Accident; analysis and prevention最新文献

{"title":"Parents protect their children when travelling: Exploring traffic safety behavioral intentions through the lens of cognitive appraisal and protection motivation theories","authors":"Nhat Dinh Quang Vo ,&nbsp;Duy Quy Nguyen-Phuoc ,&nbsp;Amjad Pervez ,&nbsp;Jaeyoung Jay Lee","doi":"10.1016/j.aap.2026.108421","DOIUrl":"10.1016/j.aap.2026.108421","url":null,"abstract":"<div><div>Child traffic safety remains a significant public health concern in developing countries such as Vietnam, where private vehicles are the primary mode of transport for children. Despite existing regulations, concerns persist regarding parental adherence to traffic safety practices. This study examines the psychological factors influencing parents’ intentions to adopt protective measures (i.e., using child safety equipment and safe driving practices). By integrating Cognitive Appraisal Theory and Protection Motivation Theory, the study develops a comprehensive model that includes perceived severity, perceived vulnerability, response efficacy, self-efficacy, and response cost, with fear acting as a key mediating variable. A structured survey was conducted with 775 parents (443 motorcyclists and 332 car drivers) in Da Nang, Vietnam, and the data were analyzed using PLS-SEM. Results show that fear significantly predicts both child-protective and safe driving behaviors. However, the key predictors of fear differ by vehicle type. For motorcyclists, fear is influenced by perceived vulnerability, response cost, and self-efficacy. For car drivers, response efficacy has the strongest effect, followed by perceived vulnerability and self-efficacy. These findings emphasize the central role of threat appraisal and coping appraisals in shaping parental coping intentions when transporting children and suggest that emotion particularly fear serves as a critical motivational force. The study contributes to traffic safety research by providing an integrated theoretical framework and offering practical implications for designing more effective public safety interventions and policies that target parental behavior in high-risk transportation environments.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"228 ","pages":"Article 108421"},"PeriodicalIF":6.2,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146058517","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-oriented passenger flow control at a congested metro hub: A microscopic approach","authors":"Jun Zhang ,&nbsp;Dongdong Shi ,&nbsp;Wei Yang ,&nbsp;Jian Ma","doi":"10.1016/j.aap.2025.108390","DOIUrl":"10.1016/j.aap.2025.108390","url":null,"abstract":"<div><div>Massive passenger influxes at a metro hub pose significant risks. While previous studies have primarily focused on service-oriented passenger flow control from a macroscopic perspective, they have lacked detailed mechanisms to mitigate crowd-related risks from a microscopic viewpoint. To address this gap, this study develops a safety-oriented passenger flow control method based on a comprehensive microscopic pedestrian simulation model for metro hubs. The simulation model operates on two levels: the tactical level, which determines route choices for each pedestrian, and the operational level, which models pedestrian movement using the Social Force Model (SFM). We then introduce a dynamic entry ticket gate service time delay strategy to regulate pedestrian flow and mitigate crowding risks. The strategy leverages Model Predictive Control (MPC) integrated with a Kalman filter to enable real-time decision-making in a stochastic environment. The MPC’s predictive model is a linear representation derived from input–output data generated by our simulation. The control objective, defined as crowd danger, represents the state-of-the-art understanding of pedestrian dynamics. Computational experiments demonstrate the effectiveness of this approach in stabilizing the crowd danger metric around a predefined target level. Furthermore, the results reveal two important management insights: (1) Increasing the crowd danger reference value intensifies fluctuations in crowd behavior, as shown by our control performance analysis and correlation analysis, highlighting the need for a lower reference value to maintain stable control. (2) While this study focuses on crowd safety in the station hall, the proposed approach also improves platform service levels under various train headways and passenger densities in carriages. Thus, this safety-oriented strategy can be integrated into broader passenger flow management efforts to enhance both safety and service efficiency.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"228 ","pages":"Article 108390"},"PeriodicalIF":6.2,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145895863","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":"From crash reports to safer roads: a multimodal framework integrating vision-language models and street view analysis","authors":"Guanhe Wu ,&nbsp;Xintong Yan ,&nbsp;Yajie Zou ,&nbsp;Yuanchang Xie","doi":"10.1016/j.aap.2026.108419","DOIUrl":"10.1016/j.aap.2026.108419","url":null,"abstract":"<div><div>Crash narratives and diagrams contain rich causal and contextual information that is often underutilized in traditional road safety analysis, which typically relies on aggregated crash counts and tabulated variables. Such approaches have limited ability to explain the mechanisms of individual crashes. This study proposes a mechanism-oriented, multimodal framework for scalable and interpretable roadway risk diagnosis. The framework employs a vision–language model to jointly analyze each crash narrative and diagram, constructing an explicit reasoning chain that distinguishes immediate crash-triggering behaviors from underlying contextual