Accident; analysis and prevention最新文献

{"title":"Burning gig, rewarding risk: Effects of dual exposure to incentive structure and heat condition on risky driving among on-demand food-delivery motorcyclists in Kaohsiung, Taiwan","authors":"Cheng-Kai Hsu","doi":"10.1016/j.aap.2024.107841","DOIUrl":"10.1016/j.aap.2024.107841","url":null,"abstract":"<div><div>The gig economy, characterized by short-term, task-based work facilitated via digital platforms, has raised various occupational safety concerns, including road safety risks and heat exposure faced by on-demand food delivery (ODFD) workers. Often using open modes of transportation, such as motorcycles and bicycles, these workers have minimal physical protection and direct environmental exposure while working long hours on the road, interacting with larger vehicles. Prior research has suggested that their road risks result from prevalent risky driving incentivized by platform-established business models, but quantitative evidence is lacking. Furthermore, while prolonged heat exposure may contribute to increased risky driving, our understanding of this relationship remains limited. This study investigates the impact of dual exposures to incentive structure and heat condition on risky driving among ODFD motorcyclists in Kaohsiung, Taiwan. A wearable sensing scheme was implemented, tracking a cohort of 40 ODFD workers during their work shifts in real time, collecting data on their speed, acceleration/deceleration patterns, incentive issuances, and heat exposure. Through a case-crossover approach, generalized linear cross-level mixed-effects models were employed to demonstrate the impact of incentive issuance on increasing risky driving among ODFD workers, including faster driving speeds, higher risks of speeding, harsher acceleration and braking, and more erratic acceleration patterns. Additionally, this study reveals that heat exposure, characterized by higher temperatures and humidity levels, exacerbates speed-related risky driving. These findings advance our understanding of causal mechanisms in two key areas of literature: firstly, the road safety risks faced by ODFD gig workers, and secondly, the broader relationship between heat exposure and risky driving. This research offers insights for policymakers to mitigate risky driving among ODFD workers, which is crucial in the context of climate change, where such urban economic dynamics may amplify climate-related inequities and place disproportionate safety burdens on vulnerable workers within the rapidly evolving gig economy.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"210 ","pages":"Article 107841"},"PeriodicalIF":5.7,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142757026","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":"Impact of speed on injury severity in single-vehicle run-off-road crashes: Insights from partially temporal constrained modeling approach","authors":"Zhe Wang ,&nbsp;Chenzhu Wang ,&nbsp;Mohamed Abdel-Aty ,&nbsp;Lei Han ,&nbsp;Helai Huang ,&nbsp;Jinjun Tang","doi":"10.1016/j.aap.2024.107848","DOIUrl":"10.1016/j.aap.2024.107848","url":null,"abstract":"<div><div>Single-vehicle run-off-road crashes accounts for approximately 35% of all the traffic fatalities in the U.S during the period of 2019–2021. This paper explores the association between driving speed and injury severity outcomes of single-vehicle run-off-road crashes. The single-vehicle run-off-road crash data from 2019 to 2021 on Interstate freeways in Florida are utilized, and categorized into periods of pre-, during-, and post-COVID-19 pandemic. The partially constrained temporal and temporal unconstrained random parameters logit models are developed considering three injury severity outcomes: no injury, minor injury and serious injury/fatality. Multiple variables in terms of driver, vehicle, roadway, environmental, crash, and temporal attributes are observed to significantly affect the injury severity. Moreover, temporal instability and transferability issues are validated through likelihood ratio test and out-of-sample prediction. In the partially constrained models, numerous variables such as indicators of new vehicle, male driver, and restraint-protected driving consistently yield identical parameter values across all periods, whereas various variables clearly illustrate the distinct differences across the three periods and three speed intervals. The marginal effects in the unconstrained models also display the obvious differences across three periods and three speed intervals. Moreover, the findings corroborate the increased risk outcomes linked to larger speed differences and the COVID-19 pandemic period. These results provide better understanding of the risk mechanisms underlying run-off-road crashes and furnish valuable direction for the formulation of effective safety interventions.