Accident; analysis and prevention最新文献

{"title":"A spatially adaptive empirical Bayes framework with dynamic dispersion parameters for enhanced crash frequency prediction across rural highway networks","authors":"Seyed Ahmadreza Almasi ,&nbsp;Jingzhen Yang","doi":"10.1016/j.aap.2025.108361","DOIUrl":"10.1016/j.aap.2025.108361","url":null,"abstract":"<div><div>Traffic crashes often exhibit strong spatial dependence that is insufficiently captured by the Empirical Bayes (EB) method recommended in the Highway Safety Manual (HSM). This study proposes a Spatially Adaptive Empirical Bayes (SA-EB) framework that integrates advanced spatial models, including Geographically Weighted Poisson Regression (GWPR) and Multiscale Geographically Weighted Regression (MGWR), with Crash Modification Factors (CMFs) to enhance the prediction accuracy of expected crash frequencies across rural divided multilane highways (RDMHs), and generates CMF-adjusted, spatially weighted forecasts for both past and future conditions. The framework was calibrated and validated using crash and roadway data from 1071 km of highways in Hamadan Province, Iran, encompassing 2995 crashes recorded between 2017 and 2019. A dynamic overdispersion parameter (ranging from 0.3 to 0.6) was incorporated to capture spatial variability in crash dispersion. Results revealed substantial spatial heterogeneity in crash predictors: a 1 % increase in roadway slope corresponded to a 3.5-unit rise in crash frequency, while a 1 km/h increase in speed deviation led to approximately 4.5 additional crashes per segment. Implementation of SA-EB–guided geometric improvements reduced predicted crash frequencies by about 20 %, outperforming the conventional EB model. Overall, the SA-EB framework advances both the theoretical understanding and practical application of spatial safety modeling by providing transportation agencies with a data-driven and location-sensitive tool to identify high-risk segments and optimize Highway Safety Improvement Program (HSIP) investments across rural highway networks worldwide.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"226 ","pages":"Article 108361"},"PeriodicalIF":6.2,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145809074","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":"Drivers’ dynamic perception of accident risk and safety in underground road merging areas","authors":"Peipei Guo ,&nbsp;Fangtong Jiao ,&nbsp;Zhigang Du ,&nbsp;Feng Sun ,&nbsp;Xinke Tang","doi":"10.1016/j.aap.2025.108347","DOIUrl":"10.1016/j.aap.2025.108347","url":null,"abstract":"<div><div>The multi-entry underpass road tunnels is affected by various factors, including long downhill approaches outside the tunnel, monotonous visual environments inside the tunnel, underground merging of the main and secondary roads, and limited sight distance and sight zone. These combined conditions can lead to perception and judgment errors among drivers, significantly increasing the accident risk of rear-end and lateral crashes. This study used video data from a real vehicle test and conducted a subjective perception experiment with a driving simulator. It collected key indicators related to crash accident risk and prevention, including Identify Merging Time (IMT), Perceive Hazard Time (PHT), and Assess Safety Time (AST), to analyze the dynamic perception of risk and safety at the entrances of the main and secondary roads under 6 different speeds. And a Linear Mixed Model (LMM) was applied to evaluate the effect of speed on each indicator. Results showed that IMT decreased with increasing speed for both main and secondary roads, with the main road exhibited the highest Identify Merging Delay Rate (IMDR) at 38.667 %, indicating that drivers traveling at higher speeds struggled to identify the underground merging area in time. The Perceive Hazard Distance (PHD) for both main and secondary roads extended with increasing speed. Compared to the main road, drivers on the secondary road perceived hazards earlier within 38.167 to 46.683 m downstream of the physical gore point. This earlier perception was related to their frequent use of rearview mirrors to assess merging opportunities and the expanded sight zone in the secondary road merging area. Through LMM analysis, secondary road drivers’ PHD is less dependent on speed and is more influenced by the merging process itself. Overall, at higher speeds, reaction time is notably reduced, leading to delayed identification, hazard perception, and safety assessment. Hence, these findings provide valuable references for optimizing underground merging area design and enhancing drivers’ safety perception in multi-entry underpass road tunnels.