Transportation Research Part C-Emerging Technologies最新文献

{"title":"Multi-reservoir traffic dynamics: Outflow network MFD and state estimation with sparse traffic data","authors":"Omid Mousavizadeh,&nbsp;Mehdi Keyvan-Ekbatani","doi":"10.1016/j.trc.2025.105504","DOIUrl":"10.1016/j.trc.2025.105504","url":null,"abstract":"<div><div>This study introduces an innovative framework for traffic state estimation in multi-reservoir networks, tackling the challenges posed by sparse data in urban traffic networks. By integrating Floating Car Data (FCD) and Loop Detector Data (LDD), the framework estimates traffic dynamics, such as inflows, outflows, transfer flows, and accumulation, without requiring detailed trip information or path flow distribution methods. A step forward in this study is a method for the estimation of internal outflow and transfer flows, enabling the direct estimation of outflow Network Macroscopic Fundamental Diagrams (outflow-NMFDs) which overcomes the shortcomings of outflow-NMFD estimation through the full set of sensors or indirect estimation via the production-NMFD. As argued, the outflow-NMFD is deemed the more robust foundation for the accumulation-based modelling in multi-reservoir systems. Accordingly, the estimated outflow-NMFD is used to build the accumulation-based model, in which the model outputs are combined with sparse real-time measurements, improving the model’s alignment with actual traffic conditions. The framework’s adaptability allows it to function effectively under varying levels of probe vehicle penetration, making it suitable for real-world scenarios where data availability can be inconsistent or limited. Simulation results validate the model’s robustness in capturing both steady-state and dynamic traffic behaviours, maintaining high accuracy even during abrupt demand changes. Furthermore, the framework performs reliably under stochastic conditions, demonstrating its resilience to daily traffic fluctuations. By reducing dependence on widespread sensor deployment across the network or its boundaries, this cost-effective approach offers a practical solution for real-time traffic monitoring and management in multi-reservoir systems, even when the boundaries of the system are not fixed in the network.</div></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":"184 ","pages":"Article 105504"},"PeriodicalIF":7.6,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145927800","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":"Uncertainty quantification for joint demand prediction of multi-modal ride-sourcing services using spatiotemporal Mixture-of-Expert neural network","authors":"Xiaobing Liu ,&nbsp;Yu Duan ,&nbsp;Yangli-ao Geng ,&nbsp;Yun Wang ,&nbsp;Qingyong LI ,&nbsp;Xuedong Yan ,&nbsp;Ziyou Gao","doi":"10.1016/j.trc.2025.105507","DOIUrl":"10.1016/j.trc.2025.105507","url":null,"abstract":"<div><div>Given the sparse uncertainty and highly imbalanced distribution of origin-destination demand in ride-sourcing, probabilistic prediction methods offer rich information to enhance operation decision-making reliability. However, existing uncertainty quantification methods face critical limitations: parametric approaches struggle with irregularities of real-world demand distributions. In contrast, nonparametric methods based on quantile regression are scarcely implemented and suffer from quantile crossing, compromising probabilistic consistency. Furthermore, current approaches seldom consider the crucial inter-service correlations among ride-sourcing services. This paper introduces the Spatiotemporal Mixture-of-Experts Residual-based Multi-quantile Regressive Network (ST-MoE-RMQRN).<span><span>¹</span></span> This novel hybrid framework integrates distribution-free quantile estimation with service correlation modeling. Our three main contributions are: 1) a Residual-based Multi-quantile Regressor that employs monotonicity-constrained residual fitting to ensure non-crossing quantiles while preserving distributional flexibility; 2) an environment-aware MoE architecture that captures service correlations through multiple shared-expert networks, addressing potential demand switches among service modals with sensitivity to temporal heterogeneity and exogenous contextual factors such as weather conditions; 3) comprehensive validation on two real-world ride-sourcing datasets demonstrating that our approach consistently outperforms recent probabilistic predictive models in both deterministic and probabilistic forecast metrics, including the Spatiotemporal Zero-Inflated Negative Binomial Graph Neural Network (STZINB-GNN). Additionally, data-wise and component-wise ablation studies confirm the effectiveness of the entire framework and each part of it, showing that explicitly modeling inter-service dependencies substantially improves both deterministic accuracy and probabilistic calibration. Our model is highly efficient in representing extreme demand fluctuations and data-sparse scenarios, which promise to address existing operational inefficiencies faced by transitioning Transportation Network Companies from reactive to anticipatory decision-making.