Transportation Research Part C-Emerging Technologies最新文献

{"title":"Noise-aware and equitable urban air traffic management: An optimization approach","authors":"Zhenyu Gao ,&nbsp;Yue Yu ,&nbsp;Qinshuang Wei ,&nbsp;Ufuk Topcu ,&nbsp;John-Paul Clarke","doi":"10.1016/j.trc.2024.104740","DOIUrl":"https://doi.org/10.1016/j.trc.2024.104740","url":null,"abstract":"<div><p>Urban air mobility (UAM), a transformative concept for the transport of passengers and cargo, faces several integration challenges in complex urban environments. Community acceptance of aircraft noise is among the most noticeable of these challenges when launching or scaling up a UAM system. Properly managing community noise is fundamental to establishing a UAM system that is environmentally and socially sustainable. In this work, we develop a holistic and equitable approach to manage UAM air traffic and its community noise impact in urban environments. The proposed approach is a hybrid approach that considers a mix of different noise mitigation strategies, including limiting the number of operations, cruising at higher altitudes, and ambient noise masking. We tackle the problem through the lens of network system control and formulate a multi-objective optimization model for managing traffic flow in a multi-layer UAM network while concurrently pursuing demand fulfillment, noise control, and energy saving. Further, we use a social welfare function in the optimization model as the basis for the efficiency-fairness trade-off in both demand fulfillment and noise control. We apply the proposed approach to a comprehensive case study in the city of Austin and perform design trade-offs through both visual and quantitative analyses.</p></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":null,"pages":null},"PeriodicalIF":7.6,"publicationDate":"2024-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141594088","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":"Competition of ride-hailing platforms in the era of autonomous vehicles: Heavy or light asset?","authors":"Xiaoyan Wang ,&nbsp;Xi Lin ,&nbsp;Meng Li ,&nbsp;Zhengtian Xu ,&nbsp;Ke Zhang","doi":"10.1016/j.trc.2024.104732","DOIUrl":"https://doi.org/10.1016/j.trc.2024.104732","url":null,"abstract":"<div><p>Business modes of ride-hailing services in the era of autonomous vehicles (AVs) could be vastly different from those of current human vehicles (HVs). In addition to the asset-heavy mode, wherein platforms both operate and have ownership of the AV assets, a new business mode known as the asset-light mode is emerging. The asset-light mode involves AV crowdsourcing, where private AV owners rent out their vehicles to platforms (Wang et al., 2021). This paper establishes a game-theoretical framework to model the competition between an asset-heavy platform and an asset-light platform. We first examine the mixed-strategy Nash equilibrium between the two AV ride-hailing services, considering that only the asset-light platform can crowdsource AVs, and then extend the model to allow both platforms to crowdsource AVs. Our analyses demonstrate that, in scenarios with insufficient AV supply, once the number of private AVs exceeds a small threshold, at least one of the platforms will crowdsource AVs, even though they cannot derive economic benefits. As the private AV market grows, the asset-light platform becomes more profitable. We then use numerical results to study the impact of price regulations on market participants and find that when urban planners aim to enhance social surplus, it is best not to regulate maximum trip fare, minimum rental payment, or maximum rental payment. These insights offer guidance for AV ride-hailing service providers and government regulators in the future era of AV commercialization.</p></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":null,"pages":null},"PeriodicalIF":7.6,"publicationDate":"2024-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141594089","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":"Predicting hurricane evacuation behavior synthesizing data from travel surveys and social media","authors":"Tanmoy Bhowmik ,&nbsp;Naveen Eluru ,&nbsp;Samiul Hasan ,&nbsp;Aron Culotta ,&nbsp;Kamol Chandra Roy","doi":"10.1016/j.trc.2024.104753","DOIUrl":"https://doi.org/10.1016/j.trc.2024.104753","url":null,"abstract":"<div><p>Evacuation behavior models estimated using post-disaster surveys are not adequate to predict real-time dynamic population response as a hurricane unfolds. With the emergence of ubiquitous technology and devices in recent times, social media data with its higher spatio-temporal coverage has become a potential alternative for understanding evacuation behaviour during hurricanes. However, these data are often associated with selection bias and population representativeness issues. To that extent, the current study proposes a novel data fusion algorithm to combine heterogeneous data sources from transportation systems and social media, in a unified framework to understand and predict real-time population response during hurricanes. Specifically, Twitter data of 2300 users are collected for evacuation response during Hurricane Irma and augmented behaviourally (probabilistically) with a representative National Household Travel Survey (NHTS) data, thus creating a hybrid dataset to improve the representativeness as well as provide a rich set of explanatory variables for understanding the evacuation behavior. The fusion process is conducted using a probabilistic matching method based on a set of common attributes across NHTS and Twitter. The fused dataset is employed to estimate the evacuation model and a comparison exercise is conducted to evaluate the performance of the model via fusion. The model fitness measures clearly demonstrate the improvement in data fit for the evacuation model through the proposed fusion algorithm. Further, we conduct a prediction assessment to illustrate the applicability of the proposed fusion technique and the results clearly highlight the improvement in the evacuation prediction rate achieved through the fused models. The proposed data-driven methods will enhance our ability to predict time-dependent evacuation demand for better hurricane response operations such as targeted warning dissemination and improved evacuation traffic management, allowing emergency plans to be more adaptive.