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"226 ","pages":"Article 108347"},"PeriodicalIF":6.2,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145735351","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":"Solution to data imbalance and complex interactions in traffic conflict modeling: a hypergraph and generative AI approach","authors":"Kaiming Guan,&nbsp;Junyi Zhang,&nbsp;Wei Ye,&nbsp;Ying Jiang","doi":"10.1016/j.aap.2025.108338","DOIUrl":"10.1016/j.aap.2025.108338","url":null,"abstract":"<div><div>Existing traffic conflict models face challenges in handling minority class samples and capturing dynamic interactions in complex traffic scenarios. These limitations hinder model generalization and real-world applicability. This study employs an enhanced Two-dimensional Time-to-collision (2D-TTC) metric combined with vehicle interaction relationships to predict traffic conflicts of multiple patterns. To address imbalance in conflict and non-conflict events, both undersampling and oversampling techniques are employed, while a generative adversarial network with self-attention layers is leveraged to overcome the shortcomings of oversampling methods. Indeed, this approach proved highly effective, elevating the model’s F1-score from 76.35 % with undersampling alone to 94.21 %. Additionally, several machine learning and deep learning models are compared, with the hypergraph attention network combined with Shapley additive explanations (S-HGAT) demonstrating the strongest learning capability. Furthermore, vehicle speed is identified as the most influential factor associated with traffic conflicts. A comprehensive re-evaluation of feature combinations reveals that the top six features—vehicle speed, the number of vehicles ahead, the standard deviation and the average of vehicle speeds within the traffic flow, distance with the road markings, and peak traffic hour indicators—result in the highest model F1-score of 98.41 % and accuracy of 97.66 %. Finally, the real-world implications of these findings are discussed.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"226 ","pages":"Article 108338"},"PeriodicalIF":6.2,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145713034","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":"Uncovering latent structures of crash typology in narcotic-involved fatal crashes for safe system interventions","authors":"Anannya Ghosh Tusti,&nbsp;Rohit Chakraborty,&nbsp;Tausif Islam Chowdhury,&nbsp;Md Monzurul Islam,&nbsp;Mahmuda Sultana Mimi,&nbsp;Subasish Das","doi":"10.1016/j.aap.2025.108382","DOIUrl":"10.1016/j.aap.2025.108382","url":null,"abstract":"<div><div>Narcotic-impaired driving increases the risk of fatal crashes, yet existing studies rarely provide narcotic-specific crash typologies that link driver impairment to roadway, traffic, and environmental conditions. This gap limits the design of Safe System interventions that can proactively address the most common high-risk configurations. Using Fatality Analysis Reporting System data from 2018 to 2022, this study applies Cramér’s V statistic for variable selection and Cluster Correspondence Analysis (CCA) to explore unsupervised crash typologies and latent patterns of narcotics-involved fatal crashes. CCA biplot coordinates group crashes into four clusters: high-speed lane changes on uncontrolled arterials, run-off-road impacts with rollovers, nighttime pedestrian or cyclist strikes on unlit roads, and moderate-speed angle crashes at signalized intersections. Results show that speed and lateral control failures dominate the first two clusters, narcotic-induced sensory and cognitive deficits under low visibility drive the third, and decision-making errors during turn phases characterize the fourth. Key factors such as posted speed limit, lighting condition, and driver age exert cluster-specific influences on incapacitating and fatal injury outcomes. These findings underscore the inadequacy of appropriate countermeasures and point to Safe System-aligned interventions, including dynamic speed management, enhanced roadside clear zones, targeted lighting upgrades, and intersection control strategies.