</div></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":"184 ","pages":"Article 105507"},"PeriodicalIF":7.6,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145885537","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":"Integrating first-and-last-mile feeder services with urban public transportation considering transfer time uncertainty","authors":"Bo Sun,&nbsp;Qiang Meng","doi":"10.1016/j.trc.2025.105499","DOIUrl":"10.1016/j.trc.2025.105499","url":null,"abstract":"<div><div>This study proposes an integrated first-and-last-mile feeder service (IFLMFS) that coordinates shared mobility solutions with urban public transportation (UPT). Riders can request travel services in the form of a single first-mile (FM) trip prior to UPT, or a single last-mile (LM) trip following UPT usage, or a combination of both, to connect with UPT hubs seamlessly. We formulate the IFLMFS problem as a tailored path-based set-packing (PSC) model. For riders requiring both FM and LM connections, new coupling constraints are introduced into the PSC model to jointly optimize routing decisions, which ensures the chronological consistency between FM and LM routes. To account for the transfer time uncertainty during UPT, arising from factors such as riders’ walking speed variations and boarding behaviors, an effective robust optimization approach is adopted and the PSC model is reformulated with tractable robust counterparts. To efficiently process real-time travel requests, we develop a Column Generation method embedded within a Rolling Horizon Framework (CG-RHF). To accelerate CG-RHF, a time-based pruning strategy is created to tighten the solution space and a decomposition strategy for pricing subproblems is designed across different UPT hubs in parallel. To validate the computational performance of the proposed CG-RHF, we benchmark it against an exact branch-price-and-cut algorithm and a customized adaptive large neighborhood search metaheuristic. Extensive numerical experiments from Singapore’s metro network demonstrate that the integrated service can efficiently deliver high-quality solutions across a variety of rider travel scenarios.</div></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":"184 ","pages":"Article 105499"},"PeriodicalIF":7.6,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145824270","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":"Resilient multi-agent reinforcement learning for centralised tactical conflict resolution under uncertain perturbations and non-cooperative traffic in urban air mobility","authors":"Rodolphe Fremond ,&nbsp;Yan Xu ,&nbsp;Junjie Zhao ,&nbsp;Antonios Tsourdos ,&nbsp;Gokhan Inalhan","doi":"10.1016/j.trc.2026.105542","DOIUrl":"10.1016/j.trc.2026.105542","url":null,"abstract":"<div><div>This research investigates tactical conflict resolution for Unmanned Aircraft Systems (UAS) and Urban Air Mobility (UAM) operations under degraded conditions and in the presence of non-cooperative UAS/UAM and manned Commercial Air Transportation and General Aviation (CAT/GA) intruders. The study adopts a centralised safety-net approach within UAS Traffic Management (UTM) architectures, envisioning ground-based conflict resolution services. We propose a set of Tactical Conflict Resolution Solvers (TCRS), each built upon a Multi-Agent Reinforcement Learning (MARL) core using a shared-policy transformer architecture and executed in a decentralised manner. To assess resilience of TCRS variants, we introduce domain-specific perturbations, including positioning noise, communication loss, and sensor-related defects. The TCRS operates with partial decision-making ability in non-cooperative traffic environments, while the perturbation model increases realism by simulating varying degrees of information availability. Results show that the perturbation-trained models achieve substantial safety gains compared with the baseline TCRS trained in ideal conditions. The most resilient variant; trained under multi-perturbation exposure and evaluated in non-cooperative environments, achieves a threefold reduction in critical safety violations compared with the baseline and remains robust under mixed cooperative/non-cooperative traffic with static intent. It exhibits a modest vulnerability under fully homogeneous non-cooperative scenarios with dynamic intent. Simulations involving concurrent CAT/GA and UAS operations further indicate that integrating UAS operations within the existing airspace classification remains hazardous for ground-based tactical conflict resolution when constrained by short look-ahead horizons and insufficient time to react.</div></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":"184 ","pages":"Article 105542"},"PeriodicalIF":7.6,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146078071","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":"Distributionally robust optimization of sailing speed, bunkering, and fuel switching for dual-fuel liner services","authors":"Ping He ,&nbsp;Lingxiao Wu ,&nbsp;Jian Gang Jin ,&nbsp;Shaorui Zhou ,&nbsp;Frederik Schulte","doi":"10.1016/j.trc.2026.105528","DOIUrl":"10.1016/j.trc.2026.105528","url":null,"abstract":"<div><div>To reduce CO₂ and SO₂ emissions, shipping companies have started deploying LNG or methanol dual-fuel ships on liner services. Unlike traditional container ships, these dual-fuel ships can use multiple types of fuels during a voyage, allowing them to comply with emission regulations while reducing operational costs through fuel switching and speed optimization. Given the significant fluctuations in bunker prices across different ports, decisions regarding fuel switching, refueling, and sailing speeds must account for price uncertainty. We develop a distributionally robust chance-constrained programming model based on the Wasserstein uncertainty set to minimize operating costs under this uncertainty. We divide each port-to-port sailing leg into sub-legs, considering regional emission requirements or canal segments. This segmentation enables the optimization of fuel usage proportions, sailing speeds, and refueling strategies for each sub-leg. The model is then reformulated as a tractable mixed-integer second-order conic programming model. We validate the model using real-world data from COSCO Shipping. Numerical experiments demonstrate that the model can identify optimal solutions for real-scale instances within practical computational time. Furthermore, the robust solutions significantly outperform those obtained using the traditional sample average approximation method. Our results suggest that the joint optimization of fuel management and sailing speeds for dual-fuel ships can effectively reduce operating costs without increasing emissions.