Accident; analysis and prevention最新文献

{"title":"Comparative safety analysis of take-over control mechanisms of conditionally automated vehicles","authors":"Arastoo Karimi ,&nbsp;Arash Hassani Barbin ,&nbsp;Abrar Hazoor ,&nbsp;Giuseppe Marinelli ,&nbsp;Marco Bassani","doi":"10.1016/j.aap.2025.108068","DOIUrl":"10.1016/j.aap.2025.108068","url":null,"abstract":"<div><div>Conditionally Automated driving (CAD) represents a pivotal point in the evolution of automotive technology, bridging full automation and human intervention through effective control mechanisms that ensure safe driver-system transitions. This research consisted of a comparative analysis of take-over mechanisms, focusing on ordinary merging and diverging maneuvers and critical collision-avoidance scenarios. Three take-over control (TOC) methods, including (i) accelerating/braking, (ii) pressing a dedicated button, and (iii) steering, were investigated. Thirty participants were recruited using a mixed factorial design with both within- and between-subject factors. The experimental simulations were conducted on the fixed-base driving simulator. The participants completed three runs on a motorway track comprising ordinary merging and diverging sections, with the final run involving a sudden critical decision to avoid the collision against two crashed vehicles. Weibull accelerated failure time models with and without shared frailty, mixed effects linear regression and multiple linear regression were used to model TOC time, maximum resultant acceleration, and minimum time to collision values.</div><div>The results indicate that the pedal mechanism generally provides faster and safer takeovers, especially in critical situations, while the button mechanism results in the longest TOC times, and lowest minimum time to collision values, indicating higher risks. The steering wheel mechanism, associated with the highest maximum resultant acceleration and TOC times in merging and diverging maneuvers, suggests that lateral control may be more cognitively demanding for drivers. These findings emphasize the importance of selecting appropriate TOC mechanisms to improve the safety and efficiency of CAD systems.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"217 ","pages":"Article 108068"},"PeriodicalIF":5.7,"publicationDate":"2025-04-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143876571","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":"Cyclist safety in the digital age: A review of advanced warning technologies","authors":"Aisyah Nur Jannah ,&nbsp;Sara Moridpour ,&nbsp;Richard Tay ,&nbsp;Bo Du","doi":"10.1016/j.aap.2025.108069","DOIUrl":"10.1016/j.aap.2025.108069","url":null,"abstract":"<div><div>Improving the safety of cyclists, who are considered vulnerable road users, is essential. Implementing a warning system that alerts cyclists to nearby hazards is an effective method to improve their safety. Nevertheless, the literature needs a comprehensive and detailed review of cyclist warning systems. This paper presents a systematic review of existing studies about warning technologies and systems developed to improve cyclist safety, following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines (n = 78). Based on the review, it was found that the most used cyclist warning technologies were cameras with artificial intelligence (AI), ultrasonic sensors, lidar, and radar. Furthermore, there were some insights from the studies that conducted cycling experiments with the use of a warning system: (1) most participating cyclists responded positively to the warning technologies/systems, (2) most cyclists preferred auditory signals over visual or haptic signals as warning modalities, (3) warning technologies/systems influenced some cyclists’ behaviour during critical situations, such as early braking, faster reactions, increased focus, maintaining a safer distance, and temporarily reducing speed, and (4) only two studies tried to explore how warning systems affect crash risk. Future research should focus on developing warning systems that are compact, lightweight, and affordable. More studies are also needed to demonstrate the impact of the cyclist warning system on cyclists’ crash risk. This review identifies gaps in the current literature and offers a solid foundation for future studies to enhance cyclist safety.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"217 ","pages":"Article 108069"},"PeriodicalIF":5.7,"publicationDate":"2025-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143874586","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":"Does connected vehicle information reduce beyond-visual-range crash risk in foggy freeway conditions? A study based on extreme value theory","authors":"Wenhao Ren,&nbsp;Xiaohua Zhao,&nbsp;Ying Yao,&nbsp;Chen Chen,&nbsp;Qiang Fu,&nbsp;Yaowen Zhang","doi":"10.1016/j.aap.2025.108060","DOIUrl":"10.1016/j.aap.2025.108060","url":null,"abstract":"<div><div>Intelligent connected vehicle technology can provide drivers with connected vehicle information (CVI) to enhance traffic safety. However, the impact of CVI on safety in the case of foggy freeway beyond-visual-range conditions is currently unclear. As such, this paper introduces extreme value theory (EVT) to assess and quantify this impact. Specifically, this paper first adopted driving simulation technology to build a connected environment experimental platform. A typical foggy freeway beyond-visual-range scenario was developed, and driving simulation experiments were carried out to collect driving behavior data in both traditional environments (without CVI) and connected environments (with CVI). With this data, the peak over threshold method of EVT was used to establish generalized Pareto distribution fitting models for the four indicators of time to collision (TTC), modified time to collision (MTTC), post-encroachment time (PET), and deceleration rate to avoid a crash (DRAC), respectively, and model comparisons and selections were performed. The optimal models were then chosen for risk assessment and impact analysis, which includes both crash probability and crash damage dimensions. The results show that the DRAC-based EVT models have better data-fitting performance and higher reliability. Additionally, CVI is effective in reducing the crash risk of beyond-visual-range events on foggy freeways, and there is diversity in crash risk and the effectiveness of CVI application between different driving groups. The study in this paper further extends the EVT and also helps to better understand the action and influence mechanisms of CVI.