Analytic Methods in Accident Research最新文献

{"title":"Econometric approaches to examine the onset and duration of temporal variations in pedestrian and bicyclist injury severity analysis","authors":"Natakorn Phuksuksakul ,&nbsp;Naveen Eluru ,&nbsp;Md. Mazharul Haque ,&nbsp;Shamsunnahar Yasmin","doi":"10.1016/j.amar.2024.100362","DOIUrl":"10.1016/j.amar.2024.100362","url":null,"abstract":"<div><div>There is considerable evidence in existing safety literature that the exogenous variable effects are likely to be time-varying in the injury severity analysis. The majority of these earlier studies tested time-varying effects of exogenous variables by crash year. However, there might be variability in the variable effects within a year, while the same effect might carry over in some or all parts of the preceding years. Towards that end, in this study, we propose a flexible framework to identify when the time-varying effect is likely to occur (the onset of temporal variation) and how long such time-varying effect lasts (duration of temporal variation) in the model estimates. In the study design, we assume that the onset of temporal variation can be any quarter of a year under consideration, while the time-varying effect can continue over different quarters after the onset of temporal variation in a variable effect. The injury severity model is estimated by using Correlated Random Parameter Generalized Ordered Logit formulation with piecewise linear functions. The empirical analysis is demonstrated by employing active traveler (pedestrian and bicyclist) crash data from Queensland, Australia for the years 2015 through 2020. The estimation results are further augmented by computing elasticity effects. The results indicate that the time-varying effects are likely to be different across years for several variables, while for other variables, the onset of time-varying effects could be different than the start of a year. Such flexibility in model specification is likely to have significant implications for devising and implementing effective countermeasures since it allows us to understand how road traffic injuries are evolving over time and when a new road safety issue might be arising.</div></div>","PeriodicalId":47520,"journal":{"name":"Analytic Methods in Accident Research","volume":"45 ","pages":"Article 100362"},"PeriodicalIF":12.5,"publicationDate":"2024-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142699737","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":"Determinants influencing alcohol-related two-vehicle crash severity: A multivariate Bayesian hierarchical random parameters correlated outcomes logit model","authors":"Miaomiao Yang,&nbsp;Qiong Bao,&nbsp;Yongjun Shen,&nbsp;Qikai Qu,&nbsp;Rui Zhang,&nbsp;Tianyuan Han,&nbsp;Huansong Zhang","doi":"10.1016/j.amar.2024.100361","DOIUrl":"10.1016/j.amar.2024.100361","url":null,"abstract":"<div><div>Alcohol-related driving remains a significant concern due to its profound association with the likelihood of traffic crashes and the severity of resulting injuries, especially between two vehicles. To investigate the determinants influencing the alcohol-related two-vehicle crash severity, a foundational framework employed was a multinomial logit model. Meanwhile, by incorporating random intercept from individual case and vehicle levels to accommodate unobserved heterogeneity, and covariance matrices to underscore correlated outcomes, a multivariate hierarchical random parameters correlated outcomes logit model was proposed. Additionally, to further explore the potential temporal instability of explanatory variables, a random slope from a per-year indicator was introduced into the model. Crash data from the US Statewide Integrated Traffic Records System (SWITRS) database spanning from January 1, 2016, to December 31, 2021, was used. Three crash injury severity categories were examined, encompassing severe injury, minor injury, and no injury, with characteristics related to the driver, vehicle, road, environment, crash, and time serving as explanatory variables. The model results highlighted significant heterogeneity, with each case and vehicle accounting for 56.9% of the total variance for minor injuries and 50.8% for severe injuries. Furthermore, a significant negative correlation was explicitly exhibited between minor injury and severe injury outcomes at the case level. In terms of potential temporal instability, we provided per-year (2016–2019) parameter estimates and identified significant instability for indicators such as non-intersection, broadside and head-on collisions, cloudy weather conditions, and drivers who had been drinking but were not under the influence. Considering the impact of the COVID-19 pandemic, we divided the accident time into pre-COVID and during-COVID periods, modeling parameter estimates for both periods. This analysis revealed significant instability in several factors influenced by the pandemic. Additionally, noteworthy disparities in the estimated results of explanatory variables emerged in comparison to those general two-vehicle crashes or alcohol-related crashes, providing valuable insights. For instance, drivers who had been drinking but were not under the influence were less likely to sustain severe injuries, but the probability of minor injuries increased. These findings underscore the significance of thorough investigations into the determinants of injury severity in alcohol-impaired two-vehicle crash severity, along with the temporal instability of such factors. They hold important implications for effective traffic safety management and the formulation of prohibitive countermeasures.