Accident; analysis and prevention最新文献

{"title":"Does visual-audio feedback impair response performance and increase workload? using detection response task and NASA-TLX to examine the effect of the HMI information on driver performance at unsignalized intersections","authors":"Yunjie Ju,&nbsp;Feng Chen,&nbsp;Xiaonan Li,&nbsp;Hailin Shi","doi":"10.1016/j.aap.2025.107972","DOIUrl":"10.1016/j.aap.2025.107972","url":null,"abstract":"<div><div>The in-vehicle HMI systems regulate driving behavior by providing advisory or warning information to the driver, contributing to improved safety, reduced fuel consumption, and lower emissions. Although the issue of driver performance changes caused by HMI systems has received substantial recent attention, the implications on drivers’ workload has not received enough attention. Additionally, most previous studies provided classic visual, auditory or concurrent visual-audio feedback information but failed to determine whether the additional information resulted in workload overload, and lacked the quantitative analysis of response performance in various conflict environments. Toward to this end, this paper conducted a driving simulator experiment to examine the response performance and workload differences in the unsignalized intersection-approach process of drivers with various HMI system and conflict conditions. More precisely, an effect analysis on the drivers’ workload (response time of the DRT, DRT accuracy, and the NASA-TLX) was conducted, the Weibull AFT model with gamma heterogeneity and rANOVA method were applied. The Weibull AFT model estimation revealed the mixed effects of HMI system conditions in the drivers’ response time. In conflict situations, drivers with comprehensive visual-audio information responded earlier to DRT and lower workload. In addition, the variables for personal characteristics, safe driving history, and experience and willingness to use HMI system significantly influenced the response time of driver, among which female driver group performed longer response time. The results of DRT accuracy and NASA-TLX, drivers with comprehensive visual-audio information have excellent situation awareness when approaching and passing the unsignalized intersections; drivers believed they can accomplish performance level with less effort than others; they felt less time pressure and the driving pace was pace slow and relatively leisurely; there were fewer negative emotions such as insecure, discouraged, irritated, stressed, and annoyed. Thus, the key is whether a comprehensive situation awareness can be established for the driver, rather than purely reducing or adding additional information. The findings of this paper provide a theoretical basis for the human–machine interaction interface design and development of in-vehicle decision-making assistance systems for unsignalized intersections.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"214 ","pages":"Article 107972"},"PeriodicalIF":5.7,"publicationDate":"2025-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143453469","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 semi-parameter copula model for vehicle damage severity in lane-changing related crashes","authors":"Ruifeng Gu ,&nbsp;Penglin Song ,&nbsp;N.N. Sze ,&nbsp;Zijin Wang ,&nbsp;Mohamed Abdel-Aty","doi":"10.1016/j.aap.2025.107979","DOIUrl":"10.1016/j.aap.2025.107979","url":null,"abstract":"<div><div>Lane changing behaviour occurs frequently on the highways. However, it also poses a major impact on traffic operation and safety since complex interactions between two or more vehicles on different traffic lanes are involved. In the lane-changing related crashes, correlation in damage level among the vehicles involved is prevalent. To this end, a copula approach is proposed to model the vehicle damage level of lane-changing related crash, with which the dependency between lane-changing and lane-keeping vehicles is accounted for. Additionally, a semi-parameter estimation approach is adopted to address the problem of heterogeneous data structure. In this study, crash data from Orlando City of Florida during the period between 2016 and 2019 are used. Then, the semi-parameter copula-based ordered logit models are estimated to measure the association between road environment, vehicle attributes, driver characteristics, crash circumstances, and vehicle damage level of two-vehicle lane-changing related crashes. Results indicate that there are major discrepancies in the influences of possible factors on vehicle damage level between lane-changing and lane-keeping vehicles. Furthermore, non-linear relationships between vehicle damage level, driver age, and time of crash are also revealed.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"214 ","pages":"Article 107979"},"PeriodicalIF":5.7,"publicationDate":"2025-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143445021","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 design and system approaches for analyzing road traffic collisions in low-income settings","authors":"Khondhaker Al Momin ,&nbsp;Omar Faruqe Hamim ,&nbsp;Md. Shamsul Hoque ,&nbsp;Rich C. McIlroy","doi":"10.1016/j.aap.2025.107965","DOIUrl":"10.1016/j.aap.2025.107965","url":null,"abstract":"<div><div>Road crashes have become a critical issue in low- and middle-income countries (LMICs), where complex social and systemic factors exacerbate road safety challenges. This study combines Systems-Theoretic Accident Model and Processes-Systems Theoretic Process Analysis (STAMP-STPA) and Design with Intent (DwI), aiming to address both systemic and design-based factors, providing a comprehensive framework for enhancing road safety interventions in LMICs. A case study of a head-on collision in Bangladesh is analyzed using STAMP-STPA and DwI independently where the former identifies control and feedback failures, and the latter