Accident; analysis and prevention最新文献

{"title":"How predictive-forward-collision-warning reduces the collision risk of leading vehicle driver","authors":"Qiang Fu,&nbsp;Xiaohua Zhao,&nbsp;Chen Chen,&nbsp;Wenhao Ren","doi":"10.1016/j.aap.2024.107891","DOIUrl":"10.1016/j.aap.2024.107891","url":null,"abstract":"<div><div>Mixed platoon with a human-driven leading vehicle may be a transition mode prior to the widespread adoption of fully autonomous platoon. Enhancing the driving safety of the leading vehicle driver is crucial for improving the overall operational safety of the mixed platoon. Predictive-Forward-Collision-Warning (PFCW), an emerging technology in transportation, holds promise in mitigating collision risks for drivers by presenting traffic information beyond their immediate visual range. However, the influence characteristics of this function and how it influences the evolution of collision risk in leading vehicle driver remain unclear. Therefore, this paper attempts to analyse the quantitative impact of PFCW on the collision risk of leading vehicle driver. A test platform for connected mixed platoon was built utilizing driving simulation technology, alongside the development of a connected Human-Machine Interface (HMI) incorporating PFCW functionality. To evaluate the longitudinal collision risk of leading vehicle driver, a time–frequency analysis method was employed, focusing on key indicators: deceleration rate to avoid collision (DRAC), time to collision (TTC), and proportion of stopping distance (PSD). The time-domain analysis results indicated that PFCW can significantly mitigate the collision risk of leading vehicle. Wavelet transform results demonstrated that PFCW can ameliorate drivers’ abnormal driving behavior and mitigate the collision risk in emergency situation of impending collision moment. Meanwhile, PFCW can enhance the overall operation safety of the mixed platoon. This paper leverages driving simulation technology and multidimensional indicators to analyze the quantitative impact of PFCW on the collision risk of leading vehicle driver during rapid deceleration of preceding vehicles. The findings can guide the development of test standards for connected mixed platoon, the promotion and application of PFCW, and the advancement of Navigate on Autopilot (NOA). Additionally, the test platform and framework developed in this study can accommodate various experimental needs for connected mixed platoon testing.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"211 ","pages":"Article 107891"},"PeriodicalIF":5.7,"publicationDate":"2024-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142913178","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":"Can we realize seamless traffic safety at smart intersections by predicting and preventing impending crashes?","authors":"B M Tazbiul Hassan Anik,&nbsp;Mohamed Abdel-Aty,&nbsp;Zubayer Islam","doi":"10.1016/j.aap.2024.107908","DOIUrl":"10.1016/j.aap.2024.107908","url":null,"abstract":"<div><div>Intersections are frequently identified as crash hotspots for roadways in major cities, leading to significant human casualties. We propose crash likelihood prediction as an effective strategy to proactively prevent intersection crashes. So far, no reliable models have been developed for intersections that effectively account for the variation in crash types and the cyclical nature of Signal Phasing and Timing (SPaT) and traffic flow. Moreover, the limited research available has primarily relied on sampling techniques to address data imbalance, without exploring alternative solutions. We develop an anomaly detection framework by integrating Generative Adversarial Networks (GANs) and Transformers to predict the likelihood of cycle-level crashes at intersections. The model is built using high-resolution event data extracted from Automated Traffic Signal Performance Measures (ATSPM), including SPaT and traffic flow insights from 11 intersections in Seminole County, Florida. Our framework demonstrates a sensitivity of 76% in predicting crash events using highly imbalanced crash data along with real-world SPaT and traffic data, highlighting its potential for deployment at smart intersections. Overall, the results provide a roadmap for city-wide implementation at smart intersections, with the potential for multiple real-time solutions for impending crashes. These include adjustments in signal timing, driver warnings using various means, and more efficient emergency response, all with major implications for creating more livable and safe cities.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"211 ","pages":"Article 107908"},"PeriodicalIF":5.7,"publicationDate":"2024-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142913699","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":"Real-time risk estimation for active road safety: Leveraging Waymo AV sensor data with hierarchical Bayesian extreme value models","authors":"Mohammad Anis ,&nbsp;Sixu Li ,&nbsp;Srinivas R. Geedipally ,&nbsp;Yang Zhou ,&nbsp;Dominique Lord","doi":"10.1016/j.aap.2024.107880","DOIUrl":"10.1016/j.aap.2024.107880","url":null,"abstract":"<div><div>Near-miss traffic risk estimation using Extreme Value Theory (EVT) models within a real-time framework offers a promising alternative to traditional historical crash-based methods. However, current approaches often lack comprehensive analysis that integrates diverse roadway geometries, crash patterns, and two-dimensional (2D) vehicle dynamics, limiting both their accuracy and generalizability. This study addresses these gaps by employing a high-fidelity, 2D time-to-collision (TTC) near-miss indicator derived from autonomous vehicle (AV) sensor