Transportation Research Part F-Traffic Psychology and Behaviour最新文献

{"title":"Microsleep episodes and vigilance failure in driving: A systematic review of definitions, detection, and transport safety relevance","authors":"Uyun Nadzirotul Faidah,&nbsp;Rio Prasetyo Lukodono,&nbsp;Qomariyatus Sholihah","doi":"10.1016/j.trf.2026.103829","DOIUrl":"10.1016/j.trf.2026.103829","url":null,"abstract":"<div><div>Drowsy driving remains a major contributor to severe transportation accidents, yet operational systems still lack sensitive markers of rapid vigilance failure. Microsleep episodes (MSEs), brief sleep-like intrusions during intended wakefulness, may provide an event-level marker of imminent performance breakdown. This systematic review synthesized 45 peer-reviewed studies published from 2011 to 1 February 2026 using a PRISMA-aligned workflow. Evidence was organized by construct validity, sensing modality, validation design, and deployment context. EEG/EOG-based criteria provided the strongest reference standard, especially for ultra-short events. Selected automated EEG studies reported accuracies from 84.7% to 98.2%, but the studies could not be ranked or statistically pooled because thresholds, datasets, class balance, temporal windows, and validation protocols differed substantially. Vision-based and behavioral systems offered greater deployability but often measured drowsiness proxies rather than EEG-confirmed MSEs. Wearable and multimodal systems provided a compromise between neurophysiological specificity and operational feasibility. The review introduces a three-level construct-validity hierarchy, distinguishes direct MSE detection from calibrated and uncalibrated proxies, and maps sensing choices to clinical, simulator, and fleet deployment contexts. Major barriers were inconsistent definitions, limited participant-independent and cross-dataset validation, underreported false-alarm rates, and insufficient evidence on acceptance, privacy, latency, and energy use. Future work should standardize duration bands and reference labels, calibrate surrogate measures against synchronized EEG/EOG, and evaluate models in diverse real-world driving conditions.</div></div>","PeriodicalId":48355,"journal":{"name":"Transportation Research Part F-Traffic Psychology and Behaviour","volume":"123 ","pages":"Article 103829"},"PeriodicalIF":5.0,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149160222","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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 role of nonverbal communication in driver yielding behavior: A VR-based analysis of pedestrian–driver interactions","authors":"Jinho Won,&nbsp;Hyunjoo Eom,&nbsp;Gi-Hyoug Cho","doi":"10.1016/j.trf.2026.103836","DOIUrl":"10.1016/j.trf.2026.103836","url":null,"abstract":"<div><div>Pedestrian safety at unsignalized crosswalks relies heavily on the successful negotiation of intent between road users, yet the mechanisms underlying such interactions remain underexplored. This study distinguishes two forms of pedestrian nonverbal behavior: monitoring behavior (e.g., head-turning toward an approaching vehicle) and claiming behavior (e.g., stepping toward the crosswalk), and examines how these behaviors, along with environmental context, eye-tracked mutual gaze, and individual characteristics shape risk perception and yielding behavior. Using an immersive multi-user virtual reality (VR) experiment with integrated eye-tracking, we classified 511 pedestrian–driver interactions and fitted multilevel regression models to the 390 interactions with synchronized gaze data. Claiming behaviors were associated with higher perceived collision risk and greater odds of yielding. Mutual gaze between pedestrians and drivers was also associated with a higher likelihood of yielding, suggesting that it may serve as a coordination cue. Drivers' perceptions were strongly influenced by pedestrian behavior, whereas pedestrians anchored their safety judgments primarily in structural cues such as the presence of marked crosswalks or drivers' compliance. These results highlight an asymmetry in the communication process that often leads to misalignment at unsignalized crossings. Methodologically, the study demonstrates the value of multi-user VR with eye-tracking in capturing real-time, bidirectional interactions. The findings contribute to pedestrian–driver interaction theory by operationalizing implicit communication behaviors and offer practical insights for crosswalk design, education programs, and communication interfaces for autonomous vehicles.