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

{"title":"What factors could enhance post-crash anxiety and depression outcomes? A SEM-based retrospective study","authors":"Navid Nadimi ,&nbsp;Fariborz Mansourifar ,&nbsp;Abbas Sheykhfard ,&nbsp;Sergio A. Useche","doi":"10.1016/j.trf.2025.06.027","DOIUrl":"10.1016/j.trf.2025.06.027","url":null,"abstract":"<div><div>Traffic crashes are a leading cause of non-natural deaths and significant economic and healthcare burdens. Mental health issues and decreased life quality have been consistently linked to traffic crashes among surviving. Several studies reveal that anxiety and depression symptoms are common among these individuals. However, the specific individual factors modulating the development of these post-crash psychosocial outcomes, as well as their relative contributions, have been scarcely addressed in the literature. Thus, the primary objective of this study was to examine different factors’ effects on two key negative self-reported psychological outcomes (namely, anxiety and depression) following road crashes. The survey was conducted on 239 individuals who experienced substantial financial loss or non-fatal physical injuries after a crash. Among the full set of study variables comprised in the path model, crash severity has a positive and significant effect on post-crash anxiety severity scores. As for antecedent anxiety and depression values, these were significantly influenced by retrospectively-reported physical conditions and both pre-crash anxiety and depression scores. Moreover, both pre-crash depression scores and post-crash anxiety indexes stand out as significant predictors of post-crash depression indexes. This study suggests that traffic crashes have a significant effect on self-reported anxiety and depression-related outcomes. At a practical level, these outcomes underscore the need for increasing attention paid to post-crash mental health outcomes, given their considerably greater prevalence among crash survivors, as well as their possible comorbidity with further impairing behaviors or life quality impairments.</div></div>","PeriodicalId":48355,"journal":{"name":"Transportation Research Part F-Traffic Psychology and Behaviour","volume":"114 ","pages":"Pages 741-757"},"PeriodicalIF":3.5,"publicationDate":"2025-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144534175","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":"E-scooter safety: How attitudinal factors influence risky behavior among shared e-scooter riders","authors":"Sina Asgharpour ,&nbsp;Mohammadjavad Javadinasr ,&nbsp;Abolfazl (Kouros) Mohammadian ,&nbsp;Nazmul Arefin Khan ,&nbsp;Joshua Auld","doi":"10.1016/j.trf.2025.06.015","DOIUrl":"10.1016/j.trf.2025.06.015","url":null,"abstract":"<div><div>In recent years, e-scooter usage for short-distance trips has grown rapidly. This surge in e-scooter use, combined with the high exposure of e-scooter riders to accident risk, has sparked concerns regarding e-scooter safety. Despite some studies focusing on e-scooter safety, little is known about how attitudinal factors lead e-scooter riders to engage in risky riding behaviors. In this paper, we developed a survey-based empirical model to identify the attitudinal factors influencing engagement in risky behaviors among e-scooter users. We used survey data collected from 420 shared e-scooter users in Chicago in 2022. The survey showed that 47.7% of respondents had experienced at least one collision or fall-off while riding e-scooters. We employed the Partial Least Squares Structural Equation Model (PLS-SEM) to examine the relationships between latent attitudinal factors and risky behavior engagement. Moreover, we conducted Permutation Multi-group Analysis (PMGA) to assess the moderating effect of socio-demographic factors within the estimated model. The findings suggest that riders’ unsafe riding attitude and riding confidence are the most influential factors shaping their risky behavior engagement. In addition, accident experience, infrastructure suitability, perceived enjoyment, traffic risk perception, and operational risk perception are among the other significant predictors. Among socio-demographic factors, gender, age, education, and car use frequency significantly influence riders’ engagement in risky behaviors. The results highlight the importance of infrastructure suitability and accident experience in analyzing e-scooter users’ riding behavior. The developed model advances our understanding of factors contributing to e-scooter riders’ risky behavior engagement. The findings offer valuable insights for policymakers and e-scooter vendors aiming to mitigate e-scooter users’ accident risk. Specifically, we recommend three safety countermeasures: (1) safety training programs to encourage a safer attitude, (2) practice-based initiatives to enhance riding confidence, and (3) infrastructure improvements, especially the expansion of bike lanes.