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"217 ","pages":"Article 108060"},"PeriodicalIF":5.7,"publicationDate":"2025-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143874585","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":"Is control necessary for drivers? Exploring the influence of human–machine collaboration modes on driving behavior and subjective perception under different hazard visibility scenarios","authors":"Yongkang Chen ,&nbsp;Jianmin Wang ,&nbsp;Fusheng Jia ,&nbsp;Xingting Wu ,&nbsp;Qi Xiao ,&nbsp;Zhaodong Wang ,&nbsp;Fang You","doi":"10.1016/j.aap.2025.108067","DOIUrl":"10.1016/j.aap.2025.108067","url":null,"abstract":"<div><div>Before full achieving automation, Autonomous Vehicle(AV) must undergo a transitional phase of human–machine collaborative driving. Therefore, designing appropriate Human-Machine Interface (HMI) modes of collaboration is key to ensuring both driving safety and user experience. However, existing research has rarely considered the design of human–machine collaboration modes under different Hazard Visibility scenarios. In this study, we conducted a simulated driving experiment (N = 28) to explore the effects of three HMI-based collaboration modes (HMI1, HMI2, and HMI3) on driving behavior and subjective perception under two hazard visibility scenarios (visible and invisible hazard). The designs of the three collaboration modes were primarily based on varying levels of explainability and control. The results show that in the invisible hazard scenario, drivers exhibited significantly lower situation awareness and preference compared to the visible hazard scenario. The design of HMI in different collaboration modes significantly influences drivers’ situation awareness, cognitive workload, trust, and attention distribution, with the highest satisfaction reported for HMI2 (high explainability, AV-led decision-making). Particularly in the invisible hazard scenario, HMI2 significantly improved drivers’ situation awareness and attention while minimizing cognitive workload. The study also indicates that during autonomous driving, drivers require a certain sense of control, though this does not necessarily mean they need to directly participate in decision-making. Instead, a sense of control can be fostered by augmenting the explainability of the HMI. These findings provide valuable insights for the design of human–machine interfaces in AV to enhance driving safety.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"217 ","pages":"Article 108067"},"PeriodicalIF":5.7,"publicationDate":"2025-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143867850","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":"Short-term safety analysis and interdependencies of mixed-operation freeways with fully separated express lanes: A copula-based poisson lognormal lindley approach","authors":"Abdulrahman Faden ,&nbsp;Mohamed Abdel-Aty ,&nbsp;Chenzhu Wang ,&nbsp;Samgyu Yang","doi":"10.1016/j.aap.2025.108050","DOIUrl":"10.1016/j.aap.2025.108050","url":null,"abstract":"<div><div>The I-4 Ultimate Express Lanes (ELs) are part of a major improvement project on Interstate 4, introducing innovative freeway design that impacts traffic safety and operations. This research examines safety performance and the interdependencies between general-purpose lanes (GPLs) and (ELs). Utilizing crash data from February 2022 to February 2024, short-term crash frequency models are developed at the lane-level, leveraging microscopic traffic detector data and unique geometric design features to estimate annual average weekday crash frequencies and capture temporal safety variations. The study employs Poisson Lognormal Lindley (PLN-L) model to address excessive zeros in crash data, particularly prevalent in ELs, while copula-based framework analyzes the dependency between GPL and EL crash frequencies. Frank copula provided the best fit among five tested copula structures, revealing significant dependency, especially at access points. The analysis incorporates lane-level traffic characteristics, geometric data (e.g., standard and I-4 Ultimate specific segment types (interaction between ELs and GPLs at access points), their lengths, and ramp lengths), and time-period effects. Significant variables for GPL and EL segments include lane-level traffic exposure and other factors. Key findings indicate that the average lane-occupancy in the rightmost lane significantly impacts EL safety, with higher crash rates at merge segments. For GPLs, I-4 Ultimate segments and their shorter lengths (i.e., Long [5000ft &gt;= (length) &gt; 3000ft]) are associated with GPL-related crashes. These findings contribute to developing safer, more efficient managed lane systems and offer guidance for future freeway design improvements. The study gives policymakers and engineers insights into EL safety, design, and operation.