</div></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":"184 ","pages":"Article 105528"},"PeriodicalIF":7.6,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145978354","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":"Modeling proactive avoidance behaviors in pedestrian flows considering congestion anticipation","authors":"Pei-Yang Wu ,&nbsp;Ying-En Ge ,&nbsp;Zhuanglin Ma ,&nbsp;Ren-Yong Guo","doi":"10.1016/j.trc.2026.105532","DOIUrl":"10.1016/j.trc.2026.105532","url":null,"abstract":"<div><div>This paper investigates proactive avoidance behaviors in pedestrian flows by means of real-world experiments and a potential field model. Typical movement patterns of pedestrians related to the proactive avoidance behaviors are provided. Based on the observed behaviors, a potential field model is proposed to combine tactical-level and operational-level modeling frameworks. This model allows pedestrians to select and move toward temporary destinations instead of moving directly to their final destinations. Pedestrian movements are guided by the potential values associated with different positions in the focused space. Three types of sub-potentials are involved to reflect the effects of route attributes, spatiotemporal congestion anticipation, and potential conflicts on pedestrian movements. Numerical experiments demonstrate that the proposed model can effectively reproduce pedestrians’ proactive avoidance behaviors. The model is validated by comparing the simulation results with existing experimental data and our own experimental data. This investigation provides an alternative explanation for pedestrian movement mechanisms.</div></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":"184 ","pages":"Article 105532"},"PeriodicalIF":7.6,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146014571","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 graph vertex-coloring-based parallel block coordinate descent method for solving the traffic assignment problem","authors":"Kai Zhang ,&nbsp;Zhiyuan Liu ,&nbsp;Honggang Zhang ,&nbsp;Yicheng Zhang ,&nbsp;Yuk Ming Tang ,&nbsp;Xiaowen Fu","doi":"10.1016/j.trc.2025.105439","DOIUrl":"10.1016/j.trc.2025.105439","url":null,"abstract":"<div><div>Traffic assignment is an essential component of the traditional four-step transportation planning methodology and significantly contributes to the prediction of traffic flow distribution and optimization of traffic planning. Existing algorithms for solving the user equilibrium traffic assignment problem typically rely on equal intervals and random sampling strategies to divide a set of origin–destination (OD) pairs. However, these sampling strategies fail to address the path overlap issue among OD pairs and often depend on sensitivity analyses to partition the OD set, hindering the efficiency of task parallelism. To address this challenge, the OD grouping problem was formulated as a vertex-coloring problem, which was translated into an integer linear programming (ILP) model. The largest degree first algorithm was proposed to solve the OD grouping problem, enabling the identification of OD pairs within each block with minimal path overlap. Thereafter, the results of the OD grouping based on vertex coloring were incorporated into the parallel block coordinate descent (PBCD) method, increasing the number of OD subproblems within each block and enhancing the parallel computation. An adaptive algorithm is further proposed to address the OD-based restricted subproblem depending on the number of paths for a given OD pair. The proposed method is evaluated based on various large-scale transportation networks and compared with existing algorithms, demonstrating its effectiveness in reducing path overlap within blocks and improving the efficiency of solving traffic assignment problems in large-scale networks.</div></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":"183 ","pages":"Article 105439"},"PeriodicalIF":7.6,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145553973","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":"On-demand DAR system considering traffic dynamics and network partitioning","authors":"Cong Quoc Tran,&nbsp;Shang Jiang,&nbsp;Mehdi Keyvan-Ekbatani","doi":"10.1016/j.trc.2025.105493","DOIUrl":"10.1016/j.trc.2025.105493","url":null,"abstract":"<div><div>On-demand shared mobility systems (e.g., pooled ride-hailing, dial-a-ride, and carpooling) could enhance urban mobility by providing more sustainable door-to-door transportation options, connecting communities to public transport hubs and combining the advantages of public transportation and private services. Among these, dial-a-ride (DAR) systems represent a classical form of shared mobility; however, most DAR models traditionally rely on traffic-independent travel times. The challenges lie in the complexity of incorporating traffic conditions and