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

{"title":"Validating risk behavior in driving simulation using naturalistic driving data","authors":"Chantal Himmels ,&nbsp;Arben Parduzi ,&nbsp;Andreas Löcken ,&nbsp;Valentin Protschky ,&nbsp;Joost Venrooij ,&nbsp;Andreas Riener","doi":"10.1016/j.trf.2024.09.026","DOIUrl":"10.1016/j.trf.2024.09.026","url":null,"abstract":"<div><div>The absence of physical accident risk in driving simulation, which allows for safely studying critical driving situations, also reduces the driver's risk perception, which may result in unrealistic driver behavior. Validation studies in this context are rare for ethical reasons, making it difficult to assess the extent of this issue at present. The present study addresses this gap by utilizing naturalistic driving data. Four critical cut-in situations on German highways were extracted from naturalistic data and replicated in a driving simulator study with <em>N</em> = 58 participants. Both in-situ self-ratings on subjective criticality and post-hoc video-based ratings (from the driver and objective observers), as well as presence ratings, were collected to supplement driver behavior. Although driver reactions in the simulator and the field were not equivalent in absolute terms, drivers in both the simulation and the real world exhibited accident-avoidance behavior through braking reactions, indicating relative validity. No clear mediating role of the sense of presence towards a more careful driver behavior was found. This work shows that drivers exhibit accident-avoiding behavior in the simulator and tend to react to hazards in the simulator similarly as they would in a real situation, while absolute numerical values should only be interpreted with caution.</div></div>","PeriodicalId":48355,"journal":{"name":"Transportation Research Part F-Traffic Psychology and Behaviour","volume":null,"pages":null},"PeriodicalIF":3.5,"publicationDate":"2024-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142438401","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":"Driving behavior in Hazardous situations: The interplay between risk scenarios and dimensional emotions","authors":"Yong Peng ,&nbsp;Xin Lou ,&nbsp;Honggang Wang ,&nbsp;Xinghua Wang ,&nbsp;Guoliang Xiang ,&nbsp;Xianhui Wu ,&nbsp;Honghao Zhang ,&nbsp;Shengen Yi ,&nbsp;Tao Li","doi":"10.1016/j.trf.2024.10.004","DOIUrl":"10.1016/j.trf.2024.10.004","url":null,"abstract":"<div><div>As the contradictions among the human-vehicle-environment elements in road traffic systems intensify, the resulting traffic safety issues are becoming increasingly severe. Emotions, as critical psychological factors influencing safe driving, directly impact drivers’ perceptions and judgments of surrounding information. Simultaneously, potential risks during the driving process can affect drivers’ decisions and driving behavior. On the basis of the definition of dimensional emotions, this study analyzes the impact of different valence, arousal, and risk level scenarios on drivers’ collision avoidance behavior. A total of 21 drivers aged 18 to 50 years, with driving experience ranging from 1 to 10 years, participated in a simulated driving experiment. This study employs the decision tree (DT) algorithm to define the quantitative relationship between the occurrence of different driving behaviors and their influencing factors. It predicts vehicle avoidance behavior in two emergency conditions to determine whether collision avoidance can be achieved. The results indicate that the model’s prediction accuracy improves when emotion and risk level information are combined, reaching 88.89 %. This is a 5.56 % improvement over the model using only emotion information and a 2.53 % improvement over the model using only risk level information. Compared with the impact of subjective emotional factors on driving behavior, risk scenarios exhibit a more stable trend in influencing driving behavior. Under the interaction of emotions and risk factors, the accuracy and generalization ability of the driving behavior prediction model based on the Decision Tree (DT) algorithm have been greatly improved. This research provides a theoretical basis for addressing adverse driving behavior caused by driver emotions and analyzing the factors influencing collision avoidance processes.