data. The proposed framework uses univariate Generalized Extreme Value (UGEV) distribution models applied to a subset of the Waymo motion dataset across six arterial networks in San Francisco, Phoenix, and Los Angeles. Extreme events are identified through the Block Maxima (BM) sampling-based approach from each conflicting pair, with 20s block sizes to account for the scarcity of samples in short-duration traffic segments. The framework also incorporates conflicting vehicle dynamics (e.g., speed, acceleration, and deceleration) as covariates within a non-stationary hierarchical Bayesian structure with random parameters (HBSRP) UGEV models, allowing for the effective management of vehicle spatial, temporal, and behavioral heterogeneity. Results show that HBSRP-UGEV models outperform other approaches, with a 6.43–10.56% decrease in DIC, especially for near-miss events in short-duration traffic segments. The inclusion of dynamic vehicle behaviors and random effects substantially enhances the model’s capability to estimate real-time traffic risks. This generalized real-time EVT model bridges the gap between active and passive safety measures, offering a precise and adaptable tool for network-level traffic safety analysis.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"211 ","pages":"Article 107880"},"PeriodicalIF":5.7,"publicationDate":"2024-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142913087","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":"Driver’s journey from historical traffic violations to future accidents: A China case based on multilayer complex network approach","authors":"Rui Zhang ,&nbsp;Bin Shuai ,&nbsp;Pengfei Gao ,&nbsp;Yue Zhang","doi":"10.1016/j.aap.2024.107901","DOIUrl":"10.1016/j.aap.2024.107901","url":null,"abstract":"<div><div>Traffic violation records serve as key indicators for predicting drivers’ future accidents. However, beyond statistical correlations, the underlying mechanisms linking historical traffic violations to future accidents remain inadequately understood. This study introduces a research framework to address this gap: Using Propensity Score Matching and an adapted mutual information-based feature selection algorithm to precisely identify correlations and optimal time windows between drivers’ historical traffic violations and future accidents. A multilayer complex network approach was then applied to abstract and model the progression from drivers’ historical traffic violations to subsequent accidents, revealing intrinsic patterns through adapted network analysis metrics and ultimately uncovering underlying mechanisms. Actual data from over 17,000 drivers in Shenzhen, China, spanning the period of 2010 to 2020, was utilized. Results revealed significant heterogeneity among driver subtypes with various driving license types regarding optimal time windows and key traffic violations indicative of future accident risks. A universal “Stable Defect Effect” was identified across all driver subtypes, characterized by persistent driving-related deficiencies resistant to temporal decay and penalties. This effect’s gradual formation and maturation appear to govern the progression from traffic violations to future accidents. In addition, multilayer complex network models demonstrated significant potential in accident risk studies, particularly in providing valuable latent information by overcoming the limitations of accident data samples.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"211 ","pages":"Article 107901"},"PeriodicalIF":5.7,"publicationDate":"2024-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142913176","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":"Causal factors identification and dynamics simulation of major road traffic accidents from China’s evidence: A high-order mixed-method design","authors":"Chuntong Dong ,&nbsp;Yulong Pei ,&nbsp;Jing Liu ,&nbsp;Yingyu Zhang ,&nbsp;Ziqi Wang ,&nbsp;Jie Zhang","doi":"10.1016/j.aap.2024.107895","DOIUrl":"10.1016/j.aap.2024.107895","url":null,"abstract":"<div><div>Mitigating the injury and severity of road traffic accidents has become a crucial objective in global road safety efforts. Major road traffic accidents (MRTAs) pose significant challenges due to their high hazard and severe consequences. Despite their widespread impact, the complex causation mechanisms behind MRTAs have not been thoroughly and systematically investigated, which hinders the development of effective control strategies and policies. This study introduces an innovative high-order embedded mixed-method design to explore the causes of MRTAs, marking the first application of mixed-method approaches in road traffic accident research. The proposed approach consists of three phases: First, qualitative analysis utilizing grounded theory examines 95 MRTAs investigation reports to identify causal factors, establish a classification framework, and derive quantitative data. The second phase employs the decision experiment and evaluation laboratory (DEMATEL) for static quantitative analysis, quantifying interactions within the classification framework, and generating cause-effect diagrams. Finally, data and results from the first two phases are integrated to construct a system dynamics (SD) model and conduct sensitivity analysis, analyzing the impact of causal factors and their interactions on MRTAs casualties, thereby evaluating the effectiveness of various control strategies. The findings reveal that the causal factors of MRTAs can be categorized into five levels: “driver errors,” “vehicle, road and environment,” “supervisory deficiencies,” “organizational management and culture,” and “outside factors.” Complex interactions exist both among and