Accident; analysis and prevention最新文献

{"title":"MineSim: A scenario-based simulation test system and benchmark for autonomous trucks in open-pit mines","authors":"Zhifa Chen ,&nbsp;Guizhen Yu ,&nbsp;Peng Chen ,&nbsp;Guoxi Cao ,&nbsp;Zheng Li ,&nbsp;Yifang Zhang ,&nbsp;Haoyuan Ni ,&nbsp;Bin Zhou ,&nbsp;Jian Sun ,&nbsp;Huanyu Ban","doi":"10.1016/j.aap.2025.107938","DOIUrl":"10.1016/j.aap.2025.107938","url":null,"abstract":"<div><div>Simulation environments are essential for validating algorithms, evaluating system performance, and ensuring safety in autonomous driving systems before real-world deployment. Existing autonomous driving simulators are designed for urban scenarios but lack coverage of unstructured road environments in open-pit mining. This paper introduces MineSim, an open-source, scenario-based simulation test system specifically developed for planning tasks in autonomous trucks operating in open-pit mines. MineSim includes several components: automated scenario parsing, state update models for the ego vehicle, state update policies for other agents, metric evaluation, and scenario visualization tools. It incorporates numerous real-world traffic scenarios from two open-pit mines that capture the unique challenges of unstructured road environments, including irregular intersections, roads without clear lane markings, and the response lags of heavy autonomous mining trucks. Furthermore, MineSim provides scenario libraries and benchmarks for static and dynamic obstacle avoidance problems, facilitating research into planning algorithms in these complex settings. By offering reproducible testing methods and scenario data, MineSim serves as a critical resource for advancing autonomous driving technologies in non-urban and unstructured road environments (see <span><span>https://buaa-trans-mine-group.github.io/minesim</span><svg><path></path></svg></span>).</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"213 ","pages":"Article 107938"},"PeriodicalIF":5.7,"publicationDate":"2025-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143350285","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":"Validation of human benchmark models for automated driving system approval: How competent and careful are they really?","authors":"Pierluigi Olleja ,&nbsp;Gustav Markkula ,&nbsp;Jonas Bärgman","doi":"10.1016/j.aap.2025.107922","DOIUrl":"10.1016/j.aap.2025.107922","url":null,"abstract":"<div><div>Over the last few decades, new technological solutions have enabled the fast development of Advanced Driver Assistance Systems (ADAS) and Automated Driving Systems (ADS). These systems are expected to improve comfort, productivity and, most importantly, safety for all road users. To ensure that the systems are safe, rules and regulations describing the systems’ approval and validation procedures are in effect in Europe. The UNECE Regulation 157 (R157) is one of those. Annex 3 of R157 describes two driver models, representing the performance of a “competent and careful” driver, which can be used as benchmarks to determine whether, in certain situations, a crash would be preventable by a human driver. However, these models have not been validated against human behavior in real safety–critical events. Therefore, this study uses counterfactual simulation to assess the performance of the two models when applied to 38 safety–critical cut-in near-crashes from the SHRP2 naturalistic driving study. The results show that the two computational models performed rather differently from the human drivers: one model showed a generally delayed braking reaction compared to the human drivers, causing crashes in three of the original near-crashes. The other model demonstrated, in general, brake onsets substantially earlier than the human drivers, possibly being overly sensitive to lateral perturbations. That is, the first model does not seem to behave as the competent and careful driver it is supposed to represent, while the second seems to be overly careful. Overall, our results show that, if models are to be included in regulations, they need to be substantially improved. We argue that achieving this will require better validation across the scenario types that the models are intended to cover (e.g., cut-in conflicts), a process which should include applying the models counterfactually to near-crashes and validating them against several different safety related metrics. Possible improvements to the models include adding components that better reflect the level of urgency of the traffic situation, something which is lacking in the current models.