</p></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":null,"pages":null},"PeriodicalIF":7.6,"publicationDate":"2024-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141594768","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":"Fusion of multi-resolution data for estimating speed-density relationships","authors":"Lu Bai ,&nbsp;Wai Wong ,&nbsp;Pengpeng Xu ,&nbsp;Pan Liu ,&nbsp;Andy H.F. Chow ,&nbsp;William H.K. Lam ,&nbsp;Wei Ma ,&nbsp;Yu Han ,&nbsp;S.C. Wong","doi":"10.1016/j.trc.2024.104742","DOIUrl":"https://doi.org/10.1016/j.trc.2024.104742","url":null,"abstract":"<div><p>Estimating traffic flow models, such as speed-density relationships, using data from multiple sources with different temporal resolutions is a prevalent challenge encountered in real-world scenarios. The resolution incompatibility is often intuitively addressed by averaging the high-resolution (HR) data to synchronize with the low-resolution (LR) data. This paper shows that ignoring the variability of HR data within the LR interval during the averaging process could lead to systematic data point distortions, resulting in biased model estimations. The average absolute biases of models estimated from the average data increase with the lost variability of HR data within the LR intervals. Subsequently, it proves that for any given complete average data dataset, there must exist an optimal dataset that minimizes the average absolute bias in model estimations introduced by the averaging process. A novel procedure for determining the practical optimal dataset is proposed. To test the proposed method, real-world HR data from four sites in Hong Kong and Nanjing, China were collected to mimic situations with multi-resolution data. Results demonstrated that the proposed method can significantly reduce the average absolute biases of models estimated from the determined practical optimal dataset, as compared to models estimated from the complete average dataset.</p></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":null,"pages":null},"PeriodicalIF":7.6,"publicationDate":"2024-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0968090X24002638/pdfft?md5=c2ee76028710362fa69f0a322eca42a9&pid=1-s2.0-S0968090X24002638-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141594102","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":"Distributionally robust origin–destination demand estimation","authors":"Jingxing Wang ,&nbsp;Jun Song ,&nbsp;Chaoyue Zhao ,&nbsp;Xuegang (Jeff) Ban","doi":"10.1016/j.trc.2024.104716","DOIUrl":"https://doi.org/10.1016/j.trc.2024.104716","url":null,"abstract":"<div><p>Gaining a good understanding of the travel demands of a city or region is extremely important for many transportation applications. For stochastic origin–destination (OD) estimation problems, an accurate distribution assumption or observation of OD estimates or data is usually desired but not always available. In this paper, we establish a novel two-stage OD estimation framework based on distributionally robust optimization (DRO) and quasi-sparsity property of large-scale OD demand matrices. The proposed two-stage Distributionally Robust Quasi-Sparsity OD estimation (DR-QSOD) model does not require an accurate or complete distribution assumption of estimates/data. Numerical results demonstrate that DR-QSOD model outperforms stochastic QSOD model in estimating OD demands when the distribution assumption of data is biased. This paper also discusses two different approaches to solve the DR-QSOD model as well as compares their computational efficiency. In addition, DR-QSOD model is shown to keep relatively high quasi-sparsity consistency, which also brings lots of meaningful practical insights.</p></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":null,"pages":null},"PeriodicalIF":7.6,"publicationDate":"2024-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141594103","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":"Navigating the non-compliance effects on system optimal route guidance using reinforcement learning","authors":"Hyunsoo Yun ,&nbsp;Eui-jin Kim ,&nbsp;Seung Woo Ham ,&nbsp;Dong-Kyu Kim","doi":"10.1016/j.trc.2024.104721","DOIUrl":"https://doi.org/10.1016/j.trc.2024.104721","url":null,"abstract":"<div><p>We consider a scenario where the transportation management center (TMC) guides future autonomous vehicles (AVs) toward optimal routes, aiming to bring the network in line with the system optimal (SO) principle. However, achieving this requires a joint decision-making process, while users may be non-compliant with the TMC’s route guidance for personal gain. This paper models a future transportation network with a microscopic simulation, to introduce a novel concept of mixed equilibrium. In this framework, AVs follow the TMC’s SO route guidance, while users can dynamically choose to either comply or manually override this autonomy based on their own judgment. We initially model a fully compliant scenario, where the centralized