Accident; analysis and prevention最新文献

{"title":"Analysis of the impact of acoustic stimulation on vigilance decrement and drowsiness on expressways and its habituation","authors":"Yasuhiro Shiomi ,&nbsp;Tingjian Zou","doi":"10.1016/j.aap.2025.108359","DOIUrl":"10.1016/j.aap.2025.108359","url":null,"abstract":"<div><div>This study aims to evaluate the effectiveness of acoustic stimulation in enhancing driver’s vigilance, improving driving performance, and preventing inattentive driving on expressways. While numerous studies have investigated the effects of acoustic stimuli on drivers’ attention, the influence of different types of stimuli on sustained attention and driving behavior remains unclear. Particularly, the habituation effect to the stimuli during driving has not been investigated. In this study, several types of acoustic stimuli—monotone sounds, verbal messages, and emotional sounds—are examined as potential countermeasures for vigilance decrement. Their effects on inattentive driving and the stability of driving behavior are assessed and compared with a control condition using a driving simulator (DS) experiment in which each participant was asked to continuously operate a DS for 20 min under each experimental condition. The experimental results with 30 participants reveal that: (1) acoustic stimulation initially produces an awakening effect, but its effectiveness tends to decline over time due to habituation; and (2) among the three types of acoustic stimuli tested, emotional sounds have a stronger and more sustained effect on maintaining driver alertness, showing less susceptibility to habituation than monotone or verbal stimuli. These findings suggest that emotional acoustic stimuli may serve as a promising basis for the development of in-vehicle or infrastructure-based systems aimed at preventing inattentive driving and improving road safety.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"226 ","pages":"Article 108359"},"PeriodicalIF":6.2,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145773040","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Considering spatial heterogeneity in modeling taxi speeding frequency: An advanced geographically weighted negative binomial regression approach","authors":"Lin Qu ,&nbsp;Yue Zhou ,&nbsp;Haibo Li ,&nbsp;Xinguo Jiang ,&nbsp;Chuanyun Fu ,&nbsp;Hongjie Wang","doi":"10.1016/j.aap.2025.108356","DOIUrl":"10.1016/j.aap.2025.108356","url":null,"abstract":"<div><div>Speeding is a major contributor to traffic crashes, posing significant risks to drivers, passengers, and other road users. This issue is exacerbated among urban taxi drivers due to factors such as time pressure for passenger pickups, potentially leading to higher speeding frequency compared to other drivers. While prior studies have identified factors influencing speeding, they often assume spatial stationarity, overlooking how these effects may vary geographically. This gap limits targeted interventions. To address spatial heterogeneity in count data with potential overdispersion, two geographically weighted negative binomial models are developed: a standard Geographically Weighted Negative Binomial (GWNBR) model to account for overdispersion, and an enhanced Geographically Weighted Negative Binomial with spatially varying dispersion (GWNBRg) that further incorporates spatially varying dispersion parameters. This suite captures both local coefficient variation and unobserved heterogeneity. To optimize the spatial resolution of regression results and capture local heterogeneity in speeding behaviors, five kernel function methods (i.e., Gaussian, Exponential, Bi-square, Tricube, and Boxcar) are adopted for adaptive bandwidth calculation. Further, a sensitivity analysis of Traffic Analysis Zones (TAZs) is conducted to identify the optimal zoning scale. The study uses over 15 million GPS trajectory data points collected from September 1 to 14, 2020, in Chengdu, China, and identifies 298,114 speeding incidents. Results indicate that the GWNBRg outperforms GWNBR in terms of Akaike Information Criterion and residual Moran’s I, confirming its robustness in handling overdispersion and spatial dependence. Global estimates show that several factors significantly increase taxi speeding frequency, such as total travel distance, bicycle lanes, on-street parking management, no parking rules, and the density of public service points of interest (POI). Conversely, median dividers, central fences, overpasses and tunnels, and residential POI density decrease taxi speeding frequency. The localized coefficient maps of these factors exhibit spatial heterogeneity across TAZs, which tend to cluster regionally when estimated with the same sign. This spatial clustering suggests that regional context drives most of the spatial heterogeneity. Additionally, the spatially varying overdispersion parameter in GWNBRg further identifies zones with unobserved risk factors, highlighting its utility for precision interventions. This framework enables policymakers to propose spatially targeted strategies for taxi speeding mitigation.