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"210 ","pages":"Article 107848"},"PeriodicalIF":5.7,"publicationDate":"2024-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142746091","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 machine learning approach to quantify effects of geometric design features and traffic control devices on wrong-way driving incidents at partial cloverleaf interchange terminals","authors":"Qing Chang ,&nbsp;Huaguo Zhou ,&nbsp;Yukun Song ,&nbsp;Jingyi Zheng ,&nbsp;Fangjian Yang","doi":"10.1016/j.aap.2024.107855","DOIUrl":"10.1016/j.aap.2024.107855","url":null,"abstract":"<div><div>This study addresses the issue of wrong-way driving (WWD) incidents at partial cloverleaf (parclo) interchange terminals in the United States. These incidents are a safety concern, often attributed to geometric design features and inadequate traffic control devices (TCDs). While previous research has acknowledged the significance of parclo interchanges as common initial entry points for WWD crashes, few studies have comprehensively quantified the impact of TCDs and design features on recurring WWD incidents.</div><div>In response, this study collected data from 75 parclo interchange terminals spanning 13 states. A subset of 28 ramp terminals exhibiting recurrent WWD incidents, amounting to 410 incidents during the data collection phase, received focused attention. Leveraging modern machine learning methodologies, two techniques: eXtreme Gradient Boosting (XGboost) and Lasso-logistic regression were applied to quantify the influence of distinct TCDs and design features on the probability of WWD incidents occurring.</div><div>The outcomes of this analysis revealed noteworthy results: the fitted XGboost model displayed an average accuracy of 80%, closely followed by the fitted Lasso-logistic regression model with an average accuracy of 78%. These models were subsequently employed to construct a practical network screening tool. This tool assists in the identification of potential WWD incident locations, predicated on the effects of TCDs and design characteristics.</div><div>The significance of this study lies in its potential to inform and guide state and local transportation agencies in enhancing the safety of parclo interchange terminals. By discerning the impacts of TCDs and design features, stakeholders can implement improvements that curtail the occurrence of WWD incidents, contributing to enhanced road safety and optimized traffic management.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"210 ","pages":"Article 107855"},"PeriodicalIF":5.7,"publicationDate":"2024-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142746087","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":"Driving against the clock: Investigating the impacts of time pressure on taxi and non-professional drivers’ safety and compliance","authors":"Qinaat Hussain ,&nbsp;Wael K.M. Alhajyaseen","doi":"10.1016/j.aap.2024.107864","DOIUrl":"10.1016/j.aap.2024.107864","url":null,"abstract":"<div><div>Drivers often encounter time pressure, which can lead to riskier driving habits, decreased safety margins, and a higher chance of accidents. Given that taxi drivers frequently experience these conditions, this study examines how time pressure impacts the driving behaviors of both taxi and non-professional drivers. In this regard, a driving simulator experiment was carried out to assess the driving behaviors of both groups under time pressure. The simulation drive included different scenarios such as a stop sign, a pedestrian crosswalk, four intersections, a school zone, a slow bus and a drop-off location. The study recruited 55 taxi drivers and 55 non-professional drivers to take part in the experiment. Each participant completed the simulation drive twice, with the second drive conducted after they were informed that a reward would be given for finishing the trip more quickly. The findings reveal that drivers exhibited significantly riskier behaviors under time pressure, including higher speeds and reduced adherence to traffic rules. When comparing both groups, non-professional drivers displayed higher speeds and riskier behaviors across various scenarios, whereas taxi drivers were more likely to commit violations associated with drop-offs. These findings call for targeted awareness campaigns and stricter enforcement to reduce risky behaviors under time pressure. Flexible scheduling for non-professional drivers and incentive programs for taxi drivers can further promote safer practices. Policymakers can use these insights to design strategies that address the risks associated with time pressure.