Accident; analysis and prevention最新文献

{"title":"MSIE-Transformer: A novel driving behavior modeling approach for virtual simulation test environment","authors":"Huihua Gao ,&nbsp;Ting Qu ,&nbsp;Xun Gong ,&nbsp;Ping Wang ,&nbsp;Hong Chen","doi":"10.1016/j.aap.2025.108039","DOIUrl":"10.1016/j.aap.2025.108039","url":null,"abstract":"<div><div>To build a natural driving environment in the virtual environment for autonomous driving safety testing, a digital twin model of human driving behavior is essential. However, due to the lack of fidelity and intelligence of the driving behavior model of the background vehicle, there is an intolerable gap between the virtual simulation test environment and the real road test environment. This paper proposes the Multi-source Information Encoding Transformer (MSIE-Transformer) to model driving behaviors of background vehicles within the virtual simulation environment. This approach improves model performance through the effective encoding of multi-source features using heterogeneous encoding networks, the comprehensive integration of these features based on the multi-head self-attention mechanism, and the combination of dynamic loss functions with Bayesian optimization. The experimental results demonstrate that, benefiting from the feature extraction and integration of multi-source information, the proposed method exhibits superior performance in fidelity compared to existing approaches. It also demonstrates good performance in statistical realism and modeling heterogeneous driving behaviors. In addition, the model’s performance is validated in multi-agent control scenarios and successfully transferred to intersection scenarios.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"217 ","pages":"Article 108039"},"PeriodicalIF":5.7,"publicationDate":"2025-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143864078","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":"Applying human factors analysis and classification system for commercial vehicles crashes investigation and critical failure routes analysis","authors":"Xuesong Wang ,&nbsp;Yanru Zhou ,&nbsp;Ashleigh Filtness ,&nbsp;Chao Wang ,&nbsp;Xiaowei Tang ,&nbsp;Shikun Liu ,&nbsp;Zhicheng Wang","doi":"10.1016/j.aap.2025.108047","DOIUrl":"10.1016/j.aap.2025.108047","url":null,"abstract":"<div><div>This study aims to develop a reliable and valid Human Factors Analysis and Classification System (HFACS) and Bayesian Network (BN) methodology to understand the causal factors of commercial vehicles (CMV) involved traffic crashes. The HFACS-CMV method has been established using learnings from 100 multi casualty crashes involving at least 10 fatalities. The extreme nature of such crashes ensures the existence of in-depth investigation reports which are necessary to generate sufficient data for HFACS analysis. By analyzing 100 road traffic investigation reports across 28 provinces in China from 2001 to 2021, the research employs odds ratio and BN which is able to quantitatively examine the relationships among contributing factors. The study identifies the highest frequencies of failures in establishing/implementing safety production systems, wrong responses to emergencies, and over speeding across four levels, 12 categories, and 53 sub-subcategories’ HAFCS-CMV framework. Numerous associations between the upper and adjacent lower levels are revealed, especially between poor company supervision and government oversight across various subcategories. The HFACS-CMV with BN model highlights a critical failure route: inadequate government oversight leading to poor company supervision or poorly planned operations, resulting in substandard operator conditions and unsafe acts. Identifying associations and failure routes is crucial for developing effective countermeasures. Specific outcomes are limited by the crash reports used which were not generated with HFACS in mind, future research using HFACS informed crash reporting systems would be beneficial. However, the successful application of the method demonstrates the efficiency and applicability of HFACS-CMV as a robust method for understanding causal factors of road crashes.