</div></div>","PeriodicalId":48355,"journal":{"name":"Transportation Research Part F-Traffic Psychology and Behaviour","volume":"114 ","pages":"Pages 758-779"},"PeriodicalIF":3.5,"publicationDate":"2025-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144534176","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":"Psychosocial predictors of active transport in Australia: Applying an extended theory of planned behaviour","authors":"Angela Melville Bickel,&nbsp;Cassandra Gauld","doi":"10.1016/j.trf.2025.05.022","DOIUrl":"10.1016/j.trf.2025.05.022","url":null,"abstract":"<div><div>Active transport (AT), which involves walking or cycling instead of using motorised transport (MT), is underused in Australia. Considering sedentary Australian lifestyles, and the negative impact MT emissions have on climate change, AT offers both health and environmental benefits. Evidence suggests that transport mode choice can be predicted by Theory of Planned Behaviour (TPB) constructs i.e., attitudes, subjective norms and perceived behavioural control (PBC). It is unknown, however, if the TPB extended with descriptive norms, habit and nature relatedness (NR) improves prediction of AT intention and use, and whether MT constructs (the alternate behaviour) are useful predictors. Two online surveys issued one-week apart, measured predictors and subsequent behaviours of adults living in Australia. Participants (<em>N</em> = 294) were aged 18 to 84 years (<em>M</em> = 40.8, <em>SD</em> = 16.0) and predominantly female (<em>n</em> = 199), with 56.5 % completing both surveys. Hierarchical multiple regressions indicated that the standard TPB constructs were significant predictors, explaining over 17 % of the variance in AT intention, after controlling for past behaviour and infrastructure sufficiency. An additional and significant 2.9 % to 3.8 % of AT intention variance was also explained by NR and AT habit. Regarding AT behaviour, AT intention and PBC significantly predicted AT use, accounting for over 46 % of the variance in behaviour. These predictors should therefore be considered when designing strategies to encourage AT uptake. Interventions targeting those higher in NR, or persuasive messaging focusing on subjective norms rather than descriptive norms, may be more effective at increasing Australian AT intentions and use.</div></div>","PeriodicalId":48355,"journal":{"name":"Transportation Research Part F-Traffic Psychology and Behaviour","volume":"114 ","pages":"Pages 721-740"},"PeriodicalIF":3.5,"publicationDate":"2025-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144523051","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":"Purchase intention of new energy vehicles: a comprehensive analysis considering total cost of ownership and personality traits","authors":"Yanyan Chen ,&nbsp;Wenchao Cao ,&nbsp;Ke Wang","doi":"10.1016/j.trf.2025.06.025","DOIUrl":"10.1016/j.trf.2025.06.025","url":null,"abstract":"<div><div>Existing research often ignores the potential impact of the total cost of ownership (TCO) and consumer personality traits on purchase intention. By conducting a stated preference experiment with 173 respondents in China, this study develops a modeling approach that combines a rank-ordered logit model with an Integrated Choice and Latent Variable (ICLV) model to explore how TCO and personality traits affect electric vehicles (EVs) purchase behavior. The results reveal that providing information on 5-year fuel costs and TCO has a significant positive impact on respondents’ stated preference for EVs. Socioeconomic and travel-related factors, such as gender, age, monthly income, household car ownership, and commuting distance, also influence purchase intentions. Among personality traits, Conscientiousness, Neuroticism, and Openness significantly affect individual preferences. These findings offer actionable insights for policies aimed at promoting the adoption of new energy vehicles.</div></div>","PeriodicalId":48355,"journal":{"name":"Transportation Research Part F-Traffic Psychology and Behaviour","volume":"114 ","pages":"Pages 701-720"},"PeriodicalIF":3.5,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144523135","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":"Development of a mental model questionnaire framework: a systematic approach to measuring mental models in automated driving","authors":"Stephanie Seupke,&nbsp;Sarukan Segar,&nbsp;Martin Baumann","doi":"10.1016/j.trf.2025.06.024","DOIUrl":"10.1016/j.trf.2025.06.024","url":null,"abstract":"<div><div>As Advanced Driver Assistance Systems evolve, new technologies are regularly introduced to the market. These technologies often differ significantly in operational functionality, communication