Journal of Advanced Transportation最新文献

{"title":"Two-Echelon Pickup and Delivery Problem Using Public Transport in City Logistics","authors":"Shuai Wang,&nbsp;Xiaoning Zhu,&nbsp;Pan Shang,&nbsp;Wenqian Liu,&nbsp;Xiao Lin,&nbsp;Lóránt Tavasszy","doi":"10.1155/2024/1203246","DOIUrl":"https://doi.org/10.1155/2024/1203246","url":null,"abstract":"<div>\u0000 <p>The rapid increase in e-commerce and the emergence of combined passenger/freight systems in urban areas have raised the question of how best to integrate public transport services into door-to-door deliveries. This paper develops a variant of the pickup and delivery problem, called the two-echelon pickup and delivery problem using public transport (2E-PDP-PT). In the 2E-PDP-PT, the transportation network is split into two echelons. Different vehicles are utilized across the first and second echelons to ensure distribution efficiency. Parcels are delivered by public transport with free capacity or via trucks between satellites in the first echelon, and logistics vehicles are operated in the second echelon. The satellites are located at the echelon borders to transfer commodities between echelons. The 2E-PDP-PT aims to minimize total delivery costs and improve public transport capacity utilization. We formulate a new mathematical model based on a space-time network and adopt an adaptive large neighborhood search (ALNS) algorithm for the 2E-PDP-PT. The effectiveness of the ALNS algorithm is validated using newly generated small-scale instances. Furthermore, we investigate large-scale instances based on the Beijing Yizhuang transportation network. The computations show that an average total delivery cost savings of 4.5% is feasible. In addition, we analyze the impact of demand distributions and compare the ALNS algorithm and the LNS algorithm. Finally, we conclude that dynamically integrating public transport into freight transport services can benefit both logistics companies and public transport operators.</p>\u0000 </div>","PeriodicalId":50259,"journal":{"name":"Journal of Advanced Transportation","volume":null,"pages":null},"PeriodicalIF":2.0,"publicationDate":"2024-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/2024/1203246","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141967810","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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":"An Efficient Approach for Identifying Potential Bus Passenger Demand Based on Multisource Data","authors":"Lianghua Li,&nbsp;Shouqiang Xue,&nbsp;Yun Xiao","doi":"10.1155/2024/5368577","DOIUrl":"https://doi.org/10.1155/2024/5368577","url":null,"abstract":"<div>\u0000 <p>Big data provide massive samples and resources for exploring the operating rules of public transportation. This article proposes a method that combines multiple data sources to identify potential bus passenger flows, aiming to address the issue of insufficient identification accuracy with a single data source. First, the spatially weighted <i>K</i>-means algorithm and improved DBSCAN algorithm are designed to partition traffic zones and residents’ travel flow OD is extracted based on mobile phone signaling data. Second, using bus IC card data and vehicle trajectory data, a method for identifying bus passenger boarding and alighting stops based on spatiotemporal clustering is proposed and the bus passenger flow OD for each traffic zone is calculated. By comparing the resident travel flow OD with the bus passenger flow OD, we set a threshold for the potential bus passenger demand proportion. Finally, the analysis is conducted using actual data from a city in central China. The city is divided into 43 traffic zones, with the maximum bus passenger flow proportion between zones being 14.9%, the minimum being 5.0%, and the average being 7.2%. The initial threshold for the potential bus passenger demand proportion is thus set to 7.2%, and a sensitivity analysis is conducted by gradually decreasing the threshold in increments of 0.5% to 6.7%, 6.2%, 5.7%, and 5.2%. The corresponding potential bus passenger demand OD pairs between traffic zones are identified as 419, 358, 245, 151, and 51. Urban managers should focus on the 51 pairs with relatively large potential flows to gradually optimize and balance the development of the bus network based on actual conditions. The method proposed provides important theoretical and practical support for effectively optimizing urban bus networks. However, there are limited indicators for identifying potential passenger flows; in the future, more multidimensional indicators will be taken into consideration.