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"217 ","pages":"Article 108047"},"PeriodicalIF":5.7,"publicationDate":"2025-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143864077","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":"Assessment of urban rail transit network passenger-centered resilience under hazards: A dynamic resilience assessment framework","authors":"Ying Lu ,&nbsp;Minmin Yi ,&nbsp;Jianqiang Cui ,&nbsp;Guangdong Wu ,&nbsp;Dong Lin","doi":"10.1016/j.aap.2025.108042","DOIUrl":"10.1016/j.aap.2025.108042","url":null,"abstract":"<div><div>Urban rail transit (URT) is vulnerable to cascading failures from disasters due to their complexity. In modern society, we should pay more attention to the experience of passengers and enhance the quality of service in URT. This paper develops a model using dynamic indicators to assess the disaster resilience of URT. It presents a novel passenger-centered resilience assessment method that reflects dynamic passenger flow demand indicators. A cascading failure model under Geometric Attack Model was established to simulate the route choice of passengers before and after cascading failure. This paper collected data on the Beijing subway, analyzed the features of dynamic passenger flow resilience indicators, and assessed the disaster resilience of the Beijing subway. Results show: (1) generalized travel time costs and transfer times for passengers increase after cascading failures, indicating reduced system resilience; and (2) two indicators are at moderately low resilience levels. This shows that passenger travel routes become more complicated after station failure, and the network’s ability to restore service and maintain service quality in the aftermath of unexpected events needs improvement. The paper suggests improving disaster resistance by adding redundant lines and other measures and also discusses adjusting assessment strategies based on the unique characteristics of different cities. A sensitivity analysis was conducted on the model, and based on this, guiding suggestions were made for optimization strategies.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"217 ","pages":"Article 108042"},"PeriodicalIF":5.7,"publicationDate":"2025-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143858905","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 unified risk metric for freeway vehicles based on the spatio-temporal overlap probability of predicted positions","authors":"Chuang Cui,&nbsp;Bocheng An,&nbsp;Linheng Li,&nbsp;Xu Qu,&nbsp;Wenquan Li","doi":"10.1016/j.aap.2025.108059","DOIUrl":"10.1016/j.aap.2025.108059","url":null,"abstract":"<div><div>Surrogate Safety Measures (SSMs) are widely used to quantify driving risks and support proactive traffic safety management. However, the existing metrics primarily focus on single scenarios, such as rear-end collisions in car-following situations. These metrics assume that the vehicles comply with specific motion equations, overlooking the uncertainties intrinsic to vehicle operation. To address these limitations, this study proposes a novel metric, the Risk Metric based on Predicted Position (RMPP), which evaluates the probability of collision. RMPP is designed to comprehensively capture all risk within a unified framework by incorporating the future positional distributions of vehicles. Firstly, the target vehicle and surrounding vehicles are constructed as a graph structure. The Generalized Dynamic Graph Convolutional Network (GDGCN) is used to predict the position distribution of vehicles. Then, spatial proximity risk and temporal proximity risk are computed based on the predicted positions. Spatial proximity risk is the sum of probabilities of predicted position overlap at the same time, while temporal proximity risk is the sum of probabilities of predicted position overlap at different times. RMPP is obtained through a weighted summation of these two risks. To validate the effectiveness of RMPP, we conducted experimental analyses using the Freeway B of CitySim dataset, which is collected in an Asian region using drones. We compared the prediction results of our GDGCN model with several baseline models. The experimental results demonstrated that our GDGCN model achieved good prediction accuracy. Additionally, using vehicle trajectories from CitySim, we compared RMPP with traditional metrics such as Time-to-Collision (TTC), Deceleration Rate to Avoid Collision (DRAC), and Safety Margin (SM) across both basic and high-risk driving scenarios. The results indicate that RMPP more accurately captures risk characteristics that align with real-world driving conditions. Furthermore, we evaluated the impact of prediction accuracy on RMPP by analyzing risk variations under different RMSE values. When the position prediction accuracy of both vehicles reaches a certain level, its impact on risk is small, confirming the reliability and stability of RMPP. With the advancement of technology and the improvement of prediction accuracy, the precision of RMPP will be further enhanced, making it a robust and powerful tool for traffic safety management.