IEEE Open Journal of Intelligent Transportation Systems最新文献

{"title":"Recognizing Distant Vehicles on GMM by Extracting Far Road Area Based on Analyzing Trajectories of Nearby Vehicles","authors":"Chinthaka Premachandra;Eigo Ito","doi":"10.1109/OJITS.2025.3614862","DOIUrl":"https://doi.org/10.1109/OJITS.2025.3614862","url":null,"abstract":"In today’s motorized society, road accidents occur frequently, and their incidence continues to rise with the increasing number of car users worldwide. A significant proportion of these accidents occur at intersections, where one promising countermeasure is the use of multi-camera systems that assist pedestrians and drivers by detecting moving vehicles in the intersection area. However, conventional vehicle detection methods suffer from reduced accuracy as vehicles move farther from the camera, since distant vehicles appear smaller in images. To address this limitation, we propose a method that first identifies the distant region of the road in an image and then applies up-sampling to enhance the visibility of faraway road area for improved vehicle detection. In the proposed approach, nearby moving vehicles are roughly extracted using inter-frame subtraction across consecutive frames, and these subtractions are accumulated over time as trajectories. Based on these trajectories, we introduce a novel method to estimate the road’s vanishing point, which is then used to determine the distant road area. This region is subsequently up-sampled in consecutive frames, and vehicle detection is performed using a Gaussian Mixture Model (GMM) to identify distant vehicles. Extensive experiments confirm the effectiveness of the proposed method. The results demonstrated that, although detection accuracy naturally decreases with distance, our method achieves more than twice the accuracy of conventional approaches under both daytime and nighttime conditions.","PeriodicalId":100631,"journal":{"name":"IEEE Open Journal of Intelligent Transportation Systems","volume":"6 ","pages":"1346-1357"},"PeriodicalIF":5.3,"publicationDate":"2025-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11182307","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145255924","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Maneuver Coordination Service With Reliability and Relevance Enhancements","authors":"Andreia Figueiredo;João Viegas;Pedro Rito;Miguel Luís;Susana Sargento","doi":"10.1109/OJITS.2025.3613990","DOIUrl":"https://doi.org/10.1109/OJITS.2025.3613990","url":null,"abstract":"The increase in vehicle density exacerbates traffic congestion, accidents, and emissions. Automated Vehicles (AVs), while promising improved safety and efficiency, require seamless coordination and communication to unlock their full potential. The European Telecommunications Standards Institute (ETSI) Maneuver Coordination Service (MCS) draft introduces Vehicle-to-Everything (V2X) communication for real-time vehicle coordination, utilizing a modular architecture designed to enhance inter-vehicle communication. However, a major limitation of the current MCS framework is its vulnerability to message loss during maneuver negotiation, which can increase latency and negatively impact maneuver efficiency. This paper proposes an acknowledgment mechanism in MCS to enhance message reliability and a Relevance Message Detector to filter out irrelevant messages, reducing processing overhead. The experimental results demonstrate that introducing an acknowledgment mechanism can reduce maneuver negotiation time by approximately 900 ms compared to standard methods under packet loss scenarios, significantly improving reliability and efficiency. Furthermore, the Relevance Message Detector effectively minimizes unnecessary message processing, enhancing overall system efficiency. Functional evaluations validate the correct execution of coordinated maneuvers, demonstrating the practical benefits of the proposed extensions. These enhancements contribute to a more robust and efficient MCS framework, improving AV coordination in real-world scenarios.","PeriodicalId":100631,"journal":{"name":"IEEE Open Journal of Intelligent Transportation Systems","volume":"6 ","pages":"1325-1345"},"PeriodicalIF":5.3,"publicationDate":"2025-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11177009","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145255974","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Simulation Framework for Evaluating Mobile Autonomous Charging Pod Operations","authors":"Mohd A. Khan;Wilco Burghout;Oded Cats;Erik Jenelius;Matej Cebecauer","doi":"10.1109/OJITS.2025.3613259","DOIUrl":"https://doi.org/10.1109/OJITS.2025.3613259","url":null,"abstract":"Recent