Accident; analysis and prevention最新文献

{"title":"A cross-sectional safety evaluation approach using generalized extreme value models: A case of right-turn safety treatment","authors":"Chenxiao Zhang ,&nbsp;Yongfeng Ma ,&nbsp;Tarek Sayed ,&nbsp;Yanyong Guo ,&nbsp;Shuyan Chen","doi":"10.1016/j.aap.2024.107907","DOIUrl":"10.1016/j.aap.2024.107907","url":null,"abstract":"<div><div>There has been an increase in the use of the extreme value theory (EVT) approach for conflict-based crash risk estimation and its application such as conducting the evaluation of safety countermeasures. This study proposes a cross-sectional approach for evaluating the effectiveness of a right-turn safety treatment using a conflict-based EVT approach. This approach combines traffic conflicts of different sites at the same period and develops the generalized extreme value (GEV) models. It introduces treatment as a dummy variable for estimating the treatment effects and adds traffic-related and conflict severity-related variables to account for unobserved confounding factors between sites. The approach was applied to a case of right-turn safety treatment at two signalized intersections in Nanjing, China. Conflict indicators (i.e., TTC, PET) and potential influencing factors of E-bike-heavy vehicle (EB-HV) right-turn interactions were extracted from aerial video data. A series of GEV models were developed considering different combinations of covariates and their link to the model parameters. Moreover, site GEV models were developed separately for each site to compare the treatment effects across different models. Based on the best-fit models, the results indicate significant safety improvements after implementing the right-turn safety treatment. In addition, the results also show that the cross-sectional GEV models indicate a significant reduction in the number of high-severity conflicts and lowering overall crash risk attributed to the treatment highlighting the applicability of the GEV cross-sectional models in evaluation safety treatments.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"211 ","pages":"Article 107907"},"PeriodicalIF":5.7,"publicationDate":"2024-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142902546","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":"Availability bias in road safety systematic reviews and its impact on the meta-analysis findings","authors":"Jiří Ambros ,&nbsp;Rune Elvik","doi":"10.1016/j.aap.2024.107905","DOIUrl":"10.1016/j.aap.2024.107905","url":null,"abstract":"<div><div>Meta-analyses, which present the best source of information on the effectiveness of interventions, are influenced by several biases. One category relates to the convenience of selective inclusion of those primary studies, which are more easily available than others. This availability bias includes bias from excluding the grey literature, bias from excluding non-English literature, and bias from excluding older studies. Existing studies are not conclusive about the impacts of this bias; in addition, none of them focus on road safety <em>meta</em>-analyses. To fill this gap, the present paper consisted of two studies: (1) exploring the presence of availability bias in road safety <em>meta</em>-analyses, and (2) demonstrating the impact of availability bias in several example <em>meta</em>-analyses. Based on an analysis of 80 existing <em>meta</em>-analyses, the first study concluded that compared to the medicine <em>meta</em>-analyses, the road safety <em>meta</em>-analyses use a longer time range, are more often restricted in terms of language, and less often involve the grey literature. The second study utilized selected unrestricted data samples to demonstrate the impact of availability bias in seven <em>meta</em>-analyses. The differences in intervention effectiveness in terms of crash frequency changes between unrestricted and restricted scenarios were identified. This shows that the search restrictions clearly lead to availability bias, which influences the differences in meta-analysis results.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"211 ","pages":"Article 107905"},"PeriodicalIF":5.7,"publicationDate":"2024-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142891214","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 comparison of traffic crash and connected vehicle event data on a freeway corridor with Hard-Shoulder Running","authors":"Nischal Gupta ,&nbsp;Qiuqi Cai ,&nbsp;Hisham Jashami ,&nbsp;Peter T. Savolainen ,&nbsp;Timothy J. Gates ,&nbsp;Timothy Barrette ,&nbsp;Wesley Powell","doi":"10.1016/j.aap.2024.107900","DOIUrl":"10.1016/j.aap.2024.107900","url":null,"abstract":"<div><div>Police crash reports have traditionally been the primary data source for research and development projects aimed at improving traffic safety. However, there are important limitations of such data, particularly