Accident; analysis and prevention最新文献

{"title":"Assessment of the collision risk on the road around schools during morning peak period","authors":"Xiaojian Hu ,&nbsp;Haoran Deng ,&nbsp;Huasheng Liu ,&nbsp;Jiayi Zhou ,&nbsp;Hongyu Liang ,&nbsp;Long Chen ,&nbsp;Li Zhang","doi":"10.1016/j.aap.2024.107854","DOIUrl":"10.1016/j.aap.2024.107854","url":null,"abstract":"<div><div>Road traffic injury is a leading cause of death among pupils worldwide, particularly around primary schools during rush hours, where heavy traffic, frequent parking, and unpredictable patterns increase accident risk. To mitigate these risks, this study employs the peak-over-threshold method with the generalized pareto distribution to evaluate the spatial–temporal collision risk near primary schools during rush hours. Specifically, the research quantifies collision risks spatially across different road segments (upstream, midstream, and downstream) and lanes (outside, middle, and inside). Temporally, it assesses risks during vehicle gathering, peak vehicle concentration, and vehicle dissipation phases. Results show that collision risk decreases from upstream to downstream but increases from the outside lane to the inside lane. Moreover, collision risks are highest in the middle and outside lanes during the gathering and peak periods in upstream and midstream sections, and in the middle lanes during the dissipation phase. These findings recommend adding parking spaces, minimizing lane changes, reducing speed limits in upstream and midstream, and increasing speed limits in downstream and inside lanes. These measures aim to improve road traffic management policies around schools.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"210 ","pages":"Article 107854"},"PeriodicalIF":5.7,"publicationDate":"2024-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142708941","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":"Gender disparities in rural motorcycle accidents: A neural network analysis of travel behavior impact","authors":"Ittirit Mohamad","doi":"10.1016/j.aap.2024.107840","DOIUrl":"10.1016/j.aap.2024.107840","url":null,"abstract":"<div><div>Rural road accidents involving motorcycle riders present a formidable challenge to road safety globally. This study offers a comprehensive gender-based comparative analysis of rural road accidents among motorcycle riders, aimed at illuminating factors contributing to accidents and discerning potential gender disparities in accident rates and severity. Employing a sophisticated Neural Network approach, the research delves into the intricate relationship between various variables and accident outcomes, with a specific emphasis on identifying gender-specific patterns. For female riders, the ANN model demonstrates impressive overall accuracy (CA) of 92 %, indicating its capability to correctly classify accident outcomes. Precision, which measures the model’s ability to avoid false positives, stands at a commendable 90.8 %. Moreover, the model exhibits high recall (92 %) and F1 score (88.4 %), indicating its effectiveness in identifying both fatal and non-fatal accidents among female riders. Additionally, the Matthews Correlation Coefficient (MCC) of 0.132 suggests a moderate level of agreement between the predicted and actual outcomes. Upon further examination, it is evident that the model performs exceptionally well in predicting non-fatal accidents for female riders, achieving a precision, recall, and F1 score of 92 %, 99.9 %, and 95.8 %, respectively. However, its performance in predicting fatalities is relatively lower, with a precision of 75.6 % and recall of 2.6 %, resulting in a lower F1 score of 5.0 %. Despite this disparity, the MCC remains consistent at 0.132, indicating a balanced performance across both classes. The findings reveal valuable insights for policymakers and road safety practitioners, providing avenues for the development of targeted interventions and the enhancement of safety measures for motorcycle riders on rural roads. By addressing the gap in understanding gender-related differences in travel habits and accident risks, this research contributes to ongoing efforts to mitigate the impact of road accidents and promote safer travel environments for all road users.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"210 ","pages":"Article 107840"},"PeriodicalIF":5.7,"publicationDate":"2024-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142695121","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":"One-pedal or two-pedal: Does the regenerative braking system improve driving safety?","authors":"Jun Ma ,&nbsp;Xu Zhang ,&nbsp;Wenxia Xu ,&nbsp;Jiateng Li ,&nbsp;Zaiyan Gong ,&nbsp;Jingyi Zhao","doi":"10.1016/j.aap.2024.107832","DOIUrl":"10.1016/j.aap.2024.107832","url":null,"abstract":"<div><div>Electric vehicles equipped with regenerative braking systems provide drivers a new driving mode, the one-pedal mode, which enables drivers to accelerate and decelerate with the throttle alone. However, there is a lack of systematic research on driving behavior in one-pedal mode, and whether it actually enhances or reduces safety remains to be validated. A driving simulator was used to analyze driving behavior and safety in the one-pedal mode in situations with different urgency level, with the two-pedal mode (the traditional driving mode in internal combustion engine vehicles) serving as a comparative group. The driver’s perception times, initial and final throttle release times, throttle to brake transition times, maximum brake pedal forces, collision ratios, and time-to-collision (TTC) were measured