This combination of instruments enabled and validated a new method by which plasma conditions in nickel and copper X-pinches can be assessed immediately before and after the ∼30 ps continuum x-ray burst produced by 370 kA hybrid X-pinches. In general, the method described here can be applied to observe otherwise highly filter-absorbed radiation in the presence of a broad spectrum of higher energy radiation by combining x-ray crystals and detectors.

Cycle-based signal timing with traffic flow prediction for dynamic environment

This article studies adaptive traffic signal control problem of single intersection in dynamic environment. A novel cycle-based signal timing method with traffic flow prediction (CycleRL) is proposed to improve the traffic efficiency under dynamic traffic flow. Firstly, the empirical mode decomposition is applied to denoise the flow data. Then a data-model hybrid driven traffic flow prediction strategy is designed to predict the traffic flow, which combines a model-based Kalman filter and an LSTM network-based predictor and adopts another Kalman filter to fuse both prediction results to improve the prediction precision. Besides, a robust signal cycle timing strategy based on human-machine collaboration is developed to deal with dynamic traffic flow, which firstly designs a rule-based signal cycle scheme according to the predicted flow data as the preliminary scheme, and then finetunes the preliminary scheme based on Soft Actor-Critic (SAC) algorithm according to the real-time traffic dynamics. The experiments in both synthetic scenario and real-world scenario show that the proposed data-model hybrid driven traffic flow prediction algorithm has better prediction performance and the proposed CycleRL method outperforms rule-based methods, flow-based allocation methods and traditional reinforcement learning method. Moreover, it is also shown that the proposed CycleRL method has better transferability to bridge the discrepancy across domains.

Vehicular Traffic Flow Prediction Model Deep Learning

Abstract: Efficient traffic flow prediction is crucial for effective traffic management and congestion reduction in urban areas. However, traditional statistical models often struggle to accurately capture the intricate dynamics of vehicular traffic flow, particularly under dynamic conditions. In this research project, we propose a novel approach that leverages deep learning techniques, specifically Long Short-Term Memory (LSTM) neural networks, AdaBoost, and gradient descent, to enhance the accuracy of traffic flow predictions .By harnessing historical traffic data, our model generates precise predictions for the next time step, empowering traffic managers to optimize signal timings and proactively reroute traffic. To boost the model's performance, we incorporate AdaBoost, which integrates LSTM predictions as additional input features. We evaluate the accuracy of our model using mean absolute error (MAE) and R2 score techniques, comparing the predicted traffic flow against the actual traffic flow .Experimental results demonstrate that our proposed model outperforms traditional statistical models, exhibiting lower MAE and higher R2 scores. This indicates its efficacy in accurately predicting traffic flow and presents a promising solution for traffic management and congestion reduction. Our research contributes to the advancement of traffic flow prediction models by offering a more reliable and accurate approach. Future work may explore the integration of real- time data streams and external factors, such as weather conditions and events, to further enhance prediction accuracy and effectively address dynamic traffic situations. By optimizing traffic management strategies, reducing congestion, and improving overall traffic flow efficiency, our proposed model holds significant potential for improving urban traffic conditions.

CitySim: A Drone-Based Vehicle Trajectory Dataset for Safety-Oriented Research and Digital Twins

The development of safety-oriented research and applications requires fine-grain vehicle trajectories that not only have high accuracy, but also capture substantial safety-critical events. However, it would be challenging to satisfy both these requirements using the available vehicle trajectory datasets do not have the capacity to satisfy both. This paper introduces the CitySim dataset that has the core objective of facilitating safety-oriented research and applications. CitySim has vehicle trajectories extracted from 1,140-min of drone videos recorded at 12 locations. It covers a variety of road geometries including freeway basic segments, weaving segments, expressway merge/diverge segments, signalized intersections, stop-controlled intersections, and control-free intersections. CitySim was generated through a five-step procedure that ensured trajectory accuracy. The five-step procedure included video stabilization, object filtering, multivideo stitching, object detection and tracking, and enhanced error filtering. Furthermore, CitySim provides the rotated bounding box information of a vehicle, which was demonstrated to improve safety evaluations. Compared with other video-based trajectory datasets, CitySim had significantly more safety-critical events, including cut-in, merge, and diverge events, which were validated by distributions of both minimum time-to-collision and minimum post encroachment time. In addition, CitySim had the capability to facilitate digital-twin-related research by providing relevant assets, such as the recording locations’ three-dimensional base maps and signal timings.

Cooperative control of multiple intersections combining agent and chaotic particle swarm optimization

The regional intersection can be regarded as an agent model, that should be controlled and optimized. The traditional particle swarm optimization algorithm has been enhanced through optimization techniques and the inclusion of chaos algorithms and agent interactive and cooperative control technology, resulting in the development of a chaotic particle swarm optimization algorithm. Multi-agent is applied to the collaborative control of intersections to realize the collaborative decision-making of multi-intersections. The state and action of intersection signal control agent are defined to reduce the dimension of state-action space and speed up the convergence rate. The proposed method takes vehicle delay, vehicle queue length and regional access ratio as optimization objectives, implements regional traffic signal control strategy and improves overall traffic efficiency. By constructing a simulation environment based on Paramics software, the effectiveness of the regional traffic signal control method based on chaotic particle swarm optimization algorithm is verified, and the convergence efficiency and signal timing optimization have interactive effects.

Specific facial signals associate with categories of social actions conveyed through questions.

The early recognition of fundamental social actions, like questions, is crucial for understanding the speaker's intended message and planning a timely response in conversation. Questions themselves may express more than one social action category (e.g., an information request "What time is it?", an invitation "Will you come to my party?" or a criticism "Are you crazy?"). Although human language use occurs predominantly in a multimodal context, prior research on social actions has mainly focused on the verbal modality. This study breaks new ground by investigating how conversational facial signals may map onto the expression of different types of social actions conveyed through questions. The distribution, timing, and temporal organization of facial signals across social actions was analysed in a rich corpus of naturalistic, dyadic face-to-face Dutch conversations. These social actions were: Information Requests, Understanding Checks, Self-Directed questions, Stance or Sentiment questions, Other-Initiated Repairs, Active Participation questions, questions for Structuring, Initiating or Maintaining Conversation, and Plans and Actions questions. This is the first study to reveal differences in distribution and timing of facial signals across different types of social actions. The findings raise the possibility that facial signals may facilitate social action recognition during language processing in multimodal face-to-face interaction.