Accident; analysis and prevention最新文献

{"title":"Exploring the impact of built environment on crash risks at transportation hubs","authors":"Chuanyao Li,&nbsp;Li Chen","doi":"10.1016/j.aap.2025.108079","DOIUrl":"10.1016/j.aap.2025.108079","url":null,"abstract":"<div><div>This study investigates the impact mechanism of the built environment surrounding transportation hubs on crash risks (CR). Three buffer zones (300 m, 500 m, and 800 m) are defined as the spatial analysis units, and Ordinary Least Squares (OLS), Geographically Weighted Regression (GWR), and Multiscale Geographically Weighted Regression (MGWR) are utilized in this study. The results reveals that the 800 m buffer zone provides deeper insights into the factors affecting CR related to the built environment surrounding transportation hubs. Additionally, MGWR demonstrates superior performance in explaining the built environment’s impact on CR compared to the other two methods, with an explanation rate of 83.7 %. To reduce CR near transportation hubs, rationally planning the surrounding land use layout and reducing population density per unit area are recommended. Moreover, the density of road networks surrounding airports and railway stations should be kept at a lower level to reduce CR. The findings of this study contribute to a deeper understanding of the relationship between the built environment surrounding transportation hubs and crashes, providing planning guidance and creating a friendly environment surrounding transportation hubs.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"218 ","pages":"Article 108079"},"PeriodicalIF":5.7,"publicationDate":"2025-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143906425","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":"Could vehicles analyze driving risks using human fuzzy semantic logic? A data-knowledge-driven new perspective","authors":"Jiming Xie ,&nbsp;Yaqin Qin ,&nbsp;Yan Zhang ,&nbsp;Jianhua Li ,&nbsp;Tianshun Chen ,&nbsp;Xiaohua Zhao ,&nbsp;Yulan Xia","doi":"10.1016/j.aap.2025.108037","DOIUrl":"10.1016/j.aap.2025.108037","url":null,"abstract":"<div><div>Accurate risk identification is crucial for ensuring the safe operation of Host vehicles (HoVs) in environments shared with Neighboring vehicles (NeVs). Traditional risk identification mechanisms typically rely on large amounts of precise numerical data, making it difficult to comprehensively and accurately identify traffic crash risks under conditions of imperfect data associated with fuzzy information. However, human drivers rely on knowledge-driven, subjective assessments using fuzzy descriptors like distance and speed semantics to evaluate driving risk. These insights provide significant value for addressing the limitations of precise data-driven methods. This study proposes a novel traffic crash risk analysis framework called Token Tree Generation and Parsing (TTGP). It integrates knowledge-driven insights from human drivers with data-driven methods. TTGP includes the Token Tree Generation Module (Module 1) and the Token Tree Parsing Module (Module 2). In Module 1, we apply the token-tree-of-thoughts method to transform natural language traffic regulations and vehicles’ traffic behaviors and attribute parameters into token tree based on semantic rules. This module simulates the generation of human fuzzy semantics in traffic scenarios. In Module 2, we integrate three encoders and decoders to extract traffic crash risk semantic features and identify traffic crash risk level from the digitized token tree. Experiments in the highway and urban expressway interweaving areas demonstrate that TTGP can accurately analyze risk using imprecise data. The TTGP performs better than traditional methods such as Tree, Naïve Bayes, RUSBoost and Efficient Logistic Regression models. This study significantly enhances the flexibility, generalization, and reliability of risk assessment. It bridges the gap in how HoVs handle fuzzy information in risk analysis.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"218 ","pages":"Article 108037"},"PeriodicalIF":5.7,"publicationDate":"2025-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143906426","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":"Collision risk prediction and takeover requirements assessment based on radar-video integrated sensors data: A system framework based on LLM","authors":"Qingchao Liu ,&nbsp;Ruohan Yu ,&nbsp;Yingfeng Cai ,&nbsp;Quan Yuan ,&nbsp;Henglai Wei ,&nbsp;Chen Lv","doi":"10.1016/j.aap.2025.108041","DOIUrl":"10.1016/j.aap.2025.108041","url":null,"abstract":"<div><div>There are safety risks when drivers take over the control of autonomous driving vehicles, and reducing unnecessary takeovers is essential to improve driving safety. This study seeks