Analytic Methods in Accident Research最新文献

{"title":"Assessing traffic conflict/crash relationships with extreme value theory: Recent developments and future directions for connected and autonomous vehicle and highway safety research","authors":"Yasir Ali ,&nbsp;Md Mazharul Haque ,&nbsp;Fred Mannering","doi":"10.1016/j.amar.2023.100276","DOIUrl":"10.1016/j.amar.2023.100276","url":null,"abstract":"<div><p>With proactive safety assessment gaining significant attention in the literature, the relationship between traffic conflicts (which form the underpinnings of proactive safety measures) and observed crashes remains a critical research need. Such a need will grow significantly with the ongoing introduction of connected and autonomous vehicles where software and hardware improvements are likely to be determined from observed traffic conflict data as opposed to data from accumulated crashes. Extreme value theory has been applied for over two decades to study the relationship between traffic conflicts and crashes. While several advancements have been made in extreme value theory models over time, the need to continually evaluate the strengths and weaknesses of these models remains, particularly considering their likely use in improving the safety–critical elements of connected and autonomous vehicles. This paper seeks to comprehensively review studies on extreme value theory applications in traffic conflict/crash contexts by providing an in-depth assessment of alternate modelling methodologies and associated issues. Critical research needs relating to the further development of extreme value theory models are identified and include identifying efficient techniques for sampling extremes, determining optimal sample size, assessing and selecting appropriate traffic conflict measures, incorporating covariates, accounting for unobserved heterogeneity, and addressing issues associated with real-time estimations.</p></div>","PeriodicalId":47520,"journal":{"name":"Analytic Methods in Accident Research","volume":null,"pages":null},"PeriodicalIF":12.9,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41582796","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":"Car-following crash risk analysis in a connected environment: A Bayesian non-stationary generalised extreme value model","authors":"Faizan Nazir ,&nbsp;Yasir Ali ,&nbsp;Anshuman Sharma ,&nbsp;Zuduo Zheng ,&nbsp;Md Mazharul Haque","doi":"10.1016/j.amar.2023.100278","DOIUrl":"10.1016/j.amar.2023.100278","url":null,"abstract":"<div><p>A connected environment provides driving aids to assist drivers in decision-making and aims to make driving manoeuvres safer by minimising uncertainty associated with decisions. The role of a connected environment becomes vital for car-following manoeuvres in a safety–critical event, whereby drivers follow a lead vehicle, and if timely action is not taken, it is likely to lead to a rear-end collision. Moreover, how different drivers perceive and react to the same information needs to be explored to understand the differential effects of a connected environment on car-following behaviour. As such, this study investigated the effects of the traditional and connected environments on car-following crash risk using traffic conflict techniques. Data were collected using the CARRS-Q advanced driving simulator, whereby 78 participants performed a car-following task in two randomised driving conditions: baseline (without driving aids) and connected environment (with driving aids). The safety–critical event in the car-following scenario was the leader’s hard braking, for which participants received advance information, besides several other driving aids. Modified time-to-collision was used as the traffic conflict measure for characterising rear-end crash risk and modelled using a generalised extreme value (GEV) model in the Bayesian framework. This model incorporated driving-related factors and driver demographics to address the non-stationarity issue of traffic extremes. Results reveal that the car-following crash risk is significantly reduced in the connected environment. Further, using the developed model, separate GEV distributions were estimated for each individual driver, providing insights into the heterogeneous effects of the connected environment on crash risk. The developed model was employed to understand the crash risk across different driver characteristics, and results suggest that crash risk decreases for all age groups and gender, with the maximum safety benefits obtained by young and female drivers. The findings of this study shed light on the efficacy of the connected environment in improving car-following behaviour and drivers’ ability to make safer decisions.