constraints. Based on inferred root cause keywords, each crash is decomposed into proportional contributions of five predefined factors: human, vehicle, road, environment, and traffic signal. These attributions are spatially aggregated to identify hotspots with elevated road-related risk. For high-risk locations, street-view imagery is analyzed in the crash context to diagnose potential roadway design deficiencies and generate targeted recommendations. The framework is evaluated using 4,302 crash reports from Massachusetts with expert-annotated attributions. Vision–language models substantially outperform locally trained regression baselines, particularly under imbalanced factor distributions. Among all tested models, the Grok 2 multimodal model achieves the highest agreement with expert annotations. Further analysis shows that the distribution of inferred root cause keywords in the factor attribution space effectively explains how relative factor contributions are formed. The street-view-based diagnostic outputs align closely with real-world engineering issues, demonstrating that combining causal reasoning with multimodal crash data enables practical, mechanism-driven road safety diagnostics beyond frequency-based screening.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"228 ","pages":"Article 108419"},"PeriodicalIF":6.2,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146023724","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":"Uncertainty-aware spatiotemporal interaction learning for pre-conflict risk evolution with a risk-increase prior","authors":"Chenhao Zhao ,&nbsp;Min Li ,&nbsp;Jiawei Liu ,&nbsp;Zhishun Zhang ,&nbsp;Shifeng Niu ,&nbsp;Dongdong Song","doi":"10.1016/j.aap.2025.108379","DOIUrl":"10.1016/j.aap.2025.108379","url":null,"abstract":"<div><div>Quantifying real-time conflict risk and revealing its evolution are of great importance for enhancing vehicle active safety. Recent studies estimate dynamic risk via conflict probability, yet annotation still relies on threshold based static views and uncertainty is only partially modeled, which limits the assessment of a model’s ability to learn conflict evolution. Addressing this gap, we posit a prior hypothesis of increasing pre-conflict risk and develop a risk quantification model that integrates driver control inputs and multi vehicle spatiotemporal interactions with explicit uncertainty outputs. The model is evaluated for accuracy and stability of risk perception, parameter sensitivity, and capacity for pattern learning. Experiments show that, relative to Time To Collision (TTC), Deceleration Rate to Avoid a Crash (DRAC), Proportion of Stopping Distance (PSD), Anticipated Collision Time (ACT),and Emergency Index (EI), the proposed model achieves stronger risk discrimination. On the test set of 15 conflict events used in this study, the proposed model detects elevated conflict risk on average 1.15 s before the conflict point. In four representative scenarios, including car following, ego lane change, unobstructed cut in and cut in under occluded view, the proposed model yields a lower false alarm rate than TTC and, on average, perceives rising conflict risk 1.44 s before the conflict point. Uncertainty analysis indicates lower uncertainty during the rising risk phase, enabling reliable capture of risk evolution. Sensitivity results support the expressiveness of the proposed hypothesis and reveal a common regularity across scenarios, where risk begins to increase approximately 4–6 s before conflict. The results establish a pre conflict risk modeling paradigm that jointly estimates risk and its confidence, supports calibration and transfer across scenarios, and provides an operational basis for proactive safety assessment.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"228 ","pages":"Article 108379"},"PeriodicalIF":6.2,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145895862","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":"Beyond the norm: Identifying rare and high-risk pedestrian crash patterns using unsupervised learning","authors":"Zeinab Bayati,&nbsp;Asad J. Khattak","doi":"10.1016/j.aap.2026.108406","DOIUrl":"10.1016/j.aap.2026.108406","url":null,"abstract":"<div><div>Pedestrian safety remains a major concern, with fatalities rising despite infrastructure and safety improvements. To make meaningful progress, efforts should focus more intensely on reducing the most dangerous and fatal cases, given the growing importance of conventional and automated vehicle safety in shaping crash outcomes. This study introduces a composite unsupervised edge case detection framework that combines Uniform Manifold Approximation and Projection (UMAP) for dimensionality reduction with Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN). Each crash receives a composite score based on its cluster membership uncertainty and its distance from the core of typical crash patterns in the UMAP space. Based on these scores, crashes are classified into three interpretive layers: Core, Moderate Edge, and Strong Edge. Core cases represent common patterns, while Strong Edge cases reflect rare and complex situations. The framework is applied to 10,108 police-reported crashes from North Carolina coded with the Pedestrian and Bicycle Crash Analysis Tool (PBCAT), a relatively clean database of pedestrian crashes. Crash severity and contextual characteristics were compared across the three layers. Strong Edge crashes were substantially more severe, with 36.6% resulting in fatal injuries compared to 8.1% in the Core group. These high-risk cases often occurred in rural areas, under poor lighting conditions, in non-intersection locations, and involved behaviors such as unusual circumstances or crossing expressways. The findings show that the built environment and crash type influence pedestrian crash patterns. The edge case framework helps detect rare, high-risk crashes often missed by traditional methods, supporting targeted safety efforts.