</p>\u0000 </div>","PeriodicalId":50259,"journal":{"name":"Journal of Advanced Transportation","volume":null,"pages":null},"PeriodicalIF":2.0,"publicationDate":"2024-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/2024/5368577","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141967785","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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":"Machine Learning-Based Prediction of Parking Space Availability in IoT-Enabled Smart Parking Management Systems","authors":"Anchal Dahiya,&nbsp;Pooja Mittal,&nbsp;Yogesh Kumar Sharma,&nbsp;Umesh Kumar Lilhore,&nbsp;Sarita Simaiya,&nbsp;Ehab Ghith,&nbsp;Mehdi Tlija","doi":"10.1155/2024/8474973","DOIUrl":"https://doi.org/10.1155/2024/8474973","url":null,"abstract":"<div>\u0000 <p>Parking space management has become a critical challenge in urban areas due to increasing vehicle numbers and limited parking infrastructure. This paper presents a comprehensive study of machine learning (ML) models in IoT-enabled environments focusing on proposing an ML-based model for predicting available parking space. The study evaluates the performance of various models including K-nearest neighbors (KNNs), support vector machines (SVMs), random forest (RF), decision tree (DT), logistic regression (LR), and Naïve Bayes (NB) based on “precision, recall, accuracy, and <i>F</i>1-score performance metrics”. The results obtained by implementing ML models on the data with 65% and 85% threshold values are compared to draw meaningful conclusions regarding their performance in predicting parking space availability. Among the evaluated models, random forest (RF) demonstrates superior performance with high precision, recall, accuracy, and <i>F</i>1-score values. It showcases its effectiveness in accurately predicting parking space availability in the IoT-enabled environment. On the other hand, models such as K-nearest neighbors (KNNs), decision tree (DT), logistic regression (LR), and Naïve Bayes (NB) show relatively lower performance in complex parking scenarios. The paper concludes that the use of advanced predictive models, particularly random forest, significantly enhances the accuracy and reliability of IoT-enabled parking management systems and also reduces the waiting time of the vehicles, leading to more efficient resource utilization, reduced traffic congestion in real-time scenarios, and better user satisfaction in the IoT-enabled environment.</p>\u0000 </div>","PeriodicalId":50259,"journal":{"name":"Journal of Advanced Transportation","volume":null,"pages":null},"PeriodicalIF":2.0,"publicationDate":"2024-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/2024/8474973","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141967816","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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":"Reliability Analysis of Horizontal Curves Using Geometric Design Consistency Assessment Criterion","authors":"Hossein Saedi,&nbsp;Ali Abdi Kordani,&nbsp;Seyed Mohsen Hosseinian","doi":"10.1155/2024/4085522","DOIUrl":"https://doi.org/10.1155/2024/4085522","url":null,"abstract":"<div>\u0000 <p>Road accidents have always been one of the important reasons for fatalities and financial losses. Since road accidents on rural highways cause more serious injuries than those on urban highways, providing a suitable method to increase safety in the curves can be a significant contributor to preventing these damages. Although speed is one of the most important variables affecting highway safety, numerous studies have been performed on the reliability analysis of horizontal curves without taking the speed variable into account. The aim of this research is reliability (probability of noncompliance) assessment in the horizontal curve design using geometric design consistency criteria. The radius, superelevation, and operating speed of 19 horizontal curves were collected by field research on the Mashhad-Torbat Heydarieh highway in Iran. Three different approaches were defined based on the geometric design consistency criterion of a single horizontal curve, and consecutively, the probability of noncompliance was calculated using these approaches. According to the obtained results, this study showed that radius enhancement increases the probability of noncompliance and the consistency level of the geometric design. Finally, the high values of the probability of noncompliance (failure) indicate that the geometric design guidelines need calibration in the design of horizontal curves, especially for higher radii.