Transportation Research Part C-Emerging Technologies最新文献

{"title":"Congestion pricing in multi-modal networks: An application of deep reinforcement learning","authors":"Nasser Parishad,&nbsp;Mehmet Yildirimoglu,&nbsp;Mark Hickman","doi":"10.1016/j.trc.2025.105166","DOIUrl":"10.1016/j.trc.2025.105166","url":null,"abstract":"<div><div>Developing a real-time dynamic pricing mechanism that proactively generates toll profiles while incorporating demand elasticity and travellers’ heterogeneity remains a significant challenge. Many existing approaches suffer from low transferability and rely heavily on precise estimation of network parameters such as critical accumulation. This study introduces a data-driven, cordon-based pricing framework using reinforcement learning to optimise traffic flow and address these limitations. A multi-modal, trip-based Macroscopic Fundamental Diagram (MFD) simulation has been developed, capable of capturing individual mode choice decisions. Traveller heterogeneity is addressed through variations in origin and destination, trip length, departure time, and value of time (VoT). To establish a tolling strategy that maximises network outflow (minimises total travel time) and proactively addresses traffic congestion, a Double Deep Q-Network (DDQN) agent has been introduced. Remarkably, without prior knowledge of network parameters, the agent successfully regulates car accumulation at critical levels to maximise network outflow. Sensitivity analysis reveals that even with a 20% margin of error in input data, the agent remains effective in mitigating congestion. Additionally, the agent’s transferability has been evaluated under various traffic conditions and dynamics by introducing different demand profiles and MFD coefficients, demonstrating robust performance. Benchmark comparisons with a feedback controller across all scenarios further confirm that the DDQN agent consistently outperforms.</div></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":"177 ","pages":"Article 105166"},"PeriodicalIF":7.6,"publicationDate":"2025-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144147321","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":"From strategic to tactical carriers’ selection: A new SDDP algorithm to handle dynamic stochastic demand","authors":"Simon Schmiedel,&nbsp;Rania Boujemaa,&nbsp;Monia Rekik,&nbsp;Adnène Hajji","doi":"10.1016/j.trc.2025.105174","DOIUrl":"10.1016/j.trc.2025.105174","url":null,"abstract":"<div><div>This paper addresses a Carrier’s Selection and Shipment Assignment Problem (CSSAP) in a distribution network where a set of products need to be shipped from warehouses to distribution centers to satisfy the demand at each distribution center at each period of a tactical planning horizon. The demand at distribution centers is uncertain and back-ordering is permitted but penalized. Shipments are ensured by external carriers either strategic or spot ones. Strategic carriers are long-term contracts carriers with commitments to respect when solving the CSSAP. The problem is formulated as a multi-stage dynamic stochastic model. New variants of the Stochastic Dual Dynamic Programming (SDDP) algorithm are proposed to solve it. They consider novel cut removal techniques and new stopping criterion inspired by the concept of regret from the field of reinforcement learning. The concept of regret additionally enables evaluating the quality of the SDDP decisions, rarely addressed in the literature. We carried out experiments and evaluated our results against other cut removal strategies and stopping criteria reported in the literature. Our results first show that the SDDP algorithm is a good approach to solve the CSSAP under different contexts yielding good-quality solutions in a reasonable time. Second, some of the new variants we propose outperform existing ones and this is mostly due to the new techniques we propose to remove what we call the detrimental cuts. The new SDDP variants can be easily adapted to be used for any other problem to which a standard SDDP algorithm may apply.</div></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":"177 ","pages":"Article 105174"},"PeriodicalIF":7.6,"publicationDate":"2025-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144138670","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 platoon-centric approach to the capacity analysis of mixed traffic comprising connected and autonomous vehicles","authors":"Peilin Zhao,&nbsp;Yiik Diew Wong,&nbsp;Feng Zhu","doi":"10.1016/j.trc.2025.105170","DOIUrl":"10.1016/j.trc.2025.105170","url":null,"abstract":"<div><div>The study of mixed traffic capacity, involving both Connected and Autonomous Vehicles (CAVs) and Human-driven Vehicles (HVs), remains a critical area of research. Traditional models have typically focused on individual vehicles, while this research shifts the focus to platoons as the fundamental units of analysis to better capture the platooning characteristics of CAVs. Specifically, we introduce a new metric, the Inter-Platoon Platooning Intensity (IPI), to facilitate the analysis of mixed traffic capacity. Through both mathematical and numerical investigations, we evaluate the impact of the proposed IPI and Maximum Platoon Size (MPS) on mixed traffic dynamics. Our findings indicate that: (1) the IPI effectively measures the clustering of CAVs in a single-lane mixed traffic environment; (2) the calculated mixed traffic capacity closely matches the actual traffic capacity, showing only minor deviations; (3) the marginal analysis demonstrates the conditions under which mixed traffic capacity correlates monotonically with either MPS or IPI; and (4) an optimal MPS is determined that maximizes mixed traffic capacity. These insights contribute significantly to the existing literature on the effects of platoon size and platooning intensity on mixed traffic flow.