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"228 ","pages":"Article 108406"},"PeriodicalIF":6.2,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145974326","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":"Evaluating underground space of rail transit hub evacuation under fire scenarios: Virtual reality meets agent-based simulation","authors":"Zhilu Yuan ,&nbsp;Shenyao Lin ,&nbsp;Zixuan Mao ,&nbsp;Changyang Xie ,&nbsp;Yunhe Tong ,&nbsp;Yongxing Li ,&nbsp;Hongfei Jia","doi":"10.1016/j.aap.2025.108389","DOIUrl":"10.1016/j.aap.2025.108389","url":null,"abstract":"<div><div>The rapid growth of urban rail transit has improved transportation efficiency but presents significant safety challenges, particularly in high-density transit hubs where aging infrastructure, overcrowding, and extreme events converge. Fires in such hubs create critical evacuation bottlenecks due to enclosed spaces and complex rescue environments. Existing methods of evacuation research lack the realism and adaptability required to address these dynamic scenarios, particularly for optimizing Selective Door Opening (SDO) strategies. To address these gaps, we developed a high-fidelity virtual environment replicating emergency scenarios in underground transit tunnels. A VR experiment was conducted to collect participants’ evacuation trajectories and eye-tracking data under different SDO strategies. Behavioral mechanisms, movement dynamics, gaze patterns and evacuation speeds in different areas were analyzed, with bivariate Gaussian distributions fitted to describe visual perception regions of pedestrians. Features derived from the foregoing VR experiment informed agent-based simulations, enabling the quantitative evaluation of SDO performance across diverse emergency scenarios. The results of the VR experiment indicate that during evacuation, pedestrians exhibit significant differences in evacuation speed and gaze patterns across various spatial regions. In addition, pedestrians show notable variation in perception scale for different types of AOIs. Furthermore, the evaluation of SDO using agent-based simulation reveals that the effectiveness of SDO is influenced by the scenario issues, with passenger volume and the weighting of safety factors dynamically impacting the selection of the optimal SDO.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"228 ","pages":"Article 108389"},"PeriodicalIF":6.2,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145923361","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":"Proactive safety at CVIS-enabled intersections: a framework based on high-fidelity trajectory reconstruction and dynamic risk assessment","authors":"Yunxuan Li ,&nbsp;Shihao Wang ,&nbsp;Lishengsa Yue ,&nbsp;Zhonghua Wei ,&nbsp;Wenhui Zheng ,&nbsp;Lin Zhang","doi":"10.1016/j.aap.2026.108410","DOIUrl":"10.1016/j.aap.2026.108410","url":null,"abstract":"<div><div>High-fidelity vehicle trajectories are critical for proactive safety at intersections, where traditional reconstruction methods based on linear models or interpolation fail to capture complex nonlinear dynamics like sudden stops and sharp turns. While deep learning can model this complexity, its computational cost is prohibitive for real-time edge deployment. To address these challenges, this paper proposes an edge-computing-enhanced two-stage framework for high-fidelity trajectory reconstruction and dynamic risk assessment, specifically designed for Cooperative Vehicle-Infrastructure Systems (CVIS) at intersections. The first stage reconstructs accurate vehicle trajectories by applying physics-informed constraints derived from vehicle dynamics, combined with adaptive wavelet transforms and a hybrid thresholding strategy, enabling robust noise reduction from low-quality, multi-source sensor data. The second stage introduces a Vehicle Outline-based Conflict Algorithm (VOCA), which elevates traditional point-based conflict detection to outline-based spatial overlap analysis. By accurately modeling the real physical boundaries of vehicles, the proposed method significantly improves the sensitivity and timeliness of conflict detection, enabling more reliable proactive safety interventions in complex urban scenarios. Validated with real-world intersection data on an NVIDIA Jetson edge device, our method effectively suppresses high-frequency noise, reducing acceleration fluctuations by 98.66%. The outline-based VOCA proves vastly superior to traditional approaches, with center-point methods detecting only 22.53% of the conflicts identified by our algorithm. The entire framework achieves real-time performance, processing complex scenarios with delays under 100 ms per frame per vehicle. This work delivers an efficient solution for generating accurate, low-latency conflict warnings, advancing the practical application of CVIS for proactive safety management in urban environments.