Accident; analysis and prevention最新文献

{"title":"Bivariate Bayesian hierarchical extreme value modeling using multi-type traffic conflict for crash estimation on freeway horizontal curves","authors":"Zhenlin Hu,&nbsp;Pengru Wei,&nbsp;Lin Sheng,&nbsp;Guorui Wang,&nbsp;Xianghai Meng","doi":"10.1016/j.aap.2025.108019","DOIUrl":"10.1016/j.aap.2025.108019","url":null,"abstract":"<div><div>Freeway horizontal curves pose great challenges to vehicle driving safety due to suboptimal road alignment, poor visual conditions, and higher demands for driving maneuvers. The interaction between multiple conflicting vehicles may generate multi-type crash risks with correlations. Modeling individual types of crash risks separately will result in biased crash estimation. In this study, a bivariate Bayesian hierarchical extreme value modeling approach, which consists of a bivariate extreme value model and a Bayesian hierarchical structure, is developed. The former integrates two different conflict indicators while also accounting for their correlation. The latter combines traffic conflicts across different sites, incorporating block-level and site-level covariates and unobserved heterogeneity. Using rear-end and lane-changing conflicts collected from 14 directional curved segments of the Yinkun freeway, several univariate Bayesian hierarchical extreme value models (UBHMS) and bivariate Bayesian hierarchical extreme value models (BBHMS) were constructed to estimate expected rear-end crashes and side crashes. The crash estimation results show that the bivariate model considering correlation between multi-type conflicts has smaller standard deviations of the model parameters and outperforms the univariate models in both accuracy and precision of crash estimation. The covariate analysis suggests that a larger proportion of large vehicles and standard deviation of speed will lead to an increase in both rear-end and side crash risks; the number of car-following vehicles and the number of lane-changing vehicles have positive influences on rear-end and side crash risks, respectively, whereas the higher the overspeed and the lane space occupancy instead reduce rear-end crash risk. Finally, when vertical curves overlap with horizontal curves, the rear-end and side crash risks on sag vertical curves exceed those on crest vertical curves.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"215 ","pages":"Article 108019"},"PeriodicalIF":5.7,"publicationDate":"2025-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143734564","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":"Effects of time interval and request modality on driver takeover responses: Identifying the optimal time interval for two-stage warning system","authors":"Jie Zhang ,&nbsp;Zhi Zhang ,&nbsp;Tingru Zhang ,&nbsp;Yijing Zhang ,&nbsp;Shanguang Chen","doi":"10.1016/j.aap.2025.108008","DOIUrl":"10.1016/j.aap.2025.108008","url":null,"abstract":"<div><div>Two-stage warning system plays a critical role in guiding drivers to prepare for takeovers in conditional automated driving. However, the optimal time interval for this system, especially under different takeover request (TOR) modalities, remains unclear. A driving simulator experiment with 36 participants was conducted to investigate the effects of time interval and TOR modality of two-stage warning system on drivers’ takeover responses from a multidimensional perspective. Each participant completed takeovers with four time intervals (3 s, 5 s, 7 s, and 9 s) and three TOR modalities (visual-only, auditory-only, and auditory-visual). Drivers’ takeover performance, mental workload, situation awareness (SA), user experience, and eye movements during the takeover process were recorded. The results indicated that drivers showed faster and higher-quality takeovers as the time interval increased from 3 s to 9 s. Their ratings of satisfaction, usefulness, effectiveness, and safeness of the warning system showed the inverted U-shaped trends, with the 7 s as a turning point. The 7 s interval was also favored for drivers to regain sufficient SA while maintaining an appropriate mental workload, as evidenced by both subjective measures and eye-tracking metrics. This allowed drivers to adopt more focused visual strategies for the takeover after receiving TOR warning, thereby improving takeover performance. Additionally, the auditory-visual TOR was found to be the most effective across all measures, followed by the auditory-only TOR, and finally the visual-only TOR. No significant interaction effects between time interval and TOR modality were observed. In conclusion, regardless of TOR modality, the 7 s time interval was generally favored for young drivers with relatively limited driving experience for swift takeover responses, high takeover quality, sufficient SA, appropriate mental workload, and good satisfaction ratings. When the interval was extended to 9 s, drivers’ takeover performance improved, but with the cost of reduced satisfaction and potential shift in visual attention from driving task to non-driving-related task. These findings had implications for the design and application of appropriate time interval of two-stage warning system for Level 3 automatic vehicles.