Analytic Methods in Accident Research最新文献

{"title":"Investigating the effects of sleepiness in truck drivers on their headway: An instrumental variable model with grouped random parameters and heterogeneity in their means","authors":"Amir Pooyan Afghari ,&nbsp;Eleonora Papadimitriou ,&nbsp;Fran Pilkington-Cheney ,&nbsp;Ashleigh Filtness ,&nbsp;Tom Brijs ,&nbsp;Kris Brijs ,&nbsp;Ariane Cuenen ,&nbsp;Bart De Vos ,&nbsp;Helene Dirix ,&nbsp;Veerle Ross ,&nbsp;Geert Wets ,&nbsp;André Lourenço ,&nbsp;Lourenço Rodrigues","doi":"10.1016/j.amar.2022.100241","DOIUrl":"10.1016/j.amar.2022.100241","url":null,"abstract":"<div><p>Sleepiness is a common human factor among truck drivers resulting from sleep loss or time of day and causing impairment in vigilance, attention, and driving performance. While driver sleepiness may be associated with increased risk on the road, sleepy drivers may drive more cautiously as a result of risk-compensating behaviour. This endogeneity has been overlooked in the previous driver behaviour studies and may provide new insight into the effects of sleepiness on driving performance. In addition, the Karolinska Sleepiness Scale (KSS) has been widely used to quantify sleepiness. However, the KSS is a subjective self-reported measure and is reliant on honest reporting and understanding of the scale. An alternative way of quantifying sleepiness is using drivers’ heart rate and correlating it with their sleepiness. While recent advances in data collection technologies have made it possible to collect heart rate data in real-time and in an unobtrusive way, their application in measuring sleepiness particularly among truck drivers has been unexplored.</p><p>This study aims to address these gaps and contribute to analytic methods in road safety research by collecting truck drivers’ heart rate data in real-time, measuring sleepiness from those data, and using it in an instrumental variable modelling framework to investigate its effect on driving performance. To this end, a driving simulator experiment was conducted in Belgium and heart rate data were collected for 35 truck drivers via sensors installed on the steering wheel of the simulator. Additional demographic data were collected using a questionnaire before the experiment. An instrumental variable model consisting of a discrete binary logit and a continuous generalized linear model with grouped random parameters and heterogeneity in their means was then developed to study the effects of driver sleepiness on headway. Results indicate that age, years of holding driver licence, road type, type of truck transport, and weekly distance travelled are significantly associated with sleepiness among the participants of this study. Sleepy driving is associated with reduced headway for 30.5% of the drivers and increased headway for the other 69.5%, and night-time shift is associated with such varied effects. These findings indicate that there may be group- or context-specific risk patterns which cannot be explicitly addressed by hours of service regulations and therefore, transport operators, driver trainers and fleet managers should identify and handle such context-specific high risk patterns in order to ensure safe operations.</p></div>","PeriodicalId":47520,"journal":{"name":"Analytic Methods in Accident Research","volume":null,"pages":null},"PeriodicalIF":12.9,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2213665722000306/pdfft?md5=368b5f5598c02639562d0c73dc43fd55&pid=1-s2.0-S2213665722000306-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46969838","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Bayesian correlated grouped random parameters duration model with heterogeneity in the means for understanding braking behaviour in a connected environment","authors":"Yasir Ali ,&nbsp;Md. Mazharul Haque ,&nbsp;Zuduo Zheng ,&nbsp;Amir Pooyan Afghari","doi":"10.1016/j.amar.2022.100221","DOIUrl":"10.1016/j.amar.2022.100221","url":null,"abstract":"<div><p>Driver’s response to a pedestrian crossing requires braking, whereby both excess and inadequate braking is directly associated with crash risk. The highly anticipated connected environment aims to increase drivers’ situational awareness by providing advanced information and assisting them during critical driving tasks such as braking. Focussing on this crucial behaviour and combined with the promise of a connected environment, the objective of this study is to examine the braking behaviour of drivers in response to a pedestrian at a zebra crossing in a connected environment. Seventy-eight participants from diverse backgrounds performed this driving task in the CARRS-Q Advanced Driving Simulator in two randomised driving scenarios: a baseline scenario (without driving aids) and a connected environment (with driving aids) scenario. A Weibull accelerated failure time duration modelling approach is adopted to model the