to develop an interpretable system framework for collision risk prediction and takeover requirements analysis (CPTR-LLM) utilizing a large language model (LLM). The model’s inference performance is enhanced through the collection of extensive perception data and the design of a two-stage training strategy, reasoning chain framework, and an error detection and correction mechanism. In terms of collision risk prediction, the experimental results show that the accuracy of CPTR-LLM can reach 0.88. The Cross-sectional-autoregressive-distributed lag (ARDL) model and Augmented Mean Groups (AMG) confirm the reliability of the model’s predictive performance by revealing the association between different variables and collision risk. Regarding takeover requirement analysis, CPTR-LLM accurately comprehends the characteristics of the pre-takeover scene and comprehensively assesses the takeover requirement level in conjunction with collision risk, thereby effectively reducing unnecessary takeovers in simple driving scenarios and unsafe takeovers in scenarios with multiple moving targets. Overall, the findings of this paper offer significant insights into the application and takeover requirements of LLM in the domain of road safety.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"218 ","pages":"Article 108041"},"PeriodicalIF":5.7,"publicationDate":"2025-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143903565","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 influence of socioeconomic factors on the relationships between road characteristics and traffic crash frequency and severity-- A hybrid structural equation modelling − artificial neural networks approach","authors":"Mahsa Jafari,&nbsp;Bhagwant Persaud","doi":"10.1016/j.aap.2025.108076","DOIUrl":"10.1016/j.aap.2025.108076","url":null,"abstract":"<div><div>Traffic crashes result from complex interactions between driver, roadway, and environmental factors, which traditional methods often fail to capture. This paper investigates the influence of road, weather, and socioeconomic factors on traffic crashes, using a two-stage hybrid Structural Equation Modelling (SEM)-Artificial Neural Networks (ANN) approach to capture the complex relationships between these factors and crash intensity, a variable that jointly captures the frequency and severity of crashes. A database from Ohio collector road segments served as the case study in this novel hybrid approach, which utilized SEM to analyze the complex and moderating relationships between different factors and crash intensity. SEM revealed significant relationships between crash intensity and factors such as “Horizontal Curve,” “Road” (AADT and surface width index), “Segment Length,” “Speed Limit,” “Vertical Curve,” and “Vehicle Possession.” Based on the SEM results, “Vehicle Possession” significantly moderated the relationship between “Horizontal Curve” and crash intensity. In the next step, ANN further identified key predictors, including “Segment Length,” “Road,” the interactions of “Vehicle Possession-Speed Limit,” “Vehicle Possession-Vertical Curve,” and “Age-Road.” The findings highlight the advantage of the complementary application of linear and nonlinear methods in providing invaluable theoretical and methodological insights for crash data analysis.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"218 ","pages":"Article 108076"},"PeriodicalIF":5.7,"publicationDate":"2025-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143903567","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":"Safety effectiveness of forward collision warning systems in the vehicle fleet: A driving simulation study","authors":"Oluwaseun Olufowobi ,&nbsp;John Ivan ,&nbsp;Kai Wang ,&nbsp;Naveen Eluru","doi":"10.1016/j.aap.2025.108078","DOIUrl":"10.1016/j.aap.2025.108078","url":null,"abstract":"<div><div>The increasing rate of crashes globally has prompted the development of strategies like the Advanced Driver Assistance systems (ADAS) to improve safety. These systems range from subtle speed adjustment alerts to automatic emergency braking. One such system is the forward collision warning (FCW), which aims to mitigate collisions, particularly rear-end crashes, by providing visual, auditory, or tactile alerts of impending collisions. However, there has not been enough attention given to how drivers might react to FCW without unintentionally distracting them, while also using serious conflicts to quantify safety. This driving simulation study aimed to assess the effectiveness of the FCW by examining serious conflict as a surrogate measure for actual crashes. The goal was to estimate a Crash Modification Factor (CMF) for FCW systems within vehicle fleet, considering varying market penetration rates ranging from 