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"226 ","pages":"Article 108356"},"PeriodicalIF":6.2,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145788092","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A cross-scale traffic-communication control framework for improving safety through proactive congestion mitigation in mixed traffic","authors":"Zhigang Wu ,&nbsp;Meng Li ,&nbsp;Yanyong Guo ,&nbsp;Zhibin Li ,&nbsp;Shunchao Wang","doi":"10.1016/j.aap.2025.108339","DOIUrl":"10.1016/j.aap.2025.108339","url":null,"abstract":"<div><div>Large-scale traffic accidents are often triggered by sudden shockwaves in congested flow, typically caused by unpredictable driving behaviors. The collaboration among connected and autonomous vehicles (CAVs) offer potential to mitigating traffic congestion and accidents, yet it remains vulnerable to failures in vehicle behavior coordination due to unstable long-range communication. To address these issues, this study proposes a Cross-Network Collaboration-based Congestion Mitigation (CNC-CM) framework, which establishes a feedback response mechanism between the traffic system and the communication network. At the communication layer, a distance-to-delay interval backtracking algorithm is developed to optimize long-range hybrid communication routing, ensuring timely and reliable command delivery under varying network conditions. At the traffic control layer, a multi-scale cooperative strategy is designed: a micro-level barrier consensus control restrains disruptive lane-changing by human-driven vehicles (HDVs), while a macro-level delay-corrected cruising control eliminates stop-and-go waves within enclosed congestion clusters. By integrating communication constraints into traffic control decisions, this cross-scale, multi-layer approach proactively dissipates incipient traffic jams before they escalate into safety hazards. Simulation results demonstrate that the proposed control framework enhances driving safety by over 54.11% through completely eliminating traffic congestion, while also significantly improving traffic efficiency, reducing energy consumption, and enhancing communication quality. Notably, the framework maintains robust performance even under low CAV penetration rates, confirming its effectiveness in mixed traffic environments with unpredictable human driving behaviors.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"226 ","pages":"Article 108339"},"PeriodicalIF":6.2,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145665619","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Exploring spatiotemporal heterogeneity and nonlinear effects in electric vehicle crash risk prediction: A hybrid modeling approach","authors":"Jianglin Lu ,&nbsp;Chunjiao Dong ,&nbsp;Xuedong Yan ,&nbsp;Jingjun Li ,&nbsp;Zhenzhen Lu","doi":"10.1016/j.aap.2025.108357","DOIUrl":"10.1016/j.aap.2025.108357","url":null,"abstract":"<div><div>Electric vehicle (EV)-related risk and uncertainty pose critical challenges for urban traffic management. Fine-grained crash risk prediction at 1 km × 1 km and hour-of-day resolution remains difficult due to rapidly evolving, strongly spatiotemporally heterogeneous crash patterns. Crash risk research spans risk measurement, prediction modeling, and factor selection, with a move toward interpretable nonlinear hybrid methods, yet temporal dynamics and local heterogeneity remain insufficiently modeled. This study addresses these limitations by first constructing a Spatio-Temporal Adaptive Network Kernel Density Estimation (ST-ANKDE) method that combines network-constrained proximity, cyclic time weighting, severity weighting, and adaptive bandwidths, and then developing a Multiscale Geographically and Temporally Weighted Regression–Extreme Gradient Boosting (MGTWR-XGBoost) method to learn local heterogeneity and nonlinear effects. To capture the influence of preceding periods and adjacent grids, we introduce temporal and spatial weighted crash risk variables (T-AccRisk and S-AccRisk). These are analyzed alongside road-network density, built-environment variables, socioeconomic variables, and EV-specific infrastructure variables. An empirical case study on 14,818 EV crashes shows that ST-ANKDE effectively captures crash risk dynamics, with a mean value of 6.57, and reveals pronounced spatiotemporal heterogeneity. The results show that MGTWR-XGBoost, enhanced by S-AccRisk and T-AccRisk to capture spatiotemporal dependence, achieves MAE = 1.54 and RMSE = 2.06 and outperforms standalone machine learning and other hybrid methods; road-network density, built-environment features, population density, and EV infrastructure coefficients exhibit significant spatiotemporal heterogeneity. Moreover, SHapley Additive exPlanations (SHAP) further analyzes nonlinear effects. These findings enable grid-level early warning, priority targeting of high-risk periods/locations, and data-driven deployment of enforcement and infrastructure for EV safety management.