under the lead vehicle decelerating at 0.1 g, 0.2 g, 0.5 g, 0.75 g, as well as uncertainty (decelerating at 0.2 g to 25 km/h, then decelerating at 0.75 g to 0), and under headways of 1.5 s and 2.5 s. Results showed: 1) The regenerative braking system did not affect driver perception and reaction of the lead vehicle braking event and drivers extended throttle release to avoid rapid speed drops when the lead vehicle braked slowly; 2) the one-pedal mode exhibited a longer throttle to brake transition time and increased uncertainty in timing of brake pedal application; 3) the one-pedal mode was safer than the two-pedal mode in low urgency situations but became unsafe in high urgency or uncertain situations due to delayed braking. The implications of this research include enhancing regenerative braking systems and developing forward collision warning systems.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"210 ","pages":"Article 107832"},"PeriodicalIF":5.7,"publicationDate":"2024-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142692537","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":"Driving characteristics of static obstacle avoidance by drivers in mountain highway tunnels − A lateral safety distance judgement","authors":"Ying Chen ,&nbsp;Zhigang Du ,&nbsp;Jin Xu ,&nbsp;Shuang Luo","doi":"10.1016/j.aap.2024.107845","DOIUrl":"10.1016/j.aap.2024.107845","url":null,"abstract":"<div><div>Static obstacles (tunnel sidewalls, barricades, etc.) on the side of mountainous highways change the spatial range of the road during driving, restricting the driver’s freedom of driving while possibly triggering the driver’s shy away effect, which poses a specific potential safety hazard. To understand the characteristics of driving behaviour in mountain highway tunnels with different tunnel lengths and lateral obstacles, nine tunnels in Chongqing were selected for real-vehicle tests, and data on driving trajectories, speeds and other metrics were collected from 40 drivers. Analyse the driver’s need for lateral safety distance in different scenarios, defines the conditions and scope of the shy away effect, and establishes a multi-scenario “distance-trajectory” offset prediction model to adjust the offset under varying lateral environments by setting different facilities. The results show that drivers exhibit some avoidance behavior towards lateral static obstacles, but the extent of the shy-away effect varies based on tunnel length. By widening the lateral clearance to 0.925 m on the left side and 1.450 m on the right side of the road to meet the driver’s requirements for lateral safety distances, unreasonable avoidance behaviour can be reduced. Combined with the trajectory fluctuation characteristics of drivers in different tunnels, it is proposed to set up the traffic safety facilities in a manner more aligned with driver behavioral habits, with a place set up 110 m before the entrance of the short tunnel, two places set up in the medium tunnel at <em>L</em>/2 − 200 m, <em>L</em>/2 + 100 m (where <em>L</em> is the length of the tunnel), and three places for long tunnels at <em>L</em>/2 − 400 m, <em>L</em>/2 m, and <em>L</em>/2 + 300 m. For extra-long tunnels, facilities are to be set up in cycles of 500 m, 1000 m, and 1500 m intervals. In the cross-section where different drivers are prone to apparent trajectory offsets, a driving behavior prompt sign is added to help correct the driving trajectory.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"210 ","pages":"Article 107845"},"PeriodicalIF":5.7,"publicationDate":"2024-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142685734","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":"Revisiting the correlation between simulated and field-observed conflicts using large-scale traffic reconstruction","authors":"Ao Qu,&nbsp;Cathy Wu","doi":"10.1016/j.aap.2024.107808","DOIUrl":"10.1016/j.aap.2024.107808","url":null,"abstract":"<div><div>Safety is a critical aspect of traffic systems. However, traditional crash data-based methods suffer from scalability and generalization issues. Although SSMs offer a proactive alternative for safety evaluation, their validation in simulated settings remains inconsistent, especially with emerging mobility technologies like autonomous driving. Our study critiques existing methodologies in SSM validation and introduces a novel framework integrating micro-level driver models with macro-level traffic states. This approach accounts for diverse external factors, including weather and geographical variations. Utilizing the Caltrans Performance Measurement System (PeMS) data, we conduct a large-scale analysis, merging traffic simulation with real-world data to extract SSMs and correlate them with crash statistics. Our results indicate a significant correlation between SSM counts and crash numbers but no clear trend with varying SSM thresholds. This suggests limitations in current public data for establishing robust links between simulated SSMs and real-world crashes. Our study highlights the need for improved data collection and simulation techniques, paving the way for more accurate and meaningful roadway safety analysis in the era of advanced mobility systems.