</div></div>","PeriodicalId":48355,"journal":{"name":"Transportation Research Part F-Traffic Psychology and Behaviour","volume":"123 ","pages":"Article 103836"},"PeriodicalIF":5.0,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149160618","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Transport modal shift: do riders prefer e-bike sharing over e-scooter sharing in Canada?","authors":"Bing-Hung Chou,&nbsp;Giovani J.C. da Silveira,&nbsp;Jaydeep Balakrishnan,&nbsp;Jiayu Chen","doi":"10.1016/j.trf.2026.103756","DOIUrl":"10.1016/j.trf.2026.103756","url":null,"abstract":"<div><div>The rapid expansion of electric micromobility sharing services, such as e-scooter and e-bike sharing, is transforming urban mobility. However, there is limited understanding of the behavioral mechanisms that drive modal shifts in electric micromobility sharing (EMS). This study investigates the factors influencing the modal transition from e-scooter sharing (ESS) to e-bike sharing (EBS) in cities where both services are available. Utilizing the theory of planned behavior, we employed a structural equation modeling approach to analyze survey data from Canadian users. The findings indicate that trip characteristics, particularly purpose and distance, are the most significant predictors of modal shift, with leisure-oriented and long-distance trips increasing the preference for EBS. Social influence also significantly shapes behavioral intentions, whereas environmental and safety concerns exert less influence. These results underscore the predominance of functional and social drivers in the adoption of electric micromobility. Further, policymakers and service providers are advised to explicitly align infrastructure improvements with these distinct spatial and trip characteristics, such as deploying e-bike mounting racks in suburban areas and implementing targeted e-scooter corrals in dense downtowns, and to leverage peer-based social marketing strategies to encourage modal shifts.</div></div>","PeriodicalId":48355,"journal":{"name":"Transportation Research Part F-Traffic Psychology and Behaviour","volume":"122 ","pages":"Article 103756"},"PeriodicalIF":5.0,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148638930","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Optimal lateral acceleration for different levels of automated driving: a test track study of passenger evaluation for curve negotiation","authors":"Chen Peng,&nbsp;Stefanie Carlowitz,&nbsp;Ruth Madigan,&nbsp;Claus Marberger,&nbsp;Michael Schulz,&nbsp;Sabine Osswalt,&nbsp;Andreas Schultz,&nbsp;Natasha Merat","doi":"10.1016/j.trf.2026.103760","DOIUrl":"10.1016/j.trf.2026.103760","url":null,"abstract":"<div><div>Lateral acceleration impacts passenger comfort in automated vehicles (AVs). This study investigated the optimal lateral acceleration range for AVs negotiating curves, considering curve radii, engagement in non-driving related activities (NDRAs), and automation level (SAE L1 vs L4). An experiment was conducted on a test track with 28 participants. Participants, seated in the driver's seat, experienced three lateral acceleration levels (2, 3, 4 m/s²) across five different road curves. Evaluations, using a 7-point bipolar scale, were collected for both SAE Level 1 (assisted driving) and Level 4 (highly automated driving), with NDRA engagement considered under L4. Data were analysed using multilevel Bayesian multinomial logistic models.</div><div>Results suggest that no single universally “optimal” lateral acceleration was identified, as preferences were contextual. Generally, 2 m/s² was often rated “too slow”, particularly without NDRAs in L4, or in L1 driving. Conversely, 4 m/s² was frequently perceived as “too fast”. Within the tested range, the level of 3 m/s² often appeared to be a more balanced option, provided that road geometry, passenger activity, and automation level are accounted for. However, its optimality depended on other factors. Larger curve radii (gentler curves) led to a preference for lower lateral accelerations. NDRA engagement in L4 driving made 2 m/s² receive fewer “too slow” ratings and amplified the “too fast” perception for higher accelerations. L4 automation, compared to L1, also made 2 m/s² receive fewer “too slow” ratings. The findings provide detailed guidelines for designing AV lateral control systems, suggesting that lateral acceleration should be dynamically adjusted based on contextual factors to ensure comfort throughout the journey.