methods, and limitations, even within the same SAE automation level. These variations can have critical safety implications, which highlight the importance of accurately assessing users’ mental models to identify potential safety gaps and optimize product communication strategies. While existing mental model questionnaires provide valuable insights, their applicability is often restricted to specific automation levels and system versions and are therefore limited in their use across diverse systems. This paper addresses this limitation by presenting a flexible questionnaire framework designed to assess users’ mental models across different levels of automation. For this purpose, a qualitative content analysis was conducted to systematically extract key information relevant to system usage from user manuals of SAE Level 2 and Level 3 automated systems. These insights guided the development of a questionnaire structure that is adaptable to the specifications of individual systems. Example items for Level 2 and Level 3 systems were created and validated through expert review (n = 4) and a quantitative online study (n = 1023). The results confirm that the proposed framework provides a valid and adaptable tool for designing mental model questionnaires tailored to specific systems, facilitating consistent assessment and comparison of system understanding across diverse automated driving technologies. This framework represents a significant step toward improving the evaluation of user interaction with automated systems and supporting the safe implementation of these technologies.</div></div>","PeriodicalId":48355,"journal":{"name":"Transportation Research Part F-Traffic Psychology and Behaviour","volume":"114 ","pages":"Pages 686-700"},"PeriodicalIF":3.5,"publicationDate":"2025-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144510772","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 effect of cognitive load on driver situational awareness under partially automated driving conditions","authors":"Xinrui He ,&nbsp;Kang Jiang ,&nbsp;Ping Zhang ,&nbsp;Zhenhua Yu ,&nbsp;Xiaoshan Lu ,&nbsp;Zhipeng Huang","doi":"10.1016/j.trf.2025.06.020","DOIUrl":"10.1016/j.trf.2025.06.020","url":null,"abstract":"<div><div>With the development of automated driving technology, level 2 (L2) automated systems have transformed the role of drivers, leading to changes in cognitive loads (including both physiological and psychological factors). Under partially automated conditions, drivers are prone to fatigue and disengagement, which reduces Situation Awareness (SA). The distribution of attention and the level of SA are critical to driving performance and safety. To investigate how SA varies under different cognitive loads during extended periods of automated driving, a high-fidelity driving simulator and eye-tracking technology were employed to collect data. The impacts of the automation level (manual vs. L2), cognitive channel (visual vs. auditory), and cognitive load (no load, 0-back, or 1-back) on drivers’ SA during extended driving were examined. In the simulation, a suburban road environment, including potential hazard scenarios and SA measurement tasks, was modeled. The results indicate that cognitive load influences drivers’ subjective cognitive loads and situational awareness scores. Overall, the cognitive load during L2 automated driving is lower than that during manual driving. As the driving time increases, drivers’ situational awareness tends to decrease. After 40 min of driving, a decrease in the subjective situational awareness score occurs. The distribution of drivers’ gaze points is influenced by automation, cognitive load, and their interaction. Gaze transition probability influenced by driving duration and cognitive load level, with the gaze concentration effect occurring after prolonged driving. Additionally, as the driving time increases, eye movement indicators such as the number of fixations on potential hazards and fixation entropy decrease, whereas the pupil coefficient of variation increases. This study reveals the relationship between cognitive load and SA, showing that visual metrics can effectively reflect drivers’ SA. These results provide valuable insights for designing driving cues that reflect drivers’ current state.