</p></div>","PeriodicalId":47520,"journal":{"name":"Analytic Methods in Accident Research","volume":null,"pages":null},"PeriodicalIF":12.9,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46584217","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":"Analysis of duration between crashes using a hazard-based duration approach with heterogeneity in means and variances: Some new evidence","authors":"Mohammad M. Hamed,&nbsp;Ahmad AlShaer","doi":"10.1016/j.amar.2023.100283","DOIUrl":"10.1016/j.amar.2023.100283","url":null,"abstract":"<div><p>This paper provides new evidence for the factors underlying crash involvement by modeling the time duration between crashes for drivers involved in one or more crashes between 2016 and 2020. Several random parameter hazard-based duration models with heterogeneous means and variances are presented. Among this study’s other findings, the results show that male drivers had a higher risk of being involved in one crash than female drivers (among drivers involved in only one crash). Female drivers were more likely to be involved in higher-order crashes however. Among female drivers involved in only one crash, millennials had the highest crash risk. However, baby boomers and Gen Z drivers had a greater risk of being involved in a crash than millennials or Gen X drivers, whether male or female. The analysis presents evidence for distinct crash risk patterns in men and women and among different age groups. The lagged duration dependence indicates that the longer the time from a previous crash, the sooner the driver will be involved in their next crash. In addition, the lagged duration dependence suggests two types of dependencies. The first is profound dependency. Drivers with this type of dependency tended to be tier-three male millennials, tier-three Gen X drivers, tier-three Gen Z drivers, or tier-four male millennials. The second is shallow dependency. Drivers with this type of dependency tended to be tier-three female millennials, tier-four male Gen X drivers, and tier-five male millennials. The likelihood of a crash was almost independent of the time that had transpired without a crash for those involved in more than one crash. Estimation results also revealed that crash survivors showed different subsequent behavior. Surviving a severe crash and experiencing crashes involving multiple vehicles may lead to hazardous habituation among male millennials. Moreover, many drivers seemed to alter their behavior after the first crash, particularly male and female drivers involved in one crash only. Other drivers did not show any behavioral changes, including tier-three female millennials, tier-four male Gen X, and tier-five male millennials, who had a shallow lagged dependency, and their likelihood of a crash was almost independent of the time that transpired without a crash.</p></div>","PeriodicalId":47520,"journal":{"name":"Analytic Methods in Accident Research","volume":null,"pages":null},"PeriodicalIF":12.9,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45733299","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":"How heterogeneity has been examined in transportation safety analysis: A review of latent class modeling applications","authors":"Sung Hoo Kim","doi":"10.1016/j.amar.2023.100292","DOIUrl":"10.1016/j.amar.2023.100292","url":null,"abstract":"<div><p>This study explores how heterogeneity has been examined in transportation safety analyses, specifically focusing on latent class modeling, which has gained popularity and has successfully captured unobserved heterogeneity. The study firstly identifies a large volume of relevant papers in the safety analysis domain and analyzes how models have been used by focusing on key elements of the latent class model (along with the proposed typology of segmentation-based heterogeneity models). In the literature, various class-specific outcome models have been used. They are determined by the type of outcome variable and are also highly associated with the analysis context. For example, crash severity and crash likelihood/frequency analyses are the main applications where crash severity is often treated as binary, nominal, or ordered, whereas crash likelihood/frequency is subject to count data or survival data modeling. The study reviews the number of classes selected in empirical applications and how they were determined. It is found that in safety analyses, it is more common to choose the number of classes based on the judgement of the analyst than quantitative measures (e.g., BIC). This implies that we value interpretability of the latent class model and solutions with many classes (i.e., greater model complexity, many parameters) often hinder the interpretation of models. This paper also covers further discussions about heterogeneity including model comparisons (homogeneity models versus latent class models and random parameters versus latent class models), modeling intra-class heterogeneity, possible alternative model specifications that have been rarely used in the literature, and issues related to temporal instability.