</div></div>","PeriodicalId":47520,"journal":{"name":"Analytic Methods in Accident Research","volume":"44 ","pages":"Article 100361"},"PeriodicalIF":12.5,"publicationDate":"2024-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142322065","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":"Effects of sample size on pedestrian crash risk estimation from traffic conflicts using extreme value models","authors":"Faizan Nazir ,&nbsp;Yasir Ali ,&nbsp;Md Mazharul Haque","doi":"10.1016/j.amar.2024.100353","DOIUrl":"10.1016/j.amar.2024.100353","url":null,"abstract":"<div><div>Sample size plays a critical role in an Extreme Value Theory (EVT) model for estimating crash risks from traffic conflicts. Many studies have raised concerns regarding sample size and its consequent negative impact on the performance of EVT models. However, the effects of sample size on EVT models are not well-known, requiring an extensive investigation and a deeper understanding of the effects of sample size on model performance. Motivated by this research gap, this study proposes a systematic approach to examine the effects of sample size on EVT models aimed at estimating pedestrian crash risks from traffic conflicts. Ten smaller and homogeneous samples of traffic conflicts are derived from a total of 144 h of video data collected from three signalised intersections in Brisbane, Australia, whereby vehicle–pedestrian conflicts are measured by post encroachment time. To ensure that each subset contains equal data from three intersections, samples are formed using a uniform distribution, and their effects are tested using non-stationary Block Maxima and Peak Over Threshold models estimated in the Bayesian framework. Results show that the sample size influences the prediction of mean crash frequencies, confidence intervals, and relative errors. Although the effect of sample size is non-uniform, the model performance appears to improve with the increase in sample size, whereby the block maxima models show higher sensitivity towards sample size variation, and the peak over threshold models reveal relatively stable and better performance. Moreover, a comparison of sample size thresholds indicates that the peak over threshold approach is more cost-efficient than its counterpart. Overall, the findings of this study demonstrate that improper sample size can lead to poor predictability, low reliability, and large uncertainties.</div></div>","PeriodicalId":47520,"journal":{"name":"Analytic Methods in Accident Research","volume":"44 ","pages":"Article 100353"},"PeriodicalIF":12.5,"publicationDate":"2024-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142358556","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 cross-comparison of different extreme value modeling techniques for traffic conflict-based crash risk estimation","authors":"Depeng Niu ,&nbsp;Tarek Sayed ,&nbsp;Chuanyun Fu ,&nbsp;Fred Mannering","doi":"10.1016/j.amar.2024.100352","DOIUrl":"10.1016/j.amar.2024.100352","url":null,"abstract":"<div><p>Extreme Value Theory (EVT) models have recently gained increasing popularity for crash risk estimation using traffic conflict data. Extreme value modeling consists of two fundamental approaches: the block maxima approach and the peak-over-threshold approach, each with several variants. However, a comprehensive comparison of these two approaches and their variants in crash risk estimation is lacking. This study bridges this gap by comparing different extreme value modeling techniques and evaluating their performance in estimating crash frequencies. Within a non-stationary Bayesian hierarchical modeling framework, the analyzed models include the block maxima model, the <span><math><mrow><mi>r</mi></mrow></math></span> largest order statistic model, and the peak-over-threshold model with the fixed and dynamic threshold, across univariate and bivariate traffic conflict cases. The analysis utilizes modified time-to-collision and post-encroachment time conflict indicator data collected from four signalized intersections in the City of Surrey, British Columbia, Canada. The results show that incorporating additional order statistics in the <span><math><mrow><mi>r</mi></mrow></math></span> largest order statistic model improves predictive performance, particularly with limited extreme conflict samples. Moreover, employing the dynamic threshold within the peak-over-threshold model enhances model goodness-of-fit and yields more accurate crash frequency estimates compared to using the fixed threshold. While the performance of the block maxima and peak-over-threshold models varies with the selected conflict indicator in the univariate case, the bivariate peak-over-threshold model with the dynamic threshold exhibits superior overall prediction accuracy over the corresponding block maxima model. This is likely due to the effectiveness of the dynamic threshold in precisely identifying truly critical extreme conflicts.</p></div>","PeriodicalId":47520,"journal":{"name":"Analytic Methods in Accident Research","volume":"44 ","pages":"Article 100352"},"PeriodicalIF":12.5,"publicationDate":"2024-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2213665724000368/pdfft?md5=e0524d026183a3a445ae1bb4c5f67a44&pid=1-s2.0-S2213665724000368-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142148447","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":"The role of posted speed limit on pedestrian and bicycle injury severities: An investigation into systematic and unobserved heterogeneities","authors":"Natakorn Phuksuksakul ,&nbsp;Mazharul Haque ,&nbsp;Shamsunnahar Yasmin","doi":"10.1016/j.amar.2024.100351","DOIUrl":"10.1016/j.amar.2024.100351","url":null,"abstract":"<div><p>The posted speed limit, as a proxy of actual speed, is one of the most fundamental predictors of active travelers’ (pedestrian and bicyclist) injury outcomes when involved in crashes with motor vehicles. Although earlier studies predominantly considered posted speed limit as an exogenous variable and provided highly insightful findings, majorities of them assume the effects of active traveler behavior to remain the same across different posted speed limits, which in turn neglect the heterogeneity in active traveler behaviors on high-speed roads vs. low-speed roads. This study proposes to develop a latent segmentation-based active traveler injury severity model to relax the homogeneity assumption of the posted speed limit by active traveler behavior. Specifically, this study proposes to estimate a latent segmentation-based correlated random parameters generalized ordered logit model to examine active travel injury severity mechanisms. The proposed model accommodates systematic heterogeneity in the effects of posted speed limit, crash year and active traveler group by using a piecewise linear function in injury severity component of the latent segment model. The model is demonstrated by using active traveler crash data from Queensland, Australia, for the years 2015 through 2019. To demonstrate the implications of the estimated models, a number of hypothetical scenario analyses are performed with a specific focus on active traveler behavior and reduction in posted speed limits. The outcomes from the hypothetical scenario analysis highlighted that a 76 % (73 %) reduction in active traveler fatalities can be achieved by converting 50–60 km/hr roadways to 10–40 km/hr roadways in the urban areas (rural areas) of Queensland. The outcomes of the study will allow us to identify effective speed management strategies while targeting those with high-risk behavior.</p></div>","PeriodicalId":47520,"journal":{"name":"Analytic Methods in Accident Research","volume":"44 ","pages":"Article 100351"},"PeriodicalIF":12.5,"publicationDate":"2024-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2213665724000356/pdfft?md5=1107e410ee25ebd24c74e3af72ce8cc7&pid=1-s2.0-S2213665724000356-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142122150","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":"Investigating work-related distraction’s impact on male taxi driver safety: A hazard-based duration model","authors":"Shi Ye ,&nbsp;Tiantian Chen ,&nbsp;Oscar Oviedo-Trespalacios ,&nbsp;N.N. Sze ,&nbsp;Sikai Chen","doi":"10.1016/j.amar.2024.100350","DOIUrl":"10.1016/j.amar.2024.100350","url":null,"abstract":"<div><p>With the increasing use of phone-based ride-hailing apps, concerns have arisen regarding road safety and driver distraction. Despite the recognized safety risks of driver distraction, limited research has explored how distractions from various ride-hailing systems affect drivers in the taxi industry. To close this gap, the current research utilized a driving simulator experiment involving 51 male taxi drivers in two road environments (urban street and motorway) and three distracted driving conditions (no distraction, auditory distraction via radio dispatching system, and visual-manual distraction via mobile application). A car-following scenario with sudden brake events was incorporated into the experiments because this is a typical safety–critical situation