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"213 ","pages":"Article 107922"},"PeriodicalIF":5.7,"publicationDate":"2025-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143177477","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Assessing conflict likelihood and its severity at interconnected intersections: Insights from drone trajectory data","authors":"Qianqian Jin,&nbsp;Mohamed Abdel-Aty,&nbsp;Chenzhu Wang,&nbsp;Siyuan Tang","doi":"10.1016/j.aap.2025.107943","DOIUrl":"10.1016/j.aap.2025.107943","url":null,"abstract":"<div><div>Unsignalized intersections are complex and hazardous due to their numerous conflicts. However, most studies analyzing crash factors at unsignalized intersections focus solely on the isolated intersection itself. This study investigates how proximity to signalized intersections affects traffic conflicts at unsignalized intersections (divided into three segments). Traditional segment-level traffic flow data often fail to capture the nuanced short-term traffic conditions that contribute to conflicts; thus, we utilized microscopic high-resolution trajectory data extracted from the CitySim drone dataset. To represent real dangerous events, conflict probability and severity were introduced and assessed using two surrogate safety measures: time-to-collision (TTC) and the predicted change in velocity post-collision (Delta-V). A Structural Equation Model (SEM) is applied to explore the interactive relationship within the interconnected intersections. Then, a hierarchical Joint Generalized Linear Mixed Model (JGLMM) was employed to identify factors contributing to conflict risks and severity across the three segments. SEM findings reveal that upstream traffic volume can significantly mitigate conflict risks downstream at interconnected intersections. Estimation results show that angled conflicts are prominent in weaving sections, with increased conflict probability and severity as the angle increases. Meanwhile, conflict potential decreases as vehicle queue length increases in right-turn lanes, but it increases with longer queues in left-turn lanes. Suggested countermeasures include clearly marking the left-turn lane at the intersection and installing clear left-turn signs in advance of the intersection. This study highlights the value of high-resolution trajectory data for in-depth variable analysis, facilitating hierarchical safety assessments and pinpointing influential interactions at interconnected intersections.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"213 ","pages":"Article 107943"},"PeriodicalIF":5.7,"publicationDate":"2025-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143177478","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":"Tunnel crash severity and congestion duration joint evaluation based on cross-stitch networks","authors":"Chenzhu Wang,&nbsp;Mohamed Abdel-Aty,&nbsp;Lei Han","doi":"10.1016/j.aap.2025.107942","DOIUrl":"10.1016/j.aap.2025.107942","url":null,"abstract":"<div><div>Tunnels, with limited space and restricted widths/heights, increase the likelihood of crashes and traffic congestion, where the severity and duration of one often exacerbate the other. However, existing studies mainly conducted separate models, which cannot simultaneously analyze the joint impacts of contributing factors on both crash severity and duration. To address such gap, a joint modeling approach was proposed to explore critical features affecting both crash severity and duration and their joint relationships. A total of 2,454 tunnel crashes in Shanxi, China were collected. Five types of characteristics were extracted as inputs: crash, vehicle, road, environment, and temporal features. Then, a joint cross-stitch network model was proposed with two sub-multilayer perceptron (MLP) networks to establish the relationships between input features with crash severity and duration, respectively. Cross-stitch units were introduced between the two sub-networks to share each model parameters with specific weights, enforcing the sub-networks to simultaneously estimate the coupling relationships between variables and two targets (i.e., crash severity and duration). Compared to existing separate models, the joint cross-stitch network models achieved best performance on estimation of both crash severity (7.0%, 10.2% increase in sensitivity and F1 score, respectively) and congestion duration (3.7% reduction in mean squared error). Though the parameter sharing mechanism, it could simultaneously learn the coupling relationships between contributing factors on both crash severity and duration to offer more reasonable interpretations than separate models. The results indicate that congested traffic conditions significantly increase injury severity, while truck-only, two-vehicle, and multi-vehicle crashes notably prolong congestion duration. Moreover, the joint models exhibited some features presenting inverse effects on injury severity in the separate models. The results enhance our understanding of crashes and congestion in tunnels and inform several recommendations for tunnel management to reduce both crash severity and congestion duration.