braking behaviour of drivers. In particular, this duration model is specified to capture the panel nature of the data and unobserved heterogeneity through correlated grouped random parameters with heterogeneity-in-the-means in the Bayesian framework. Results indicate that, for most drivers in the connected environment, it takes longer to reduce their speed with less speed variation and a larger safety margin. In addition, a decision tree analysis for the braking time suggests that for older drivers, when the distance to the zebra crossing is larger in the connected environment than that in the baseline scenario, braking time is likely to increase. The model also reveals that the braking time of female drivers is longer in the connected environment compared to that of male drivers. Overall, the connected environment is associated with increased braking time by providing advanced information, giving drivers additional time to smoothly reduce their speed in response to a pedestrian at a zebra crossing, and ultimately making the vehicle–pedestrian interaction safer.</p></div>","PeriodicalId":47520,"journal":{"name":"Analytic Methods in Accident Research","volume":null,"pages":null},"PeriodicalIF":12.9,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42867061","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":"Using traffic flow characteristics to predict real-time conflict risk: A novel method for trajectory data analysis","authors":"Chen Yuan ,&nbsp;Ye Li ,&nbsp;Helai Huang ,&nbsp;Shiqi Wang ,&nbsp;Zhenhao Sun ,&nbsp;Yan Li","doi":"10.1016/j.amar.2022.100217","DOIUrl":"10.1016/j.amar.2022.100217","url":null,"abstract":"<div><p>The real-time conflict prediction model using traffic flow characteristics is much less studied than the crash-based model. This study aims at exploring the relationship between conflicts and traffic flow features with the consideration of heterogeneity and developing predictive models to identify conflict-prone conditions in a real-time manner. The high-resolution trajectory data from the HighD dataset is used as empirical data. A novel method with the virtual detector approach for traffic feature extraction and a two-step framework is proposed for the trajectory data analysis. The framework consists of an exploratory study by random parameter logit model with heterogeneity in means and variances and a comparative study on several machine learning methods, including eXtreme Gradient Boosting (Boosting), Random Forest (Bagging), Support Vector Machine (Single-classifier), and Multilayer-Perceptron (Deep neural network). Results indicate that (1) traffic flow characteristics have significant impacts on the probability of conflict occurrence; (2) the statistical model considering mean heterogeneity outperforms the counterpart and lane differences variables are found to significantly impact the means of random parameters for both lane variables and lane differences variables; (3) eXtreme Gradient Boosting trained on an under-sampled dataset turns out to be the best model with the highest AUC of 0.871 and precision of 0.867, showing that re-sampling techniques can significantly improve the model performance. The proposed model is found to be sensitive to the conflict threshold. Sensitivity analysis on feature selection further confirms that the conflict risk prediction should consider both subject lane features and lane difference features, which verifies the consistency with exploratory analysis based on the statistical model. The consistency between statistical models and machine learning methods improves the interpretability of results for the latter one.</p></div>","PeriodicalId":47520,"journal":{"name":"Analytic Methods in Accident Research","volume":null,"pages":null},"PeriodicalIF":12.9,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48568549","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 alternate crash severity multicategory modeling approach with asymmetric property","authors":"Dawei Li ,&nbsp;Mustafa F.M. Al-Mahamda ,&nbsp;Yuchen Song ,&nbsp;Siqi Feng ,&nbsp;N.N. Sze","doi":"10.1016/j.amar.2022.100218","DOIUrl":"10.1016/j.amar.2022.100218","url":null,"abstract":"<div><p>The logit model and its variations have been used extensively in the field of traffic safety in general, and crash severity analysis in particular. Attempts were made to overcome the logit's shortcomings and limitations by generalizing its binary form to a more relaxed and unconstrained setting. Such attempts include the addition of shape parameters in order to add more flexibility to the probability distribution, while maintaining the straightforwardness provided in the logit-type models, with the least computational effort. A well-known form that provides an extra parameter to the base logit is the scobit model. In this study, we explore several generalizations of the binary scobit model by applying the same