</div></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":"177 ","pages":"Article 105170"},"PeriodicalIF":7.6,"publicationDate":"2025-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144133995","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":"Joint optimization of vehicle platoon and traffic signal with mixed traffic flow at intersections: Deep reinforcement learning approach","authors":"Xin Huang ,&nbsp;Penghao Jing ,&nbsp;Yongfu Li ,&nbsp;Xiaoyang Wang ,&nbsp;Yibing Wang","doi":"10.1016/j.trc.2025.105184","DOIUrl":"10.1016/j.trc.2025.105184","url":null,"abstract":"<div><div>Vehicle formation and the management of right-of-way conflicts at intersections can realize both the one-dimensional and two-dimensional benefits of connected and automated vehicles (CAVs). Although researchers have proposed joint optimization of vehicle trajectories and traffic signals in fully connected environments to exploit these benefits, the advantages of CAVs still need to be examined in mixed traffic scenarios. To fully realize the benefits of CAVs at intersections with mixed traffic flow, this study proposes an innovative approach known as the mixed platoon and intersection coordination strategy. Specifically, the mixed platoon and intersection coordination strategy refers to constructing a formation area and a speed planning area at the intersection, achieving mixed multi-vehicle formation and right-of-way conflict resolution. However, the mixed traffic consisting of CAV and HDV is a partially controllable system. Therefore, we propose a bi-level control framework where the upper-level determines the right-of-way for traffic demands from different directions while the lower-level implements speed planning for CAVs. To efficiently assess the available crossing span for vehicles and calculate their acceleration, we propose a mixed traffic resource allocation algorithm in conjunction with a reinforcement learning-based speed planning algorithm. Simulation experiments demonstrate that the proposed strategy can rapidly identify available crossing spans for vehicles and guide them to the intersection in their target states. Compared to traditional intersection control methods, the proposed approach enhances traffic system efficiency, increasing effectiveness as the penetration rate of CAVs rises.</div></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":"177 ","pages":"Article 105184"},"PeriodicalIF":7.6,"publicationDate":"2025-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144124135","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 novel 2D motion planning method for vehicles considering the impact of lane configurations","authors":"Haozhan Ma ,&nbsp;Chen Qian ,&nbsp;Linheng Li ,&nbsp;Huhe manda ,&nbsp;Xu Qu ,&nbsp;Bin Ran","doi":"10.1016/j.trc.2025.105186","DOIUrl":"10.1016/j.trc.2025.105186","url":null,"abstract":"<div><div>Lane changes inherently escalate collision risks, while lane lines mitigate undue cross-lane impacts from lateral perturbations or unsuccessful lane change maneuvers. To quantify these dynamics, this paper introduces an Extended Omnidirectional Risk Indicator (EORI). Building on a novel risk equivalence hypothesis and our previous research, the EORI effectively measures the influence of vehicle relative motion states. A Risk-Quantification based longitudinal planning Model using EORI (ERQM) and an EORI-Based Lane Change model (ELC) are proposed. Unlike conventional models that rely on lane markings to first identify the preceding vehicle, ERQM prioritizes the vehicle presenting the highest risk as the focal object for car-following, allowing it to proactively detect and respond to vehicles that show potential for cutting in. Its mapping relationship between longitudinal steady-state speed and risk offers a novel potential approach for future lane width settings. Besides, ELC dynamically changes the risk search range during the vehicle lane change process, and makes lane change decisions based on EORI. As a model-driven and parameter-free model, ELC enables lane change decisions, duration determination, and trajectory generation. Simulation experiments validate ERQM’s capability to prevent collisions induced by cut-in to a certain extent. Moreover, within the segments selected from the NGSIM dataset, the combination of ERQM and ELC completes lane change with a high success rate, producing more comfortable lane change trajectories. The results demonstrate that EORI effectively represents risk under lane line constraints. The ERQM and ELC models, both based on EORI, adapt well to dynamic multi-participant traffic scenarios, providing a novel model-driven approach for Connected and Automated Vehicles in bidimensional traffic environments.