where attention will determine the outcome. The collected performance indicators include brake reaction time, time headway, and car-following distance. The grouped random parameters Weibull accelerated failure time model was applied to model the duration data under different road conditions. The brake reaction time and time headway are dependent variables, while the car-following distance is a covariate in the models. The results indicate that although taxi drivers show longer brake reaction time when distracted by mobile app and radio system, this does not necessarily equate with greater risk or reduced safety since they compensate for the risk of rear-end crashes by maintaining a longer time headway. In general, taxi drivers’ brake reaction time and time headway are more profoundly affected by mobile apps when distracted in both urban and motorway scenarios. This highlights the elevated risks associated with such technologies. In addition, significant interaction effects revealed the observed heterogeneity, which suggests that drivers’ personal characteristics influence the relationship between distraction type and driving performance. This research provides valuable insights for designing safer ride-hailing operations and systems.</p></div>","PeriodicalId":47520,"journal":{"name":"Analytic Methods in Accident Research","volume":"44 ","pages":"Article 100350"},"PeriodicalIF":12.5,"publicationDate":"2024-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142077395","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":"Rethinking cycling safety: The role of gender in cyclist crash injury severity outcomes","authors":"Natalia Barbour,&nbsp;Mohamed Abdel-Aty","doi":"10.1016/j.amar.2024.100349","DOIUrl":"10.1016/j.amar.2024.100349","url":null,"abstract":"<div><p>Given the ongoing climate crisis and the need for environmentally friendly communities, there has been an increasing interest in sustainable mobility solutions such as cycling. This study seeks to incorporate an equitable component to studying cycling safety and uses one full year’s data of 4,457 single bicycle-single motor vehicle crashes that took place in 2022 in the state of Florida to estimate a series of random parameters multinomial logit models with heterogeneity in the means and variances to capture gender differences in outcome severities. A comparison of advanced statistical models such as unconstrained and partially constrained approaches, that were previously employed in the literature to test for temporal stability, is undertaken in a new application. A partially constrained model is estimated to best identify gender specific factors and argue the need to evaluate and promote safety of female and male cyclists separately. The study finds substantial differences between how the contributing factors and crash circumstances impact the crash injury severity of women and men cyclists. It evaluates factors such as age, location, cyclist behavior, weather, and road design as well as performs out-of-sample simulation to gain additional insights. The findings of this research emphasize the need for targeted approaches in designing our cities and policy making that account for the collective differences in behavior and experience of women and men cyclists.</p></div>","PeriodicalId":47520,"journal":{"name":"Analytic Methods in Accident Research","volume":"44 ","pages":"Article 100349"},"PeriodicalIF":12.5,"publicationDate":"2024-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142049357","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 nonlinear mixed logit model of occupant severity in autonomous vehicle crashes","authors":"Lan Ventura ,&nbsp;Rohan Shrestha ,&nbsp;Narayan Venkataraman ,&nbsp;Venkataraman Shankar ,&nbsp;Nardos Feknssa","doi":"10.1016/j.amar.2024.100348","DOIUrl":"10.1016/j.amar.2024.100348","url":null,"abstract":"<div><p>This paper presents a nonlinear mixed logit to capture heterogeneous effects of contributing factors on autonomous involved occupant severity. Autonomous level information to this point has been quite sparse in the context of real-world crash scenarios and police reporting. However, the Texas Department of Transportation (TxDOT) began reporting autonomous involvement in April of 2023. With reporting still in its early stages, this analysis incorporated three distinct vehicle technologies: non-autonomous internal combustion engine (ICE) vehicles; ICE and hybrid electric autonomous vehicles; and fully electric autonomous vehicles. Crash data included any crash in Texas from April to December of 2023 that involved at least one autonomous-indicated vehicle (either the second or third distinct vehicle technology). Random parameters were found with respect to: an indicator for occupant involvement in the first harmful crash sequence event, with that event being collision with a fixed object, for no injury; proportion of autonomous vehicles for no injury; an intersection related indicator for possible injury; total occupant count for possible injury; and total vehicle count for injury. The count and proportion variables were expressed as nonlinear relationships, for which random parameters improved prediction accuracy by 37.50 % and 30.00 %, respectively, for possible injury and injury outcomes, as compared to fixed parameters. The findings in this study highlight the applicability of the nonlinear mixed logit for severity analysis with respect to complex autonomous interactions in crashes.