generates creative design solutions for road safety improvements. The design ideas generated in DwI workshops are analyzed using natural language processing (NLP) techniques, including topic modeling and bigram analysis, to extract key themes and reveal frequent word pairs for deeper insights. The STAMP-STPA analysis revealed critical systemic vulnerabilities related to road safety policies, vehicle standards, intersection design, and enforcement practices, which aided in proposing ten countermeasures across system levels. The DwI workshop yielded additional design ideas emphasizing infrastructure improvements, driver education, and technological solutions. NLP analysis of workshop data identified four key themes and prominent bigrams, highlighting critical areas for targeted interventions. By combining STAMP-STPA’s systematic analysis with DwI’s participatory, design-focused ideation, stakeholders can identify and implement countermeasures addressing both infrastructural and human factors to enhance road safety. Although this framework, tailored to LMIC contexts, demonstrates its applicability to a real-world case in Bangladesh, future research could expand it by incorporating multiple cases from various LMICs to enhance the generalizability of the findings.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"214 ","pages":"Article 107965"},"PeriodicalIF":5.7,"publicationDate":"2025-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143445022","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 personalized human-machine shared driving system: A case study of obstacle avoidance","authors":"Zhiyao Pan ,&nbsp;Hongyu Zheng","doi":"10.1016/j.aap.2025.107961","DOIUrl":"10.1016/j.aap.2025.107961","url":null,"abstract":"<div><div>This paper presents a personalized human–machine shared driving (HMSD) system aimed at aiding drivers in obstacle avoidance path planning and tracking based on driving styles. Initially, a driver-in-the-loop experimental bench is utilized to collect the driver characteristic data. Then, the collected data undergoes normalization and clustering to quantify driving styles. Furthermore, a personalized path planning approach is developed to enhance driving safety, vehicle stability, and traffic efficiency. Finally, the proposed system is validated using the driver-in-the-loop experimental setup and a questionnaire survey. Results show the system’s capability to provide personalized assistance, reduce driving load, and make driver-vehicle interaction more reliable and smoother, garnering high satisfaction. Notably, the study reveals significant individual differences in the perception of the system’s effectiveness and trustworthiness. Aggressive drivers, confident in their driving abilities, display the lowest level of trust in the system. Conversely, cautious drivers perceive the system as reliable assistance, leading to the highest level of trust. Moderates maintain a modest level of trust, seeking a balance. These findings provide valuable insights for developing intelligent vehicle human–machine interaction systems, suggesting that customization to individual driving styles can significantly improve system acceptance and effectiveness.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"214 ","pages":"Article 107961"},"PeriodicalIF":5.7,"publicationDate":"2025-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143453468","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":"Simulation of human–vehicle interaction at right-turn unsignalized intersections: A game-theoretic deep maximum entropy inverse reinforcement learning method","authors":"Wenli Li ,&nbsp;Xianglong Li ,&nbsp;Lingxi Li ,&nbsp;Yuanhang Tang ,&nbsp;Yuanzhi Hu","doi":"10.1016/j.aap.2025.107960","DOIUrl":"10.1016/j.aap.2025.107960","url":null,"abstract":"<div><div>The safety of pedestrians in urban transportation systems has emerged as a significant research topic. As a vulnerable group within this transportation framework, pedestrians encounter heightened safety risks in complex urban road environments. Protecting this group and safeguarding their rights and interests in urban transportation has garnered attention from academia and industry. The objective of this study is to develop a reliable simulation model that represents pedestrian crossing behavior at unsignalized crosswalks. A data-driven human–vehicle interaction behavior modeling framework is proposed, describing the human–vehicle interaction process at right-turning unsignalized intersections as a standard Markov decision-making process. In this framework, pedestrians are treated as the primary agents, and human–vehicle interactions are described using game theory. The Deep Maximum Entropy Inverse Reinforcement Learning (DMIRL) approach, combined with game theory, is employed to identify a reward function that encapsulates these interactions. The Deep Q-network (DQN) algorithm is then designed to simulate pedestrian crossing behavior based on the derived reward function. Finally, a comparison with a baseline algorithm that does not account for the game dynamics validates the proposed framework’s effectiveness and feasibility.