</div></div>","PeriodicalId":48355,"journal":{"name":"Transportation Research Part F-Traffic Psychology and Behaviour","volume":"114 ","pages":"Pages 633-664"},"PeriodicalIF":3.5,"publicationDate":"2025-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144471571","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":"Automated driving training: a scoping review examining the use of instructional design and learning principles in current training research","authors":"Albraa A. Rajkhan,&nbsp;Wayne C.W. Giang","doi":"10.1016/j.trf.2025.06.022","DOIUrl":"10.1016/j.trf.2025.06.022","url":null,"abstract":"<div><div>As Advanced Driver Assistance Systems (ADAS) and Automated Driving Systems (ADS) become increasingly prevalent in vehicles, there is a critical need to ensure that drivers are effectively trained to use these technologies safely. This scoping review examines how learning and instructional design principles are addressed in current ADAS/ADS training research. We assessed literature from the past 20 years, focusing on how research on training interventions address learner engagement, knowledge acquisition, and skills development. A content representation classification taxonomy was introduced alongside an analysis of six dimensions: use of instructional design frameworks, training purpose, training content topics, content representation, training styles, and learner differences. The analysis revealed that while some training practices effectively align with educational principles, significant gaps remain. Our findings identify three key themes: the evolution of training objectives and interventions, the alignment of training content with appropriate knowledge types and delivery methods, and the need for specifically tailored training for distinct populations. This synthesis can guide future training developments, making a crucial impact on user competence and safety outcomes in automated driving systems.</div></div>","PeriodicalId":48355,"journal":{"name":"Transportation Research Part F-Traffic Psychology and Behaviour","volume":"114 ","pages":"Pages 665-685"},"PeriodicalIF":3.5,"publicationDate":"2025-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144471572","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":"Modeling behavioral intentions to adopt autonomous vehicles: A dual SEM–FsQCA approach to trust, technology acceptance, and the moderating role of social influence","authors":"Arsenyan Ani ,&nbsp;Fan Xing ,&nbsp;Chrispus Zacharia Oroni","doi":"10.1016/j.trf.2025.06.021","DOIUrl":"10.1016/j.trf.2025.06.021","url":null,"abstract":"<div><div>Autonomous vehicles have the potential to transform transportation by increasing efficiency, safety, and sustainability. However, their acceptance remains low, particularly in emerging markets like Armenia, where issues of public trust and perception pose significant barriers. This study aims to examine behavioral intents to embrace autonomous vehicles by extending the Technology Acceptance Model with trust, social influence, and user attitudes. The study employs a dual analytical method, including structural equation modeling and fuzzy-set qualitative comparative analysis, to find both linear and configurational pathways that influence adoption intentions. The results of structural equation modeling show that trust highly affects behavioral intention, both directly and indirectly through perceived usefulness and attitudes. In addition, social influence moderates the relationships, particularly those between perceived ease of use and attitude, as well as trust and intention. The results of fuzzy-set Qualitative Comparative Analysis reveal multiple sufficient combinations of factors leading to high behavioral intention. Attitude emerged as a central factor in most configurations that indicated a strong intention to adopt. This study increases our understanding of how social and cognitive factors influence the adoption of autonomous vehicles. It highlights the importance of building trust and improving the usability of the design to generate positive perceptions and a desire to utilize it. The findings suggest recommendations for regulators, autonomous vehicle manufacturers, and marketers looking to promote the use of self-driving cars in emerging nations.</div></div>","PeriodicalId":48355,"journal":{"name":"Transportation Research Part F-Traffic Psychology and Behaviour","volume":"114 ","pages":"Pages 597-620"},"PeriodicalIF":3.5,"publicationDate":"2025-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144364966","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":"Investigating driver responses to automated vehicles in a bottleneck scenario: The impact of lateral offset and eHMI","authors":"Li Yang ,&nbsp;Yee Mun Lee ,&nbsp;Ruth Madigan ,&nbsp;Armin Grunwald ,&nbsp;Barbara Deml ,&nbsp;Natasha Merat","doi":"10.1016/j.trf.2025.06.016","DOIUrl":"10.1016/j.trf.2025.06.016","url":null,"abstract":"<div><div>This driving simulator study investigated drivers’ responses to an approaching automated or manual vehicle in a bottleneck scenario. Participants were asked to decide whether to pass through the bottleneck, or yield for the approaching vehicle, across numerous trials. Prior to each trial, they were informed whether the approaching vehicle was an automated vehicle (AV) or a manually driven vehicle (MV). Although participants were told that the MV was controlled by the experimenter using a distributed simulator, both vehicles were actually controlled by the system, and behaved in the same way. The kinematics of the approaching vehicle, such as its yielding behaviour (with or without lateral offset), and the presence of external Human Machine Interfaces (eHMIs, AV only) were manipulated. 