</p>\u0000 </div>","PeriodicalId":50259,"journal":{"name":"Journal of Advanced Transportation","volume":null,"pages":null},"PeriodicalIF":2.0,"publicationDate":"2024-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/2024/4085522","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141967356","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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":"Super-Efficiency-Malmquist Model-Based Efficiency Evaluation of Logistics Distribution Center considering Truck Traffic Restriction","authors":"Jiao Yao,&nbsp;Xiurong Wu,&nbsp;Hao Li,&nbsp;Beibei Xie,&nbsp;Cong Zhang","doi":"10.1155/2024/8989408","DOIUrl":"https://doi.org/10.1155/2024/8989408","url":null,"abstract":"<div>\u0000 <p>Combining the super-efficiency model based on data envelopment analysis (DEA) with the Malmquist index model, this paper evaluated the efficiency of the logistics distribution center comprehensively considering the truck traffic restriction and provided decision suggestions to improve the efficiency of the logistics distribution center. This paper takes 20 logistics distribution centers as the research objects and uses economic factors, transportation factors, quality of distribution center business activities, and quality of customer service as the primary input indicators; selects eight indicators such as construction cost, transportation cost, labor cost, road facilities, accessibility, business demand, number of laborers, and customer satisfaction as the secondary input indicators; chooses distribution time and profit as the output indicators; and measures the static efficiency of logistics distribution centers from two perspectives, including the traditional unconstrained super-efficiency model and the truck- restricted conditions, using the super-efficiency model of data envelopment analysis (DEA). The Malmquist index model was used to measure the dynamic efficiency and change trend efficiency of the logistics distribution center, and a unified and comprehensive analysis was also made. The results of the case study show that the average efficiency of the logistics distribution center in the driving and nondriving restriction area is 0.872 and 0.914, respectively, and the average efficiency in the driving restriction area is about 4.5% lower than that of the nondriving restriction area, and variance is 1.58 times of the latter. Therefore, it can be concluded that the measures of truck driving restriction have an impact on the efficiency of the logistics distribution center, and the results of the super-efficiency model with the restriction constraint have a greater impact on the logistics efficiency of the logistics distribution center than the traditional unconstrained super-efficiency model. According to the evaluation results, suggestions on reasonable assignment of labor and other resources input are put forward for logistics distribution centers in areas where driving is restricted to improve efficiency.</p>\u0000 </div>","PeriodicalId":50259,"journal":{"name":"Journal of Advanced Transportation","volume":null,"pages":null},"PeriodicalIF":2.0,"publicationDate":"2024-08-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/2024/8989408","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141967066","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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":"Examining Causation of Fatal Traffic Crashes Involving Commercial Vehicles over the Last Decade in China","authors":"Hongwen Xia,&nbsp;Rengkui Liu,&nbsp;Pengfei Cui,&nbsp;Wei Zhou,&nbsp;Wenhui Luo","doi":"10.1155/2024/1903508","DOIUrl":"https://doi.org/10.1155/2024/1903508","url":null,"abstract":"<div>\u0000 <p>Fatal traffic crashes involving commercial vehicles exhibit distinct characteristics and mechanisms compared to general traffic crashes, influenced by numerous factors that impact the resulting fatalities. This study presents a comprehensive analysis of significant commercial vehicle crashes in China over a nine-year period (2014–2022), exploring an extensive range of factors including driver behavior, road conditions, vehicle characteristics, and environmental aspects. Utilizing a hierarchical Bayesian ordered probit model that incorporates both categorical and random effects, the research offers nuanced insights into the probabilistic outcomes of fatal traffic crashes. The model’s hierarchical structure enables the exploration of unobserved heterogeneities at individual and group levels. Key findings indicate that driver’s behaviors like speeding and overloading significantly escalate the likelihood of fatal traffic crashes, particularly those resulting in 10 or more fatalities. The study also highlights the role of road class in fatal crashes, with primary and secondary roads being associated with higher risks of more severe fatal crashes. The analysis extends to the impact of vehicle type, noting a distinct increase in the probabilities of fatal crashes with passenger vehicles, while freight vehicles exhibit a more complex relationship with fatal crashes severity. The insights from this study underscore the urgent need for enhanced enforcement of speed limit and vehicle weight regulations, particularly through the deployment of advanced monitoring technologies on highways frequented by commercial vehicles, and targeted infrastructure improvements on primary and secondary roads. This approach offers a novel analytical framework for evaluating commercial traffic crashes, assisting policymakers in devising targeted safety interventions to reduce the incidence of commercial vehicle crashes.