</p></div>","PeriodicalId":47520,"journal":{"name":"Analytic Methods in Accident Research","volume":null,"pages":null},"PeriodicalIF":12.9,"publicationDate":"2023-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46794557","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":"Modelling the continuum of serious traffic injuries in police-hospital linked data by applying the random parameters hazard-based duration model","authors":"Khalid Alzaffin ,&nbsp;Sherrie-Anne Kaye ,&nbsp;Angela Watson ,&nbsp;Md Mazharul Haque","doi":"10.1016/j.amar.2023.100291","DOIUrl":"10.1016/j.amar.2023.100291","url":null,"abstract":"<div><p>Injury severity in police crash reports is usually recorded in three to five classes, including property damage, slight, moderate, serious, and fatal injuries. Among these classifications, serious injuries are commonly classified as cases where a road user is admitted to a hospital. In this classification, the length of hospital stay is not differentiated, whether one day or ten days, as long as the road user has been admitted to the hospital. As such, the inferences drawn from assuming that all serious injuries (1 if a road user is admitted to the hospital; 0 otherwise) are at the same severity level inherently suffer from aggregation bias and may not provide a thorough understanding of this severity category. This study proposes a hazard-based duration modelling approach to examine the severity of serious injury crashes measured in a continuous spectrum. Specifically, using the length of hospital stay as the measure of serious injuries, a random parameters hazard-based duration model with heterogeneity in means was applied to model serious injury crashes obtained by linking crash records in police and hospital databases. To address temporal instability, the injury records sources from Abu Dhabi, United Arab Emirates (UAE), between 2015 and 2019 were modelled separately for each year. The results showed that factors positively associated with more serious injury severity (prolonged length of hospital stay) are rural areas, high posted speed limits of 100–160 km/h, overturned crashes, speeding, impaired driving, involvements of a heavy vehicle, nighttime crashes, lack of restraint usage, and injuries to the head or lower extremities. In particular, speeding violations during nighttime are positively associated with more serious injuries. Furthermore, the means of the random parameters of head injury are positively influenced by speeding, lack of restraint usage, and motorcycle involvement through the heterogeneity-in-mean specification of the hazard-based duration model. The proposed modelling approach to model serious traffic injuries using a hazard-based duration model provides a comprehensive understanding of the factors associated with serious injuries.</p></div>","PeriodicalId":47520,"journal":{"name":"Analytic Methods in Accident Research","volume":null,"pages":null},"PeriodicalIF":12.9,"publicationDate":"2023-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41576855","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":"Modelling speed reduction behaviour on variable speed limit-controlled highways considering surrounding traffic pressure: A random parameters duration modelling approach","authors":"Yasir Ali ,&nbsp;Mark P.H. Raadsen ,&nbsp;Michiel C.J. Bliemer","doi":"10.1016/j.amar.2023.100290","DOIUrl":"10.1016/j.amar.2023.100290","url":null,"abstract":"<div><p>Variable speed limits are frequently used to improve traffic safety and harmonise traffic flow. This study investigates how, and to what extent, drivers reduce their speed upon passing a variable speed limit sign. We specifically consider the impact on braking behaviour due to the systematic inclusion of different social pressures exerted by surrounding traffic. This social pressure is the natural result of having two vehicle cohorts created by a change in the variable speed limit (the new speed limit being higher than the original). The cohort with the higher speed limit overtakes vehicles with the lower speed limit, instigating a specific passing rate on drivers in the lower speed cohort. A driving simulator study is employed to obtain individual driver data whilst being able to systematically change the social pressure applied. A sample comprising sixty-seven participants conducted multiple randomised drives, with varying passing rates from as low as 90 veh/h to as high as 360 veh/h. The speed reduction behaviour of the participants is modelled using a <em>random parameter duration modelling approach</em>. Both the panel nature of the data and unobserved heterogeneity are captured through a <em>correlated grouped random parameters with heterogeneity-in-the-mean</em> model. The random parameters are predicated on the different passing rate scenarios, allowing drivers to take shorter or longer to reduce their speeds compared to the reference passing rate. It is shown that the extent of social pressure impacts braking behaviour and therefore affects safety measures, which is a function of the magnitude of the speed limit change. In addition, an extensive decision tree analysis is conducted to understand differential braking behaviour. Results reveal that, on average, female drivers take a shorter time to reduce their speed under a high passing rate but longer in a low passing rate scenario compared to males. Similarly, young drivers are found to take longer to reduce their speeds in a high passing rate scenario compared to other age groups. Our main findings indicate that the within-cohort safety is lowest under low passing rates due to comparatively larger speed differences between drivers. Yet, under a high passing rate, we observe an increase in violation of the speed limit by the lower speed limit vehicles (but less within cohort speed differences). Whilst normally this would be an undesired effect across cohorts, this violation is argued to lead to increased safety due to the smaller discrepancy in speed.