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"210 ","pages":"Article 107808"},"PeriodicalIF":5.7,"publicationDate":"2024-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142685735","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":"V-FCW: Vector-based forward collision warning algorithm for curved road conflicts using V2X networks","authors":"Xiangpeng Cai ,&nbsp;Bowen Lv ,&nbsp;Hanchen Yao ,&nbsp;Ting Yang ,&nbsp;Houde Dai","doi":"10.1016/j.aap.2024.107836","DOIUrl":"10.1016/j.aap.2024.107836","url":null,"abstract":"<div><div>The implementation of advanced driver assistance systems (ADAS) has significantly impacted the prevention of traffic accidents, particularly through the forward collision warning (FCW) algorithm. Nevertheless, traffic conflicts on traffic routes remain a significant issue, since most FCW algorithms cannot accurately determine the distance between the host vehicle (HV) and remote vehicle (RV) on curved roads. Hence, this study proposes a vector-based FCW (V-FCW) algorithm to address the issue of false warnings on unconventional road sections. The V-FCW algorithm employs vector relationships to estimate the poses of HV and RV at the current and next moments, thereby effectively calculating the relative angles. Firstly, the HV and RV transmit their position vector, velocity vector, and heading angle in real time via the vehicle-to-vehicle (V2V) communication technique. Subsequently, the localization of lanes is conducted through the vehicle-to-infrastructure (V2I) communication technique, with the assistance of roadside unit (RSU)-based local maps. Finally, a V-FCW algorithm was implemented on the Simcenter Prescan simulation platform and a cellular vehicle-to-everything (C-V2X, i.e., the combination of V2V and V2I) communication platform. The simulation results demonstrate that the proposed V-FCW algorithm can accurately identify and warn dangerous vehicles on both straight and curved roads. Moreover, the experimental results obtained from the hardware-in-the-loop approach illustrate the efficacy of the proposed V-FCW algorithm in accurately forecasting four warning levels on both straight and curved roads. Consequently, this study yields a significant contribution to the field of vehicle-road cooperation in C-V2X-enable intelligent driving.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"210 ","pages":"Article 107836"},"PeriodicalIF":5.7,"publicationDate":"2024-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142685736","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":"Detection and analysis of corner case scenarios at a signalized urban intersection","authors":"Clemens Schicktanz ,&nbsp;Kay Gimm","doi":"10.1016/j.aap.2024.107838","DOIUrl":"10.1016/j.aap.2024.107838","url":null,"abstract":"<div><div>One of the major challenges in automated driving is ensuring that the system can handle all possible driving scenarios, including rare and critical ones, also referred to as corner case scenarios. For the validation of automated driving functions, it is necessary to test the corner cases in simulation environments. However, the effectiveness of simulation-based testing depends on the availability of realistic test data that accurately reflect real-world scenarios. This work aims to detect, cluster, and analyze rare and critical traffic scenarios based on real-world traffic data from an urban intersection and prepare the data for usage in simulation environments. The scenarios are detected by filtering hard braking maneuvers, red light violations, and near misses under adverse weather conditions. A long-term analysis of trajectory, weather, and traffic light data was conducted to find these rare scenarios. Our results show that 24 hard braking maneuvers are included in our dataset with a duration of half a year. They occur due to failure to yield, emergency vehicle operations, and a red light violation. Some of the scenarios include crashes, lateral evasive maneuvers, or are under adverse weather conditions like fog. Altogether, we provide methods to extract corner case scenarios based on multiple data sources and reveal diverse types of corner case scenarios at an urban intersection. In addition, we analyze the behavior of road users in critical scenarios and show influencing factors to avoid crashes. By combining and converting the data to an industry standard for simulation we provide realistic test cases for the validation of automated vehicles. Therefore, the results are relevant for both, traffic safety researchers to learn from road user behavior in these rare scenarios and developers of automated driving systems to test their functions.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"210 ","pages":"Article 107838"},"PeriodicalIF":5.7,"publicationDate":"2024-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142685733","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":"Decision-making of autonomous vehicles in interactions with jaywalkers: A risk-aware deep reinforcement learning approach","authors":"Ziqian Zhang ,&nbsp;Haojie Li ,&nbsp;Tiantian Chen ,&nbsp;N.N. Sze ,&nbsp;Wenzhang Yang ,&nbsp;Yihao Zhang ,&nbsp;Gang Ren","doi":"10.1016/j.aap.2024.107843","DOIUrl":"10.1016/j.aap.2024.107843","url":null,"abstract":"<div><div>Jaywalking, as a hazardous crossing behavior, leaves little time for drivers to anticipate and respond promptly, resulting in high crossing risks. The prevalence of Autonomous Vehicle (AV) technologies has offered new solutions for mitigating jaywalking risks. In this study, we propose a risk-aware deep reinforcement learning (DRL) approach for AVs to make decisions safely and efficiently in jaywalker-vehicle interactions. Notably, a risk prediction module is incorporated into the traditional DRL framework, making the AV agent risk-aware. Considering the complexity of jaywalker-vehicle conflicts, an encoder-decoder model is adopted as the risk prediction module, which comprehensively integrates multi-source data and predicts probabilities of the final conflict severity levels. The risk-aware DRL approach is applied in a simulated environment established in Anylogic, where the motion features of jaywalkers and vehicles are calibrated using real-world survey data.