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"217 ","pages":"Article 108050"},"PeriodicalIF":5.7,"publicationDate":"2025-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143868033","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":"MSIE-Transformer: A novel driving behavior modeling approach for virtual simulation test environment","authors":"Huihua Gao ,&nbsp;Ting Qu ,&nbsp;Xun Gong ,&nbsp;Ping Wang ,&nbsp;Hong Chen","doi":"10.1016/j.aap.2025.108039","DOIUrl":"10.1016/j.aap.2025.108039","url":null,"abstract":"<div><div>To build a natural driving environment in the virtual environment for autonomous driving safety testing, a digital twin model of human driving behavior is essential. However, due to the lack of fidelity and intelligence of the driving behavior model of the background vehicle, there is an intolerable gap between the virtual simulation test environment and the real road test environment. This paper proposes the Multi-source Information Encoding Transformer (MSIE-Transformer) to model driving behaviors of background vehicles within the virtual simulation environment. This approach improves model performance through the effective encoding of multi-source features using heterogeneous encoding networks, the comprehensive integration of these features based on the multi-head self-attention mechanism, and the combination of dynamic loss functions with Bayesian optimization. The experimental results demonstrate that, benefiting from the feature extraction and integration of multi-source information, the proposed method exhibits superior performance in fidelity compared to existing approaches. It also demonstrates good performance in statistical realism and modeling heterogeneous driving behaviors. In addition, the model’s performance is validated in multi-agent control scenarios and successfully transferred to intersection scenarios.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"217 ","pages":"Article 108039"},"PeriodicalIF":5.7,"publicationDate":"2025-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143864078","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":"Applying human factors analysis and classification system for commercial vehicles crashes investigation and critical failure routes analysis","authors":"Xuesong Wang ,&nbsp;Yanru Zhou ,&nbsp;Ashleigh Filtness ,&nbsp;Chao Wang ,&nbsp;Xiaowei Tang ,&nbsp;Shikun Liu ,&nbsp;Zhicheng Wang","doi":"10.1016/j.aap.2025.108047","DOIUrl":"10.1016/j.aap.2025.108047","url":null,"abstract":"<div><div>This study aims to develop a reliable and valid Human Factors Analysis and Classification System (HFACS) and Bayesian Network (BN) methodology to understand the causal factors of commercial vehicles (CMV) involved traffic crashes. The HFACS-CMV method has been established using learnings from 100 multi casualty crashes involving at least 10 fatalities. The extreme nature of such crashes ensures the existence of in-depth investigation reports which are necessary to generate sufficient data for HFACS analysis. By analyzing 100 road traffic investigation reports across 28 provinces in China from 2001 to 2021, the research employs odds ratio and BN which is able to quantitatively examine the relationships among contributing factors. The study identifies the highest frequencies of failures in establishing/implementing safety production systems, wrong responses to emergencies, and over speeding across four levels, 12 categories, and 53 sub-subcategories’ HAFCS-CMV framework. Numerous associations between the upper and adjacent lower levels are revealed, especially between poor company supervision and government oversight across various subcategories. The HFACS-CMV with BN model highlights a critical failure route: inadequate government oversight leading to poor company supervision or poorly planned operations, resulting in substandard operator conditions and unsafe acts. Identifying associations and failure routes is crucial for developing effective countermeasures. Specific outcomes are limited by the crash reports used which were not generated with HFACS in mind, future research using HFACS informed crash reporting systems would be beneficial. However, the successful application of the method demonstrates the efficiency and applicability of HFACS-CMV as a robust method for understanding causal factors of road crashes.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"217 ","pages":"Article 108047"},"PeriodicalIF":5.7,"publicationDate":"2025-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143864077","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":"Assessment of urban rail transit network passenger-centered resilience under hazards: A dynamic resilience assessment framework","authors":"Ying Lu ,&nbsp;Minmin Yi ,&nbsp;Jianqiang Cui ,&nbsp;Guangdong Wu ,&nbsp;Dong Lin","doi":"10.1016/j.aap.2025.108042","DOIUrl":"10.1016/j.aap.2025.108042","url":null,"abstract":"<div><div>Urban rail transit (URT) is vulnerable to cascading failures from disasters due to their complexity. In modern society, we should pay more attention to the experience of passengers and enhance the quality of service in URT. This paper develops a model using dynamic indicators to assess the disaster resilience of URT. It presents a novel passenger-centered resilience assessment method that reflects dynamic passenger flow demand indicators. A cascading failure model under Geometric Attack Model was established to simulate the route choice of passengers before and after cascading failure. This paper collected data on the Beijing subway, analyzed the features of dynamic passenger flow resilience indicators, and assessed the disaster resilience of the Beijing subway. Results show: (1) generalized travel time costs and transfer times for passengers increase after cascading failures, indicating reduced system resilience; and (2) two indicators are at moderately low resilience levels. This shows that passenger travel routes become more complicated after station failure, and the network’s ability to restore service and maintain service quality in the aftermath of unexpected events needs improvement. The paper suggests improving disaster resistance by adding redundant lines and other measures and also discusses adjusting assessment strategies based on the unique characteristics of different cities. A sensitivity analysis was conducted on the model, and based on this, guiding suggestions were made for optimization strategies.