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"210 ","pages":"Article 107864"},"PeriodicalIF":5.7,"publicationDate":"2024-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142723794","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":"Spatiotemporal analysis of roadway terrains impact on large truck driver injury severity outcomes using random parameters with heterogeneity in means and variances approach","authors":"Muhammad Faisal Habib ,&nbsp;Nawaf Alnawmasi ,&nbsp;Diomo Motuba ,&nbsp;Ying Huang","doi":"10.1016/j.aap.2024.107849","DOIUrl":"10.1016/j.aap.2024.107849","url":null,"abstract":"<div><div>This study employs a partially temporally constrained modeling approach to examine spatiotemporal variations in driver injury severity in single-vehicle large truck crashes across different terrains in California, allowing for a nuanced understanding of how specific factors influencing injury outcomes may change over time. Utilizing crash data from January 1st, 2015, to December 31st, 2017, obtained from the Highway Safety Information System, this study categorizes terrains as flat, rolling, and mountainous terrain and employs a random parameter multinomial logit model with heterogeneity in means and variance to account for potential heterogeneity in crash injury outcomes. This approach helps understand how different terrains influence injury severities while allowing for parameter variability across observations. The analysis is further enriched by likelihood ratio tests to verify the stability and temporal transferability of the model estimates across different terrains and years. Notably, the study identifies truck overturning as the first and second event in a crash as a consistent parameter influencing injury severity across all years, emphasizing its importance regardless of terrain or time in single-vehicle large truck crashes. Furthermore, this study takes into account a wide range of variables, including driver characteristics, crash attributes, roadway characteristics, vehicle features, and environmental and temporal aspects. The findings highlight the importance of terrain-specific elements in traffic safety assessments and the need for focused measures to reduce serious injuries in truck crashes. The out-of-sample simulation revealed a significant increase in minor and severe injuries when flat terrain parameters were replaced with those from rolling or mountainous terrains. This research not only contributes to the existing literature by detailing the dynamics of injury severity in single-vehicle large truck crashes but also announces the utility of partially temporally constrained models in enhancing traffic safety management strategies.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"210 ","pages":"Article 107849"},"PeriodicalIF":5.7,"publicationDate":"2024-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142723768","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 the safety impact of mid-block pedestrian signals (MPS)","authors":"Md Jamil Ahsan,&nbsp;Mohamed Abdel-Aty,&nbsp;Ahmed S. Abdelrahman","doi":"10.1016/j.aap.2024.107847","DOIUrl":"10.1016/j.aap.2024.107847","url":null,"abstract":"<div><div>The Florida Department of Transportation (FDOT) has recently started implementing a new signal system at mid-blocks called Mid-block Pedestrian Signals (MPS). This study aims to evaluate the effectiveness of these newly implemented MPSs. A total of 260 h of video data were collected from five locations across Florida, with 130 h recorded before MPS installation and 130 h after installation, including both weekdays and weekends. State-of-the-art computer vision technology was employed to detect and track various road users. A random parameters multinomial logit model with heterogeneity in the means was implemented to assess safety of vehicle–pedestrian interaction by three conflict categories: No Conflict, Moderate Conflict, and Serious Conflict. Relative-Time-to-Collision (RTTC) values were utilized to classify these level of conflicts. The analysis demonstrates that the presence of MPS significantly enhances safety outcomes by increasing the likelihood of avoiding conflicts and reducing the probabilities of both moderate and serious conflicts. Key factors influencing conflict probabilities were identified, including pedestrian and vehicle counts, average leading vehicle speed, standard deviation of leading vehicle speeds, and land-use mix, all of which increase the probability of serious conflicts. Interestingly, the analysis identified three significant interaction variables with MPS: average leading vehicle speed, standard deviation of leading vehicle speeds, and land-use mix. While these factors individually had a higher probability of leading to serious conflicts, the presence of MPS effectively mitigates these risks by moderating their adverse effects, increasing the likelihood of no conflicts. These results underscore the importance of MPS as an effective measure to improve safety at mid-block crossings.