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"217 ","pages":"Article 108059"},"PeriodicalIF":5.7,"publicationDate":"2025-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143854545","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":"Perceived vs. actual multitasking abilities: Predicting texting while driving efficacy and behavior from overconfidence","authors":"Claire E. Bowman-Callaway,&nbsp;Benjamin D. Schulte,&nbsp;Stephanie C. Payne","doi":"10.1016/j.aap.2025.108058","DOIUrl":"10.1016/j.aap.2025.108058","url":null,"abstract":"<div><div>Whereas numerous studies have reported drivers’ overconfidence in their driving ability, this study examines overconfidence in one’s multitasking abilities operationalized as overestimation (perception relative to one’s actual performance) and overplacement (perception relative to others’ abilities) as predictors of texting while driving (TWD). This study also examines TWD self-efficacy as an explanatory mechanism for the relationship between overconfidence and TWD. A sample of 611 undergraduate students (34 % male, mean age of 19.52 years) from a southwestern US university completed an online task-switching paradigm to assess their multitasking ability and multiple self-report measures of TWD-related constructs. TWD was also measured using phone application data. Results indicated that overconfidence (both overestimation and overplacement) was more strongly related to TWD self-efficacy than self-efficacy to resist TWD. TWD self-efficacy explained the relationships between overconfidence and TWD. Additionally, TWD self-efficacy predicted self-reported and actual TWD above and beyond self-efficacy to resist TWD and vice versa. Actual multitasking ability was not significantly related to actual or self-reported TWD. Overall, these findings provide evidence for the influence of overconfidence in multitasking and two forms of self-efficacy on TWD. Implications as well as future directions for research are discussed.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"217 ","pages":"Article 108058"},"PeriodicalIF":5.7,"publicationDate":"2025-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143851383","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":"REViVe: Development of a reactive environmental vigilance in-vehicle system to mitigate drowsiness-induced inattention during automated driving","authors":"Chiho Lim ,&nbsp;Ryan Thomas Villarreal ,&nbsp;Mansoor Nasir ,&nbsp;Chiu Yu-Chin ,&nbsp;Denny Yu","doi":"10.1016/j.aap.2025.108045","DOIUrl":"10.1016/j.aap.2025.108045","url":null,"abstract":"<div><div>With monotonous or conditionally automated driving conditions that may lead to the degradation of driver vigilance and increase the risk of drowsy driving, it is crucial to implement advanced systems that assist drivers in returning to a state of optimal driving readiness. While these systems have shown significant effects in reducing the risks related to drowsy driving, most warning systems heavily rely on auditory and visual sensory channels. These modalities are susceptible to “alarm fatigue” due to frequent and annoying alarms, which may lead drivers to ignore or deactivate the systems entirely, thus rendering them less suitable for preemptive cautionary warnings. To address these limitations, a Reactive Environmental Vigilance in-Vehicle (REViVe) system was developed to counteract driver drowsiness by utilizing alternative sensory modalities. A total of 35 drivers were divided into three condition groups: olfactory, climate, and control. To evaluate the effectiveness of the system, five dependent measurements were analyzed: time taken for PERCLOS to return to baseline and engagement index to measure salient effect; time interval between drowsiness events and peripheral detection task score difference to measure sustained arousal effect; and satisfaction rating to measure driver acceptability. Both the olfactory and climate REViVe systems showed potential as effective preemptive warnings compared to control. With REViVe, drowsy drivers quickly returned to an awake state and sustained vigilance significantly longer than control, while driver satisfaction was positive. Thus, the REViVe system provides a balanced solution for alert functionality and driving experience, suggesting a novel approach to designing preemptive warning systems.