Q-network, analogous to a TMC, is trained using reinforcement learning (RL) to minimize total system travel time (TSTT), providing optimal routes to users. Subsequently, we extend the problem setting to a multi-agent reinforcement learning (MARL) scenario, where users can comply or deviate from the TMC’s guidance based on their own decision-making. Through neural fictitious self-play (NFSP), we employ a modulating hyperparameter to investigate the impact of varying degrees of non-compliance on the overall system. Results indicate that our RL approach holds significant potential for addressing the dynamic system optimal assignment problem. Remarkably, the TMC’s route guidance retains the essence of SO while integrating some level of non-compliance. However, we also demonstrate that dominant user-centric decision-making may lead to system inefficiencies while creating disparities among users. Our framework serves as an innovative tool in an AV-dominant future, offering a realistic perspective on network performance that aids in formulating effective traffic management strategies.</p></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":null,"pages":null},"PeriodicalIF":7.6,"publicationDate":"2024-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0968090X24002420/pdfft?md5=9dc535009d27953f74f5e574dc779a57&pid=1-s2.0-S0968090X24002420-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141541337","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":"How did international air transport networks influence the spread of COVID-19? A spatial and temporal modeling perspective","authors":"Chi Li ,&nbsp;Linhao Yu ,&nbsp;Jianfeng Mao ,&nbsp;Wei Cong ,&nbsp;Zibin Pan ,&nbsp;Yuhao Du ,&nbsp;Lianmin Zhang","doi":"10.1016/j.trc.2024.104730","DOIUrl":"https://doi.org/10.1016/j.trc.2024.104730","url":null,"abstract":"<div><p>The international air transport network is pivotal in the global propagation of emerging infectious diseases. Gaining insights into the nuances of this transmission mechanism can pave the way for more strategic and effective interventions. While previous studies have delved into the subject, an integrated spatial and temporal modeling framework, specifically tailored to distinct phases of COVID-19 and its variants, has yet to be fully explored. This research aims to address this gap by exploring the spatial and temporal impacts of the air transport network on the spread of COVID-19 and its Omicron variant. We introduce an improved effective distance metric to assess the spatial correlation between various distance metrics and the onset of infectious diseases in selected nations. Subsequently, we employ a network-based heterogeneous susceptible-unreported infectious-confirmed-recovered-death (SUCRD) mathematical model to delineate the temporal evolution of infections by country. Our findings underscore the air transport network’s instrumental role in the pandemic’s spatial dynamics. Moreover, our model has been validated, demonstrating robustness and reliability. Through rigorous validation and simulation experiments, we discern the significance of the timing and intensity of interventions in shaping the pandemic’s trajectory. Notably, while the air transport network exerts a profound influence during the phases of both COVID-19 and Omicron, international travel restrictions exhibit diminishing returns once the disease achieves widespread prevalence. Through comparative analysis and discussion, we highlight the advantages of our experimental outcomes and methodological approach compared to previous studies. Based on our findings, we identify six key policy implications that offer critical perspectives for aviation stakeholders. This study illuminates the role of the air transport network in affecting the spatial accessibility and temporal dynamics of pandemic transmission, thereby providing valuable insights for informed policy-making in the aviation sector.</p></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":null,"pages":null},"PeriodicalIF":7.6,"publicationDate":"2024-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141541107","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":"Modelling level 1 situation awareness in driving: A cognitive architecture approach","authors":"Umair Rehman ,&nbsp;Shi Cao ,&nbsp;Carolyn G. MacGregor","doi":"10.1016/j.trc.2024.104737","DOIUrl":"https://doi.org/10.1016/j.trc.2024.104737","url":null,"abstract":"<div><p>The goal of this research is to computationally model and simulate the situation awareness (SA) of drivers. A computational model in a cognitive architecture was developed that can interact with a driving simulator to infer quantitative predictions of drivers’ SA. The model uses the Queueing Network Adaptive Control of Thought-Rational (QN-ACTR) framework as a foundation and integrates a dynamic visual sampling model (SEEV) to create QN-ACTR-SA, which simulates attention allocation patterns of human drivers at SA Level 1 (i.e., perception of critical elements). QN-ACTR-SA also incorporates a driver model that can interact with a driving simulator. A validation study was conducted to determine whether Level 1 SA results produced with the QN-ACTR-SA model correspond to empirical data collected from human drivers (14 participants) for the same tasks. Both QN-ACTR-SA and human participants were probed for SA using two approaches: within-task queries using the Situation Awareness Global Assessment Technique (SAGAT) and post-experiment questions. A comparative assessment demonstrated that QN-ACTR-SA could reasonably simulate drivers’ Level 1 SA for two driving conditions: easy (with few vehicles and signboards) and complex (with dense traffic and signboards). QN-ACTR-SA fit for human SAGAT scores (possible range 0–100) resulted in a mean absolute percentage error (MAPE) of 5.0% and the root means square error (RMSE) of 3.5. Model fit for post-experiment human SA results was MAPE of 6.7% and RMSE of 6.1. Limitations of QN-ACTR-SA as a predictive model and areas of future research are discussed.