</div></div>","PeriodicalId":48355,"journal":{"name":"Transportation Research Part F-Traffic Psychology and Behaviour","volume":"122 ","pages":"Article 103760"},"PeriodicalIF":5.0,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148638933","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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":"Location design for intelligent vehicle warning information: level 1 situational awareness modeling based on attention allocation and cognitive mechanisms","authors":"Ya Gao,&nbsp;Tao Gu,&nbsp;Zhongxiang Feng,&nbsp;Yubing Zheng,&nbsp;Jingyu Li","doi":"10.1016/j.trf.2026.103768","DOIUrl":"10.1016/j.trf.2026.103768","url":null,"abstract":"<div><div>The traditional layout of warning information positions mostly relies on experience or local experiments, lacking systematic theoretical guidance. The objective of this study is to simulate and model the human performance of drivers under different warning information position conditions through computational methods. Firstly, a comprehensive area division for the head-up display (HUD) and head-down display (HDD) interfaces was conducted. Based on the SEEV model, the N-SEEV calculation framework was introduced to achieve theoretical predictions of drivers' time to first fixation on the target under different warning information position conditions. Additionally, by innovatively integrating attention allocation and cognitive mechanisms, the QN-ACTR-SA1 computational model was developed to achieve mechanism modeling and performance prediction for level 1 situational awareness (SA1). A driving simulator experiment was conducted, recruiting 40 drivers to validate the time to first fixation on the target and the SA1 theoretical values across various scenarios. Results indicate that the lower-central HUD area delivers the most optimal overall performance in target acquisition efficiency, SA1 values, and vehicle control stability. More importantly, the proposed model can effectively estimate both time to first fixation on the target and SA1 values. This provides quantifiable design criteria for optimizing warning interface placement in intelligent vehicles, thereby helping reduce the reliance on large-scale end-user experimental validation during the early design stage.</div></div>","PeriodicalId":48355,"journal":{"name":"Transportation Research Part F-Traffic Psychology and Behaviour","volume":"122 ","pages":"Article 103768"},"PeriodicalIF":5.0,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148732131","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Willingness to use intelligent speed assistance among UK drivers: the role of personality and driving attitudes","authors":"Ian Edwards,&nbsp;Harriet M. Baird,&nbsp;Paul Norman,&nbsp;Richard Rowe","doi":"10.1016/j.trf.2026.103764","DOIUrl":"10.1016/j.trf.2026.103764","url":null,"abstract":"<div><div>Speeding is a well-documented contributor to crash risk. Intelligent Speed Assistance (ISA) is an in-car system that helps drivers remain within the speed limit that is becoming more common in vehicles. This paper examines associations between personality, driving attitudes, and willingness to use ISA among UK drivers. A total of 176 participants (69% female; <em>M</em> age = 38.61, <em>SD</em> = 13.97), all of whom held a full UK driving licence with a mean of 20 years (<em>SD</em> = 13.5 years) of driving experience, completed an online questionnaire. Participants completed measures of attitudes to driving violations, impulsivity, sensation seeking, and the Big Five personality traits, as well as a new set of items developed for this study to measure willingness to use ISA. Principal component analysis identified two distinct and reliable components representing willingness to avoid ISA and willingness to override it. Regression modelling found that attitudes to driving violations were related to both willingness to avoid and override ISA. Mediation analysis found that agreeableness was related to willingness to avoid ISA indirectly through attitudes towards driving violations. These findings highlight a key challenge to realising the full potential safety impact of ISA. Drivers with positive attitudes towards violations, who would benefit most from ISA, are also the most likely to avoid using or override the system. Potential educational methods to address this challenge are discussed.