40 participants took part in this study. Results indicated that participants’ subjective responses and behaviours did not differ between the AVs and MVs. The approaching vehicle’s lateral offset was seen to be the most influential source of information for participants, followed by information from the eHMI. Participants were more likely to pass through the bottleneck first, and had a shorter decision time, when encountering yielding vehicles with “away offsets”, which involved the vehicle moving away from the road centre line. This condition also led to higher perceived safety, comprehension, and trust ratings. Conversely, drivers were more likely to yield and had a shorter decision time when encountering non-yielding vehicles without any lateral offset. The lateral offset of non-yielding vehicles did not have an impact on drivers’ perceived safety and trust. However, non-yielding with “towards offsets” (towards the centre line) led to a higher comprehension score. Participants also passed through the bottleneck significantly more often and provided higher ratings for perceived safety and trust when the yielding vehicles presented an eHMI. This was regardless of lateral deviation. However, the eHMI only led to a higher rating of comprehension when the AV yielded without an offset. This study shows the value of using lateral offsets to communicate vehicles’ intentions in bottleneck scenarios. While the eHMI could enhance the driver’s understanding of the yielding AV, some participants also noted that it introduced uncertainty. Therefore, the need for eHMI should be further discussed.</div></div>","PeriodicalId":48355,"journal":{"name":"Transportation Research Part F-Traffic Psychology and Behaviour","volume":"114 ","pages":"Pages 621-632"},"PeriodicalIF":3.5,"publicationDate":"2025-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144364967","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":"Research on drivers’ hazard perception and visual characteristics before vehicle-to-powered two-wheeler collisions","authors":"Xianhui Wu ,&nbsp;Chenxi Li ,&nbsp;Xinghua Wang ,&nbsp;Guoliang Xiang ,&nbsp;Hanwen Deng ,&nbsp;Zhuoxi Jiang ,&nbsp;Yong Peng","doi":"10.1016/j.trf.2025.06.014","DOIUrl":"10.1016/j.trf.2025.06.014","url":null,"abstract":"<div><div>Understanding drivers’ hazard perception levels and visual behavior in conflict scenarios is crucial for improving traffic safety and advancing intelligent driving systems, especially given the growing complexity of traffic conditions and the rapid evolution of intelligent driving technologies. This study examines typical near-collision scenarios involving vehicles and powered two-wheelers, focusing on the effects of collision scenarios, driving states, and risk conditions on drivers’ hazard perception and visual characteristics. Using quantile regression and generalized linear mixed models, the study quantitatively assesses how these factors influence hazard perception and visual behavior, uncovering the visual response mechanisms underlying hazard perception. The results reveal that different vehicle-to-powered two-wheeler collision scenarios significantly affect drivers’ hazard perception and visual behavior. Drivers exhibited higher hazard perception levels and collision avoidance success rates in “Crossing from Right” and “Cut-in from Right” scenarios, whereas lower hazard perception abilities were observed in “Crossing from Left” and “Oncoming” scenarios. Fatigue was shown to severely impair drivers’ alertness and visual search abilities, resulting in diminished hazard perception levels. Under high-risk conditions, while drivers exhibited reduced collision avoidance success rates, their heightened attention and vigilance toward powered two-wheeler enhanced hazard perception. Besides, the study also highlights a strong correlation between visual characteristics and drivers’ hazard perception. These findings are significant for understanding the mechanisms underlying drivers’ hazard perception in intersection scenarios and may provide a scientific basis for future developments in human–machine collaborative monitoring and intelligent traffic safety strategies.</div></div>","PeriodicalId":48355,"journal":{"name":"Transportation Research Part F-Traffic Psychology and Behaviour","volume":"114 ","pages":"Pages 575-596"},"PeriodicalIF":3.5,"publicationDate":"2025-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144321749","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}