conventional methods associated with the generalized logit forms, principally to cover the multinomial nature of crash severity outcomes. Those are the multinomial and the ordinal forms. Furtherly, we utilize mixed distributions to provide crash-specific random parameters with heterogeneity in means and variances. Crash severity dataset taken from Guangdong province, China, was used to compare the different forms. The multinomial scobit models provided better results in terms of sample and out-of-sample fit, with the cost of some complexity in the heterogeneous forms. Other forms did not show a substantial or consistent advantage over their logit counterparts. All models exhibit temporal instability when applied to multiple time periods.</p></div>","PeriodicalId":47520,"journal":{"name":"Analytic Methods in Accident Research","volume":null,"pages":null},"PeriodicalIF":12.9,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46179907","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 multiple membership multilevel negative binomial model for intersection crash analysis","authors":"Ho-Chul Park ,&nbsp;Byung-Jung Park ,&nbsp;Peter Y. Park","doi":"10.1016/j.amar.2022.100228","DOIUrl":"10.1016/j.amar.2022.100228","url":null,"abstract":"<div><p>Many intersections belong to more than one zone, but most research has not considered the effects of multiple zones in intersection crash analysis. This issue is known as a boundary problem. Unobserved heterogeneity between zones can lead to model misspecification which can result in biased parameter estimates and poor model fitting performance. This study investigated the issue using five years of intersection crash data from the City of Regina, Saskatchewan, Canada. The study developed three multiple membership multilevel negative binomial models to reduce unobserved zonal-level heterogeneity. Each multiple membership multilevel model used a different weight strategy. When the fitting performance of the three multiple membership multilevel models was compared with two additional models, a traditional single level model and a conventional multilevel model, all three multiple membership multilevel models had a better fitting performance. Five individual-level and seven group-level variables were statistically significant (90% confidence level) in all the models with five of the individual-level and four of the group-level variables statistically significant at the 99% confidence level. The multiple membership multilevel models also helped to prevent the underestimation of group-level variance and type I statistical errors that tend to occur with single level models and conventional multilevel models. In particular, the three multiple membership multilevel models produced more accurate results for intersections with a large AADT. As intersections with a large AADT are known to have more crashes, multiple membership multilevel models are likely to be more useful than single level models and conventional multilevel models when selecting intersections for safety improvement. The study recommends the adoption of a multiple membership multilevel model to improve fitting performance and reduce the boundary problem for intersections affected by more than one zone.</p></div>","PeriodicalId":47520,"journal":{"name":"Analytic Methods in Accident Research","volume":null,"pages":null},"PeriodicalIF":12.9,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2213665722000173/pdfft?md5=09f84d2d54f5d53ebf847f6860d121f1&pid=1-s2.0-S2213665722000173-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44686499","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Temporal stability of factors affecting injury severity in rear-end and non-rear-end crashes: A random parameter approach with heterogeneity in means and variances","authors":"Chenzhu Wang ,&nbsp;Fei Chen ,&nbsp;Yunlong Zhang ,&nbsp;Shuyi Wang ,&nbsp;Bin Yu ,&nbsp;Jianchuan Cheng","doi":"10.1016/j.amar.2022.100219","DOIUrl":"10.1016/j.amar.2022.100219","url":null,"abstract":"<div><p>Rear-end crashes have become a serious global issue, with increasing injuries and fatalities accounting for massive property loss. The purpose of this study is to investigate the variation in the influence of factors affecting injury severity in rear-end and non-rear-end crashes and the change in impact degree over time. Using the three-year crash data of the Beijing–Shanghai Expressway from 2017 to 2019, the heterogeneity and temporal stability of contributing factors affecting rear-end and non-rear-end crashes were investigated through a group of random parameter logit models with unobserved heterogeneity in means and variances. Then, the temporal stability and transferability of the models were evaluated using likelihood ratio tests. Moreover, the marginal effects were calculated to explore the temporal stability and potential heterogeneity of the contributing variables from year to year. Using four possible injury severity outcomes, namely, fatal injury, severe injury, minor injury, and no injury, a wide variety of possible factors significantly affecting injury severity outcomes including environmental, temporal, spatial, traffic, speed, geometric, and sight distance characteristics were analyzed. Considerable differences were observed in the rear-end and non-rear-end crashes, and the contributing factors indicated statistically significant temporal instability in both crashes over the three-year period. This study can be of value in promoting highway safety aimed at rear-end and non-rear-end crashes and developing suitable safety countermeasures.