</div><div>The trained driving policies are evaluated from perspectives of safety and efficiency across three scenarios with escalading levels of jaywalker volume. Regarding safety performance, the <em>Baseline</em> policy performs the worst in “medium jaywalker volume” scenario and “high jaywalker volume” scenario, while our <em>Proposed risk-aware</em> method outperforms the other methods, with the “low TTC ratio” metric stabilizing near 0.08. Moreover, as the scenario gets more complex, the superiority of our <em>Proposed risk-aware</em> policy gets more evident. In terms of efficiency performance, our <em>Proposed risk-aware</em> policy ranks the second best, achieving an “AV delay” metric around 8.1 s in the “medium jaywalker volume” scenario and 8.5 s in the “high jaywalker volume” scenario. In practice, the proposed risk-aware DRL approach can help AV agents perceive potential risks in advance and navigate through potential jaywalking areas safely and efficiently, further enhancing pedestrian safety.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"210 ","pages":"Article 107843"},"PeriodicalIF":5.7,"publicationDate":"2024-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142680178","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":"Why they take the risk to perform a direct left turn at intersections: A data-driven framework for cyclist violation modeling","authors":"Hui Bi ,&nbsp;Xuejun Zhang ,&nbsp;Weiwei Zhu ,&nbsp;Hui Gao ,&nbsp;Zhirui Ye","doi":"10.1016/j.aap.2024.107846","DOIUrl":"10.1016/j.aap.2024.107846","url":null,"abstract":"<div><div>Bicycle crashes at intersection areas are posed a worrying traffic safety issue, and one of the main reasons for bicycle crashes is failing to avoid conflicts with motor vehicles and other bicycles. Clearly, cyclists are more exposed to risk if they perform a direct left turn (DLT) being mixed with left-turning vehicle under a left-turn phase. Owing to the lack of exposure data, the detection of DLT event and the mechanism behind the risky riding behavior have yet to be discovered. To bridge these gaps, this study proposes a DLT detection framework based on bike sharing trajectories. Moreover, this study seeks to understand the contributing factors to DLT behavior using the random parameters logit model with heterogeneity in means and variances (RPLHMV) to account for unobserved heterogeneity in the DLT cases dataset. Statistical analysis shows that DLT is most likely to occur on weekdays during peak periods under large commuting demand. As to what caused the DLT violations, law-obeying cyclists are more susceptible to external events, while risk-taking cyclists are subtly undermined by their habits. In addition, the model of RPLHMV reveals several significant contributing factors to the propensity of DLT violations, such as event time, available passing time for left-turning bicycles, and average cycling speed, whereas the indicator variables of actual waiting time, available passing space for left-turning bicycles, and preference for DLT violation become the emerging influential variables. This study is expected to help better understand DLT occurrence and propose countermeasures more efficiently for reducing cyclists’ DLT rate.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"209 ","pages":"Article 107846"},"PeriodicalIF":5.7,"publicationDate":"2024-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142674737","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":"Assessing the safety impacts of winter road maintenance operations using connected vehicle data","authors":"Minsoo Oh,&nbsp;Jing Dong-O’Brien","doi":"10.1016/j.aap.2024.107837","DOIUrl":"10.1016/j.aap.2024.107837","url":null,"abstract":"<div><div>This paper investigates the impacts of winter maintenance operations (WMO) on road safety under different weather conditions using connected vehicle data. In particular, the impacts of WMO on incident-induced delays (IID) and harsh braking events are highlighted, representing the influence on traffic flow and vehicle stability, respectively. Taking advantage of emerging connected vehicle data, the impacts of WMO on IIDs and vehicle harsh braking events are estimated. Data analysis revealed that WMO plays an important role in reducing the mean IID and the average number of harsh braking events, particularly when roads were covered with ice, frost, slush, or snow in snowy weather. The presence of WMO reduced the mean IID from 145.93 veh-h to 57.70 veh-h, representing a 60% decrease, and the number of harsh braking events from 3.58 cases per crash to 2.90 cases per crash, making a 19% reduction. Last, the multiple linear regression (MLR) model highlights that WMO effectively reduces IID by 23.36 veh-h. In addition, the MLR model indicates that IID is influenced by traffic volume, driving behaviors immediately before a crash, crash severity, road weather conditions, with more severe crashes and worse pavement conditions contributing to longer delays. These findings suggest that the WMO can improve road safety by reducing incident-induced delays and improving traffic stability in winter weather conditions.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"209 ","pages":"Article 107837"},"PeriodicalIF":5.7,"publicationDate":"2024-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142646745","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}