</div></div>","PeriodicalId":48355,"journal":{"name":"Transportation Research Part F-Traffic Psychology and Behaviour","volume":null,"pages":null},"PeriodicalIF":3.5,"publicationDate":"2024-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142424173","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":"Exploring the influence of a virtual reality experience on user acceptance of shared autonomous vehicles: A quasi-experimental study in Brussels","authors":"Fatima-Zahra Debbaghi,&nbsp;Evy Rombaut,&nbsp;Lieselot Vanhaverbeke","doi":"10.1016/j.trf.2024.10.003","DOIUrl":"10.1016/j.trf.2024.10.003","url":null,"abstract":"<div><div>This paper investigates the intention to use shared autonomous vehicles in Brussels before and after an experience in virtual reality, in which a scenario of a city with shared autonomous vehicles was visualized taking into account transformations that are not possible to implement in real life such as urban form changes. 51 participants took part in a quasi-experiment and their acceptance of autonomous vehicles was measured before and after, using surveys based on the unified theory of acceptance and use of technology (UTAUT). Using partial least squares structural equation modeling, we model user acceptance pre- and post-VR experience and compare the change in influencing factors. Results show that, under the conditions we defined, the intention to use autonomous shuttles was influenced by the virtual reality experience, as there was an increase in the behavioral intention and a change in the significant predictors. Overall, we found evidence that the virtual reality experience did influence the acceptance of autonomous vehicles, and can then be an effective test bed to visualize future impacts of autonomous vehicles that are impossible to project in real life. Our study constitutes then a first step into exploring potential usages of virtual reality to project long-term visions of future developments of cities with shared autonomous vehicles.</div></div>","PeriodicalId":48355,"journal":{"name":"Transportation Research Part F-Traffic Psychology and Behaviour","volume":null,"pages":null},"PeriodicalIF":3.5,"publicationDate":"2024-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142424272","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":"Which electric vehicle charging station to upgrade? Biased judgments based on differences in station efficiency","authors":"Ola Svenson ,&nbsp;Ilkka Salo ,&nbsp;Inés Duce Gimeno","doi":"10.1016/j.trf.2024.10.001","DOIUrl":"10.1016/j.trf.2024.10.001","url":null,"abstract":"<div><div>One way of controlling global warming is to substitute fuel driven cars with electric cars. Electric vehicles need to be charged. For maximal efficiency the charging times should be as short as possible. In the US charging stations are classified as Level 1 charging 5–10 miles/h, Level 2 25 miles/h and Fast DCFC stations 150–1000 miles/h. We asked participants to select one of two upgrades of charging stations that would save most charging time for a vehicle. The alternatives were upgrading L1 (5miles/h) to L2 (25 miles/h) or L2 (25miles/h) to Fast (250 miles/h). In all, 86% of the participants wanted to upgrade to a Fast station, which objectively saves less time than L1 to L2. The second study replicated the first study and 91% of the participants wanted to upgrade to the Fast (250) station. The third study offered alternatives with smaller objective efficiency differences than the earlier studies: upgrading L2 (30) to Fast (150) and Fast (150) to Fast (600) and 68% of the participants preferred the second incorrect alternative. Verbal justifications showed that many participants seemed to assume that differences in charging time are proportional to charging time saved. The results have practical implications and illustrate the difficulty to process reciprocal variables leading to incorrect decisions. Finally, we suggest two strategies for counteracting biased intuitive decision making when charging efficiencies are compared.</div></div>","PeriodicalId":48355,"journal":{"name":"Transportation Research Part F-Traffic Psychology and Behaviour","volume":null,"pages":null},"PeriodicalIF":3.5,"publicationDate":"2024-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142424271","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":"Willingness to utilize autonomous vehicles following accidents: A fresh perspective from mixed-methods research","authors":"Yongjiang Zhou ,&nbsp;Hanying Guo ,&nbsp;Luping Tang ,&nbsp;Yuxin Deng ,&nbsp;Hongguo Shi","doi":"10.1016/j.trf.2024.09.022","DOIUrl":"10.1016/j.trf.2024.09.022","url":null,"abstract":"<div><div>While autonomous vehicles (AVs) show promise, several challenges remain in their implementation. In this regard, adverse incidents can alter