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"210 ","pages":"Article 107847"},"PeriodicalIF":5.7,"publicationDate":"2024-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142723767","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 streetscape environmental characteristics associated with road traffic crashes using street view imagery and computer vision","authors":"Han Yue","doi":"10.1016/j.aap.2024.107851","DOIUrl":"10.1016/j.aap.2024.107851","url":null,"abstract":"<div><div>Examining the relationship between streetscape features and road traffic crashes is vital for enhancing roadway safety. Traditional field surveys are often inefficient and lack comprehensive spatial coverage. Leveraging street view images (SVIs) and deep learning techniques provides a cost-effective alternative for extracting streetscape features. However, prior studies often rely solely on semantic segmentation, overlooking distinctions in feature shapes and contours. This study addresses these limitations by combining semantic segmentation and object detection networks to comprehensively measure streetscape features from Baidu SVIs. Semantic segmentation identifies pixel-level proportions of features such as roads, sidewalks, buildings, fences, trees, and grass, while object detection captures discrete elements like vehicles, pedestrians, and traffic lights. Zero-inflated negative binomial regression models are employed to analyze the impact of these features on three crash types: vehicle-vehicle (VCV), vehicle–pedestrian (VCP), and single-vehicle crashes (SVC). Results show that incorporating streetscape features from combined deep learning methods significantly improves crash prediction. Vehicles have a significant impact on VCV and SVC crashes, whereas pedestrians predominantly affect VCP crashes. Road surfaces, sidewalks, and plants are associated with increased crash risks, while buildings and trees correlate with reduced vehicle crash frequencies. This study highlights the advantages of integrating semantic segmentation and object detection for streetscape analysis and underscores the critical role of environmental characteristics in road traffic crashes. The findings provide actionable insights for urban planning and traffic safety strategies.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"210 ","pages":"Article 107851"},"PeriodicalIF":5.7,"publicationDate":"2024-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142708947","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":"An emergency operation strategy and motion planning method for autonomous vehicle in emergency scenarios","authors":"Tianyang Gong ,&nbsp;Xiumin Yu ,&nbsp;Qunli Zhang ,&nbsp;Zilin Feng ,&nbsp;Shichun Yang ,&nbsp;Yaoguang Cao ,&nbsp;Jingyun Xu ,&nbsp;Xinjie Feng ,&nbsp;Zhaowen Pang ,&nbsp;Yu Wang ,&nbsp;Peng Wang","doi":"10.1016/j.aap.2024.107842","DOIUrl":"10.1016/j.aap.2024.107842","url":null,"abstract":"<div><div>Ensuring driving operational safety in emergency scenarios is paramount for autonomous vehicles to prevent accidents, particularly when vehicle motion completely depends on autonomous systems. Numerous factors must be evaluated when designing emergency collision avoidance strategies for critical situations, such as trajectory feasibility, vehicle motion stability, and driver comfort. Therefore, this study proposes a framework for emergency operation that uses collision-free area calculations to inform maneuver decisions and facilitate collision avoidance trajectory planning, preventing vehicle collisions. In case of danger, the emergency maneuver decision module evaluates the safety level and selects safety terminal state by considering a pre-specified cluster of candidate maneuvers before generating trajectories. This process avoids infeasible trajectories and selects maneuvers for greater driver comfort when available. Subsequently, the dynamic trajectory planning module converts the collision-free area into mixed-integer constraints, utilizing time-varying Nonlinear Model Predictive Control (NMPC) for trajectory planning and ensuring vehicle motion stability by integrating dynamic and collision-free constraints throughout the motion planning process. Eventually, simulations and field testing validate the framework’s effectiveness, mitigating collisions in emergency scenarios with prompt and safe operations. The framework is designed to function autonomously, independent of the intelligent driving system, engaging only during risk events and restoring control to the driver or the intelligent system after the event.