10 percent to 50 percent in increments of 10 percent. Scenarios were created where drivers encountered different road and traffic conditions to evaluate their responses to unexpected events. A total of 133 participants completed a between subject design in which half of them drove the course with the FCW programmed into the simulation, and the other half did not have the FCW. The evaluation of serious conflicts utilized the Swedish Traffic Conflict Technique, employing two key indicators: Time to Accident (TA) and Conflicting Speed. Conflict severity was measured by considering two variables − the lane position and speed of the participants which was used to compute the TA. As expected, drivers in each scenario experienced fewer serious conflicts when assisted by FCW systems compared to the drivers without them. The resulting CMFs from the study can be integrated into crash prediction models to support efforts to keep crash prediction models up to date by accounting for the effects of increasing numbers of vehicles having FCW systems.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"218 ","pages":"Article 108078"},"PeriodicalIF":5.7,"publicationDate":"2025-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143903575","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":"Quantile-based scenario generation for automated vehicle safety evaluation","authors":"Hang Zhou,&nbsp;Chengyuan Ma,&nbsp;Ke Ma,&nbsp;Xiaopeng Li","doi":"10.1016/j.aap.2025.108043","DOIUrl":"10.1016/j.aap.2025.108043","url":null,"abstract":"<div><div>As automated vehicles (AVs) are increasingly deployed, ensuring their safety and reliability is crucial before widespread adoption. Existing safety evaluation methods typically focus on generating a testing scenario library with a large number of safety-critical scenarios; however, this approach presents two key limitations. First, the safety testing of the testing scenario library is time-consuming, making it impractical to apply to the production qualification test for every individual production AV. Second, most methods aim to maximize the risks of the scenario, often overlooking that some highly hazardous situations are unavoidable. Considering these research gaps, this study introduces a quantile-based scenario generation method for AV safety evaluation. The proposed method generates scenarios with varying levels of risk, determined by a specified quantile of the risk index, enabling a comprehensive and efficient assessment of AV safety. With the knowledge of the quantile of the scenario library, safety evaluation can rapidly identify the safety performance of each individual AV with a theoretical bound using a limited number of tests. To address the challenge posed by the rarity of safety-critical events, an adaptive variance reduction framework based on importance sampling theory, combined with Particle Swarm Optimization, is employed to minimize estimation variance and optimize scenario distribution. Experiments validate the method’s ability to reduce estimation variance in the multi-lane scenario and demonstrate how it compares the safety performance of commercialized AVs.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"218 ","pages":"Article 108043"},"PeriodicalIF":5.7,"publicationDate":"2025-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143903566","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 framework for real-time traffic risk prediction incorporating cost-sensitive learning and dynamic thresholds","authors":"Dan Wu ,&nbsp;Lu Xing ,&nbsp;Ye Li ,&nbsp;Yiik Diew Wong ,&nbsp;Jaeyoung Jay Lee ,&nbsp;Changyin Dong","doi":"10.1016/j.aap.2025.108087","DOIUrl":"10.1016/j.aap.2025.108087","url":null,"abstract":"<div><div>In recent years, researchers have explored an innovative approach that leverages real vehicle trajectory data to simultaneously derive traffic state and risk level for real-time risk prediction, which is crucial for traffic safety. However, existing studies largely overlook the costs associated with incorrect predictions and the varying consequences of different misclassifications, which undermines the reliability of the obtained prediction results. To address these gaps, this study refined traffic risk classification into four levels (i.e., no, low, medium, and high risks) and incorporated misclassification costs into the prediction process through cost-sensitive learning (CSL). Furthermore, considering that multi-class prediction tasks often face performance degradation and increased risk level granularity worsens class imbalance, further amplifying this degradation, this study introduced dynamic thresholds (DTs) to improve model performance. The aforementioned cost coefficients and thresholds were pinpointed using a genetic algorithm (GA). Furthermore, the employed data, comprising variables related to traffic state and associated risk data, were sourced from the HighD dataset. Subsequently, CSL-DTs-based models were built by integrating CSL and DTs with four distinct baseline machine/deep learning models, and the prediction performance (e.g., precision) and computation time of these models were compared. Results show that, compared to the corresponding baseline models, the proposed models perform better for multi-class prediction tasks. Additionally, the computation time of the CSL-DTs-based models is found to be acceptable for real-time prediction purposes. Finally, to ensure the reliability of the results obtained through the GA optimization (e.g., avoiding local optima), convergence curves were plotted, confirming the robustness of the optimization process. A robustness analysis also demonstrates that the models are highly stable under slight perturbations of cost coefficients and thresholds, with minimal impact on performance. Findings of this study are expected to enhance the reliability of real-time traffic risk prediction, holding the promise of significantly promoting proactive traffic safety management.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"218 ","pages":"Article 108087"},"PeriodicalIF":5.7,"publicationDate":"2025-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143906424","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":"Interchange configurations safety comparison tool","authors":"Wei Zhang ,&nbsp;Scott Himes","doi":"10.1016/j.aap.2025.108074","DOIUrl":"10.1016/j.aap.2025.108074","url":null,"abstract":"<div><div>The Interstate System is a fully access controlled surface transportation network connecting diverse geographical areas for the movement of people and goods. It is of national interest to preserve and enhance this system. Service interchanges are critical facilities linking State and Local roads to the Interstate System. When the need arises to insert a new interchange or modify an existing interchange, the request is presented in the Interchange Justification Report (IJR). Although many IJRs are submitted annually, the types of interchanges proposed are limited. Improvements in traffic operation due to proposed interchanges are usually well-described based on traffic analysis results. However, the safety performance of proposed interchanges is often not well substantiated due to a lack of safety modeling tools. Historic data indicates that high-traffic facilities like interchanges exhibit stable traffic crash patterns over time, meaning they should be predictable. Although many factors are known to induce traffic crashes, the safety community hasn’t fully grasped how to properly quantify those factors. Decomposing an interchange into simple components and adding up the components’ annual crash predictions generally doesn’t yield results that match field data. The Federal Highway Administration (FHWA) developed the Interchange Safety Analysis Tool (ISAT) and supported the development of its enhanced version, ISATe. Both of these tools decompose interchanges into simple components for analysis. The FHWA recently completed a study treating the service interchange as a whole and using input data typically available in the planning phase to predict the safety performance of planned service interchanges. It includes the safety predictions of eight interchange configurations representing 78 percent of all interchanges considered in IJRs reviewed by FHWA. This paper presents the functionalities of this tool and explains how the tool may be used. This tool gives the IJR reviewers a consistent methodology for assessing the safety performance of proposed service interchange projects.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"218 ","pages":"Article 108074"},"PeriodicalIF":5.7,"publicationDate":"2025-05-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143900379","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 do freeway tunnel portal locations and the corresponding horizontal alignment affect traffic safety? Insights from driving simulation experiment and reliability analysis","authors":"Yonghong Yang ,&nbsp;Yixi Hu ,&nbsp;Chuangbo Xu ,&nbsp;Yu Zhang ,&nbsp;Tao Zheng","doi":"10.1016/j.aap.2025.108082","DOIUrl":"10.1016/j.aap.2025.108082","url":null,"abstract":"<div><div>Previous research has indicated that freeway tunnel portals are prone to traffic accidents, with alignment at tunnel portal being a significant factor influencing crash occurrence. However, how to ensure safe design of freeway tunnel portal alignment remains unclear. This study developed a freeway model with five tunnels, whose portals are located at different positions along a tangent-curve section, to