stakeholders’ involvement in the decision-making process, particularly under dynamic traffic settings. This study proposes a congestion-aware DAR framework to maximize the system’s profit while considering the dynamic nature of traffic conditions, real-time passenger requests and their sharing preferences. The proposed framework is able to capture the heterogeneity of traffic patterns by incorporating a regional dynamic traffic assignment (DTA) with a novel network partitioning strategy. Solution algorithms (i.e., quasi-dynamic partition selection, MSA-based algorithm for the regional DTA problem and dynamic on-demand DAR algorithm) are developed and numerically tested using the central business district network of Christchurch City, New Zealand, considering specific traffic scenarios. Numerical tests and comparative results demonstrate the practical applicability and operational efficiency (i.e., utilization rates of vehicles and rejection rates of customers’ requests) of the regional-based dynamic on-demand DAR system compared to the traditional dynamic DAR approach. Managerial insights and potential research directions are also provided.</div></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":"183 ","pages":"Article 105493"},"PeriodicalIF":7.6,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145731236","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":"VLM-MPC: Model predictive controller augmented vision language model for autonomous driving","authors":"Keke Long ,&nbsp;Haotian Shi ,&nbsp;Jiaxi Liu ,&nbsp;Chaowei Xiao ,&nbsp;Xiaopeng Li","doi":"10.1016/j.trc.2025.105487","DOIUrl":"10.1016/j.trc.2025.105487","url":null,"abstract":"<div><div>Motivated by the emergent reasoning capabilities of Vision Language Models (VLMs) and their potential to improve the comprehensibility of autonomous driving systems, this paper introduces a closed-loop autonomous driving controller called VLM-MPC, which combines a VLM with Model Predictive Controller (MPC) to evaluate how model-based control could enhance VLM decision-making. The proposed VLM-MPC is structured into two asynchronous components: The upper level VLM generates driving parameters (e.g., desired speed, desired headway) for lower-level control based on front camera images, ego vehicle state, traffic environment conditions, and reference memory; The lower-level MPC controls the vehicle in real-time using these parameters, considering engine lag and providing state feedback to the entire system. Experiments based on the nuScenes dataset and Carla simulation validated the effectiveness of the proposed VLM-MPC across various environments (e.g., night, rain, fog, and intersections). The results demonstrate that the VLM-MPC consistently maintains Post Encroachment Time (PET) above safe thresholds, in contrast to some scenarios where the VLM-based control posed collision risks. Additionally, the VLM-MPC enhances smoothness compared to the real-world trajectories and VLM-based control. By comparing behaviors under different environmental settings, we highlight the VLM-MPC’s capability to understand the environment and make reasoned inferences. Moreover, we validate the contributions of two key components, the reference memory and the environment encoder, to the stability of responses through ablation tests.</div></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":"183 ","pages":"Article 105487"},"PeriodicalIF":7.6,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145689760","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":"V2X-VLM: End-to-End V2X cooperative autonomous driving through large vision-Language models","authors":"Junwei You ,&nbsp;Zhuoyu Jiang ,&nbsp;Zilin Huang ,&nbsp;Haotian Shi ,&nbsp;Rui Gan ,&nbsp;Keshu Wu ,&nbsp;Xi Cheng ,&nbsp;Xiaopeng Li ,&nbsp;Bin Ran","doi":"10.1016/j.trc.2025.105457","DOIUrl":"10.1016/j.trc.2025.105457","url":null,"abstract":"<div><div>Vehicle-to-everything (V2X) cooperation has emerged as a promising paradigm to overcome the perception limitations of classical autonomous driving by leveraging information from both ego-vehicle and infrastructure sensors. However, effectively fusing heterogeneous visual and semantic information while ensuring robust trajectory planning remains a significant challenge. This paper introduces V2X-VLM, a novel end-to-end (E2E) cooperative autonomous driving framework based on vision-language models (VLMs). V2X-VLM integrates multiperspective camera views from vehicles and infrastructure with text-based scene descriptions to enable a more comprehensive understanding of driving environments. Specifically, we propose a contrastive learning-based mechanism to reinforce the alignment of heterogeneous visual and textual characteristics, which enhances the semantic understanding of complex driving scenarios, and employ a knowledge distillation strategy to stabilize training. Experiments on a large real-world dataset demonstrate that V2X-VLM achieves state-of-the-art trajectory planning accuracy, significantly reducing L2 error and collision rate compared to existing cooperative autonomous driving baselines. Ablation studies validate the contributions of each component. Moreover, the evaluation of robustness and efficiency highlights the practicality of V2X-VLM for real-world deployment to enhance overall autonomous driving safety and decision-making.</div></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":"183 ","pages":"Article 105457"},"PeriodicalIF":7.6,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145651056","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}