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"217 ","pages":"Article 108045"},"PeriodicalIF":5.7,"publicationDate":"2025-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143842900","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 before-after safety evaluation of wide centerline treatment considering the simultaneous changes in lane width and sealed shoulder width","authors":"Yanqi Lian ,&nbsp;Shamsunnahar Yasmin ,&nbsp;Jaeyoung Jay Lee ,&nbsp;Md Mazharul Haque","doi":"10.1016/j.aap.2025.108049","DOIUrl":"10.1016/j.aap.2025.108049","url":null,"abstract":"<div><div>Wide Centerline Treatment (WCLT) is typically implemented by simultaneous adjustments to lane widths and sealed shoulder widths to minimize the need for road widening. Studies to date, however, have overlooked these concurrent changes when assessing the safety effectiveness of WCLT, potentially leading to unreliable conclusions. Therefore, this paper aims to assess the safety effectiveness of WCLT while accounting for simultaneous changes in the lane width and sealed shoulder width to provide a more reliable and comprehensive evaluation. A before-after simulation-based Empirical Bayes approach is adopted by using a Panel Random Parameters Negative Binomial model with parameterized overdispersion. Specifically, the study evaluates crash modification factors of WCLT for nine treatment conditions in combinations of lane width and sealed shoulder width changes (increase, decrease, and constant). The results suggest that WCLT with both increased lane width and sealed shoulder width reduces total injury crashes by 74.33%, fatal and serious injury crashes by 73.40%, head-on crashes by 41.16%, and run-off-road crashes by 72.54%. On the other hand, WCLT with both decreased lane width and sealed shoulder width is found to be less effective, with a reduction in total injury crashes by 43.79%, fatal and serious injury crashes by 42.54%, head-on crashes by 60.66% and run-off-road crashes by 0.76%. This study will assist roadway designers in making informed decisions for implementing WCLT.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"217 ","pages":"Article 108049"},"PeriodicalIF":5.7,"publicationDate":"2025-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143842901","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":"Impact of anger-inducing situations on driver takeover behavior in highly automated vehicles","authors":"Robin Cazes ,&nbsp;Valérie Camps ,&nbsp;Céline Lemercier","doi":"10.1016/j.aap.2025.108051","DOIUrl":"10.1016/j.aap.2025.108051","url":null,"abstract":"<div><div>Motor vehicle accidents, often caused by human error, remain a significant concern. While automated vehicles have the potential to reduce these accidents by handling driving tasks, unnecessary human takeovers, especially when the automated system is operational, can reintroduce error. This study investigates how situational factors known to trigger anger influence takeover behavior and emotional responses in levels 4 and 5 automated vehicles. Using a driving simulator, 60 participants were randomly assigned to either a goal-aligned condition (clear weather, on-time departure, no traffic) or a goal-conflicting condition (dense fog, delayed departure, slow vehicles). Participants could freely choose between manual and automated driving modes. Results showed a significant increase in takeover frequency, higher negative affect and anger in the goal-conflicting condition compared to the goal-aligned condition. Qualitative data gathered from open-ended questions revealed increased stress and frustration leading to more frequent manual takeovers in goal-conflicting conditions, while participants felt calmer with fewer takeovers in goal-aligned conditions. No link was found between takeover behavior and trust in driving automation. These findings highlight the importance of designing Automated Driving Systems (ADS) that minimize stressors and consider drivers’ emotional states to enhance safety and comfort. In this regard, incorporating real-time emotional monitoring and employing cognitive behavioral therapy (CBT) strategies (e.g., situation reappraisal) may help mitigate driver emotional states and prevent unnecessary takeovers.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"217 ","pages":"Article 108051"},"PeriodicalIF":5.7,"publicationDate":"2025-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143842899","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 crash risk factors of farm equipment vehicles on county and non-county roads using interpretable tabular deep learning (TabNet)","authors":"Md Monzurul Islam,&nbsp;Jinli Liu,&nbsp;Rohit Chakraborty,&nbsp;Subasish Das","doi":"10.1016/j.aap.2025.108048","DOIUrl":"10.1016/j.aap.2025.108048","url":null,"abstract":"<div><div>Crashes involving farm equipment vehicles are a significant safety concern on public roads, particularly in rural and agricultural regions. These vehicles display unique challenges due to their slow-moving operational speed and interactions with faster vehicles, often leading to severe crashes. This study analyzed crashes involving farm equipment vehicles to examine the factors influencing crash severity, with a particular focus on comparing incidents on county roads to those on non-county roads. The dataset included key variables such as road geometry, lighting conditions, and traffic interactions, with preprocessing techniques like Synthetic Minority Over-sampling Technique (SMOTE) applied to address class imbalance. The TabNet model, a tabular deep learning model, was employed to analyze crash dynamics, offering both predictive accuracy and interpretability through feature importance and SHapley Additive exPlanations (SHAP) plots.