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"215 ","pages":"Article 108008"},"PeriodicalIF":5.7,"publicationDate":"2025-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143725735","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":"Understanding e-scooter rider crash severity using a built environment typology: A two-stage clustering and random parameter model analysis","authors":"Amirhossein Abdi ,&nbsp;Steve O’Hern","doi":"10.1016/j.aap.2025.108018","DOIUrl":"10.1016/j.aap.2025.108018","url":null,"abstract":"<div><div>E-scooters are an emerging transport mode that is transforming urban mobility; however, their proliferation has raised concerns about safety. This study combines UK e-scooter crash data with built environment characteristics from the crash locations. A two-stage framework was followed: first, a typology of built environments was developed using K-means++; second, crash severity within each cluster was analysed using a random parameter binary logit model. Four built environment clusters were identified: (1) car-centric and mixed-use zones, (2) commercial and industrial zones, (3) intersection-dense areas, and (4) residential and central areas. Collisions with motor vehicles, younger e-scooter riders, and higher speed limits were the most common risk factors across the clusters, with the first two clusters showing a higher impact of these factors on the likelihood of severe crashes. In the first and second clusters, riding on the carriageway significantly increased injury severity. In the second cluster, three collision types were significant, more than in other clusters where only side-impact collisions were significant. This indicates high e-scooter–motor vehicle friction in the second cluster. Among all collision types, head-on collisions increased the likelihood of severe outcomes more than others. In the third and fourth clusters, peak hours were associated with a lower likelihood of severe crashes, while this variable showed the opposite impact in the first cluster. The results highlight that consideration of the surrounding built environment is paramount when analysing e-scooter crash severity, as unique contributing factors were identified specific to each built environment type, along with varying magnitudes or directions of marginal effects.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"215 ","pages":"Article 108018"},"PeriodicalIF":5.7,"publicationDate":"2025-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143725734","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A systematic review on test performance of the driver takeover process in automated driving","authors":"Haolin Chen ,&nbsp;Xiaohua Zhao ,&nbsp;Chen Chen ,&nbsp;Zhenlong Li ,&nbsp;Haijian Li ,&nbsp;Qiuhong Wang","doi":"10.1016/j.aap.2025.108012","DOIUrl":"10.1016/j.aap.2025.108012","url":null,"abstract":"<div><div>Much research has been conducted on takeover behavior in automated driving, and integrating these studies into a knowledge system can help to gain a deeper understanding of the current research status and guide critical future research. The takeover focused in this study refers to the takeover related to human intervention (i.e. the transfer of control between the human driver and the auto drive system), rather than the context of overtaking another vehicle (e.g., lane changes and acceleration). The takeover behavior is a multi-stage process consisting of situation awareness, decision &amp; reaction, and takeover performance stages. An in-depth review of takeover behavior characteristics from the three takeover stages is helpful to describe the takeover process and analyze takeover behavior characteristics systematically. Therefore, this paper aims to review driver’s takeover performance from the three levels of driver, automated vehicle, and road environment based on the takeover behavior mode. First, we identified 1329 articles through a systematic literature search. 122 articles were included in this review. Second, we use the knowledge graph method for bibliometric analysis. Third, we systematically review the characteristics of takeover behavior in three stages (situation awareness, decision &amp; reaction, takeover performance) from three dimensions: driver, vehicle, and road environment. At the same time, this study develops scoring rules that quantify each factor’s contribution to takeover behavior. Fourth, based on the reviewed literature and scores, 18 suggestions were proposed to improve takeover behavior from three levels: drivers, vehicles, road environment. Finally, we have outlined the future fundamental research of takeover behavior. This review summarizes the research content of takeover behavior testing and forms a knowledge system, which provides researchers with a window to understand the research status and development context. This review can guide future research on takeover behavior.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"215 ","pages":"Article 108012"},"PeriodicalIF":5.7,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143680343","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":"Incorporating multi-path risk assessment in transformer-based pedestrian crossing action prediction","authors":"Bowen Liu ,&nbsp;Meng Li ,&nbsp;Ruyi Feng ,&nbsp;Wei Zhou ,&nbsp;Zhibin Li","doi":"10.1016/j.aap.2025.108002","DOIUrl":"10.1016/j.aap.2025.108002","url":null,"abstract":"<div><div>This paper proposes a Transformer-based framework for predicting pedestrian crossing actions that uses visualized pedestrian-vehicle collision risks, which are assessed from multiple potential paths. Our framework contains two sequential steps: (1) multi-path risks of a pedestrian-vehicle interaction (PVIs) at each time point are estimated and encoded into an RGB image, which captures a high-density array of safety information. (2) a multi-modal fusion architecture that incorporates both risk images and historical sequential data (e.g., pedestrian action and vehicle velocity) is developed based on the Cross-Attention Transformer. The model outputs are also risk-informed, categorized as yielding, risky crossing, and safe crossing. Experiments are conducted on real-world data from the Euro-PVI dataset. Through two-dimensional mapping tests, risk images are validated to have significant spatiotemporal feature differences and transition associations under different PVIs. The Transformer architecture proves to be an effective method for processing multi-path risk images. Prediction accuracy reaches 87.34% for short-term forecasts (0.5 s ahead), maintains stability as the prediction time horizon progressively extends to 2 s, and improves the prediction of abrupt action switches. For further exploration and validation, the risk image data and imaging code are available at <span><span>www.github.com/Sivan0227/PVI-Risk-Image</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"215 ","pages":"Article 108002"},"PeriodicalIF":5.7,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143680346","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":"Estimating safety benefit of in-vehicle work zone safety technology alerts: A counterfactual Monte-Carlo simulation approach","authors":"Qishen Ye ,&nbsp;Yihai Fang ,&nbsp;Nan Zheng","doi":"10.1016/j.aap.2025.108014","DOIUrl":"10.1016/j.aap.2025.108014","url":null,"abstract":"<div><div>Work Zone Safety Technologies (WSTs) have exhibited great potential to improve road work zone safety by detecting safety risks and providing warnings to drivers and workers involved. Yet, it remains extremely challenging to quantify the actual safety benefits of such technologies in reducing work zone intrusion accidents, mainly due to a lack of empirical data and robust evaluation methods. This paper aims to explore the patterns of drivers’ behavioural responses when approaching work zones and estimate the safety benefits of in-vehicle WSTs. First, a VR-based driving simulation experiment was conducted to collect human behavioural data on drivers’ responses when approaching work zones in critical scenarios. Second, a Linear Mixed Effect (LME) model was developed to capture the impact of in-vehicle WST alerts and scenario criticality, i.e., speed and Time-to-Collision (TTC), on drivers’ behavioural responses. Finally, the safety benefits of in-vehicle WST alerts were estimated through counterfactual Monte-Carlo simulations of vehicle trajectories. The findings highlight the mechanism by which in-vehicle WST alerts improve driver response in various critical driving scenarios involving work zones and provide crucial evidence for future decision-making regarding the evaluation of WSTs.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"215 ","pages":"Article 108014"},"PeriodicalIF":5.7,"publicationDate":"2025-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143680345","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Examining collision avoidance behavior of distracted drivers: A correlated grouped random parameters accelerated failure time model with heterogeneity-in-means","authors":"Qikai Qu ,&nbsp;Yasir Ali ,&nbsp;Yongjun Shen ,&nbsp;Qiong Bao ,&nbsp;Md Mazharul Haque","doi":"10.1016/j.aap.2025.108016","DOIUrl":"10.1016/j.aap.2025.108016","url":null,"abstract":"<div><div>Current research mostly studied the driving behavior of distracted drivers in abrupt situations, but different types of distraction may lead to differential crash risks, particularly during collision avoidance, which has been overlooked in the literature. As such, this study investigated and compared drivers’ collision avoidance performance under different distraction conditions. Forty-four licensed drivers completed driving simulation experiments under normal driving, cognitive distraction, and manual distraction conditions. To comprehend the collision avoidance behavior under different distraction conditions, this study analyzed hazard response time, deceleration time, and collision avoidance time—critical components in avoiding secondary collisions. For these performance measures, correlated grouped random parameters accelerated failure time models were developed, considering repeated experiment design and unobserved heterogeneity. Results indicate that drivers in manual distraction took more time to respond during the hazard response phase compared to cognitive distraction. The majority of distracted drivers were observed to spend less time decelerating and avoiding collisions than in normal driving conditions, indicating a risk compensation behavior. Further, this study found that gender was associated with differential increase in collision avoidance time, with male drivers having longer collision avoidance time under manual distraction than female drivers and female drivers having longer collision avoidance time under cognitive distraction than male drivers. Overall, this study provides insights into collision avoidance behavior and aids in developing automated collision avoidance assistance systems.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"215 ","pages":"Article 108016"},"PeriodicalIF":5.7,"publicationDate":"2025-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143680344","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Critical scenarios adversarial generation method for intelligent vehicles testing based on hierarchical reinforcement architecture","authors":"Bing Zhu,&nbsp;Rui Tang,&nbsp;Jian Zhao,&nbsp;Peixing Zhang,&nbsp;Wenxu Li,&nbsp;Xinran Cao,&nbsp;Siyuan Li","doi":"10.1016/j.aap.2025.108013","DOIUrl":"10.1016/j.aap.2025.108013","url":null,"abstract":"<div><div>The widespread deployment of intelligent vehicles necessitates comprehensive testing across diverse driving scenarios. A significant challenge is generating critical testing scenarios to accurately evaluate vehicle performance. To overcome the limitations of existing methods, including inadequate diversity and validity, this study proposes an adversarial generation method grounded on a hierarchical reinforcement learning framework. This approach comprises three modules: a hierarchical scheduling module, a conflict prediction module, and a scenario evaluation module. The hierarchical scheduling module segments the testing procedure into guidance, adversarial, and exploration periods, effectively managing reward sparsity to promote varied scenario generation. The conflict prediction module employs kinematic conflict prediction and adaptive action strategies to enhance learning speed and efficiency, directing traffic entities in producing critical scenarios. The evaluation module assesses scenario validity and diversity by analyzing relative trajectories, temporal characteristics, and spatial configurations, in addition to employing a perception-limited model and replay testing to assess performance within the system’s operational limits. Experimental results using the HighD dataset in the highway environment demonstrate that the proposed method efficiently generates varied critical test scenarios, improving the collision rate and period contributions throughout the testing process. When producing an equivalent number of critical scenarios, the overall testing resource utilization decreases by 49.49% relative to the conventional Deep Q-Network method.