the relative infrequency of crashes on a site-by-site basis in many contexts. Crash analyses often require multiple years of data and the use of such data for short-term evaluations creates challenges. Recently, connected vehicle (CV) event data have emerged as a promising means for addressing these limitations. CV events, which are reported when a vehicle engages in rapid longitudinal or lateral acceleration, can be obtained both at larger scale and in a timelier manner as compared to crash data. However, research as to the relationships between CV events and crashes is still in its nascent stages. This study examined the frequency of CV driving events and traffic crashes on a freeway corridor in Southeastern Michigan that operates with hard-shoulder running during periods of heavy congestion. This corridor uses the inside (left) shoulder as a temporary travel lane during peak periods and also provides dynamic advisory speeds based upon traffic congestion levels as monitored by microwave vehicle detection systems. Consequently, comparisons were made as to the general relationships of CV events and crashes with respect to traffic volumes, as well as whether the shoulder lane was open or closed. As the study was conducted during 2020, this also allowed for comparisons between each metric over the early stages of the COVID-19 pandemic. A series of analyses show strong correlation between traffic conditions along the corridor and the frequency of crash and CV driving events. Both crashes and CV events occurred more frequently during periods of congestion. However, significant differences were observed between crashes and CV events depending on whether the inside shoulder was open to traffic or not. Furthermore, the CV events were more reflective of changes in travel patterns that occurred following the introduction of travel restrictions in response to the COVID-19 pandemic.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"211 ","pages":"Article 107900"},"PeriodicalIF":5.7,"publicationDate":"2024-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142891201","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":"Assessing the impact of car-following driving style on traffic conflict risk using asymmetric behavior model and explainable machine learning","authors":"Xiao-chi Ma ,&nbsp;Yun-hao Zhou ,&nbsp;Jian Lu ,&nbsp;Yiik Diew Wong ,&nbsp;Jun Zhang ,&nbsp;Junde Chen ,&nbsp;Chao Gu","doi":"10.1016/j.aap.2024.107904","DOIUrl":"10.1016/j.aap.2024.107904","url":null,"abstract":"<div><div>To deepen the understanding of the impact of car-following driving style (CFDS) on traffic conflict risk and address the lack of clear CFDS evaluation metrics, this study proposes an improved CFDS metric based on the Asymmetric Behavior (AB) theory. Interpretable machine learning models were utilized for regression analysis to examine the relationship between CFDS and conflict risk. The generalized AB model calculates the difference between vehicle trajectories and the Newell trajectory, constructing the driving style evaluation metric, which quantifies driver aggressiveness in a manner that is both computationally straightforward and easily interpretable. High-precision vehicle trajectory data were collected using radar-camera integrated devices, enabling the use of various interpretable machine learning methods to model and analyze the impact of driving style on conflict risk. The results demonstrate that the proposed car-following driving style evaluation metric consistently shows the highest importance across multiple datasets with different risk levels and sampling windows, indicating a strong correlation with conflict risk. Interpretations using Shapley Additive Explanations reveal a nuanced, yet mostly monotonic impact pattern of driving style across high, medium, and low-risk scenarios, with more aggressive drivers being more prone to high-risk situations. Furthermore, Partial Dependence Plot analysis reveals a complex, saddle-shaped risk curve related to driving style and its interactions, highlighting that aggressive and “pseudo-timid” drivers exhibit higher risks in specific contexts. In summary, this research constructs clear and interpretable CFDS evaluation metrics, validated through case analysis for their rationality and effectiveness, thereby providing new theoretical support for traffic risk prediction and intervention.