</div></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":"177 ","pages":"Article 105186"},"PeriodicalIF":7.6,"publicationDate":"2025-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144131039","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":"Hybrid-phase-enabled multi-mode-band approach to arterial traffic control in mixed traffic environment with self-organized connected and automated vehicles","authors":"Jinjue Li ,&nbsp;Chunhui Yu ,&nbsp;Wanjing Ma ,&nbsp;Jiaqi Liu","doi":"10.1016/j.trc.2025.105177","DOIUrl":"10.1016/j.trc.2025.105177","url":null,"abstract":"<div><div>With the development of connected and automated vehicle (CAV) technology, mixed traffic with CAVs and regular vehicles (RVs) are expected to persist for a long time in the foreseeable future. Research on mixed traffic control often assumes that CAV trajectories can be fully controlled by traffic controllers or that their trajectory planning strategies are known. However, this assumption may not hold in the near term due to limitations in communication technology or concerns over data privacy. In recent years, several studies have addressed traffic control while considering the uncontrollability of CAVs and the limitations of available CAV information. However, these studies typically focus on isolated intersections or the fully CAV environment. This study introduces a hybrid-phase-enabled multi-mode-band-based (HPMM-based) traffic control for arterials with CAV-dedicated lanes in the mixed traffic environment with CAVs and RVs. In this study, CAVs are not controlled by traffic controllers and conduct trajectory planning themselves, which are called self-organized CAVs. For simplicity, they are referred to as CAVs throughout this paper. There are dedicated lanes for CAVs in each arm at each intersection along the arterial. In the proposed model, left-turn and through CAVs share CAV-dedicated lanes and cross the intersection during the shared phases using the standard NEMA ring barrier structure with RVs or during the CAV-dedicated phase. A two-level hierarchical optimization model is developed, which consists of the arterial and the intersection levels. The arterial level introduces a multi-mode-band model to address the signal coordination challenge for arterials with multiple phases (i.e., shared phases and CAV-dedicated phases) and multiple modes (i.e., CAVs and RVs) and CAV-dedicated lanes in the mixed traffic environment. The model is formulated as a mixed integer linear programming problem to maximize weighted bandwidth for CAVs and RVs. At the intersection level, a three-sub-level model optimizes signal timings based on the estimation of RV queue lengths and prediction of CAV passing states without directly controlling CAV trajectories or assuming prior knowledge of their trajectory planning strategies, and a rolling horizon scheme is designed. Numerical results demonstrate the proposed HPMM control framework outperforms existing methods under distinct scenarios: it reduces average vehicle delay and unnecessary stops compared to max-pressure-blue-phase-based control in under-saturated traffic, and surpasses normal control (which lacks CAV-dedicated lane and phase) when CAV penetration rates exceed 10%.</div></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":"177 ","pages":"Article 105177"},"PeriodicalIF":7.6,"publicationDate":"2025-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144115608","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":"Data-driven planning of large-scale electric vehicle charging hubs using deep reinforcement learning","authors":"Karsten Schroer ,&nbsp;Ramin Ahadi ,&nbsp;Wolfgang Ketter ,&nbsp;Thomas Y. Lee","doi":"10.1016/j.trc.2025.105126","DOIUrl":"10.1016/j.trc.2025.105126","url":null,"abstract":"<div><div>We consider the problem of planning large-scale service systems, specifically electric vehicle (EV) charging hubs (EVCHs). EVCHs are locally concentrated clusters of charging infrastructure, e.g. in large parking lots, and are often integrated with on-site generation, storage and adjacent building infrastructure. Planning such complex operational systems over a multi-year investment horizon represents a high-dimensional, dynamic and stochastic decision problem. Such planning problems typically rely on mathematical optimization frameworks which are subject to computational challenges (e.g., NP-hardness) that can limit scalability to practical system sizes. As a result, simplifying assumptions related to, for example, temporal granularity, operational detail, system size, decision horizon or stochasticity are required to achieve tractability. Modern reinforcement learning (RL) approaches, in combination with fine-grained data-driven simulation frameworks, also known as Digital Twins (DTs), may circumvent these shortcomings. We develop a scalable soft actor-critic (SAC) reinforcement learning method, that learns near-optimal EVCH configurations against a minimum cost objective. Our method uses a highly detailed DT of the EVCH environment that is bootstrapped with unique real-world sensor data from parking lots, charging stations, office buildings, and solar generation facilities, along with microscopic simulations of practical parking and charging policies. In extensive computational experiments, we provide empirical evidence that the proposed SAC RL algorithm converges closely to the global optimum (4%–15% gap) outperforming alternative popular RL approaches such as Deep Q Networks (DQN) and Deep Deterministic Policy Gradients (DDPG). We also demonstrate the superior scalability characteristic of our method to real-world problem sizes of up to 1000 charging spots. Finally, we run scenario analyses that explore the impact of user preferences and operational choices on planning decisions, thus providing actionable and novel policy guidance for EVCH planners and operators.