</div><div>Findings revealed that crash severity on county roads is primarily influenced by crash speed limit, first harmful event, traffic control, and person age, reflecting the role of road geometry and demographic risk in rural settings. In contrast, non-county roads were more affected by lighting conditions, intersection-related features, and population group, emphasizing the impact of visibility and traffic complexity in urban areas. Speed limit consistently emerged as a critical factor across all road types and severity levels. The study emphasized the need for targeted safety interventions, including visibility enhancements, speed management, and enhanced education campaigns for county and non-county areas.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"217 ","pages":"Article 108048"},"PeriodicalIF":5.7,"publicationDate":"2025-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143842902","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 game theoretical model to examine pedestrian behaviour and safety on unsignalised slip lanes using AI-based video analytics","authors":"Md Eaysir Arafat ,&nbsp;Sherrie-Anne Kaye ,&nbsp;Ronald Schroeter ,&nbsp;Md Mazharul Haque","doi":"10.1016/j.aap.2025.108034","DOIUrl":"10.1016/j.aap.2025.108034","url":null,"abstract":"<div><div>Left-turn slip lanes, also known as channelised right-turn lanes in right-hand driving countries, are widely implemented to facilitate left-turning at signalised intersections. However, pedestrian safety on slip lanes is not well known. At unsignalised crosswalks, the joint decision-making process of both pedestrians and motorists is complex, involving joint communication dynamics, yet current research primarily focuses on examining individual decisions. This study proposes a game theory-based approach, formulating the interaction as a two-player, non-cooperative, simultaneous game to examine those joint decision-making dynamics, their resulting behaviours, and associated crash risks. The approach compares two well-known equilibriums of game theory, namely Quantal Response Equilibrium (QRE) and Nash Equilibrium (NE), based on real-world pedestrian-motorist interaction video data collected over two days, each day spanning 12 h, from two slip lanes with zebra crossings at signalised intersections in Brisbane, Australia. Artificial intelligence-based video analytics extracted interaction data, which was modelled using binary logit models to understand the crossing and yielding decisions of pedestrians. Results demonstrate that the QRE outperforms the NE in predicting the crossing intention of pedestrians and the yielding intention of motorists. Results indicate that a) motorists are less likely to yield to pedestrians when the vehicle speed is higher, b) pedestrians are more likely to let the motorists go first if they start crossing from the curbside of the road, and c) motorists are more likely to yield to pedestrians when the distance between pedestrians and vehicles is longer. According to the QRE model, the probability of conflict is 5.8%, indicating that 5.8% of pedestrian interactions with vehicles in these slip lanes result in conflicts. Similarly, the confusion probability indicates that about 5.2% of pedestrians were confused about initiating crossing in the presence of zebra crossing even though drivers yielded them on slip lanes at signalised intersections. This study highlights the significance of using game theory-based approaches to understand road users’ behaviour on slip lanes. These findings can help to identify pedestrian crossing intentions and support connected automated vehicles in making stopping decisions to enhance pedestrian safety and reduce potential conflicts with other road users.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"217 ","pages":"Article 108034"},"PeriodicalIF":5.7,"publicationDate":"2025-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143838783","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}