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"213 ","pages":"Article 107942"},"PeriodicalIF":5.7,"publicationDate":"2025-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143177479","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":"Face to planning risk: A hierarchical risk-aware prediction module for the safe planning system","authors":"Jiahui Xu ,&nbsp;Wenbo Shao ,&nbsp;Bingbing Nie ,&nbsp;Weida Wang ,&nbsp;Chao Yang ,&nbsp;Hong Wang","doi":"10.1016/j.aap.2024.107906","DOIUrl":"10.1016/j.aap.2024.107906","url":null,"abstract":"","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"213 ","pages":"Article 107906"},"PeriodicalIF":5.7,"publicationDate":"2025-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143177527","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 comprehensive multi-objective framework for the estimation of crash frequency models","authors":"Zeke Ahern ,&nbsp;Paul Corry ,&nbsp;Mohammadali Shirazi ,&nbsp;Alexander Paz","doi":"10.1016/j.aap.2024.107844","DOIUrl":"10.1016/j.aap.2024.107844","url":null,"abstract":"<div><div>A common and challenging data and modeling aspect in crash analysis is unobserved heterogeneity, which is often handled using random parameters and special distributions such as Lindley. Random parameters can be estimated with respect to each observation for the entire dataset, and grouped across segments of the dataset, with variable means, or variable variances. The selection of the best approach to handle unobserved heterogeneity depends on the data characteristics and requires the corresponding hypothesis testing. In addition to dealing with unobserved heterogeneity, crash frequency modeling often requires explicit consideration of functional forms, transformations, and identification of likely contributing factors. During model estimation, it is important to consider multiple objectives such as in- and out-of-sample goodness-of-fit to generate reliable and transferable insights. Taking all of these aspects and objectives into account simultaneously represents a very large number of modeling decisions and hypothesis testing. Limited testing and model development may lead to bias and missing relevant specifications with important insights. To address these challenges, this paper proposes a comprehensive optimization framework, underpinned by a mathematical programming formulation, for systematic hypothesis testing considering simultaneously multiple objectives, unobserved heterogeneity, grouped random parameters, functional forms, transformations, heterogeneity in means, and the identification of likely contributing factors. The proposed framework employs a variety of metaheuristic solution algorithms to address the complexity and non-convexity of the estimation and optimization problem. Several metaheuristics were tested including Simulated Annealing, Differential Evolution and Harmony Search. Harmony Search provided convergence with low sensitivity to the choice of hyperparameters. The effectiveness of the framework was evaluated using three real-world data sets, generating sound and consistent results compared to the corresponding published models. These results demonstrate the ability of the proposed framework to efficiently estimate sound and parsimonious crash data count models while reducing costs associated with time and required knowledge, bias, and sub-optimal solutions due to limited testing. To support experimental testing for analysts and modelers, the Python package “MetaCountRegressor,” which includes algorithms and software, is available on PyPi.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"210 ","pages":"Article 107844"},"PeriodicalIF":5.7,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142765362","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evaluating the effectiveness of rhythmic visual guidance technology for mitigating driving risks in highway tunnel groups: A simulation study","authors":"Haoran Zheng ,&nbsp;Zhigang Du ,&nbsp;Chengfeng Jia ,&nbsp;Linna Zhu ,&nbsp;Shiming He ,&nbsp;Jialin Mei","doi":"10.1016/j.aap.2025.107940","DOIUrl":"10.1016/j.aap.2025.107940","url":null,"abstract":"<div><div>Driving in highway tunnel groups necessitates frequent adaptation to drastic changes in the traffic environment, thereby increasing driving difficulty and risk. This study integrates drivers’ preferences for rhythmic information with the inherent rhythmic characteristics of tunnel group structures to propose a