advances in automation have accelerated the development of autonomous electric vehicles (AEVs), which offer the potential for continuous operation, constrained primarily by the need for recharging. We propose a dynamic charging strategy based on Mobile Autonomous Charging Pods (MAPs), which are battery-equipped electric vehicles capable of transferring energy to AEVs while in motion. We introduce a dedicated simulation framework within the microscopic traffic simulator SUMO, incorporating MAP-specific modules for assignment, navigation, and real-time energy transfer under realistic traffic constraints. We model the behavior of both MAPs and AEVs in a stylized looped network and evaluate system-level performance under various demand and fleet configurations. Key performance indicators include energy consumption, charging efficiency, battery utilization, and reductions in AEV battery capacity requirements. Simulation results demonstrate that MAPs can effectively support continuous AEV operation, achieving up to 14% battery downsizing with minimal infrastructure investment, while also reducing travel time by 7%, relative to fixed charging solutions. This study lays the foundation for simulation-based evaluation of MAP-based dynamic charging as a scalable, flexible, and efficient alternative to fixed charging solutions.","PeriodicalId":100631,"journal":{"name":"IEEE Open Journal of Intelligent Transportation Systems","volume":"6 ","pages":"1282-1297"},"PeriodicalIF":5.3,"publicationDate":"2025-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11175572","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145255929","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Interaction of Macroscopic Optimization and Microscopic Traffic Flow With Communication Uncertainty in Intelligent Vehicle Cyber–Physical System","authors":"Huamin Li;Moye Lu;Junfeng Mao;Xiaojun Yu","doi":"10.1109/OJITS.2025.3610928","DOIUrl":"https://doi.org/10.1109/OJITS.2025.3610928","url":null,"abstract":"This study addresses the challenge of bridging macroscopic optimization and microscopic driving behavior under communication uncertainty in Intelligent Vehicle Cyber-Physical Systems (IVCPS). A multi-objective macroscopic optimization model is first developed to generate recommended speeds, with different evolutionary algorithms systematically compared. Through experiments with Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO), and Real-Coded Genetic Algorithm (RCGA), RCGA is identified as the most effective solver. The recommended speeds are subsequently integrated into the microscopic layer, where a modified Intelligent Driver Model (IDM) accounts for both multi-preceding vehicle interactions and macroscopic guidance. Communication uncertainty in the transmission process is modeled and quantified using soft set theory, enabling robust adaptation of vehicle behaviors. Simulation results under both ideal and uncertain communication conditions demonstrate that: (i) the proposed framework consistently outperforms the baseline IDM and the conventional IDM with recommended speeds, validating its effectiveness; (ii) variations in optimization weights significantly influence the performance of the modified IDM; and (iii) the modified IDM achieves superior traffic efficiency and fuel economy across different traffic demand scenarios. Overall, the findings highlight the necessity of incorporating uncertainty-aware speed guidance to effectively link macroscopic optimization with microscopic control, offering new insights into building resilient and efficient intelligent transportation systems.","PeriodicalId":100631,"journal":{"name":"IEEE Open Journal of Intelligent Transportation Systems","volume":"6 ","pages":"1265-1281"},"PeriodicalIF":5.3,"publicationDate":"2025-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11168868","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145210119","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Koopman-Theoretic Approach to Car-Following and Multi-Lane Interaction Modeling","authors":"Shakib Mustavee;Shaurya Agarwal","doi":"10.1109/OJITS.2025.3610456","DOIUrl":"https://doi.org/10.1109/OJITS.2025.3610456","url":null,"abstract":"This paper presents a Koopman operator-based approach for the car-following model using SwarmDMD, a dynamic mode decomposition (DMD)-type algorithm designed to capture multi-agent interactions. A central challenge in Koopman operator-based car-following dynamics modeling lies in selecting an appropriate dictionary of observable functions. While previous studies have demonstrated various techniques, including