within these levels, collectively influencing MRTAs. Moreover, in reducing MRTAs casualties, combined control strategies demonstrate significant superiority over single control strategies, especially when targeting key factors. It should also be noted that the importance ranking of causal factors dynamically adjusts with changes in the control environment, and the effectiveness of combined control strategies becomes more pronounced as the number of control factors increases. Specifically, comprehensive prevention strategies across all five levels exhibit the most remarkable efficacy. In conclusion, preventing MRTAs requires emphasizing the shared responsibility of all stakeholders and judiciously allocating control resources, while avoiding excessive reliance on interventions targeting any specific factor. This study provides a methodological foundation for a deeper understanding of the causation mechanisms behind MRTAs, and its results offer robust evidence to support the formulation of future prevention measures and policies.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"211 ","pages":"Article 107895"},"PeriodicalIF":5.7,"publicationDate":"2024-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142913700","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":"Investigating the safety influence path of right-turn configurations on vehicle–pedestrian conflict risk at signalized intersections","authors":"Mingjie Feng ,&nbsp;Jing Zhao ,&nbsp;Chaofan Hou ,&nbsp;Chunting Nie ,&nbsp;Jianke Hou","doi":"10.1016/j.aap.2024.107910","DOIUrl":"10.1016/j.aap.2024.107910","url":null,"abstract":"<div><div>Right-turning vehicles and pedestrians share the right-of-way during the permitted signal phase at intersections in countries with right-handed traffic. Although right-turning vehicles are required to stop or yield to pedestrians according to the traffic rules, there still remains circumstances where the two will compete, posing significant safety risks to pedestrians. To investigate the impact mechanism of right-turn configurations, driver characteristics, and traffic operational features on vehicle–pedestrian conflict risk, a driving simulator experiment was conducted. The driving trajectory data of 51 drivers across 28 different scenarios encompassing customized intersection configurations and various traffic conditions were collected. Evaluation indicators, including average crossing speed, maximum deceleration, and post encroachment time (PET) were extracted, of which the first two represented the driving performance of right-turning vehicles, and the last was used to assess vehicle–pedestrian conflict risk. Using a categorical boosting (CatBoost)–Shapley additive explanations (SHAP) approach, pedestrian volume was identified as the most significant influencing factor, with its two levels having the most differential impact on each of the three evaluation indicators. Consequently, a multigroup path analysis was conducted to explore the varying safety influence paths of diverse factors on vehicle–pedestrian conflict risk under different pedestrian volumes. The mediating effects of the average crossing speed and maximum deceleration were found to be significant only under low pedestrian volumes, indicating that intersection configurations not only affect right-turn safety directly but also produce significant indirect effects by influencing driving performance. However, in scenarios with high pedestrian volumes, intersection configurations influenced right-turn safety directly but with no significant indirect effects. The corresponding quantitative insights can help urban road designers construct safer intersections.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"211 ","pages":"Article 107910"},"PeriodicalIF":5.7,"publicationDate":"2024-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142909098","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":"Quantifying learning algorithm uncertainties in autonomous driving systems: Enhancing safety through Polynomial Chaos Expansion and High Definition maps","authors":"Ruihe Zhang ,&nbsp;Chen Sun ,&nbsp;Minghao Ning ,&nbsp;Reza Valiollahimehrizi ,&nbsp;Yukun Lu ,&nbsp;Krzysztof Czarnecki ,&nbsp;Amir Khajepour","doi":"10.1016/j.aap.2024.107903","DOIUrl":"10.1016/j.aap.2024.107903","url":null,"abstract":"<div><div>Autonomous driving systems (ADS), leveraging advancements in learning algorithms, have the potential to significantly enhance traffic safety by reducing human errors. However, a major challenge in evaluating ADS safety is quantifying the performance uncertainties inherent in these black box algorithms, especially in dynamic and complex service environments. Addressing this challenge is crucial for maintaining public trust and promoting widespread ADS adoption. In this work, we propose a Polynomial Chaos Expansion (PCE) approach, utilizing High Definition (HD) maps to quantify positional uncertainties from an ADS object detection algorithm. The PCE-based approach also offers the flexibility for online self-updating, accommodating data shifts due to changing operational conditions. Tested in both simulation and real-world experiments, the PCE method demonstrates more accurate uncertainty quantification than baseline models. Additionally, the results highlight the importance and effectiveness of the self-updating capability, particularly when encountering weather changes.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"211 ","pages":"Article 107903"},"PeriodicalIF":5.7,"publicationDate":"2024-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142902549","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 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}