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"217 ","pages":"Article 108042"},"PeriodicalIF":5.7,"publicationDate":"2025-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143858905","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 unified risk metric for freeway vehicles based on the spatio-temporal overlap probability of predicted positions","authors":"Chuang Cui,&nbsp;Bocheng An,&nbsp;Linheng Li,&nbsp;Xu Qu,&nbsp;Wenquan Li","doi":"10.1016/j.aap.2025.108059","DOIUrl":"10.1016/j.aap.2025.108059","url":null,"abstract":"<div><div>Surrogate Safety Measures (SSMs) are widely used to quantify driving risks and support proactive traffic safety management. However, the existing metrics primarily focus on single scenarios, such as rear-end collisions in car-following situations. These metrics assume that the vehicles comply with specific motion equations, overlooking the uncertainties intrinsic to vehicle operation. To address these limitations, this study proposes a novel metric, the Risk Metric based on Predicted Position (RMPP), which evaluates the probability of collision. RMPP is designed to comprehensively capture all risk within a unified framework by incorporating the future positional distributions of vehicles. Firstly, the target vehicle and surrounding vehicles are constructed as a graph structure. The Generalized Dynamic Graph Convolutional Network (GDGCN) is used to predict the position distribution of vehicles. Then, spatial proximity risk and temporal proximity risk are computed based on the predicted positions. Spatial proximity risk is the sum of probabilities of predicted position overlap at the same time, while temporal proximity risk is the sum of probabilities of predicted position overlap at different times. RMPP is obtained through a weighted summation of these two risks. To validate the effectiveness of RMPP, we conducted experimental analyses using the Freeway B of CitySim dataset, which is collected in an Asian region using drones. We compared the prediction results of our GDGCN model with several baseline models. The experimental results demonstrated that our GDGCN model achieved good prediction accuracy. Additionally, using vehicle trajectories from CitySim, we compared RMPP with traditional metrics such as Time-to-Collision (TTC), Deceleration Rate to Avoid Collision (DRAC), and Safety Margin (SM) across both basic and high-risk driving scenarios. The results indicate that RMPP more accurately captures risk characteristics that align with real-world driving conditions. Furthermore, we evaluated the impact of prediction accuracy on RMPP by analyzing risk variations under different RMSE values. When the position prediction accuracy of both vehicles reaches a certain level, its impact on risk is small, confirming the reliability and stability of RMPP. With the advancement of technology and the improvement of prediction accuracy, the precision of RMPP will be further enhanced, making it a robust and powerful tool for traffic safety management.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"217 ","pages":"Article 108059"},"PeriodicalIF":5.7,"publicationDate":"2025-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143854545","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":"Perceived vs. actual multitasking abilities: Predicting texting while driving efficacy and behavior from overconfidence","authors":"Claire E. Bowman-Callaway,&nbsp;Benjamin D. Schulte,&nbsp;Stephanie C. Payne","doi":"10.1016/j.aap.2025.108058","DOIUrl":"10.1016/j.aap.2025.108058","url":null,"abstract":"<div><div>Whereas numerous studies have reported drivers’ overconfidence in their driving ability, this study examines overconfidence in one’s multitasking abilities operationalized as overestimation (perception relative to one’s actual performance) and overplacement (perception relative to others’ abilities) as predictors of texting while driving (TWD). This study also examines TWD self-efficacy as an explanatory mechanism for the relationship between overconfidence and TWD. A sample of 611 undergraduate students (34 % male, mean age of 19.52 years) from a southwestern US university completed an online task-switching paradigm to assess their multitasking ability and multiple self-report measures of TWD-related constructs. TWD was also measured using phone application data. Results indicated that overconfidence (both overestimation and overplacement) was more strongly related to TWD self-efficacy than self-efficacy to resist TWD. TWD self-efficacy explained the relationships between overconfidence and TWD. Additionally, TWD self-efficacy predicted self-reported and actual TWD above and beyond self-efficacy to resist TWD and vice versa. Actual multitasking ability was not significantly related to actual or self-reported TWD. Overall, these findings provide evidence for the influence of overconfidence in multitasking and two forms of self-efficacy on TWD. Implications as well as future directions for research are discussed.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"217 ","pages":"Article 108058"},"PeriodicalIF":5.7,"publicationDate":"2025-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143851383","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}