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"227 ","pages":"Article 108382"},"PeriodicalIF":6.2,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145882092","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":"TGLD: A trust-aware game-theoretic lane-changing decision framework for automated vehicles in heterogeneous traffic","authors":"Jie Pan ,&nbsp;Yongjun Shen ,&nbsp;Chengyu He ,&nbsp;Jing Shi","doi":"10.1016/j.aap.2025.108365","DOIUrl":"10.1016/j.aap.2025.108365","url":null,"abstract":"<div><div>Automated vehicles (AVs) face a critical need to adopt socially compatible behaviors and cooperate with human-driven vehicles (HVs) in heterogeneous traffic environments. However, existing lane-changing decision frameworks for AVs rarely account for the dynamic trust levels of HVs, thereby impeding the accurate prediction of HV behaviors due to inherent uncertainties. This study proposes a trust-aware, game-theoretic lane-changing decision framework for AVs. First, a trust-aware multi-vehicle coalition game for heterogeneous traffic environment is developed, which encompasses a fully cooperative game among AVs and a partially cooperative model for HVs based on each HV’s real-time trust level. Second, an online evaluation method to dynamically estimate HV’s trust during lane-change interactions is designed, guiding AVs to select appropriate cooperative maneuvers. Finally, social compatibility objectives are considered by minimizing disruption to surrounding vehicles and enhancing the predictability of AV behavior, ensuring the overall lane-changing strategy remains both human-friendly and context-adaptive. To test the proposed model, a human-in-the-loop experiment was conducted in a highway on-ramp scenario. Results showed that the AV adjusted its lane-changing strategy accordingly for human drivers of varying driving styles and trust levels. Moreover, the proposed model facilitated safe, efficient, and comfortable merging behavior for the AV. Ablation studies revealed that incorporating the trust mechanism yields higher average lane-change speeds than models without it, demonstrating improved efficiency while maintaining safety in heterogeneous traffic. This research contributes to enhance the interpretability of HV-AV interaction and promotes the design of more transparent and adaptive lane-changing strategies in automated driving systems.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"227 ","pages":"Article 108365"},"PeriodicalIF":6.2,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145808393","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":"Spatial-temporal gated transformer network for freeway secondary crash prediction considering the impact of class imbalance","authors":"Ling Deng ,&nbsp;Chengcheng Xu ,&nbsp;Pan Liu ,&nbsp;Yuxuan Wang ,&nbsp;Yanli Jiao ,&nbsp;Kequan Chen","doi":"10.1016/j.aap.2025.108387","DOIUrl":"10.1016/j.aap.2025.108387","url":null,"abstract":"<div><div>The primary objective of this paper is to improve the prediction accuracy of freeway secondary crashes by jointly modeling spatiotemporal dynamics and addressing the challenge of class imbalance. Using secondary-crash data from Interstate 5 (I-5) in California, we develop a Spatial-Temporal Gated Transformer Network (STGT-Net). STGT-Net employs a tri-branch encoder to model upstream, downstream, and differential traffic flows, and dual Transformer modules with a gated fusion mechanism to capture temporal and inter-feature dependencies. To address data rarity, we employ an LSTM-based Wasserstein GAN with gradient penalty (LSTM-WGAN-GP) to generate realistic, sequence-aware crash samples. Comparative results show that the secondary crash data generated by our method aligns more closely with real-world conditions than those produced by existing approaches. STGT-Net achieves substantial relative improvements of 13.08% in F1-score and 13.95% in Matthews Correlation Coefficient (MCC) compared with the best baseline. Finally, SHapley Additive exPlanations (SHAP)-based analysis reveals that upstream traffic variables contribute most strongly to the model’s predictions, with downstream and upstream–downstream differential indicators also providing informative cues.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"227 ","pages":"Article 108387"},"PeriodicalIF":6.2,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145882003","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":"Time-to-event crash severity prediction at highway-rail grade crossings with monotonic neural networks","authors":"Amin Keramati ,&nbsp;Pan Lu ,&nbsp;Yi Hao Ren","doi":"10.1016/j.aap.2025.108385","DOIUrl":"10.1016/j.aap.2025.108385","url":null,"abstract":"<div><div>Despite advances in highway-rail grade crossing (HRGC) safety, including widespread use of active control devices, crashes at these intersections still lead to severe outcomes. Conventional crash prediction models often fail to capture severity-level dependencies, rely on