</p>\u0000 </div>","PeriodicalId":50259,"journal":{"name":"Journal of Advanced Transportation","volume":null,"pages":null},"PeriodicalIF":2.0,"publicationDate":"2024-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/2024/1903508","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141966484","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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":"Vulnerability Analysis of China-Europe Railway Express Network Based on Improved Nonlinear Load-Capacity Model","authors":"Chao Zhu,&nbsp;Xiaoning Zhu","doi":"10.1155/2024/5910244","DOIUrl":"https://doi.org/10.1155/2024/5910244","url":null,"abstract":"<div>\u0000 <p>The China-Europe Railway Express (C-ER Express) provides a transcontinental rail container service between China and Europe. As most C-ER Expresses are affected by frequent natural disasters and public health incidents, it faces the increasing risk of network vulnerability. When previous studies investigated the evolution of network vulnerability through local information, they often overlook the complexity of the network’s multidimensional characteristics. The nonlinear load-capacity (NLC) model proposed in this paper integrates local and global information of the network. This approach enables a detailed investigation into how condition thresholds and different types of nodes influence network vulnerability. Firstly, a feature matrix is constructed for C-ER Express based on the topological measures, freight information, and external environment scores. Then, the autoencoder is used to extract the low-dimensional dense information, and the DBSCAN is used to classify C-ER Express into distinct clusters. Secondly, The NLC model integrates feature coefficient to describe the initial capacity of nodes. Subsequently, the failure load is redistributed proportionally to neighboring nodes and remaining normal nodes based on time-varying load and initial capacity of nodes. Finally, the improved NLC model is applied to the C-ER Express under different simulation scenarios. Simulation results show that a reasonable condition threshold can mitigate the impact of small-scale node failures on the network. The DBSCAN attack strategy can effectively identify the node types and prevent the network from chain reactions brought by different types of node failures. This research study is expected to provide some reference value for relevant research about vulnerability analysis of the C-ER Express network.</p>\u0000 </div>","PeriodicalId":50259,"journal":{"name":"Journal of Advanced Transportation","volume":null,"pages":null},"PeriodicalIF":2.0,"publicationDate":"2024-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/2024/5910244","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141967721","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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":"Research on Intelligent Vehicle Operation Risk Assessment and Early Warning Based on Predictive Risk Field","authors":"Ruibin Zhang,&nbsp;Yingshi Guo","doi":"10.1155/2024/7504378","DOIUrl":"https://doi.org/10.1155/2024/7504378","url":null,"abstract":"<div>\u0000 <p>In order to enhance the driving safety of intelligent vehicles in complex road scenarios, a method for vehicle operation risk assessment and early warning based on the predictive risk field is proposed. The temporal feature vector composed of the spatiotemporal state characteristics of the ego vehicle and surrounding traffic participants is taken as input data for the Attention-Bidirectional Long-Short Term Memory (Attention-BiLSTM) model, which is trained to establish the desired mapping relationship. By predicting the motion state of the target vehicle and utilizing an improved risk field model based on the target vehicle of heading angle, the predictive risk field is obtained. This allows for the assessment of the ego vehicle operational risks. The risk warning model is integrated to provide risk early warning, and the safety path for the ego vehicle is planned based on the interaction between the predictive risk field equipotential lines and the cubic spline curves. Experimental results demonstrate that the proposed vehicle operation risk assessment and early warning model is effective in providing early warnings and safe path references for the ego vehicle in complex urban road test scenarios.