public perceptions and acceptance of this technology. This study used latent Dirichlet allocation (LDA), a random-parameters ordered probit model and a structural causal model to investigate public concerns regarding AV incidents and their influence on the public’s willingness to adopt AVs. Using LDA to analyse mass media data, this study identified seven latent variables related to AV incidents. To consider changes in public attitudes following incidents, attitude was introduced as a new latent variable. The impact of various variables on public willingness to use AVs was analysed using a random-parameters ordered probit model. The findings indicated that perceived trust, attitude, perceived risk, mass media, perceived value, brand effect, privacy concerns and policies and regulations are crucial factors influencing AV adoption. Subsequently, a causal structure model was developed to determine the inter-relationships between variables. The model indicated that policy interventions increased public willingness to adopt AVs by 28%, suggesting that policymakers’ interventions help create an early market for AVs. In addition, good branding and mass media campaigns influence the public’s psychological characteristics and encourage the use of AVs. These findings hold crucial implications for mitigating the negative impacts of adverse incidents, fostering public acceptance and providing valuable insight for theoretical understanding and practical implementation of AV usage.</div></div>","PeriodicalId":48355,"journal":{"name":"Transportation Research Part F-Traffic Psychology and Behaviour","volume":null,"pages":null},"PeriodicalIF":3.5,"publicationDate":"2024-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142424267","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":"Beyond private-sphere pro-environmental action: Explaining shared mobility using the Theory of Planned Behavior and solidarity-oriented variables","authors":"Nadine Richter ,&nbsp;Marcel Hunecke ,&nbsp;Paula Blumenschein","doi":"10.1016/j.trf.2024.09.014","DOIUrl":"10.1016/j.trf.2024.09.014","url":null,"abstract":"<div><div>Shared mobility behaviors can decrease the negative environmental effects of the transport sector, yet they have received limited attention. Models such as the Theory of Planned Behavior (TPB) have been widely used to explain primarily individual private-sphere pro-environmental behaviors (PEB). However, as shared mobility behaviors are not completely limited to the private sphere but require social cooperation, solidarity-focused variables that emphasize social interactions may meaningfully complement the TPB variables. In three university samples (Study 1: <em>N</em>=261, Study 2: <em>N</em>=1411; Study 3: <em>N</em>=544), we tested relationships between the TPB variables and shared mobility, and whether solidarity-oriented variables are relevant predictors of shared mobility beyond the TPB variables. The analyses confirmed attitude (<span><math><mrow><mi>β</mi></mrow></math></span> = 0.38 − 0.59, <em>p</em> &lt; 0.001) and social norm (<span><math><mrow><mi>β</mi></mrow></math></span> = 0.17 − 0.43, <em>p</em> &lt; 0.001) as predictors of shared mobility intention. Furthermore, the solidarity-oriented variables universalism (<span><math><mrow><mi>β</mi></mrow></math></span> = 0.09 − 0.18, <em>p</em> &lt; 0.05) as well as specific collective efficacy (<span><math><mrow><mi>β</mi></mrow></math></span> = 0.08 − 0.10, <em>p</em> &lt; 0.05) and opinion-based social identification (<span><math><mrow><mi>β</mi></mrow></math></span> = 0.18, <em>p</em> &lt; 0.001) positively predicted shared mobility intention. Psychological variables showed no consistent connection to self-reported shared mobility behaviors in logistic regressions. We discuss the scope of solidarity-oriented variables that need social cooperation to complementarily explain PEB beyond the private sphere.