</div></div>","PeriodicalId":48355,"journal":{"name":"Transportation Research Part F-Traffic Psychology and Behaviour","volume":"122 ","pages":"Article 103764"},"PeriodicalIF":5.0,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148732560","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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 nudge does not fit all: How hazard characteristics and individual differences shape drivers' stated monitoring decisions in conditionally automated driving?","authors":"Yingli Zhang,&nbsp;Chunxi Huang,&nbsp;Hao Tan","doi":"10.1016/j.trf.2026.103788","DOIUrl":"10.1016/j.trf.2026.103788","url":null,"abstract":"<div><div>Ensuring effective human supervision in conditionally automated driving is critical yet challenging, as directive alerts may increase attentional burden or foster passive compliance. This study examined whether nudges could encourage stated monitoring decisions without degrading user experience, and how hazard characteristics and individual differences modulate their effectiveness. Through a scenario-based experiment (<em>N</em> = 1032), we evaluated eight between-subject message conditions: four framing nudges combining perspective (self-focused vs. prosocial) and goal framing (gain vs. loss) in a 2 × 2 design, two social norm nudges (i.e., descriptive and injunctive), a reminder nudge, and a control. For within-subject factors, participants responded to hazards that varied in visibility (i.e., visible vs. invisible) and source (i.e., vehicles vs. vulnerable road users (VRUs)). Results revealed that self-gain framing, self-loss framing, prosocial-gain framing, and descriptive norm increased stated monitoring decisions relative to control, while the injunctive norm nudge performed consistently worse than other nudge conditions. Nudge effects on subjective evaluations varied by individual characteristics and hazard context: perceived safety improvements under multiple nudge conditions were primarily observed among participants with a High Big Five profile, and the acceptance gap between hazard sources observed in the control condition disappeared under nudge conditions. Hazard characteristics and individual differences also systematically shaped stated monitoring decisions and subjective evaluations. These findings offer design implications for adaptive human–machine interfaces that may support drivers' stated monitoring decisions from a human-centered perspective, contributing to the safe and scalable deployment of automated driving.</div></div>","PeriodicalId":48355,"journal":{"name":"Transportation Research Part F-Traffic Psychology and Behaviour","volume":"122 ","pages":"Article 103788"},"PeriodicalIF":5.0,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148844578","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Trust in vehicle automation: A bibliometric synthesis of in-use interaction and public perception, 2001–2024","authors":"Huimin Ge,&nbsp;Yanan Liu,&nbsp;Wenkai Zang,&nbsp;Xia Zhao,&nbsp;Maocan Song,&nbsp;Qing Jiang,&nbsp;Jiajun Lin","doi":"10.1016/j.trf.2026.103762","DOIUrl":"10.1016/j.trf.2026.103762","url":null,"abstract":"<div><div>Trust in vehicle automation has become a key construct in traffic psychology and human–automation interaction, yet its conceptualisation and measurement remain fragmented across research contexts. This study analysed 729 Web of Science publications from 2001 to 2024 using bibliometric analysis, science mapping, and topic modelling to clarify the field's intellectual structure and conceptual differentiation. The findings show that, especially after 2018, trust shifted from a secondary issue in studies of driver behaviour, driver assistance systems, and human–machine interaction to a central construct for explaining acceptance, system use, and safety-related outcomes. Cross-validation based on bibliographic coupling, thematic mapping, and LDA topic modelling identifies two related but distinct knowledge streams: public perception research and in-use user–vehicle interaction research. Public perception studies generally treat trust as a belief-based pre-use attitude that shapes acceptance, intention to use, and perceived risk. In-use interaction studies, by contrast, conceptualise trust as a dynamic state that evolves during interaction and affects reliance, monitoring, and takeover performance. This distinction has important safety implications: higher trust may reflect stronger acceptance before use, but may indicate unsafe over-reliance during use when it exceeds system capability. The review calls for future research linking pre-use expectations with in-use trust dynamics through longitudinal designs, cross-context comparisons, and dynamic trust measurement, thereby supporting a more context-sensitive understanding of appropriate reliance in vehicle automation.