comprehensively investigate the impact of portal locations on driving behavior and traffic safety. Microscopic driving parameters were obtained through experiments and further analyzed using reliability analysis. Based on real-world crash data, two failure modes were considered: insufficient stopping sight distance and excessive lane departure. The probability of failure (PoF) was calculated using the Monte-Carlo sampling algorithm as an effective indicator of driving risk. To further explore various features affect tunnel portal traffic safety, sensitivity analysis was conducted on four key indicators, include curve radius, spiral length, pavement friction coefficient, and driving speed. The results show that the design locations of the tunnel portal significantly affect drivers’ speed and lane departure behavior. When the portal is located on a tangent section, the distribution of driver speed and lane departure behavior are the most concentrated. In contrast, when the portal is situated on a circular curve or spiral section, the distribution becomes more dispersed. The failure modes and PoF are related to the portal location. Besides, the PoF based on insufficient stopping sight distance increases continuously with the curvature at the portal, while the PoF based on excessive lane departure increases with the deviation of curvature. The synthetic PoF indicates that when the portal is located 3/4 of the spiral section, the PoF is the highest, reaching up to 35.66% at the entrance and 25.31% at the exit. The curve radius, spiral length, pavement friction coefficient, and driving speed all influence the PoF at the tunnel portal. Among these factors, increasing the curve radius and ensuring a sufficient pavement friction coefficient have the most significant impact on reducing the PoF. This study proposes recommendations for the alignment design of freeway tunnel portals and traffic safety management, providing valuable references for road designers and freeway administrators to enhance the traffic safety of freeway tunnels.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"218 ","pages":"Article 108082"},"PeriodicalIF":5.7,"publicationDate":"2025-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143900196","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":"Exploring the trust and behavior of experienced advanced driver assistance system drivers: An on-road study","authors":"Ruifo Zhang ,&nbsp;Zhengyu Tan ,&nbsp;Zemin Lin ,&nbsp;Ruiying Zhang ,&nbsp;Chenhui Liu","doi":"10.1016/j.aap.2025.108071","DOIUrl":"10.1016/j.aap.2025.108071","url":null,"abstract":"<div><div>Trust in automation is crucial for the optimal utilization of advanced driver assistance systems (ADAS). While previous studies have examined trust in automated driving (TiAD) and its impact on behavior, there remains a need to explore how experienced drivers interact with partially automated systems in real-world contexts. This study investigates the trust and behavior of 34 experienced ADAS drivers, divided into trustful and distrustful groups, during on-road driving encompassing six typical scenarios. This study evaluates the initial and final TiAD, situational trust across six driving scenarios; and behaviors, including hands-off the steering wheel, engagement in non-driving-related activities (NDRAs), and visual behavior. Results reveal no significant change in TiAD between pre- and post-driving evaluations, but there are significant differences in TiAD and situational trust across six scenarios between the trustful and distrustful groups. Regarding behavior, trustful drivers exhibit more hands-off events and delay responses to warnings. Both groups engage in risky NDRAs with different patterns, while trustful drivers showing a higher tendency for high-risk NDRAs. Visual behavior analysis shows that trustful drivers spend less time monitoring the driving environment, particularly in complex scenarios such as lane addition/reduction, but more time focusing on the human–machine interface (HMI) overall compared to distrustful drivers. The study also explores the impact of ADAS type and mileage, showing that drivers with advanced functionality exhibit higher trust and reduced monitoring, while mileage influence trust with a turning point at around 3,000 km. With these findings, this study highlights safety risks and proposes strategies to address them. This study is expected to provide insights into trust research and ADAS optimization, enhancing driving safety and user experience.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"217 ","pages":"Article 108071"},"PeriodicalIF":5.7,"publicationDate":"2025-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143881368","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}