</div></div>","PeriodicalId":48355,"journal":{"name":"Transportation Research Part F-Traffic Psychology and Behaviour","volume":null,"pages":null},"PeriodicalIF":3.5,"publicationDate":"2024-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142424270","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":"The Predictive Role of visual attention bias in aggressive driving decisions among violation-involved drivers on attitudes of right-of-way","authors":"Jinfei Ma ,&nbsp;Hao Chen ,&nbsp;Yi Cui ,&nbsp;Tingru Zhang","doi":"10.1016/j.trf.2024.09.024","DOIUrl":"10.1016/j.trf.2024.09.024","url":null,"abstract":"<div><div>This study, based on dual-process theory, explores the aggressive driving decision-making characteristics and cognitive processing of violation-involved drivers in right-of-way infringement scenarios. It aims to identify an eye-movement indicator that can predict drivers’ prosocial behaviors. The study recruited 75 drivers aged 19–58 years, who completed a video-based aggressive driving decision-making test and the Driver Attitudes of Right-of-way Questionnaire (DARQ). The results indicate that age moderates the relationship between violation history and visual attentional bias. Younger drivers with a history of violations exhibit an attentional bias towards aggressive words, whereas older drivers do not. After controlling for age, violation-involved drivers demonstrated a higher rate of aggressive decision-making, especially in situations with very short lane-change time headway. Visual attentional bias towards aggressive words can effectively predict positive emotions in attitudes towards the right-of-way. This suggests that early eye movement indicators during the driving decision-making process represent a form of socioemotional characteristic. The more positive the driver’s right-of-way attitude, the stronger their prosocial behavior and the weaker their intuitive impulsiveness during the early cognitive processing stages of driving decision-making. This indicates that the driving decision-making eye-movement assessment paradigm is an objective and effective method for evaluating drivers’ pro-sociality.</div></div>","PeriodicalId":48355,"journal":{"name":"Transportation Research Part F-Traffic Psychology and Behaviour","volume":null,"pages":null},"PeriodicalIF":3.5,"publicationDate":"2024-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142424269","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":"Road-crossing behavior and safety of pedestrians facing autonomous vehicles with an acceleration indicator eHMI in VR traffic flow","authors":"Yuanming Song ,&nbsp;Xing Chen ,&nbsp;Jingyu Zhang ,&nbsp;Jingyi Tian ,&nbsp;Liwen Zhang ,&nbsp;Guojie Ma ,&nbsp;Xiangling Zhuang","doi":"10.1016/j.trf.2024.09.023","DOIUrl":"10.1016/j.trf.2024.09.023","url":null,"abstract":"<div><div>To support pedestrian road-crossing behavior in the presence of autonomous vehicles (AVs), we propose a novel external Human-Machine Interface (eHMI) that enhances the vehicle’s inherent motion cues, which pedestrians already use in traditional traffic. Based on the Beta Movement, the eHMI design consists of light bands with multiple arrows moving forward or backward to indicate the vehicle’s accelerating or decelerating motion state. We measured pedestrians’ behavior and perceived safety when faced with AVs equipped with this acceleration indicator eHMI in a virtual reality (VR) street scene, where pedestrians had road-crossing tasks and vehicles did not always yield. We found that pedestrians exhibited crossing behavior according to the motion state of the oncoming vehicle. They almost always crossed if the vehicle yielded but considered the gap size to cross when it did not. With the acceleration indicator eHMI, they crossed more frequently if the vehicle yielded, yet less frequently if it did not. Although risky crossings could not be fully prevented, pedestrians crossed faster and maintained larger safety margins when the non-yielding vehicle used this eHMI. Additionally, pedestrians started to cross earlier when the eHMI indicated the vehicle’s decelerating state. Regarding pedestrians’ perceived safety, the eHMI did not increase perceived safety for decelerating vehicles but effectively decreased the perceived safety for accelerating vehicles. These results demonstrate the potential of enhancing vehicles’ inherent motion cues with eHMIs, but also imply that making vehicles’ motion state more apparent might not simply increase pedestrians’ perceived safety. Overall, a trade-off seems to exist that it is pedestrians’ sense of unsafety that leads to their safe road-crossing behavior. Suggestions for future research and VR experiments were also discussed.</div></div>","PeriodicalId":48355,"journal":{"name":"Transportation Research Part F-Traffic Psychology and Behaviour","volume":null,"pages":null},"PeriodicalIF":3.5,"publicationDate":"2024-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142424268","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":"Human-Machine interfaces and vehicle Automation: A review of the literature and recommendations for system Design, Feedback, and alerts","authors":"Meng Wang ,&nbsp;Shashank Mehrotra ,&nbsp;Nicholas Wong ,&nbsp;Jah’inaya Parker ,&nbsp;Shannon C. Roberts ,&nbsp;Woon Kim ,&nbsp;Alicia Romo ,&nbsp;William J. Horrey","doi":"10.1016/j.trf.2024.08.014","DOIUrl":"10.1016/j.trf.2024.08.014","url":null,"abstract":"<div><div>The effectiveness of the human–machine interface (HMI) in a driving automation system is based, in part, on how it issues alerts and requests to the driver—it must quickly and sufficiently orient the driver to the driving task. While much research on the design of<!