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"228 ","pages":"Article 108410"},"PeriodicalIF":6.2,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146017096","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":"Determinants influencing risks in e-bike cyclists under mix traffic condition: a partially constrained random parameters approach using experimental study data","authors":"Yuntong Zhou ,&nbsp;Xin Gu ,&nbsp;Mohamed Abdel-Aty ,&nbsp;Yanyan Chen","doi":"10.1016/j.aap.2025.108364","DOIUrl":"10.1016/j.aap.2025.108364","url":null,"abstract":"<div><div>The global adoption of e-bikes improves urban mobility but can impact traffic safety. Braking behavior is a vital task with a high association with e-bike cyclists’ safety, especially under mixed traffic conditions. Although traditional studies on cyclists’ traffic safety analyses have been conducted based on crash data in transportation research, most studies focus on crash severity rather than the specific behavior risks associated with different traffic conditions. This paper uses field experiment data to study the factors that affect the observed braking behavior of e-bike cyclists under mixed and non-mixed traffic conditions. To simultaneously account for this flexibility to model varying effects across traffic conditions and capture unobserved heterogeneity, a partially constrained random parameters logit model with heterogeneity in the means is estimated. The analysis controls cyclists’ characteristics, behavioral factors and roadway characteristics. Our main findings indicate that safety is affected by mixed traffic conditions, which has implications for infrastructure design and cyclist characteristics. Different head-turning and handlebar turning behavior reflect cyclists’ risk perception and risk avoidance responses under various road conditions. Overall, accounting for unobserved heterogeneity and cross-condition correlation provides a more nuanced understanding of e-bike cyclists’ braking behavior.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"227 ","pages":"Article 108364"},"PeriodicalIF":6.2,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145852944","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":"Enhancing collaborative perception through multi-scale contextual information integration","authors":"Mingyue Ma ,&nbsp;Dongyang Hong ,&nbsp;Hui Zhang ,&nbsp;Zelin Miao ,&nbsp;Weiqing Wang ,&nbsp;Guangming Zhao","doi":"10.1016/j.aap.2025.108367","DOIUrl":"10.1016/j.aap.2025.108367","url":null,"abstract":"<div><div>In autonomous driving, perception systems face challenges from dynamic environments such as occlusions, changing lighting, and unpredictable traffic. These conditions make it hard to capture fine local details and broad context, while real-time operation demands high efficiency and low communication cost. In this paper, we propose a multi-scale contextual information integration (MSCI) framework designed to enhance collaborative perception. The method employs multi-scale adaptive attention augmentation to focus on relevant features across spatial scales, capturing fine-grained details and wider contextual cues to improve perception accuracy. The context-aware perception enhancement module then combines local and global information. It refines features to keep perception robust and stable in changing or challenging environments. Finally, the GRU-based dynamic booster embeds a motion-aware mechanism into the recurrent unit. This strengthens temporal modeling for sequential data and improves real-time decision making. Experimental results demonstrate that the proposed method achieves notable improvements in both detection accuracy and communication efficiency.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"227 ","pages":"Article 108367"},"PeriodicalIF":6.2,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145882094","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":"Enhancing safety in remote takeover of highly automated mining trucks: What are the different effects of audiovisual takeover requests under daytime and nighttime conditions?","authors":"Dong Liu ,&nbsp;Hui Li ,&nbsp;Yongkang Chen ,&nbsp;Jun Yi ,&nbsp;Ye Tian ,&nbsp;Renxi Wang","doi":"10.1016/j.aap.2025.108383","DOIUrl":"10.1016/j.aap.2025.108383","url":null,"abstract":"<div><div>Highly automated mining trucks significantly enhance operational safety and efficiency. However, until full automation is realized, remote takeover remains a necessary safeguard. Consequently, designing appropriate takeover request (TOR) is critical for improving driving safety and user experience. Nevertheless, existing research has seldom considered the design of audiovisual TORs under different lighting conditions or their adaptation for non-passenger vehicles. To address this gap, this study conducted a remote takeover experiment on mining trucks, involving 48 human participants, to investigate the effects of nine distinct audiovisual TORs on takeover performance, attention, driving stress, and subjective perceptions under two lighting conditions (daytime and nighttime). Statistical analysis was performed using Generalized Estimating Equation (GEE). The results indicate that both lighting conditions and the type of audiovisual TOR significantly influenced the dependent variables. Furthermore, TORs exhibited differential effectiveness depending on the lighting condition. During nighttime, drivers demonstrated greater reliance on the auditory channel, adopting a strategy of visual concentration and more conservative driving. Although heightened tension responses were observed, drivers exhibited better stress regulation capabilities. Speech-based cues effectively enhanced driving safety, particularly during daytime. TORs relying solely on beep sounds were prone to inducing tension and reducing acceptability. Excessive visual elements could distract attention, impairing takeover performance and situational awareness, although they partly alleviated physiological stress. The optimal TOR configuration was condition-specific: a combination of text and speech was most effective during daytime, whereas a combination of icon, beep, and speech yielded the best results at night. This study provides valuable insights for optimizing TOR design in remote takeover interfaces for mining trucks.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"227 ","pages":"Article 108383"},"PeriodicalIF":6.2,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145861727","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}