</p>\u0000 </div>","PeriodicalId":50259,"journal":{"name":"Journal of Advanced Transportation","volume":null,"pages":null},"PeriodicalIF":2.0,"publicationDate":"2024-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/2024/7504378","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141967739","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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":"Spatial Modeling of Travel Demand Accounting for Multicollinearity and Different Sampling Strategies: A Stop-Level Case Study","authors":"Samuel de França Marques,&nbsp;Cira Souza Pitombo,&nbsp;J. Jaime Gómez-Hernández","doi":"10.1155/2024/7967141","DOIUrl":"https://doi.org/10.1155/2024/7967141","url":null,"abstract":"<div>\u0000 <p>Stop-level ridership data serve as a basis for various studies toward increasing bus patronage and promoting sustainable land use planning. To address limitations found in previous studies, this study proposes a novel approach based on Geographically Weighted Principal Component Analysis (GWPCA) and Ordinary Kriging to predict the stop-level boarding or alighting data along bus lines in São Paulo (Brazil), considering four different sampling methods. The main contributions are as follows: by accounting for the spatial heterogeneity of the predictor dataset, the GWPCA can identify the most important factor affecting transit ridership even in bus stops with no information on boarding and alighting; the spatial modeling of stop-level ridership data using GWPCA components as explanatory variables allows visualizing the spatially varying effects from predictors on ridership, supporting the land use planning at a local level; GWPCA coupled with kriging simultaneously addresses the multicollinearity of predictor data, its spatial heterogeneity, and the spatial dependence of the stop-level ridership variable, thus enhancing the goodness-of-fit measures of the transit ridership prediction in unsampled stops; and a balanced sample on predictor data and well-spread in the geographic space might be preferred to accurately estimate missing stop-level ridership data. In addition to solve the lack of stop-level ridership data, supporting a reliable bus system planning, the proposed method indicates what predictors should be addressed by policymakers to stimulate a transit-oriented development. The method can be successfully applied to other travel demand variables facing a lack of data such as traffic volume in road segments and mode choice at the household level.</p>\u0000 </div>","PeriodicalId":50259,"journal":{"name":"Journal of Advanced Transportation","volume":null,"pages":null},"PeriodicalIF":2.0,"publicationDate":"2024-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/2024/7967141","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141968402","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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":"Predicting Flight Trajectory in Convective Weather through Boosted Spatiotemporal Deep Learning Ensemble","authors":"Xi Zhu,&nbsp;Ke Zhang,&nbsp;Zhuxi Zhang,&nbsp;Lifei Tan","doi":"10.1155/2024/6400839","DOIUrl":"https://doi.org/10.1155/2024/6400839","url":null,"abstract":"<div>\u0000 <p>Flight trajectory prediction is one of the key issues in ensuring the safety of air traffic, providing the air traffic controller with the foresight of flight conflicts so that control instructions for pilots can be preconceived. In a complicated mechanism, flight trajectories can be severely affected by convective weather, making accurately predicting trajectories challenging. To address this problem, we propose a boosted spatiotemporal deep learning ensemble for mining the law of how convective weather affects flight trajectory stretching. Instead of conventionally representing trajectory data in a geographic coordinate system, we design a relative coordinate system for gaining new trajectory features which tangibly reflect trajectory’s relations with planned route and convective weather. Besides, we raise a boosted ensemble framework of spatiotemporal deep learning models, trained by the samples pairing sequential trajectory with graphical weather, dedicating to strengthen the mining of the high-value training samples that involve explicit flight deviations caused by convective weather. The experiments using actual flight and weather data demonstrate our method’s superiority in predicting flight trajectory affected by convective weather.</p>\u0000 </div>","PeriodicalId":50259,"journal":{"name":"Journal of Advanced Transportation","volume":null,"pages":null},"PeriodicalIF":2.0,"publicationDate":"2024-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/2024/6400839","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141968279","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}