</p></div>","PeriodicalId":47520,"journal":{"name":"Analytic Methods in Accident Research","volume":null,"pages":null},"PeriodicalIF":12.9,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49668372","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":"Differences of overturned and hit-fixed-object crashes on rural roads accompanied by speeding driving: Accommodating potential temporal shifts","authors":"Xintong Yan ,&nbsp;Jie He ,&nbsp;Guanhe Wu ,&nbsp;Changjian Zhang ,&nbsp;Chenwei Wang ,&nbsp;Yuntao Ye","doi":"10.1016/j.amar.2022.100220","DOIUrl":"10.1016/j.amar.2022.100220","url":null,"abstract":"<div><p>Overturned crashes are associated with a disproportionate number of severe injuries and fatalities, while hit-fixed-object crashes are acknowledged as the most frequent single-vehicle crashes. To investigate the temporal stability and differences of contributing factors determining different injury severity levels<span> in overturned and hit-fixed-object crashes on rural roads<span> accompanied by speeding driving, this paper estimates two groups of correlated random parameters logit models with heterogeneity in the means (one group relating to overturned crashes and the other relating to hit-fixed-object crashes). Three injury-severity categories are determined as outcome variables: severe injury, minor injury and no injury, while multiple factors are investigated as explanatory variables including driver, vehicle, roadway, environmental, and crash characteristics. The overall temporal instability and non-transferability between overturned and hit-fixed-object crashes are captured through likelihood ratio tests<span>. Marginal effects are adopted to further exhibit temporal variations of the explanatory variables. Despite the overall temporal instability, some variables still present relative temporal stability such as alcohol, truck, aggressive driving, vehicle age (&gt;14 years old), and speed limit (&lt;45 mph). This non-transferability between overturned and hit-fixed-object crashes provides insights into developing differentiated strategies targeted at mitigating and preventing different types of crashes. Besides, out-of-sample prediction is undertaken given the captured temporal instability and non-transferability of overturned and hit-fixed-object crash observations. More studies can be conducted to accommodate the spatial instability, under-reporting of severe-injury crashes, the trade-off between model predictive capability, inference capability, and selectivity bias.</span></span></span></p></div>","PeriodicalId":47520,"journal":{"name":"Analytic Methods in Accident Research","volume":null,"pages":null},"PeriodicalIF":12.9,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42126118","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":"Evaluating the safety of autonomous vehicle–pedestrian interactions: An extreme value theory approach","authors":"Abdul Razak Alozi,&nbsp;Mohamed Hussein","doi":"10.1016/j.amar.2022.100230","DOIUrl":"10.1016/j.amar.2022.100230","url":null,"abstract":"<div><p>With the increasing advancements in autonomous vehicle (AV) technologies, the forecasts of AV market shares seem to follow an ever-growing trend. This leads to the inherent need for proactive safety evaluations of AV impacts on other road users. To that end, this study proposes a modeling framework for the proactive assessment of pedestrian safety in AV environments. The proposed framework relies on the Extreme Value Theory (EVT), with the peak over threshold modeling technique, to develop an estimate of AV-pedestrian collisions using AV-pedestrian conflicts. The proposed framework was applied to two AV datasets, collected from three locations in the US and Singapore, using the operating AV fleets of two developers, Motional and Lyft. Both datasets included trajectory data for the subject AV, as well as LiDAR point clouds and annotated video data from AV cameras to capture the trajectories of surrounding road users. The datasets were processed to extract the AV-pedestrian conflicts along with the corresponding conflict indicators, mainly the post-encroachment time (PET) and time-to-collision (TTC). Relevant covariates were introduced