deep learning, to learn the Koopman operator, they do not yield analytical forms. To address this, we revisit classical physics-based car-following models and propose candidate observables inspired by their mathematical structures. These observables are used within the Koopman and DMD framework to reconstruct a follower’s acceleration. The corresponding speed and trajectory are then estimated from the reconstructed acceleration. We evaluate the framework using both simulated and real-world datasets, demonstrating strong potential for accuracy and interpretability. While this study focuses on single-lane human-driven vehicles (HDVs), the framework is easily extendable to multi-lane traffic and connected and autonomous vehicle (CAV) scenarios, highlighting its generality and versatility. We presented a comparative evaluation of the proposed model by contrasting its acceleration reconstruction performance with that of both physics-based and data-driven models. Additionally, we interpreted the individual entries of the SwarmDMD matrix by establishing their connections to parameters of physics-based models. The codes and data used in the paper are available at our GitHub page.","PeriodicalId":100631,"journal":{"name":"IEEE Open Journal of Intelligent Transportation Systems","volume":"6 ","pages":"1358-1376"},"PeriodicalIF":5.3,"publicationDate":"2025-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11165106","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145255946","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Adaptive System Architecture for Intelligent Multimodal Transport: Challenges and Fundamental Design Aspects","authors":"Fatemeh Golpayegani;Abdollah Malekjafarian;Muhammad Farooq;Saeedeh Ghanadbashi;Nima Afraz","doi":"10.1109/OJITS.2025.3609482","DOIUrl":"https://doi.org/10.1109/OJITS.2025.3609482","url":null,"abstract":"Multimodal Intelligent Transportation Systems (M-ITS) encompass a range of transportation services utilizing various modes of transport (e.g., buses, trains, ride-sharing) and incorporating intelligent technologies for enhanced efficiency and user experience. Traditional, non-adaptive system architectures struggle to respond to dynamic changes in real-time traffic conditions, user demands, and operational disruptions. These rigid systems lack flexibility in integrating new technologies, managing fluctuating demand, and ensuring seamless operation across multiple transport modes. Consequently, inefficiencies in data handling, scalability, and real-time decision-making emerge, hindering the potential of M-ITS. In this paper, we provide a conceptual layered architecture that can adapt to various needs of multimodal transportation systems. The proposed architecture focuses on aspects such as scalability, adaptability, seamless integration, and interoperability of various subcomponents that are owned and managed by different stakeholders (parties with an interest or role in the system, such as users, city planners, service operators, and technology providers). In addition to the component architecture, we propose a data architecture that emphasizes the crucial role of integrating multimodal, multisource data to enable intelligent decision-making. We illustrate the functionality of the proposed architecture through two use cases at a conceptual level: a traffic monitoring system and a traffic flow prediction system. These examples demonstrate how the data and system architecture can be fused and serve multimodal intelligent transport services, highlighting its ability to adapt to complex urban environments. Furthermore, we present results for an emergency vehicle approaching scenario, showcasing the architecture’s responsiveness and adaptability in critical situations.","PeriodicalId":100631,"journal":{"name":"IEEE Open Journal of Intelligent Transportation Systems","volume":"6 ","pages":"1298-1324"},"PeriodicalIF":5.3,"publicationDate":"2025-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11160689","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145255930","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Lightweight Recurrent Architecture for Robust Urban Traffic Forecasting With Missing Data","authors":"Chung-I Huang;Lucas Biechy;Jih-Sheng Chang;Chien-Hao Tseng;Jyh-Horng Wu;Wen-Yi Chang;Yun-Te Lin","doi":"10.1109/OJITS.2025.3609339","DOIUrl":"https://doi.org/10.1109/OJITS.2025.3609339","url":null,"abstract":"Real-time traffic flow prediction plays a vital role in alleviating urban congestion and improving transportation efficiency. However, urban traffic data are often subject to sensor anomalies, missing values, and unstable model