</div></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":"177 ","pages":"Article 105126"},"PeriodicalIF":7.6,"publicationDate":"2025-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144108096","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":"Corrigendum to “The container drayage problem for electric trucks with charging resource constraints” [Transp. Res. Part C 174 (2025) 105100]","authors":"Liyang Xiao ,&nbsp;Luxian Chen ,&nbsp;Peng Sun ,&nbsp;Gilbert Laporte ,&nbsp;Roberto Baldacci","doi":"10.1016/j.trc.2025.105167","DOIUrl":"10.1016/j.trc.2025.105167","url":null,"abstract":"","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":"176 ","pages":"Article 105167"},"PeriodicalIF":7.6,"publicationDate":"2025-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144115203","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":"Multi-agent reinforcement learning with causal communication for ride-sourcing pricing in mixed autonomy mobility","authors":"Ningke Xie ,&nbsp;Yong Chen ,&nbsp;Wei Tang ,&nbsp;Xiqun (Michael) Chen","doi":"10.1016/j.trc.2025.105164","DOIUrl":"10.1016/j.trc.2025.105164","url":null,"abstract":"<div><div>The burgeoning self-driving technology has provided a solid impetus for the ride-sourcing market and new demand and supply management challenges. Under the context of a long-haul mixed operation of autonomous vehicles and human-driven vehicles, this paper focuses on profit-maximizing pricing for both demand and supply sides, in which the prices are differentiated by service type, time, and location. Diverging from most studies limited to centralized control for small-scale problems, we align with distributed and scalable requirements in practice and tackle the coordination challenge from a causal communication perspective. Based on the spatial supply–demand interdependencies inherent in the ride-sourcing market, operation areas are modeled as collaborative intelligent agents. The pricing problem is formulated as a decentralized partially observable Markov game augmented with neighborhood communication. Then a multi-agent reinforcement learning with causal communication method is developed to jointly optimize pricing policy and communication mechanism through end-to-end learning. The bidirectional communication mechanism is ensured to be effective and succinct by maximizing the causal effect of the communication message. Leveraging theoretical analysis, the proposed method is proven to cope with partial observability and non-stationary environments through collaborative communication. Besides, an agent-based simulator for mixed autonomy mobility is established on a real-world large-scale network, emulating the causal communication process among decentralized areas, as well as the heterogeneity, elasticity, and uncertainty of ride-sourcing demand and supply. Two representative scenarios are designed to demonstrate the dynamic evolutions of mixed autonomy mobility: (a) smaller-sized autonomous vehicles and conservative passenger acceptance (conservative stage), and (b) larger-sized autonomous vehicles and liberal passenger acceptance (liberal stage). The results highlight that incorporating the causal communication mechanism can speed up the learning process and guide informed pricing decisions. Furthermore, the proposed method gains managerial insights into proactively regulating pricing schemes for a smooth transformation into fully autonomous ride-sourcing services.</div></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":"176 ","pages":"Article 105164"},"PeriodicalIF":7.6,"publicationDate":"2025-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144070728","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":"Can AV crash datasets provide more insight if missing information is supplemented? Employing Generative Adversarial Imputation Networks to Tackle Data Quality Issues","authors":"Hongliang Ding ,&nbsp;Zhuo Liu ,&nbsp;Hanlong Fu ,&nbsp;Xiaowen Fu ,&nbsp;Tiantian Chen ,&nbsp;Jinhua Zhao","doi":"10.1016/j.trc.2025.105154","DOIUrl":"10.1016/j.trc.2025.105154","url":null,"abstract":"<div><div>The growing prevalence of autonomous vehicles (AVs) offers new opportunities for enhancing traffic efficiency. However, AVs still face significant challenges that impact their safety and effectiveness in preventing accidents. Real-world operational data is therefore essential to identifying the factors contributing to AV crashes. Despite this, the analysis of AV crashes is still hampered by a lack of data, missing information, and underreporting, which negatively impacts its accuracy and comprehensiveness. To address this challenge, a method based on Generative Adversarial Networks (GANs) was used for data imputation, leveraging their advantage in handling heterogeneous data. An evaluation of the performance of our proposed data imputation approach was performed by comparing it with two established methods, namely conventional case deletion and Random Forest (RF) imputation. Synthetic data obtained from these three methods were modelled using the random parameters logit model with heterogeneity in means. Data from the California Department of Motor Vehicles (DMV) and the National Highway Traffic Safety Administration (NHTSA) covering 2021–2023 were used. Our results showed that the model based on Generative Adversarial Imputation Networks (GAIN)- processing data outperformed other candidate methods in terms of fitting, predictive accuracy, and factor interpretation. Our results suggest that factors including speed limit, roadway types, head-on crashes, and takeover of ADAS-equipped vehicles are positively associated with serious injury crashes. On the other hand, ADS engagement and crashes with fixed objects exhibit a negative association with serious injury crashes. Additionally, heterogeneous effects of posted speed limits and ADS engagement on AV crash severity were captured to provide a deeper insight into implications.</div></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":"176 ","pages":"Article 105154"},"PeriodicalIF":7.6,"publicationDate":"2025-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144070729","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}