to the proposed models to enhance their performance and prediction accuracy, including turning movements and conflict speeds. The results indicate an alarming risk to pedestrians when interacting with AVs, especially at the early stages of AV adoption. The expected number of collisions ranged from 4 to 5.5 per million vehicle kilometers travelled (VKT) of the AVs. With the addition of the covariates, the expected number of collisions went down to a range of 2.3–3.7 per million VKT, but with a narrower confidence interval of the resulting estimate and a better fit. The introduced approach shows promising prospects for the application of EVT methods to address AV safety impacts. It also invites future applications to address issues of concern for pedestrian safety in different conditions of urban traffic.</p></div>","PeriodicalId":47520,"journal":{"name":"Analytic Methods in Accident Research","volume":null,"pages":null},"PeriodicalIF":12.9,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48290211","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":"The impact of weekday, weekend, and holiday crashes on motorcyclist injury severities: accounting for temporal influence with unobserved effect and insight from out-of-sample prediction","authors":"Chamroeun Se, Thanapong Champahom, Sajjakaj Jomnonkwao, N. Kronprasert, V. Ratanavaraha","doi":"10.1016/j.amar.2022.100240","DOIUrl":"https://doi.org/10.1016/j.amar.2022.100240","url":null,"abstract":"","PeriodicalId":47520,"journal":{"name":"Analytic Methods in Accident Research","volume":null,"pages":null},"PeriodicalIF":12.9,"publicationDate":"2022-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48729408","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 generative approach for crash frequency model with heterogeneous imbalanced data","authors":"Hongliang Ding ,&nbsp;Yuhuan Lu ,&nbsp;N.N. Sze ,&nbsp;Tiantian Chen ,&nbsp;Yanyong Guo ,&nbsp;Qinghai Lin","doi":"10.1016/j.amar.2022.100212","DOIUrl":"10.1016/j.amar.2022.100212","url":null,"abstract":"<div><p>Crash frequency model is often subject to excessive zero observation because of the rare nature of crashes. To address the problem of imbalanced crash data, a deep generative approach – augmented variational autoencoder – was proposed to generate synthetic crash data for the association measure between crash and possible explanatory factors. This approach was characterized by a factorized generative model and refined objective function. For instance, the generative model can handle heterogeneous data including real-valued, nominal and ordinal distributions. On the other hand, the refined objective function can control for the random effect by better recognizing both the zero-crash and non-zero crash cases. In this study, comprehensive traffic and crash data of multiple distribution types in Hong Kong in the period between 2014 and 2016 were used. To assess the data generation performance of the proposed augmented variational autoencoder method, a conventional data synthesis technique (synthetic minority oversampling technique-nominal continuous) was also considered. Performances of crash frequency models of total crashes and fatal and severe injury crashes are assessed. For total crashes, the results of parameter estimation, in terms of statistical fit, prediction accuracy, and explanatory factors identified, of the crash frequency model based on synthetic data using the augmented variational autoencoder method adhered closer to that based on original data, compared to that based on synthetic data using the synthetic minority oversampling technique-nominal continuous method. For fatal and severe injury crashes, zero-crash observations were prevalent, with the ratio of zero-crash to non-zero crash cases of 9 to 1. Crash data was first balanced using the proposed augmented variational autoencoder method. Then, fatal and severe injury crash frequency models using correlated random parameter models based on original data and balanced data were estimated respectively. Results indicate that fatal and severe injury crash frequency model based on balanced data outperforms its counterpart, with the lowest root mean square error, lowest mean absolute error, and highest number of crash explanatory factors identified. More importantly, correlation between the random parameters can be revealed. Findings of this study should shed light to both researchers and practitioners for the development of crash frequency models, with which the problem of excessive zero observations is prevalent when highly disaggregated traffic and crash data by time and space are used.</p></div>","PeriodicalId":47520,"journal":{"name":"Analytic Methods in Accident Research","volume":null,"pages":null},"PeriodicalIF":12.9,"publicationDate":"2022-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49094989","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}