performance. To address these challenges, this article proposes a lightweight and robust recurrent neural network architecture designed to enhance the accuracy and reliability of traffic flow forecasting under data incompleteness. We introduce two enhanced recurrent models—Extended Long Short-Term Memory (xLSTM) and Extended Gated Recurrent Unit (xGRU)—which incorporate exponential gating mechanisms and matrix-valued memory updates. These enhancements significantly reduce model complexity and training overhead while preserving high prediction accuracy. Furthermore, a three-tier imputation strategy is proposed to handle missing data, adaptively applying linear interpolation, temporal averaging, or seasonal decomposition based on the length and characteristics of the missing intervals. Extensive experiments were conducted on a six-month multivariate traffic sensor dataset collected from Taichung City, Taiwan. The results demonstrate that xGRU achieves comparable or superior forecasting accuracy to mainstream Transformer-based models, such as Informer, Autoformer, and TFT, despite using significantly fewer parameters. These findings highlight the proposed architecture’s practical potential for real-world urban traffic forecasting with enhanced efficiency, robustness, and data resilience.","PeriodicalId":100631,"journal":{"name":"IEEE Open Journal of Intelligent Transportation Systems","volume":"6 ","pages":"1235-1244"},"PeriodicalIF":5.3,"publicationDate":"2025-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11162586","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145141698","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Investigating Factors Influencing Takeover Performance in Conditionally Automated Driving: The Role of Uncertainty Communication","authors":"Sule Tekkesinoglu;Lars Kunze","doi":"10.1109/OJITS.2025.3609629","DOIUrl":"https://doi.org/10.1109/OJITS.2025.3609629","url":null,"abstract":"Safe transition following a Takeover Request (ToR) in Level 3 automated driving requires a high situational awareness, which is influenced by traffic complexity, non-driving-related tasks (NDRTs), and communication strategies. These factors are often studied in isolation, focusing on individual elements such as the effects of NDRT, ToR timing, or alert design. However, a holistic investigation that considers the interdependencies of these factors is lacking. This study provides a comprehensive analysis of the human, environmental, situational, and vehicle-system factors affecting takeover performance, examining their interactions under conditions with and without uncertainty communication. We explored the effectiveness of adaptive uncertainty communication—preparatory alert prior to the ToR—in enhancing driver readiness and response in time-constrained scenarios. A simulation-based online study (N = 60) was conducted, where participants experienced multimodal takeover alerts across diverse scenarios involving varying traffic conditions and NDRT types. Our findings indicate that uncertainty communication mitigated the impact of human factors such as trust, fatigue, and stress. Without such communication, these effects were more pronounced. It also improved participants’ understanding of the ToR reason and perceived appropriateness of ToR timing. While task-switching remained a challenge under high distraction, uncertainty communication modestly reduced task-switching difficulty. Overall, scenarios involving the preparatory alert received higher ratings for notice time, preparedness, and alert effectiveness. Drawing on insights from the correlation analysis across all factors, we outline key challenges and directions for future research undertaking adaptive alert strategies that support real-time mode transitions.","PeriodicalId":100631,"journal":{"name":"IEEE Open Journal of Intelligent Transportation Systems","volume":"6 ","pages":"1245-1264"},"PeriodicalIF":5.3,"publicationDate":"2025-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11162601","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145141735","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Self Healing of a Mixed Autonomy Traffic System Using Reinforcement Learning and Attention","authors":"Safia Fatima;Kai Olav Ellefsen;Leon Moonen","doi":"10.1109/OJITS.2025.3606539","DOIUrl":"https://doi.org/10.1109/OJITS.2025.3606539","url":null,"abstract":"As urban traffic becomes increasingly complex with the integration of connected and autonomous vehicles alongside human-driven vehicles, there is a critical need for adaptive traffic management systems capable of self-healing in response to disruptions. This paper