</div></div>","PeriodicalId":48355,"journal":{"name":"Transportation Research Part F-Traffic Psychology and Behaviour","volume":"122 ","pages":"Article 103762"},"PeriodicalIF":5.0,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148732133","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Fear of crashes or fear of fines? Understanding what motivates parents to travel safely","authors":"Duy Quy Nguyen-Phuoc,&nbsp;Nhat Dinh Quang Vo,&nbsp;Amjad Pervez","doi":"10.1016/j.trf.2026.103772","DOIUrl":"10.1016/j.trf.2026.103772","url":null,"abstract":"<div><div>Road traffic crashes remain a leading cause of death among children in Vietnam, where children are commonly transported by parents on motorcycles and in private cars and the use of protective devices remains limited. While legal measures and enforcement campaigns have improved compliance temporarily, parental safety behaviours often fluctuate in response to perceived enforcement rather than sustained risk awareness. This study examines whether parents' safety intentions are more strongly associated with fear of crash consequences or fear of legal penalties. Drawing on the Theory of Planned Behaviour (TPB), the study proposes an extended behavioural model that integrates crash-related risk perceptions (perceived child vulnerability and crash severity), enforcement-related perceptions (certainty and severity of fines), and parental safety habits. Data were collected from parents in Vietnam, including 427 motorcycle-riding parents and 324 car-driving parents, and analysed using Partial Least Squares Structural Equation Modelling (PLS-SEM). The model demonstrates moderate explanatory power, accounting for 57.2% of the variance in parental safety intentions among motorcycle-riding parents (R² = 0.572) and 53.7% among car-driving parents (R² = 0.537). The results indicate that perceived severity of crashes, parental safety habits, and perceived behavioural control are strong predictors of safety intentions for both motorcycle-riding and car-driving parents. While perceived severity of legal punishment is significantly associated with car-driving parents' intentions to engage in safety behaviours, this association is not observed among motorcycle-riding parents. By jointly modelling psychological risk beliefs, enforcement perceptions, and habits, this study provides a comprehensive understanding of parental decision-making in child road safety and informs interventions aimed at promoting sustained compliance in everyday travel.</div></div>","PeriodicalId":48355,"journal":{"name":"Transportation Research Part F-Traffic Psychology and Behaviour","volume":"122 ","pages":"Article 103772"},"PeriodicalIF":5.0,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148732559","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Bifactor instrument for measuring user perceptions of technology in bus rapid transit: development, ontological classification, and internal validation","authors":"Mario Prokopiuk,&nbsp;Isabella Tioqueta,&nbsp;Jean Carlos Alberini","doi":"10.1016/j.trf.2026.103774","DOIUrl":"10.1016/j.trf.2026.103774","url":null,"abstract":"<div><div>Research on bus transit service quality relies on correlational factor models that cannot determine whether extracted dimensions represent autonomous constructs or manifestations of a single evaluative disposition. This study develops and internally validates a bifactor-based instrument for measuring user perceptions of intelligent transportation technologies in Curitiba's bus rapid transit system (<em>N</em> = 382, self-administered online questionnaire, quota-sampled). An integrated pipeline combining exploratory factor analysis, confirmatory bifactor modeling (WLSMV), ontological classification, and dual bootstrap validation was applied. The general factor accounted for 91.9% of the reliable variance (ωh/ω = 0.919). Of the four EFA-derived domains, two were reclassified as thematic after ontological classification; only Service Adequacy (ωS = 0.317 initial, 0.355 post-OC) and Fare Value (ωS = 0.261 initial, 0.311 post-OC) retained autonomous specific variance. Users recognized the city's technological capacity (M = 7.0) but rejected fare compensation (M = 2.9). Bootstrap validation confirmed structural stability (93.5% of converged replications achieving CFI ≥ 0.90). The resulting three-score architecture offers transit agencies a parsimonious monitoring framework grounded in the distinction between thematic groupings and structural dimensions.</div></div>","PeriodicalId":48355,"journal":{"name":"Transportation Research Part F-Traffic Psychology and Behaviour","volume":"122 ","pages":"Article 103774"},"PeriodicalIF":5.0,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148732561","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}