new and adaptive method to mitigate driving risks using rhythmic visual guidance (RVG) technology. Unlike traditional visual guidance systems, which often rely on static signals, RVG utilizes dynamic, rhythmically varying cues to capture drivers’ attention and improve situational awareness more effectively. By employing principles of fuzzy mathematics, the study quantifies the applicability of various rhythmic forms in visual guidance technology and establishes priority application principles for undulating and staggered rhythms. After verifying the accuracy of the simulation model, the effectiveness of RVG technology in mitigating driving risks in highway tunnel groups was analyzed using lateral offset, driving speed, and vehicle acceleration as evaluation metrics. The findings reveal that RVG technology significantly reduces vehicle lateral offset and enhances drivers’ perception and control of tunnel sidewalls and driving trajectories. This effect is particularly pronounced under limited lighting conditions or in large tunnel groups with extended driving distances. Regardless of whether the lighting level is set at 0% or 100% of the standard brightness, the implementation of RVG results in reduced vehicle driving speeds. The variation in the 25th to 75th percentile distribution of driving speeds was insignificant, demonstrating that RVG technology effectively regulates driving speed and is not significantly affected by lighting conditions. Furthermore, when the lighting level is set at 100% of the standard brightness, the 25th to 75th percentile distribution interval of driving speeds is [89.576, 102.416], indicating the highest and least stable driving speeds suggests that blindly increasing tunnel lighting levels not only raises operating costs but may also adversely affect driving safety. This study provides novel insights into applying dynamic visual cues for highway tunnel groups’ traffic operation and safety management. It has significant practical engineering value for guiding the low-carbon design of tunnel groups.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"212 ","pages":"Article 107940"},"PeriodicalIF":5.7,"publicationDate":"2025-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143073212","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":"Risk quantification based Adaptive Cruise control and its application in approaching behavior at signalized intersections","authors":"Haozhan Ma ,&nbsp;Chen Qian ,&nbsp;Linheng Li ,&nbsp;Xu Qu ,&nbsp;Bin Ran","doi":"10.1016/j.aap.2025.107939","DOIUrl":"10.1016/j.aap.2025.107939","url":null,"abstract":"<div><div>Traffic signals, while reducing conflicts within intersections, often lead to stop-and-go behaviors in approaching vehicles, negatively impacting traffic flow in terms of safety, efficiency, and fuel consumption. Aimed at minimizing the traffic oscillations caused by traffic signals through Connected and Autonomous Vehicles (CAVs) and meeting real-time operational needs, this paper proposes a Risk-Based Adaptive Cruise Control (RACC). RACC designs the constraints of approaching a signalized intersection as expected risks, enabling compliance with all constraints while being adaptable to basic road scenarios. Theoretical analysis indicates that RACC, under specific parameter conditions, achieves string stability and overdamped characteristics while maintaining high throughput efficiency. Simulations confirm RACC’s sensitivity to risks, allowing it to timely adjust to return to a stable state, thus ensuring platoon safety under high throughput conditions. At signalized intersections, RACC enables CAVs to cross stop lines with smooth trajectories, significantly reducing risk, delays, and fuel consumption for all downstream vehicles. Further simulations demonstrate that RACC significantly reduces average travel time delay and fuel consumption across various traffic volumes and Market Penetration Rates (MPRs), with reductions of up to 87.1% in delays and 54.8% in fuel consumption, showcasing substantial computational efficiency improvements over benchmarks. Furthermore, extending this study to scenarios with higher traffic conflicts, such as multi-lane roads or intersections, while considering the impact of lane-changing behavior, is a promising direction for future research.