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"226 ","pages":"Article 108357"},"PeriodicalIF":6.2,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145802869","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Impact of the enhanced Fatality Analysis Reporting System on drug detection in fatally injured drivers","authors":"Stanford Chihuri ,&nbsp;Guohua Li","doi":"10.1016/j.aap.2025.108354","DOIUrl":"10.1016/j.aap.2025.108354","url":null,"abstract":"<div><div>The prevalence of drugged driving has been increasing in the United States. In 2018, the Fatality Analysis Reporting System (FARS) enhanced toxicology data collection by expanding the number of reportable nonalcohol drugs beyond the previous limit of three. In this repeated cross-sectional study, we used difference-in-differences analyses, Poisson regression, and joinpoint regression to assess changes in the detection of alcohol and nonalcohol drugs among fatally injured drivers before (2013–2017; n = 68,741) and after (2018–2022; n = 69,764) the enhancement. The prevalence of alcohol remained stable over time, averaging 37 %, with similar values in 2013–2017 (37.1 %) and 2018–2022 (37.9 %). In contrast, the prevalence of any nonalcohol drug increased from 39.9 % to 55.0 % (p &lt; 0.001). The prevalence of marijuana rose from 14.4 % to 26.4 % (p &lt; 0.001), stimulants from 9.3 % to 25.1 % (p &lt; 0.001), and depressants from 9.1 % to 13.7 % (p &lt; 0.001). Between the two periods, the prevalence of ≥ 1 nonalcohol drug increased by 9 %, with marijuana and stimulants showing the largest gains. Drivers who tested positive for both alcohol and marijuana increased from 8.3 % to 11.8 % (p &lt; 0.001). The proportion that tested positive for ≥ 2 nonalcohol drugs rose from 13.6 % to 19.2 % (p &lt; 0.001), and for ≥ 3 drugs from 2.0 % to 5.8 % (p &lt; 0.001). The mean number of nonalcohol drugs detected increased from 1.36 to 1.50 (p &lt; 0.001). In adjusted models, the odds of detecting any nonalcohol drug associated with the enhanced toxicology data collection increased 42 % [adjusted odds ratio (aOR) 1.42, 95 % confidence interval (CI) 1.37, 1.47], with greater increase in detecting ≥ 2 drugs (aOR 1.46, 95 % CI 1.40, 1.52) and ≥ 3 drugs (aOR 3.01, 95 % CI 2.80, 3.24). These findings highlight improved detection of polysubstance use among fatally injured drivers following the adoption of a comprehensive toxicological testing data file by FARS in 2018.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"226 ","pages":"Article 108354"},"PeriodicalIF":6.2,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145766778","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Identifying environmental factors related to motorcyclist crash rates: variable selection using spatial Random Forest with network distance and barriers","authors":"Bimo Harya Tedjo ,&nbsp;Pei-Fen Kuo ,&nbsp;Febrian Fitryanik Susanta ,&nbsp;I Gede Brawiswa Putra","doi":"10.1016/j.aap.2025.108362","DOIUrl":"10.1016/j.aap.2025.108362","url":null,"abstract":"<div><div>Motorcycle crashes remain a major global safety concern, particularly in many Asian countries where motorcycles are the primary mode of transportation. While previous studies have identified factors associated with motorcycle crashes using traditional variable selection methods, they often overlook spatial patterns and contextual constraints (such as motorcycles being prohibited from freeways), leading to lower model accuracy. To address this limitation, we introduce a novel variable selection framework, designated as Spatial Random Forest with Network Distance and Barriers (spatialRF–NDBAR), which incorporates network distance and road barrier constraints to improve spatial variable selection. The study focuses on the Central Business District of Taipei City, where motorcycle access to certain roads is explicitly prohibited by road barriers. We utilize motorcycle crash data from 2016 to 2020, combined with Point of Interest (POI) data extracted from OpenStreetMap. We compare the performance of the proposed framework with that of the conventional spatialRF and spatialRF–ND (network distance constraint only) methods using both a traditional Ordinary Least Squares model and a Multiscale Geographically Weighted Regression (MGWR) model. Among the evaluated models, MGWR using the variables selected by spatialRF–NDBAR shows the best performance, yielding the lowest AICc (1801.212) and highest R² (0.867). Overall, our results show that incorporating spatial predictors reduces residual spatial autocorrelation, improving model performance. Furthermore, POIs such as marketplaces, restaurants, bars, hotels, banks, bus stops, and metro stations are associated with a higher crash risk. These findings provide valuable insights for local governments and support targeted traffic enforcement efforts around high-risk POIs to improve motorcycle safety.