</p></div>","PeriodicalId":47520,"journal":{"name":"Analytic Methods in Accident Research","volume":"44 ","pages":"Article 100348"},"PeriodicalIF":12.5,"publicationDate":"2024-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142148444","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":"Stochastic method based on copulas for predicting severe road traffic interactions","authors":"Zhankun Chen,&nbsp;Oksana Yastremska-Kravchenko,&nbsp;Aliaksei Laureshyn,&nbsp;Carl Johnsson,&nbsp;Carmelo D’Agostino","doi":"10.1016/j.amar.2024.100347","DOIUrl":"10.1016/j.amar.2024.100347","url":null,"abstract":"<div><p>A major difficulty in assessing road traffic safety is the scarcity of historical accident data. xxThis is a common problem in contexts where a certain level of safety has been reached or where exposure is low, such as mixed traffic conditions with different levels of transport automation. Recent studies have demonstrated how severe interactions between road users and/or road users and infrastructure can be a direct measure of safety. However, limiting the investigation to only the most extreme events may lead to inconclusive results considering the lack of prediction robustness and the possible selection bias. In this context, extreme value theory (EVT) is commonly used to extrapolate crashes from road traffic interactions, even combining several indicators. The present work extends the EVT paradigm by proposing a method based on copula functions and EVT, which enables a more specific and continuous evaluation of interaction severity. Compared with pure EVT, this new approach extends the boundary to interactions of all severities while implicitly assuming that the relationship between safety-relevant events and road casualties is stochastic. This EVT-copula approach was also compared with bivariate peaks over threshold (BPOT). It was found that the two approaches yield similar prediction results for crash probabilities. Furthermore, the proposed approach applies to events not properly defined in BPOT and provides more accurate predictions for severe (and less severe) interactions compared with BPOT, when benchmarked against observations.</p></div>","PeriodicalId":47520,"journal":{"name":"Analytic Methods in Accident Research","volume":"44 ","pages":"Article 100347"},"PeriodicalIF":12.5,"publicationDate":"2024-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2213665724000319/pdfft?md5=32e39c35f91b2aa9db0b85ad1053c599&pid=1-s2.0-S2213665724000319-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141732062","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":"Incorporating inconsistency patterns on road networks into crash modeling","authors":"R.N. Shilpa,&nbsp;B.K. Bhavathrathan","doi":"10.1016/j.amar.2024.100340","DOIUrl":"https://doi.org/10.1016/j.amar.2024.100340","url":null,"abstract":"<div><p>This paper expands the scope of geometric design inconsistency analysis from corridor scales to network-wide perspectives, exploring the impact of inconsistencies’ spatial-patterns on crashes, which remains largely under-explored. We define spatial-patterns of segment-level inconsistencies, focusing on their spread, contiguity, frequency, density, and magnitude. We devise a new method to measure inconsistency-contiguity and inconsistency-frequency based on adjacent segment-triplets within regions. Through micro–macro integrated models, we reveal the scalable influence of inconsistency which remain significant at the segment-level but gets modulated by spatial-patterns at the regional-level. The integrated models consistently outperform their non-integrated counterparts, emphasizing the importance of this integrated approach. This study highlights that regions with rare inconsistency occurrences demonstrate higher crash counts, while regions with uniform inconsistency occurrences exhibit lower crash rates, unveiling insights into the road conditions’ impact on driver behavior. Finally, we also propose a novel tool - vulnerability contours on <em>frequency-hyperplane</em> to map regions’ relative safety.</p></div>","PeriodicalId":47520,"journal":{"name":"Analytic Methods in Accident Research","volume":"43 ","pages":"Article 100340"},"PeriodicalIF":12.5,"publicationDate":"2024-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141487182","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}