</p></div>","PeriodicalId":47520,"journal":{"name":"Analytic Methods in Accident Research","volume":null,"pages":null},"PeriodicalIF":12.9,"publicationDate":"2023-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48974602","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":"Temporal instability and age differences of determinants affecting injury severities in nighttime crashes","authors":"Xintong Yan,&nbsp;Jie He,&nbsp;Changjian Zhang,&nbsp;Chenwei Wang,&nbsp;Yuntao Ye,&nbsp;Pengcheng Qin","doi":"10.1016/j.amar.2023.100268","DOIUrl":"10.1016/j.amar.2023.100268","url":null,"abstract":"<div><p><span>Driving at nighttime may make drivers more likely to be involved in fatal crashes. To investigate the temporal instability and age differences of contributors determining different injury severity levels in nighttime crashes, this paper estimates three groups of random parameters logit models with heterogeneity in the means and variances (young/middle-age/old groups). Nighttime single-vehicle crashes in this study are gathered over four years in California, from January 1, 2014, to December 31, 2017, provided by Highway Safety Information System, including single-vehicle crashes occurring under dark, dawn, and dusk lighting conditions. Simultaneously, to investigate the temporal instability and transferability of nighttime crash severity relating to drivers of different ages, three disaggregate groups are defined: young drivers (15–29 years old), middle-age drivers (30–49 years old), old drivers (over 49 years old). 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 characteristics, vehicle characteristics, roadway characteristics, environmental characteristics, crash characteristics, and temporal characteristics. Two series of </span>likelihood ratio tests are undertaken to unveil the contributors determining nighttime crash injury severities varying among drivers of different ages over time. Besides, the current study also compares the differences between out-of-sample and within-sample predictions. The results indicate the unstable direction of predictions across different age groups over time and underscore the necessity to adequately accommodate the temporal instability and age differences in accident prediction. More studies can be conducted to accommodate the self-selectivity issue and the out-of-sample prediction differences between using the parametric models and non-parametric models.</p></div>","PeriodicalId":47520,"journal":{"name":"Analytic Methods in Accident Research","volume":null,"pages":null},"PeriodicalIF":12.9,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42506567","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 empirical investigation of driver car-following risk evolution using naturistic driving data and random parameters multinomial logit model with heterogeneity in means and variances","authors":"Qiangqiang Shangguan ,&nbsp;Junhua Wang ,&nbsp;Ting Fu ,&nbsp;Shou'en Fang ,&nbsp;Liping Fu","doi":"10.1016/j.amar.2022.100265","DOIUrl":"https://doi.org/10.1016/j.amar.2022.100265","url":null,"abstract":"<div><p>This study aims to address the questions of how driving risk evolves during car-following processes and what factors contribute to the underlying evolution patterns. An empirical study is conducted using real world car-following data collected in the Shanghai Naturalistic Driving Study (SH-NDS). The evolution of the driving risk induced by the dynamic coupling between the leading and following vehicles during the car-following process is characterized by how an instantaneous crash-risk measure - rear crash risk index (RCRI) - changes by time. A spectral clustering analysis is first conducted to classify the driving risk evolution of the observed car-following maneuvers, showing the existence of five distinctive risk evolution patterns in the car-following processes. In order to investigate the relationship between the identified driving risk evolution clusters and their contributing factors, a regression analysis<span> employing a random parameter multinomial logit model with heterogeneity in means and variances is followed, revealing several significant contributing factors to the car-following risk evolution patterns, such as congestion level, driver’s ability to maintain stable headways, and vehicle deceleration. This study has provided important insights into driving risk from the new perspective of risk evolution patterns, which is expected to have significant implications for the future development of advanced traffic management and traveler information systems (ATMS/ATIS) strategies, advanced driver assistance systems (ADAS), and connected and autonomous vehicles (CAV).