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"214 ","pages":"Article 107960"},"PeriodicalIF":5.7,"publicationDate":"2025-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143437488","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":"An analysis of bus drivers’ interactions with motorists","authors":"Rodrigo Mora ,&nbsp;Natan Waintrub ,&nbsp;Cristhian Figueroa-Martínez","doi":"10.1016/j.aap.2025.107964","DOIUrl":"10.1016/j.aap.2025.107964","url":null,"abstract":"<div><div>Driving a bus in the city is a task that demands attention to changing road conditions while dealing with passengers’ needs. Bus drivers often experience aggression from passengers and other road users, which sometimes escalate, eventually leading to violence. However, many road conflicts are rarely reported and, hence, difficult to study. This article analyses bus drivers’ reactions to conflicts with other motorists in Santiago (Chile), where public transport accounts for 35% of the total trips. Four percent of the population of bus drivers (639) responded to a survey with questions about job satisfaction, the bus route, and experiences of conflicts and accidents with other vehicles. A Mixed Logit model was estimated to explore bus drivers’ reactions to a conflict with motorised vehicles. The results show that bus driver’s most frequent reaction is ignoring and carrying on. If a conflict escalates, bus drivers display aggressive reactions such as violently blowing the horn, insult louder, chucking the bus onto the other vehicle, overtake the vehicle off, or start a fist fight. Being a bus driver with no previous experience of violence with other public transport buses decreases the likelihood of violent reactions, while the opposite occurs with young drivers. Interestingly, the chances of having violent response increase when the driver was female. Public policies should start paying attention to the series of minor conflicts endured by bus drivers in their routines. These conflicts often are not reported to the authorities but nonetheless, exacerbate the chronic stress that bus drivers experience.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"214 ","pages":"Article 107964"},"PeriodicalIF":5.7,"publicationDate":"2025-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143437375","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":"Generation of critical pedestrian scenarios for autonomous vehicle testing","authors":"Lan Yang ,&nbsp;Songyan Liu ,&nbsp;Shuo Feng ,&nbsp;Hong Wang ,&nbsp;Xiangmo Zhao ,&nbsp;Guangyue Qu ,&nbsp;Shan Fang","doi":"10.1016/j.aap.2025.107962","DOIUrl":"10.1016/j.aap.2025.107962","url":null,"abstract":"<div><div>Current autonomous vehicle (AV) testing scenarios predominantly focus on interactions between AV and surrounding vehicles, with limited consideration given to high-risk pedestrian interactions. This paper presents a method for generating critical test scenarios specifically designed for pedestrian-oriented evaluations. First, microscopic traffic data were collected from 12 signalised intersections in 4 cities across China. By extracting overlapping vehicle and pedestrian trajectory data within the same spatiotemporal context, a vehicle–pedestrian interaction scenario library was created. Second, a three-stage autonomous emergency braking model was used to simulate the decision-making and control processes of AV, replacing the vehicle agency in the original scenario library. In addition, the artificial potential field method was applied to assess real-time interaction risks, enabling the identification of high-risk scenarios. A pedestrian-oriented critical test scenario generation framework was then developed, defining key decision variables such as speed differences, relative lateral distances, and relative longitudinal distances between pedestrians and vehicles. An importance sampling function, incorporating both scenario exposure frequency and interaction risk, was designed to generate critical scenarios. The process was further refined with an auxiliary objective function to guide the search direction. To improve computational efficiency, swarm optimisation and flood-fill algorithms were employed. Using this method, 50 high-value vehicle–pedestrian interaction test scenarios, characterised by high exposure frequency and risk, were generated. These scenarios encompass diverse and high-risk interaction dynamics, providing robust support for high-fidelity pedestrian safety testing of AV.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"214 ","pages":"Article 107962"},"PeriodicalIF":5.7,"publicationDate":"2025-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143437487","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 deep transfer learning approach for Real-Time traffic conflict prediction with trajectory data","authors":"Qinzhong Hou,&nbsp;Yonghao Yang,&nbsp;Jiatong Liang,&nbsp;Xiaoyan Huo,&nbsp;Junqiang Leng","doi":"10.1016/j.aap.2025.107966","DOIUrl":"10.1016/j.aap.2025.107966","url":null,"abstract":"<div><div>Recently, real-time traffic conflict prediction has drawn increasing attention due to its significant potential in proactive traffic safety systems. While various statistical and machine learning models have been developed for conflict prediction, transferability remains a fundamental issue across these models. Specifically, the predictive performance of a real-time conflict prediction model developed for a specific location can significantly decline when directly applied to a new location without any modifications, primarily due to substantial differences in traffic environments between these areas. To address this gap, this study proposed a novel deep transfer learning approach aimed at enhancing the transferability of real-time conflict prediction models. Initially, a real-time conflict prediction framework was designed utilizing trajectory data for merging areas with consideration of temporal variations in traffic flow characteristics. Subsequently, the Gated-Transformer, Fully Convolutional Networks (FCN), Long Short-Term Memory Fully Convolutional Networks (LSTM-FCN), and Multivariate Long Short-Term Memory Fully Convolutional Networks (MLSTM-FCN) were employed as backbone feature extraction networks to capture the hidden correlations between time-varying traffic flow characteristics and traffic conflicts. After that, an independent transfer learning architecture was established to assess the similarity of the distribution of traffic flow characteristics at different locations, based on the maximum mean discrepancy criteria. For empirical evaluation, merging areas