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"211 ","pages":"Article 107904"},"PeriodicalIF":5.7,"publicationDate":"2024-12-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142891209","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":"Examining the nonlinear effects of traffic and built environment factors on the traffic safety of cyclist from different age groups","authors":"M. Baran Ulak ,&nbsp;Mehrnaz Asadi ,&nbsp;Karst T. Geurs","doi":"10.1016/j.aap.2024.107872","DOIUrl":"10.1016/j.aap.2024.107872","url":null,"abstract":"<div><div>In the Netherlands and all over the world, traffic safety problem has been growing particularly for cyclists over the last decades with more people shifting to cycling as a healthy and sustainable mode of transport. Literature shows that age is an important factor in crash involvement and consequences; however, few studies identify the risk factors for cyclists from across different age groups. Therefore, this study aims to identify and understand the effects of traffic, infrastructure, and land use factors on vehicle-to-bike injury and fatal crashes involving cyclists from different age groups. For this purpose, we adopted an approach consisting of resampling and machine learning (XGBoost-Tweedie) techniques to analyse police-reported crashes between the years 2015 and 2019 in the Netherlands. The analysis shows that effects of external variables on crashes widely vary among different age groups and the analysis of total crash rates may not disclose the nature of crashes of cyclist from different age groups. The analysis also shed light on the nonlinear effects of traffic and built environment factors on cyclist crashes, which are usually disregarded in the traffic safety literature. The proposed approach and findings provide a profound understanding of the nature of cyclist crashes and the complex relationships between factors, which can contribute to developing effective crash prevention strategies tailored to different age groups.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"211 ","pages":"Article 107872"},"PeriodicalIF":5.7,"publicationDate":"2024-12-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142891218","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":"Cooperative control of self-learning traffic signal and connected automated vehicles for safety and efficiency optimization at intersections","authors":"Gongquan Zhang ,&nbsp;Fengze Li ,&nbsp;Dian Ren ,&nbsp;Helai Huang ,&nbsp;Zilong Zhou ,&nbsp;Fangrong Chang","doi":"10.1016/j.aap.2024.107890","DOIUrl":"10.1016/j.aap.2024.107890","url":null,"abstract":"<div><div>Cooperative control of intersection signals and connected automated vehicles (CAVs) possess the potential for safety enhancement and congestion alleviation, facilitating the integration of CAVs into urban intelligent transportation systems. This research proposes an innovative deep reinforcement learning-based (DRL) cooperative control framework, including signal and speed modules, to dynamically adapt signal timing and CAV velocities for traffic safety and efficiency optimization. Among the DRL-based signal modules, a traffic state prediction model is merged with the current state to augment characteristics and the agent-learning process. A multi-objective reward function is designed to evaluate safety and efficiency using a traffic conflict prediction model and vehicle waiting time. The double deep Q network (DDQN) model is used to design the agent observing the traffic state, learning the optimal signal control policy, and then inputting the signal phase into the speed module. Based on the green duration analysis and constraints of mixed traffic flow of CAVs and human-driven vehicles, a speed planning model is constructed to optimize CAVs’ speed and alter traffic state, which in turn affects the agent’s next signal decisions. The proposed framework is tested at isolated intersections simulated by two real-world intersections in Changsha, China. The results reveal the superiority of the proposed method over DRL-based traffic signal control (DRL-TSC) in terms of coverage speed and computation time. Compared to actuated signal control, adaptive traffic signal control, and DRL-TSC, the proposed method significantly optimizes traffic safety and efficiency across diverse intersections, temporal spans, and traffic demands. Furthermore, the advantage of the proposed method substantially amplifies with the increased CAV penetration, regardless of the intersection types.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"211 ","pages":"Article 107890"},"PeriodicalIF":5.7,"publicationDate":"2024-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142871036","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":"Do automation and digitalization distract drivers? A systematic review","authors":"Neelima C. Vijay ,&nbsp;Amit Agarwal ,&nbsp;Kamini Gupta","doi":"10.1016/j.aap.2024.107888","DOIUrl":"10.1016/j.aap.2024.107888","url":null,"abstract":"<div><div>Driving is a multifaceted activity involving a complex interplay of cognitive, perceptual, and motor skills, demanding continuous attention on the road. In recent years, the increased integration of automation and digitalization technologies in vehicles has improved drivers’ convenience and safety. However, the spare attentional capacity available during