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"226 ","pages":"Article 108362"},"PeriodicalIF":6.2,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145788095","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhancing vehicle platoon safety assessment: A novel cascading risk measure","authors":"Ying Luo ,&nbsp;Xiaomeng Li ,&nbsp;Ashish Bhaskar ,&nbsp;Dong Ngoduy ,&nbsp;Mohammed Elhenawy ,&nbsp;Sebastien Glaser","doi":"10.1016/j.aap.2025.108360","DOIUrl":"10.1016/j.aap.2025.108360","url":null,"abstract":"<div><div>Surrogate Safety Measures (SSMs) have demonstrated substantial potential in identifying collision risks and supporting traffic safety evaluation. However, conventional SSMs are primarily designed for individual vehicle–level risk assessment, which limits their ability to capture inter-vehicle risk coupling and the mechanisms of potential risk propagation within a vehicle platoon. To address this limitation, this study proposes a novel Cascading Risk Measure (CRM) to characterize potential cascading risks and enhance platoon-level safety assessment. First, we introduce the concept of critical deceleration, representing the maximum deceleration disturbance from a leading vehicle that a follower can tolerate, to characterize each vehicle’s risk state. Building on this, we develop a recursive mathematical framework that captures inter-vehicle coupling and the propagation of cascading risks by establishing recursive relationships between the critical decelerations of successive vehicles. This formulation reveals both the direction and the fundamental manner of risk propagation, thereby offering clear physical interpretability. In addition, the recursive framework ensures that CRM naturally degenerates into its single-vehicle form (denoted as DCRM) when no cascading risk is present, thereby generalizing the single-vehicle view, ensuring full compatibility with conventional SSMs, and providing a coherent and interpretable bridge between vehicle-level and platoon-level risk evaluation. The framework also guarantees complete coverage of cascading risks under arbitrary disturbance-propagation scenarios, ensuring that cascading effects are not omitted by design. Validation results show that, compared with existing SSMs, CRM enables earlier identification of peak platoon risk and generates smoother, more consistent risk profiles by better aligning with the evolution of platoon-level risk. These advantages are quantitatively supported by performance gains of 36.18% in MAE, 17.56% in sMAPE, and 22.22% in RMSSD, as well as an 18.84% increase in Kendall’s <span><math><msub><mrow><mi>τ</mi></mrow><mrow><mi>b</mi></mrow></msub></math></span> relative to the best-performing baseline. CRM also effectively captures the impact of platoon size and vehicle spatial distribution—factors to which conventional SSMs are largely insensitive. These findings highlight CRM’s potential for supporting proactive platoon-level risk warning and management.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"227 ","pages":"Article 108360"},"PeriodicalIF":6.2,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145839755","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Real-time highway crash prediction based on SHAP-RFECV and electronic toll collection data: a new feature selection strategy","authors":"Junda Huang ,&nbsp;Pengpeng Xu ,&nbsp;Kunhuo Huang ,&nbsp;Zhe Chen ,&nbsp;Yuqing Hu ,&nbsp;Huiying Wen","doi":"10.1016/j.aap.2025.108352","DOIUrl":"10.1016/j.aap.2025.108352","url":null,"abstract":"<div><div>Real-time crash prediction has emerged as a critical area of research in traffic safety, aiming to improve safety performance through proactive crash anticipation and management strategies. However, the accuracy and reliability of such prediction models largely depend on the selection of traffic features and prediction algorithms. Although numerous features have been investigated, they are often distributed across varying temporal and spatial dimensions and exhibit strong correlations, which can increase model variance and the risk of overfitting. Moreover, the selection and combination of features significantly affect both model performance and interpretability. To address these challenges, this study proposes a SHAP-RFECV feature selection algorithm that integrates SHapley Additive exPlanations (SHAP) values into the Recursive Feature Elimination with Cross-Validation (RFECV) framework. This method enhances feature selection by identifying the optimal subset of features most relevant to real-time crash prediction. Using a comprehensive dataset derived from the electronic toll collection system in Guangzhou, China, various machine learning models including extreme gradient boosting (XGBoost), categorical boosting, and light gradient boosting were trained and evaluated to demonstrate the effectiveness of the proposed algorithm. The results show that the XGBoost outperformed in terms of out-of-sample prediction, yielding an area under the curve of 0.8350, a recall of 73.12%, a false alarm rate of 18.77%, and an accuracy of 79.54%. Compared with the RFECV strategy, the number of optimal features selected by the XGBoost was substantially reduced from 47 to 21 under the SHAP-RFECV. Four benchmark models, i.e., logistic regression, long short-term memory, convolutional neural network, and support vector machine, were developed to further validate the effectiveness of the feature selection method. The results demonstrate that the proposed SHAP-RFECV algorithm outperformed traditional feature selection methods such as RFECV, least absolute shrinkage and selection operator, Boruta, and permutation importance in terms of both predictive accuracy and interpretability. By applying SHAP, we systematically measured the importance and interaction effects of risk-related features. Notably, features related to the average speed in the 5 to 10 min prior to a crash were identified as the most critical. Features such as the proportion of truck flow and speed standard deviation were also found to have significant and nonlinear impacts on crash occurrence.