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"210 ","pages":"Article 107842"},"PeriodicalIF":5.7,"publicationDate":"2024-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142708936","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 the collision risk on the road around schools during morning peak period","authors":"Xiaojian Hu ,&nbsp;Haoran Deng ,&nbsp;Huasheng Liu ,&nbsp;Jiayi Zhou ,&nbsp;Hongyu Liang ,&nbsp;Long Chen ,&nbsp;Li Zhang","doi":"10.1016/j.aap.2024.107854","DOIUrl":"10.1016/j.aap.2024.107854","url":null,"abstract":"<div><div>Road traffic injury is a leading cause of death among pupils worldwide, particularly around primary schools during rush hours, where heavy traffic, frequent parking, and unpredictable patterns increase accident risk. To mitigate these risks, this study employs the peak-over-threshold method with the generalized pareto distribution to evaluate the spatial–temporal collision risk near primary schools during rush hours. Specifically, the research quantifies collision risks spatially across different road segments (upstream, midstream, and downstream) and lanes (outside, middle, and inside). Temporally, it assesses risks during vehicle gathering, peak vehicle concentration, and vehicle dissipation phases. Results show that collision risk decreases from upstream to downstream but increases from the outside lane to the inside lane. Moreover, collision risks are highest in the middle and outside lanes during the gathering and peak periods in upstream and midstream sections, and in the middle lanes during the dissipation phase. These findings recommend adding parking spaces, minimizing lane changes, reducing speed limits in upstream and midstream, and increasing speed limits in downstream and inside lanes. These measures aim to improve road traffic management policies around schools.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"210 ","pages":"Article 107854"},"PeriodicalIF":5.7,"publicationDate":"2024-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142708941","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":"Gender disparities in rural motorcycle accidents: A neural network analysis of travel behavior impact","authors":"Ittirit Mohamad","doi":"10.1016/j.aap.2024.107840","DOIUrl":"10.1016/j.aap.2024.107840","url":null,"abstract":"<div><div>Rural road accidents involving motorcycle riders present a formidable challenge to road safety globally. This study offers a comprehensive gender-based comparative analysis of rural road accidents among motorcycle riders, aimed at illuminating factors contributing to accidents and discerning potential gender disparities in accident rates and severity. Employing a sophisticated Neural Network approach, the research delves into the intricate relationship between various variables and accident outcomes, with a specific emphasis on identifying gender-specific patterns. For female riders, the ANN model demonstrates impressive overall accuracy (CA) of 92 %, indicating its capability to correctly classify accident outcomes. Precision, which measures the model’s ability to avoid false positives, stands at a commendable 90.8 %. Moreover, the model exhibits high recall (92 %) and F1 score (88.4 %), indicating its effectiveness in identifying both fatal and non-fatal accidents among female riders. Additionally, the Matthews Correlation Coefficient (MCC) of 0.132 suggests a moderate level of agreement between the predicted and actual outcomes. Upon further examination, it is evident that the model performs exceptionally well in predicting non-fatal accidents for female riders, achieving a precision, recall, and F1 score of 92 %, 99.9 %, and 95.8 %, respectively. However, its performance in predicting fatalities is relatively lower, with a precision of 75.6 % and recall of 2.6 %, resulting in a lower F1 score of 5.0 %. Despite this disparity, the MCC remains consistent at 0.132, indicating a balanced performance across both classes. The findings reveal valuable insights for policymakers and road safety practitioners, providing avenues for the development of targeted interventions and the enhancement of safety measures for motorcycle riders on rural roads. By addressing the gap in understanding gender-related differences in travel habits and accident risks, this research contributes to ongoing efforts to mitigate the impact of road accidents and promote safer travel environments for all road users.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"210 ","pages":"Article 107840"},"PeriodicalIF":5.7,"publicationDate":"2024-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142695121","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}