</span></p></div>","PeriodicalId":47520,"journal":{"name":"Analytic Methods in Accident Research","volume":null,"pages":null},"PeriodicalIF":12.9,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49701524","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":"Evidence of sample selectivity in highway injury-severity models: The case of risky driving during COVID-19","authors":"Mouyid Islam ,&nbsp;Asim Alogaili ,&nbsp;Fred Mannering ,&nbsp;Michael Maness","doi":"10.1016/j.amar.2022.100263","DOIUrl":"10.1016/j.amar.2022.100263","url":null,"abstract":"<div><p>Research in highway safety continues to struggle to address two potentially important issues; the role that unobserved factors may play on resulting crash and injury-severity likelihoods, and the issue of identification in safety modeling caused by the self-selective sampling inherent in commonly used safety data (the fact that drivers in observed crashes are not a random sample of the driving population, with riskier drivers being over-represented in crash data bases). This paper addresses unobserved heterogeneity using mixing distributions and attempts to provide insight into the potential sample-selection problem by considering data before and during the COVID-19 pandemic. Based on a survey of vehicle usage (vehicle miles traveled) and subsequent statistical modeling, there is evidence that riskier drivers likely made up a larger proportion of vehicle miles traveled during the pandemic than before, suggesting that the increase in injury severities observed during COVID-19 could potentially be due to the over-representation of riskier drivers in observed crash data. However, by exploring Florida crash data before and during the pandemic (and focusing on crashes where risky behaviors were observed), the empirical analysis of observed crash data suggests (using random parameters multinomial logit models of driver-injury severities with heterogeneity in means and variances) that the observed increase in injury severity during the COVID-19 pandemic (calendar year 2020) was likely due largely to fundamental changes in driver behavior and less to changes in the sample selectivity of observed crash data. The findings of this paper provide some initial guidance to future work that can begin to more rigorously explore and assess the role of selectivity and resulting identification issues that may be present when using observed crash data.</p></div>","PeriodicalId":47520,"journal":{"name":"Analytic Methods in Accident Research","volume":null,"pages":null},"PeriodicalIF":12.9,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45994803","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":"Incorporating real-time weather conditions into analyzing clearance time of freeway accidents: A grouped random parameters hazard-based duration model with time-varying covariates","authors":"Qiang Zeng ,&nbsp;Fangzhou Wang ,&nbsp;Tiantian Chen ,&nbsp;N.N. Sze","doi":"10.1016/j.amar.2023.100267","DOIUrl":"10.1016/j.amar.2023.100267","url":null,"abstract":"<div><p>To minimize non-recurrent congestion, a better understanding of the factors that affect accident clearance time is crucial, in order to optimize incident management strategies. A number of methods have been developed to predict incident clearance duration, but few of those have considered the time-varying nature of certain observed factors. In addressing this gap in the literature, this study developed a grouped random parameters hazard-based duration model with time-varying covariates, while accounting for unobserved heterogeneity. Data on accidents, traffic, road inventory, and real-time weather condition were compiled for the Kaiyang freeway in 2014. Comparison of candidate models shows that the proposed model with Weibull distribution exhibits the best fit performance. The results suggest that the effects of rear-end accident, involvements of trucks or other vehicles, evening hours, and shoulder blockage on the hazard function are heterogeneous across observations. Other variables such as angle accident, injury severity, traffic volume and composition, morning or pre-dawn hours, and blockage of overtaking lane were also found to have significant but homogenous effects on accident clearance time. More importantly, the results also reveal the significant effects of the time-varying covariates (wind speed, temperature, and humidity). Accordingly, the viability and superiority of the proposed model in analyzing accident clearance time are confirmed. Overall, the results of this study are expected not only to improve traffic incident management by allowing government agencies to better understand factors affecting accident clearance times, but also to facilitate incident clearance through the recognition of time-varying pattern.</p></div>","PeriodicalId":47520,"journal":{"name":"Analytic Methods in Accident Research","volume":null,"pages":null},"PeriodicalIF":12.9,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42932697","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}