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"226 ","pages":"Article 108352"},"PeriodicalIF":6.2,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145788094","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Analysis of AV merging behavior in mixed traffic using large-scale AV driving datasets","authors":"Md Tanvir Ashraf,&nbsp;Kakan Dey","doi":"10.1016/j.aap.2025.108353","DOIUrl":"10.1016/j.aap.2025.108353","url":null,"abstract":"<div><div>Merging is one of the key maneuvers where autonomous vehicles (AVs) can perform significantly better in decision-making and execution than human-driven vehicles (HDVs). However, past studies have investigated AV merging in simulation environments but have not comprehensively analyzed the interactions between AVs and HDVs during merging events using real-world merging event datasets. This study examines AV merging behavior in mixed traffic environments by extracting and analyzing merging events from the Argoverse-2 and Waymo AV testing datasets. A Weibull random parameter hazard-based duration model was developed to examine the effect of different driving volatilities and traffic measures (i.e., relative speed, velocity standard deviation, and merging location) on merging Gap Time (GT), representing the aggressiveness and merging event crash risk. Descriptive analysis of the AV and HDV merging events showed that the merging GT distributions for AVs and HDVs were similar. Higher GT reduced the following vehicle’s speed variation at the target lane for both AV and HDV merging events. Merging crash risk was estimated using the extreme value theory (EVT) approach. The crash risk analysis revealed similar crash risks irrespective of the presence of AVs in merging events. However, AVs were associated with a reduced severity of crashes/conflicts, suggesting that the programmed behavior of AVs, such as smoother acceleration and adherence to traffic rules, contributed to safer merging in the mixed traffic environment. This study’s findings identified a need for improvement in AV technology to ensure safer and more efficient operations in mixed traffic. Merging event safety improvements can be achieved by optimizing AV algorithms for dynamic and human-centric traffic conditions during complex driving scenarios.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"226 ","pages":"Article 108353"},"PeriodicalIF":6.2,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145788096","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Loudness of In-Vehicle auditory Warnings: Sustained counteraction of task-related driver fatigue and individual differences in alertness maintenance","authors":"Mengjiao Wu ,&nbsp;Xuesong Wang ,&nbsp;Ashleigh Filtness ,&nbsp;Lishengsa Yue ,&nbsp;Ziyuan Huang ,&nbsp;Yujun Jiao","doi":"10.1016/j.aap.2025.108381","DOIUrl":"10.1016/j.aap.2025.108381","url":null,"abstract":"<div><div>In-vehicle fatigue warnings alert drivers when signs of fatigue are detected. These warnings can temporarily restore alertness, helping drivers respond before a crash occurs. They play a key role in reducing fatigue-related crash risk. However, the optimal loudness and effective duration of such warnings are not yet well understood. This study aims to analyze the effectiveness of auditory warnings from two perspectives: first, by employing the Kaplan-Meier method and the Random Survival Forest model to analyze alertness maintenance time across different warning loudness levels and to identify influencing factors. And second, by applying a Bayesian Cox proportional hazards model with random intercepts and slopes to examine individual differences in baseline alertness maintenance risk and sensitivity to 50 dB auditory warnings within the driver population. The results indicate that auditory warnings heighten drivers’ awareness of fatigue and extend the duration over which drivers maintain alertness following a warning. For mild task-related fatigue, one to three warnings produced an incremental benefit compared with no warning, as indicated by longer alertness maintenance. Compared to novice drivers, experienced drivers exhibited a higher baseline risk of reduced vigilance, greater sensitivity to the 50 dB warning, and a lower risk of post-warning fatigue. Overall, this study provides practical guidance for fatigue warning design and accounts for individual differences in alertness maintenance risk. It also introduces a new perspective for evaluating warning effectiveness based on alertness maintenance time.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"227 ","pages":"Article 108381"},"PeriodicalIF":6.2,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145882093","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}