introduces TS2RLA (“Traffic System Recovery using Reinforcement Learning and Attention”), a novel framework for self-healing in mixed-autonomy traffic systems by combining deep reinforcement learning with an attention mechanism to optimize traffic flow and recover from faults in various scenarios in a mixed-autonomy traffic environment. We evaluated TS2RLA in four complex traffic scenarios: bottleneck, figure-eight, grid, and merge. Our results demonstrate significant improvements over the baseline model, showing an average of 86.74% reduction in crashes, 71% improvement in speed and traffic throughput, and robust performance under diverse and complex traffic conditions. Moreover, our experiments show that TS2RLA leads to a significant reduction in CO2 emissions and fuel consumption. TS2RLA’s attention-based approach shows particular benefits in bottleneck and figure-eight scenarios, demonstrating its ability to adapt to complex, multi-factor traffic situations. For scenarios that TS2RLA had not been trained on before, it performs even more favorably than the baseline, with a 96.8% crash reduction and 95.3% throughput improvement. This shows its ability to adapt effectively to new traffic conditions. Overall, we conclude that TS2RLA could significantly improve the safety, efficiency, and capacity of real-world traffic systems, particularly in dynamic urban environments. As such, our work contributes to the field of intelligent transportation systems by offering a versatile self-healing framework capable of managing the complexities of mixed-autonomy traffic.","PeriodicalId":100631,"journal":{"name":"IEEE Open Journal of Intelligent Transportation Systems","volume":"6 ","pages":"1200-1220"},"PeriodicalIF":5.3,"publicationDate":"2025-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11152593","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145089948","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Intelligent Parking Guidance System Using Computer Vision, IoT, and Edge Computing","authors":"Pranav Rajesh;Dheeraj Kallakuri;Sudeeksha Chagarlamudi;Sai Manoj Vissavajhula;Jingxiong Meng;Junfeng Zhao","doi":"10.1109/OJITS.2025.3605318","DOIUrl":"https://doi.org/10.1109/OJITS.2025.3605318","url":null,"abstract":"With the increasing population, the surge in vehicle owners has made parking challenges a pressing issue. Navigating a crowded parking lot in urban centers, commercial areas, and public places is often time-consuming, leading to wasted fuel and increased greenhouse gas emissions. Another concern in parking lots is the occurrence of traffic accidents, frequently caused by distracted drivers searching for parking spaces or navigating through congested lots. Many parking lots worldwide have implemented smart parking systems that provide information about the number of available parking spaces at each instant but do not guide drivers about where these parking spaces are in the parking lot. This paper introduces an intelligent parking guidance system that monitors the parking lot using an infrastructure monocular camera to identify the available parking spaces using computer vision and edge computing to process the data and avoid latency. The processed video from the camera is streamed on a website interface for remote monitoring or viewing by drivers accessing the parking lot. The website also provides the driver with navigation route guidance to the nearest available parking spot among all the vacant parking spaces from the driver’s location at each instance. An experimental evaluation was performed to assess the robustness of the developed prototype system. Additionally, multi-camera-based vacant parking space detection was performed to upgrade the current system. This can be seamlessly integrated with the developed system to mitigate the problems of occlusions and false positives/negatives. This innovative, intelligent parking guidance solution significantly simplifies the parking process for human drivers, making it safer by reducing the risk of accidents and making it more convenient and environmentally friendly. The system’s convenience will make the audience feel more comfortable, knowing that finding a parking spot will be a hassle-free experience and that they are less likely to be involved in a parking lot accident.","PeriodicalId":100631,"journal":{"name":"IEEE Open Journal of Intelligent Transportation Systems","volume":"6 ","pages":"1185-1199"},"PeriodicalIF":5.3,"publicationDate":"2025-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11146687","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145061963","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}