automation and the prevalence of various infotainment systems in vehicles enable drivers to perform some secondary tasks not related to driving, which may divert their attention away from the road, increasing the chances of accidents. The objective of the present study is to conduct a comprehensive systematic review of existing literature utilizing an eye tracker to analyze driver distraction due to automation and/or digitalization in motorized vehicles, with a focus on identifying the key factors leading to visual distraction. Through a literature search on five databases: Google Scholar, PubMed, ScienceDirect, Scopus, and Web of Science, a total of 4769 articles were initially identified. After a systematic screening, 65 research articles are considered for the review. The findings of the study indicate an increase in the research conducted on driver distraction due to automation and/or digitalization over recent years, with the highest contribution of studies from the United States and China. The lack of studies from other parts of the world like South America, Africa and the limited representation from larger parts of Asia, specifically India, highlights the need for future research in the area. Studies report a diversion in drivers’ visual attention away from the roadway, in terms of long and frequent off-road glances, while engaging in secondary tasks during automation and/or digitalization. Studies also demonstrate changes in the pattern of drivers’ visual attention with respect to different factors like HMI information, type of secondary task, type of input modality, in-vehicle display characteristics, and vehicle automation. Studies have also found success in using feedback to reduce visual distraction and to bring back drivers’ attention on the roads. In light of the findings observed, the review provides a discussion on the effects of automation and/or digitalization technologies on drivers’ visual attention. The study also highlights the areas that are not explored despite the wealth of research available on the topic.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"211 ","pages":"Article 107888"},"PeriodicalIF":5.7,"publicationDate":"2024-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142871037","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":"Influence of road safety policies on the long-term trends in fatal Crashes: A Gaussian Copula-based time series count model with an autoregressive moving average process","authors":"Yanqi Lian ,&nbsp;Shamsunnahar Yasmin ,&nbsp;Md Mazharul Haque","doi":"10.1016/j.aap.2024.107795","DOIUrl":"10.1016/j.aap.2024.107795","url":null,"abstract":"<div><div>Time series analysis plays a vital role in modeling historical crash trends and predicting the possible changes in future crash trends. In existing safety literature, earlier studies employed multiple approaches to model long-term crash risk profiles, such as integer-valued autoregressive Poisson regression model, integer-valued generalized autoregressive conditional heteroscedastic model, and generalized linear autoregressive and moving average models. However, these modeling frameworks often fail to fully capture several key properties of crash count data, especially negative serial correlation, and nonlinear dependence structures across temporal crash counts. To address these methodological gaps in existing safety literature, this study proposes to use a Gaussian Copula-based model for the long-term crash trend analysis. Specifically, this study proposes to use a Gaussian Copula-based Time Series Count Model with an Autoregressive Moving Average Process for the analysis of long-term trends in fatal crashes. The proposed approach can accommodate several data properties, which include (1) non-negative discrete property of count data, (2) positive and negative serial correlations among time series data, and (3) nonlinear dependence among time-series observations. The performance of the Gaussian Copula-based time series count model is compared with the generalized linear autoregressive and moving average model. The proposed modeling approaches are demonstrated by using yearly fatal crash count data for the years 1986 through 2022 from Queensland, Australia. The major safety interventions implemented in Queensland over those years are also highlighted to assess the possible and plausible impacts of these safety interventions in reducing fatal crash risks. Further, elasticity effects and overall percentage changes in fatal crashes across different time points are computed to demonstrate the implications of the proposed model. The policy analysis exercise shows that the implemented road safety interventions are likely to have diminishing marginal returns, underscoring the need for new and effective road safety policies to achieve the goal of zero fatalities within the set timeframe.