assumption-driven and computationally intensive methods, and overlook links between severity and time between events. This study introduces a predictive framework based on positive monotonic neural networks (MNNs) for modeling time-to-crash outcomes with severity at HRGCs. Considering the relative newness of the time-to-crash paradigm in HRGC safety, the Neural Fine–Gray model is adopted as a core MNN implementation to estimate severity-specific crash likelihoods. This approach eliminates the numerical integration required in traditional time-to-event models, substantially reducing computational burden and accelerating training for large datasets. The framework naturally handles imbalanced HRGC data by treating event-free records as right-censored, avoiding the resampling required in traditional machine-learning approaches. To examine severity-level dependencies—an aspect largely overlooked in the literature—four MNN architectures are developed and evaluated. Using a 29-year North Dakota HRGC dataset, results show trade-offs between predictive accuracy and computational efficiency. The cause-specific MNN performs best for medium- and long-term horizons, whereas the multi-head MNN converges faster and excels at short horizons. Moreover, benchmarking against traditional time-to-event models—cause-specific Cox and Fine–Gray—shows modest calibration gains and 2%–50% stronger discrimination, reflecting the alignment between MNNs and the nonlinear, high-dimensional HRGC data. The framework also enhances interpretability by revealing paradoxical effects, including the “adding flashing lights paradox” and the “adding stop signs paradox.”</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"227 ","pages":"Article 108385"},"PeriodicalIF":6.2,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145882091","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":"Roadway traffic crash during extreme heat days: insights from hazards-exposure-vulnerability-adaptation","authors":"Zongni Gu ,&nbsp;Ilir Bejleri ,&nbsp;Binbin Peng","doi":"10.1016/j.aap.2025.108386","DOIUrl":"10.1016/j.aap.2025.108386","url":null,"abstract":"<div><div>The escalating frequency of extreme heat events poses a potential threat to roadway safety, yet the spatial patterns of crash risk from more multi-dimensional perspectives remain underexplored. Using the Hazards-Exposure-Vulnerability-Adaptation paradigm, this study examines how traffic crash rates on extreme-heat days vary across roadway segments and road design characteristics in the City of Miami, Florida. This study analyzed traffic exposure and crash rates across three yearly extreme heat thresholds (90th, 95th, and 97th percentiles) from 2011 to 2015. A CatBoost model, interpreted via SHAP analysis, is used to identify key roadway and contextual features associated with higher or lower crash rates during extreme-heat days. The key findings are as follows: 1) Crash risk on extreme-heat days shows a threshold-dependent pattern across the examined percentiles. As the heat threshold intensifies from the 90th to the 97th percentile, the average network-wide crash rate increases, while the number of road segments with above-average crash rates follows a V-shaped pattern—first declining and then rising sharply at the highest threshold. This suggests that inherent adaptive characteristics of many roadways may be sufficient to moderate crash risk under moderately extreme heat but become increasingly inadequate once heat reaches very abnormally high threshold (e.g., the 97th percentile). 2) Models based on higher extreme-heat thresholds provide clearer insight into vulnerability patterns. Compared to the 90th and 95th percentile models, the 97th percentile model more clearly isolates roadway and contextual features most strongly associated with elevated crash rates on extreme-heat days, whereas lower thresholds appear more affected by noise from other coincident factors. 3) Roadway investment emerges as the primary adaptive factor associated with reduced risk. Physical attributes such as construction cost and geometric design are the most influential correlates of crash vulnerability on extreme-heat days, with higher-quality roadway investment linked to substantially lower crash rates. In contrast, the observed associations for safety control measures and emergency service accessibility are comparatively limited. These findings characterize which roadway environments are more vulnerable when extreme-heat conditions occur.