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"215 ","pages":"Article 108013"},"PeriodicalIF":5.7,"publicationDate":"2025-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143681062","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":"The existence and impacts of sequential traffic conflicts: Investigation of traffic conflict in sequences encountered by left-turning vehicles at signalized intersections","authors":"Shuke Xie ,&nbsp;Zhenyu Zhao ,&nbsp;Qiangqiang Shangguan ,&nbsp;Ting Fu ,&nbsp;Junhua Wang ,&nbsp;Hangbin Wu","doi":"10.1016/j.aap.2025.108015","DOIUrl":"10.1016/j.aap.2025.108015","url":null,"abstract":"<div><div>The traffic paths of vehicles, pedestrians and non-motorized traffic at signalized intersections are complicated, and the phenomenon of not strictly obeying the right of way is frequent, which leads to more conflict points at the intersection. Vehicles are prone to Sequential conflicts while passing through intersections, which depletes attention resources, reduces response capacity, and increases accident risk. Therefore, analyzing Sequential Traffic Conflicts at intersections is a key focus of traffic safety research. The purpose of this study is to prove the existence of complex scenarios of sequential traffic conflicts, demonstrate the impact of initial conflicts on subsequent conflicts, and discuss the impact of different class variables on the severity of sequential traffic conflicts. A multi-dimensional severity assessment system was developed, and two nonlinear binary logistic regression models were established: one considering the correlation between conflicts and one assuming independence. Significant variables influencing Sequential traffic conflicts were categorized and analyzed. A typical left-turn scenario was selected for analysis. The results show that the occurrence of the initial conflict significantly influences the subsequent conflict. The model considering correlation outperforms the independent conflict model, confirming the existence of interdependence between conflicts. The severity of the first and second conflicts is negatively correlated, with the second conflict being more severe. Factors such as participant speed, group size, arrival time at the conflict zone, non-motorized vehicle direction, and left-turning vehicles’ willingness to continue crossing significantly affect conflict severity. Based on this, effective strategies for enhancing the safety of sequential traffic conflict scenarios are proposed.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"215 ","pages":"Article 108015"},"PeriodicalIF":5.7,"publicationDate":"2025-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143673157","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":"Integrating machine learning and a large language model to construct a domain knowledge graph for reducing the risk of fall-from-height accidents","authors":"Zhipeng Zhou ,&nbsp;Xinhui Yu ,&nbsp;Joseph Jonathan Magoua ,&nbsp;Jianqiang Cui ,&nbsp;Haiying Luan ,&nbsp;Dong Lin","doi":"10.1016/j.aap.2025.108009","DOIUrl":"10.1016/j.aap.2025.108009","url":null,"abstract":"<div><div>Fall-from-height (FFH) accidents remain a major source of workplace injuries and fatalities. Fall protection systems (FPS) are critical for preventing falls in the work-at-height (WAH) environment. However, challenges in designing and selecting effective FPS persist across various industries, and existing tools often lack practical references. This study aims to develop an FFH-specific knowledge graph (FFH-KG) to support FPS design. By structuring accident data, the FFH-KG provides empirical insights to help designers improve FPS frameworks, aiding safety planning and decision-making. It serves as a decision support system for FPS designers and safety professionals, guiding the selection and design of appropriate protection solutions for diverse WAH scenarios. The FFH-KG was constructed using a hybrid natural language processing approach, combining manual extraction, entity recognition, text segmentation, and rule-based relation extraction. It was grounded in a schema layer (i.e., ontology) established by experts. A text-mining approach, integrating machine learning with a large language model, facilitated the categorization of fall types, refinement of WAH scenarios, and identification of fall causes, enhancing the content and applicability of knowledge graph. A total of 2,200 entities and 4,820 relationships were created based on fall protection equipment standard documents and fall-from-height accident investigation reports, forming a foundation for developing countermeasures. The retrieval performance of FFH-KG was validated through three case studies. This research has also made significant progress in intelligent safety engineering and management across industries.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"215 ","pages":"Article 108009"},"PeriodicalIF":5.7,"publicationDate":"2025-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143673145","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}