</p></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":null,"pages":null},"PeriodicalIF":7.6,"publicationDate":"2024-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141541105","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":"Autonomous interval management of multi-aircraft based on multi-agent reinforcement learning considering fuel consumption","authors":"Jie Yuan ,&nbsp;Yang Pei ,&nbsp;Yan Xu ,&nbsp;Yuxue Ge ,&nbsp;Zhiqiang Wei","doi":"10.1016/j.trc.2024.104729","DOIUrl":"https://doi.org/10.1016/j.trc.2024.104729","url":null,"abstract":"<div><p>Real-time autonomous interval management in multi-aircraft operational scenarios addresses safety, efficiency, and economic issues in air transportation. This study proposes an autonomous interval management supporter (AIMS) prototype system with high scalability potential to address these issues. The system utilizes a multi-agent deep reinforcement learning method, specifically the deep deterministic policy gradient (DDPG) algorithm, which enables interval management and fuel-saving by providing speed decisions in a continuous action space amidst uncertainty. This study innovatively incorporates aircraft performance-related parameters as observational features. These features are categorized into interval- and performance-related groups as inputs, and trained using a separate reconstructed critic network structure. Experiments are focused on the enroute descent phase to validate the performance of the proposed AIMS. Compared with real flight data based on traffic controller decisions, the AIMS demonstrated superior speed change decision-making regardless of the aircraft type or classification criteria. Simulation results suggest that incorporating aircraft performance-related states and utilizing a separate critic network training structure positively improve the success rate of decision-making and reduce fuel consumption. By utilizing aircraft performance-related states, the success rate increases by an average of 49.64%, with a corresponding average fuel consumption decrease of 4.42%. Additionally, employing a separate critic network training structure results in an average success rate increase of 16.10%, with an average fuel reduction of 1.09%. To further reduce fuel consumption and achieve a shortened interval, it is recommended to set the initial altitude of the aircraft sequence appropriately high based on flight altitude constraints.</p></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":null,"pages":null},"PeriodicalIF":7.6,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0968090X2400250X/pdfft?md5=c26f5ec62797f36063cee03f673675b5&pid=1-s2.0-S0968090X2400250X-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141485100","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":"Innovation diffusion in EV charging location decisions: Integrating demand & supply through market dynamics","authors":"Xiangyong Luo ,&nbsp;Michael J. Kuby ,&nbsp;Yudai Honma ,&nbsp;Mouna Kchaou-Boujelben ,&nbsp;Xuesong Simon Zhou","doi":"10.1016/j.trc.2024.104733","DOIUrl":"https://doi.org/10.1016/j.trc.2024.104733","url":null,"abstract":"<div><p>This paper offers a strategic approach to Electric Vehicles (EVs) charging network planning, emphasizing the integration of demand and supply dynamics. This is accomplished through the utilization of continuous-time fluid queue models alongside discrete flow refueling location modeling, all in the context of innovation diffusion principles. Firstly, we employ a continuous-time approximation based on Ordinary Differential Equations (ODEs) to design multi-year supply curves, a method that stands in contrast to conventional practices which often overlook inter-year transitions and ongoing processes. Then, for medium-term charging station location planning (CSLP), we apply a flow refueling location model (FRLM) within grid-based multi-level networks, considering both multiple-path networks and capacity constraints. Furthermore, the grid-based network planning strategy uses a three-tier (Macro-Meso-Micro) approach for thorough EV charging station placement, with the macro-level covering entire cities, the <em>meso</em>-level assessing detailed EV routes and bridging the macro to micro levels, and the micro-level focusing on precise station placement for accessibility<!--> <!-->and<!--> <!-->efficiency. Lastly, our exploration of both overutilization and underutilization scenarios provides valuable insights for policymaking and conducting cost-benefit analyses. Illustrating our approach with the example of the Chicago sketch network, we introduce an integrated demand–supply model suitable for a single region and extendable to multiple regions, thereby addressing a gap in the existing literature. Our proposed methodology focuses on EV station placement, taking into account future needs, geographical capacities, and the importance of scenario analysis, which empowers strategic resource planning for EV charging networks over extended timeframes, thus aiding the transition towards a more sustainable and efficient transportation system.</p></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":null,"pages":null},"PeriodicalIF":7.6,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141481865","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}