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"212 ","pages":"Article 107939"},"PeriodicalIF":5.7,"publicationDate":"2025-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143063016","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":"Reliable crash analysis: Comparing biases and error rates of empirical Bayes before-after analyses to mixed-models","authors":"Matthew A. Albrecht,&nbsp;Razi Hasan","doi":"10.1016/j.aap.2025.107921","DOIUrl":"10.1016/j.aap.2025.107921","url":null,"abstract":"<div><div>Estimating reliable causal estimates of road safety interventions is challenging, with a number of these challenges addressable through analysis choices. At a minimum, developing reliable crash modification factors (CMFs) needs to address three critical confounding factors, i.e., 1) the regression-to-the-mean (RTM) phenomenon, 2) the effect of traffic volume, and 3) the time trend for the occurrence of crashes. The current preferred crash analysis method is the empirical Bayes (EB) before-after analysis but requires complex bespoke analysis and may not be the best performing method. We compare in a simulation experiment various EB methods to a more straightforward negative binomial generalized linear mixed model (NB-GLMM) with an interaction term between treatment group and time for analysing treatment effects in crash data. Data were simulated using two broad scenarios: 1) an idealized randomized controlled design, and 2) a moderately biased site-selection scenario commonly encountered in road safety crash analyses. Both scenarios varied treatment effects, overdispersion, and sample sizes. The NB-GLMM performed best, maintaining type I error rate and providing least biased estimates across most analyses. Most standard EB methods were too liberal or generally more biased, with the exception of the EB method that incorporated a varying dispersion parameter. Incorporating mixed-effects modelling into the EB procedure improved bias. Overall, we found that using a “standard” NB-GLMM with an interaction term is sufficient for crash analysis, reducing complexity compared to bespoke EB solutions. Chosen methods should also be the least biased and possess the marginal error rates under both ideal and selection-bias conditions. Mixed-effects approaches to analysis of road safety interventions satisfy these criteria outperforming standard or other empirical Bayes approaches tested here.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"212 ","pages":"Article 107921"},"PeriodicalIF":5.7,"publicationDate":"2025-01-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143035814","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A coordinated control framework of freeway continuous merging areas considering traffic risks and energy consumption","authors":"Weihua Zhang ,&nbsp;Fan Zhang ,&nbsp;Zhongxiang Feng ,&nbsp;Hanchu Zhou ,&nbsp;Lishengsa Yue ,&nbsp;Lijun Xiong ,&nbsp;Zeyang Cheng","doi":"10.1016/j.aap.2025.107924","DOIUrl":"10.1016/j.aap.2025.107924","url":null,"abstract":"<div><div>Freeway continuous merging areas in a short distance exist continuous multiple ramps. In these areas, traffic flow and vehicle interactions are more complex, and traffic crashes and congestion are more frequent, which has been a major concern influencing traffic operation of freeways. Active traffic management (ATM) measures can improve traffic efficiency and reduce traffic risks in merging areas. Previous studies have focused on variable speed limit (VSL) control or ramp metering (RM) to address traffic problems in merging areas, whereas the problem of comprehensively ameliorating for traffic risks on mainlines and ramps by coordinating VSL and RM control strategies has rarely been explored. This study introduces a Bi-level Programming Model capable of coordinating controls of traffic risks (e.g., Crash Risk and Congestion Risk) in freeway continuous merging areas. The upper-level model aims to minimize the crash risk, the congestion risk, and vehicle energy consumption by VSL control. While the lower-level model focuses on the ramp control by minimizing the congestion risk and energy consumption of the ramp. Then an extended Cell Transmission Model (CTM) (it is based on VSL and RM control) is utilized to simulate the traffic flow of merging areas, based on which a traffic risk evaluation model and a Bi-level coordinated control model for the continuous merging areas are developed. The results demonstrate the constructed method outperforms other control strategies for improving the safety and efficiency of freeways. Specifically, the proposed control framework in the continuous merging areas of freeways reduces the average crash risk (ACR), average mainline congestion risk (AMCI), and average energy consumption (AEC) by 14.10%, 19.52%, and 8.86%, respectively. The research results could be potentially applied to active and coordinated traffic management of freeways.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"212 ","pages":"Article 107924"},"PeriodicalIF":5.7,"publicationDate":"2025-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143021704","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}