--> <!-->HMIs<!--> <!-->exists, unique associations and meaningful relationships must be considered collectively to offer guidance for vehicle automation. The purpose of the current study was to: (a) review and synthesize existing research and guidance on HMI design as it related to requests to intervene (RTI) for driving automation systems and (b) propose a clear and comprehensive set of recommendations that could inform future system development and implementation. A thorough literature search and selection process was followed the PRISMA guidelines and resulted in the review of 93 articles. The review’s outcome indicates multimodal alerts are prominent, useful, and effective. Many of the HMIs reported in the literature were similar in that they abided by well-established HMI-design guidelines. A small subset of<!--> <!-->HMIs<!--> <!-->established evidence for distinct and innovative design principles. Taken together, this study proposed 10 recommendations for HMI design in driving automation systems, ranging from the utility of auditory versus visual alerts to the timing, sensitivity, and location of alerts.</div></div>","PeriodicalId":48355,"journal":{"name":"Transportation Research Part F-Traffic Psychology and Behaviour","volume":null,"pages":null},"PeriodicalIF":3.5,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142356900","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":"In the driver's mind: Modeling the dynamics of human overtaking decisions in interactions with oncoming automated vehicles","authors":"Samir H.A. Mohammad ,&nbsp;Haneen Farah ,&nbsp;Arkady Zgonnikov","doi":"10.1016/j.trf.2024.09.020","DOIUrl":"10.1016/j.trf.2024.09.020","url":null,"abstract":"<div><div>Understanding human behavior in overtaking scenarios is crucial for enhancing road safety in mixed traffic with automated vehicles (AVs). Computational models of behavior play a pivotal role in advancing this understanding, as they can provide insight into human behavior generalizing beyond empirical studies. However, existing studies and models of human overtaking behavior have mostly focused on scenarios with simplistic, constant-speed dynamics of oncoming vehicles, disregarding the potential of AVs to proactively influence the decision-making process of the human drivers via implicit communication. Furthermore, despite numerous studies in other scenarios, so far it remained unknown whether overtaking decisions of human drivers are affected by whether they are interacting with an AV or a human-driven vehicle (HDV). To address these gaps, we conducted a “reverse Wizard-of-Oz” driving simulator experiment with 30 participants who repeatedly interacted with oncoming AVs and HDVs, measuring the drivers' gap acceptance decisions and response times. The oncoming vehicles featured time-varying dynamics designed to influence the overtaking decisions of the participants by briefly decelerating and then recovering to their initial speed. We found no evidence of differences in participants' overtaking behavior when interacting with oncoming AVs compared to HDVs. Furthermore, we did not find any evidence of brief decelerations of the oncoming vehicle affecting the decisions or response times of the participants. Cognitive modeling of the obtained data revealed that a generalized drift-diffusion model with dynamic drift rate and velocity-dependent decision bias best explained the gap acceptance outcomes and response times observed in the experiment. Overall, our findings highlight that cognitive models of the kind considered here can provide a generalizable description of human overtaking decisions and their timing. Such models can thus help further advance the ongoing development of safer interactions between human drivers and AVs during overtaking maneuvers.</div></div>","PeriodicalId":48355,"journal":{"name":"Transportation Research Part F-Traffic Psychology and Behaviour","volume":null,"pages":null},"PeriodicalIF":3.5,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142424265","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}