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"211 ","pages":"Article 107795"},"PeriodicalIF":5.7,"publicationDate":"2024-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142871038","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":"Nudges may improve hazard perception in a contextual manner","authors":"Shiran Zadka-Peer,&nbsp;Tova Rosenbloom","doi":"10.1016/j.aap.2024.107899","DOIUrl":"10.1016/j.aap.2024.107899","url":null,"abstract":"<div><div>This research investigates the effectiveness of nudge presentation on Hazard Perception (HP) during a computerized Hazard Perception Test (HPT). Three types of nudges were examined: Reminder, Social Norm, and Negative Reinforcement. Their effects on drivers’ reaction times, hazard misidentifications (errors), and hazard recognition failures (misses) were analyzed. Additionally, the study explored how demographic and personality factors relate to individual differences in nudge responses. Results indicated that nudge presentation, regardless of type, improved reaction times and reduced errors. Reduction in errors was uniquely associated with personal characteristics, showing a positive correlation with age. Specifically, female participants and individuals low in conscientiousness exhibited fewer errors following the Social Norm nudge, while males and highly conscientious individuals showed reduced errors after the Reminder nudge. However, misses were unaffected by nudge presentation. All tested dependent variables were influenced by the order of hazard presentation, reflecting both contextual and nudge presentation effects. To further investigate the order’s impact, a follow-up study examined specific hazards sensitive to nudge presentation. Findings revealed that some hazards were more influenced by nudge/contextual factors, while others were unaffected, highlighting the need to consider complex contextual dynamics in HP research. Overall, the study supports the conclusion that nudge presentation can positively influence HP without distracting drivers, offering a promising strategy for improving road safety.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"211 ","pages":"Article 107899"},"PeriodicalIF":5.7,"publicationDate":"2024-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142871039","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":"Conflict resolution behavior of autonomous vehicles at intersections under mixed traffic environment","authors":"Md Tanvir Ashraf,&nbsp;Kakan Dey","doi":"10.1016/j.aap.2024.107897","DOIUrl":"10.1016/j.aap.2024.107897","url":null,"abstract":"<div><div>Navigating intersections is a major challenge for autonomous vehicles (AVs) because of the complex interactions between different roadway user types, conflicting movements, and diverse operational and geometric features. This study investigated intersection-related AV-involved traffic conflicts by analyzing the Arogoverse-2 motion forecasting dataset to understand the driving behavior of AVs at intersections. The conflict scenarios were categorized into AV-involved and no AV conflict scenarios. Depending on whether AVs passed the conflict region first or second in AV-involved scenarios, AV-involved scenarios were further classified into AV-first and AV-second scenarios. An agglomerative hierarchical clustering with t-SNE dimension reduction technique was applied to categorize the driving styles, and a three-layer Bayesian hierarchical model was applied to analyze the effect of driving volatility measures and traffic characteristics on relative crash risks. The clustering result showed that about 29% of the conflict events in the AV-first scenario (human-driven vehicle (HDV) was the following vehicle in passing the conflict region) exhibited <em>high-risk</em> of conflicts. In contrast, all conflicts events in the AV-second category were either <em>low-risk</em> or <em>medium-risk</em> conflicts. Parameter estimates showed that AVs had safer interactions with the other roadway users (i.e., HDVs, pedestrians/cyclists) while maintaining higher speeds and uniform driving profiles. AV’s interaction with vulnerable road users (i.e., pedestrians and cyclists) showed lower crash risk compared to HDVs, indicating AV’s safer driving behavior. AVs also demonstrated safer conflict resolution behavior in performing unprotected left turns compared to HDVs. This study discovered some unique insights into the challenges of introducing AVs in diverse intersection types (i.e., signalized, unsignalized, stop-controlled), which can be used to identify AV technology’s improvement need to better adapt to the mixed traffic driving environment.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"211 ","pages":"Article 107897"},"PeriodicalIF":5.7,"publicationDate":"2024-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142862749","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}