from the exiD dataset were differentiated into source and target domains. The results demonstrated that the Gated-Transformer model outperforms other baseline models (FCN, LSTM–FCN and MLSTM–FCN) in both feature extraction and predictive performance, achieving an F1 score of 0.864 and an area under the curve (AUC) of 0.980. Furthermore, the transfer learning architecture can substantially enhance the predictive performance of a model trained in the source domain when applied to the target domain. In particular, the F1 score and AUC for the Gated-Transformer model improved by 11.9% and 10.2%, respectively, after incorporating the transfer learning architecture. Finally, the optimal values of key model parameters, including the sliding time window (6 s) and the prewarning time (5 s), were recommended for practical applications through sensitivity analysis. This study illustrates the potential of the deep transfer learning approach as a reliable and effective alternative to improve the transferability of real-time conflict prediction models. Additionally, results from this study can offer valuable insights for practical applications in traffic safety warning systems, particularly in vehicle-to-infrastructure traffic environments.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"214 ","pages":"Article 107966"},"PeriodicalIF":5.7,"publicationDate":"2025-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143421093","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":"Multi-scenario driving style research based on driving behavior pattern extraction","authors":"Yi He ,&nbsp;Yingrui Hu ,&nbsp;Jipu Li ,&nbsp;Ke Sun ,&nbsp;Jianhua Yin","doi":"10.1016/j.aap.2025.107963","DOIUrl":"10.1016/j.aap.2025.107963","url":null,"abstract":"<div><div>Accurately analyzing drivers’ driving styles is crucial for road safety and enhancing intelligent driving systems. However, existing studies have not fully explored the hidden information in driving sequences or considered the influence of driving environments on driving styles. Based on natural driving data from electric vehicles in Wuhan, a framework for driving style analysis based on driving behavior pattern extraction was proposed. Driving sequences were extracted under free-driving and car-following scenarios, where the convergence of driving features was verified using kernel density estimation and relative entropy. A driving propensity indicator based on a dynamic threshold was constructed, and combined with the Hierarchical Dirichlet Process Hidden Semi-Markov Model (HDP-HSMM) and K-means clustering algorithm, 4 and 5 types of driving behavior pattern were extracted under free-driving and car-following scenarios, respectively. Energy consumption distribution was introduced to verify the validity of driving pattern extraction. Jensen–Shannon (JS) divergence was utilized to calculate the difference in the distribution of the driving propensity indicator among different drivers. By quantifying behavioral differences, drivers were categorized into aggressive, moderate, and conservative types. The results show that the statistical characteristics of driving patterns are consistent with the distribution of energy consumption, with the highest energy consumption occurs in aggressive acceleration and high-speed steady-state patterns, and the highest braking energy recovery occurs in aggressive deceleration pattern. Furthermore, the driving environment influences driving styles to certain degree while exhibiting consistent or diverse driving styles in different driving scenarios and patterns.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"214 ","pages":"Article 107963"},"PeriodicalIF":5.7,"publicationDate":"2025-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143421094","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":"Changes in road safety following regional administrative reforms in Norway","authors":"Rune Elvik ,&nbsp;Tor-Olav Nævestad ,&nbsp;Jenny Blom ,&nbsp;Lars Even Egner ,&nbsp;Vibeke Milch ,&nbsp;Markus Bugge ,&nbsp;Håkon Endresen Normann ,&nbsp;Erland Skogli","doi":"10.1016/j.aap.2025.107969","DOIUrl":"10.1016/j.aap.2025.107969","url":null,"abstract":"<div><div>A reform of regional government in Norway was implemented on January 1, 2020. The management of county roads was transferred from the National Public Roads Administration to the counties. The number of counties was reduced from 19 to 11. In 2022 it was decided to split some of the counties that were merged in 2020, and the number of counties increased to 15 from January 1, 2024. This paper studies whether these reforms were associated with changes in the number of injured road users on county roads. Four counties and the city of Oslo were included in the study. The city of Oslo was not affected by the reforms in 2020 and 2024. The other counties were affected either by: (1) Transfer of the management of county roads only (one county); (2) transfer of the management of county roads and merger with another county (one county); or (3) transfer of the management of county roads and merger with another county, followed by splitting up the merged counties (two counties). A before-and-after study with comparison group using county roads as treated group and other public roads as comparison group, and 2010–2019 as before-period and 2020–2023 as after-period found very small changes in the number of injured road users. Trends established during 2010–2019 continued almost unchanged after 2020. It is concluded that the regional administrative reforms were not associated with any detectable changes in road safety in the counties included in the study.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"214 ","pages":"Article 107969"},"PeriodicalIF":5.7,"publicationDate":"2025-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143429549","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}