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"227 ","pages":"Article 108386"},"PeriodicalIF":6.2,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145882096","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":"Intelligent defensive driving for autonomous vehicles: Framework, strategy and verification","authors":"Ting Zhang ,&nbsp;Zixuan Wang ,&nbsp;Hong Wang,&nbsp;Jun Li","doi":"10.1016/j.aap.2025.108355","DOIUrl":"10.1016/j.aap.2025.108355","url":null,"abstract":"<div><div>As autonomous driving advances to higher levels, conventional decision-making algorithms for autonomous vehicles (AVs) often inadequately address long-tail issues composed of low-frequency, high-uncertainty, and extreme scenarios, leading to recurrent safety incidents. How to leverage humans’ excellent defensive driving experience refined through expert knowledge to upgrade autonomous driving to intelligent defensive driving, thereby systematically improving driving safety serves as the core objective of this paper. To tackle this challenge, this paper proposes an integrated research scheme for the intelligent defensive driving of AVs. Firstly, an overall framework for intelligent defensive driving of AVs is constructed, which includes a systematic classification method for defensive driving scenarios and a hierarchical design process for defensive driving. Secondly, an online defensive driving monitoring mechanism for AVs is designed based on experience-triggered conditions, and a safety decision-making strategy is developed integrating formalized defensive driving experience. Finally, intelligent defensive driving performance for AVs is verified based on the “perception insufficiency” classification. The simulation results show that the proposed research scheme can utilize the defensive driving trigger mechanism to anticipate potential risks in multiple risk scenarios, improving the average values of the minimum relative braking distance, minimum time-to-collision and advance braking time by 7.49 m, 1.56 s, and 2.84 s, respectively, while significantly reducing the probability of emergency accidents and maintaining system robustness and real-time computational performance.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"226 ","pages":"Article 108355"},"PeriodicalIF":6.2,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145773011","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":"Avoidance behavior and movement characteristics of pedestrians under mobile phone distraction","authors":"Shuchao Cao ,&nbsp;Yuhang Wang ,&nbsp;Guang Zeng ,&nbsp;Xiaolian Li ,&nbsp;Peng Wang","doi":"10.1016/j.aap.2025.108363","DOIUrl":"10.1016/j.aap.2025.108363","url":null,"abstract":"<div><div>Smartphone use while walking has become increasingly prevalent, which significantly affects pedestrian safety and increases the risk of traffic accidents, especially in avoidance scenarios. Therefore, to reveal the avoidance mechanism and gait features under mobile phone use distractions, a series of walking experiments under varying distraction conditions including no phone use, text-browsing, message-sending, and video-watching were conducted. Multiple types of data such as physiological signals including the electrocardiogram (ECG) and electrodermal activity (EDA), and high-precision motion trajectories were synchronously obtained. To characterize the entire avoidance process, three critical positions including the start point, maximum point and end point are identified in this paper. The results show that distracted pedestrians initiate evasive maneuvers when they are close to obstacles, whereas non-distracted pedestrians prefer to avoid obstacles in advance. Moreover, the right-side avoidance strategy is adopted by most pedestrians under different distraction levels. Physiological signal indicates that distracted pedestrians experience elevated levels of psychological stress. Besides, the walking speed is significantly decreased in distracted circumstances. The gait analysis demonstrates that distracted pedestrians exhibit shorter step length, larger step width and longer step time during the avoidance phase. Furthermore, the real-time messaging task consumes large cognitive resources of pedestrians, which has the most pronounced impact on pedestrian safety. The study elucidates how the use of smartphones affects the avoidance mechanism of pedestrians, which can help develop intervention measures and safety strategies to reduce the risk of distracted walking in public places.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"226 ","pages":"Article 108363"},"PeriodicalIF":6.2,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145788093","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}