Journal of Intelligent Transportation Systems最新文献_第2页

LiDAR-camera fusion: dual-scale correction for vehicle multi-object detection and trajectory extraction 激光雷达-相机融合：车辆多目标检测和轨迹提取的双尺度校正

IF 2.8 3区工程技术

Journal of Intelligent Transportation Systems Pub Date : 2026-01-02 Epub Date: 2024-10-11 DOI: 10.1080/15472450.2024.2416164

Ting Fu , Shuke Xie , Weichao Hu , Junhua Wang , Zixuan Cui

{"title":"LiDAR-camera fusion: dual-scale correction for vehicle multi-object detection and trajectory extraction","authors":"Ting Fu , Shuke Xie , Weichao Hu , Junhua Wang , Zixuan Cui","doi":"10.1080/15472450.2024.2416164","DOIUrl":"10.1080/15472450.2024.2416164","url":null,"abstract":"<div><div>The different principles of sensor technology determine their distinct performance in vehicle detection and microscopic tracking. Vision-based sensors can provide rich semantic information but lack reliable spatial information, and their reliability is reduced in complex lighting conditions. On the other hand, LiDAR can offer high-precision spatial information independent of lighting conditions, but it suffers from low resolution and effective sampling rate limitations. Considering the strong complementarity between images and point clouds, efficient object detection can be achieved by leveraging their synergy. However, existing research has not fully explored the correlation between the features of these two types of data. This paper proposes a novel dual-scale correction strategy for feature-level fusion of camera and LiDAR data. This strategy captures spatial features of point clouds and semantic features of images at both global and local scales and establishes mapping relationships separately. The global correction results are iteratively updated based on the results of local precision correction. To validate the effectiveness of the proposed method, data is collected from highway and urban expressway scenarios. The results indicate improvements in both object detection and microscopic trajectory tracking performance compared to using single sensors alone. Furthermore, the fusion approach outperforms other methods in terms of detection accuracy and processing time. This research offers a method for real-time and accurate extraction of vehicle trajectories using roadside cameras and LiDAR devices, with potential applications in real-time trajectory tracking and traffic management.</div></div>","PeriodicalId":54792,"journal":{"name":"Journal of Intelligent Transportation Systems","volume":"30 1","pages":"Pages 55-68"},"PeriodicalIF":2.8,"publicationDate":"2026-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147855400","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A traffic incident detection method of highway based on spatial-temporal characteristics 基于时空特征的高速公路交通事件检测方法

IF 2.8 3区工程技术

Journal of Intelligent Transportation Systems Pub Date : 2026-01-02 Epub Date: 2024-10-16 DOI: 10.1080/15472450.2024.2416532

Jiali Peng , Wei Shangguan , Cong Peng , Linguo Chai , Rui Luo

{"title":"A traffic incident detection method of highway based on spatial-temporal characteristics","authors":"Jiali Peng , Wei Shangguan , Cong Peng , Linguo Chai , Rui Luo","doi":"10.1080/15472450.2024.2416532","DOIUrl":"10.1080/15472450.2024.2416532","url":null,"abstract":"<div><div>In this study, we propose a method for detecting traffic incidents by leveraging tensor decomposition in conjunction with spatial-temporal constraints. The method comprises three main stages. Firstly, we develop an initialization and noise reduction procedure for the traffic data pre-processing model, which includes Tucker decomposition and truncating higher-order Singular Value Decomposition (SVD). Subsequently, we construct spatial and temporal coefficient matrices based on the Pearson test and introduce a Laplace penalty term to quantify the disparity between predicted and actual data. Next, we validate the method using traffic loop detector data and incident records from I90 in Seattle, USA, in 2015, comparing its performance with seven other traffic prediction methods. The results demonstrate the superior prediction accuracy, correct detection rate, and low false alarm rate of our traffic incident detection method. Specifically, the mean square error of speed prediction is <span><math><mrow><mn>5.22</mn><mtext>km</mtext><mo>/</mo><mi>h</mi></mrow><mo>,</mo></math></span> the average relative error is <span><math><mrow><mn>6.88</mn><mi>%</mi></mrow><mo>,</mo></math></span> and the detection rate reaches <span><math><mrow><mn>98.92</mn><mi>%</mi></mrow><mo>.</mo></math></span> Our method effectively evaluates the spatial-temporal characteristics of traffic data, enabling accurate prediction and detection of traffic incidents. Its technical applicability holds promise for enhancing the capacity and efficiency of future traffic control systems.</div></div>","PeriodicalId":54792,"journal":{"name":"Journal of Intelligent Transportation Systems","volume":"30 1","pages":"Pages 92-107"},"PeriodicalIF":2.8,"publicationDate":"2026-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147855402","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Scene adaptation in adverse conditions: a multi-sensor fusion framework for roadside traffic perception 不利条件下的场景适应：用于路边交通感知的多传感器融合框架

IF 2.8 3区工程技术

Journal of Intelligent Transportation Systems Pub Date : 2025-11-02 Epub Date: 2024-08-12 DOI: 10.1080/15472450.2024.2390844

Kong Li , Zhe Dai , Chen Zuo , Xuan Wang , Hua Cui , Huansheng Song , Mengying Cui

{"title":"Scene adaptation in adverse conditions: a multi-sensor fusion framework for roadside traffic perception","authors":"Kong Li , Zhe Dai , Chen Zuo , Xuan Wang , Hua Cui , Huansheng Song , Mengying Cui","doi":"10.1080/15472450.2024.2390844","DOIUrl":"10.1080/15472450.2024.2390844","url":null,"abstract":"<div><div>Robust roadside traffic perception requires integrating the strengths of multi-source sensors under various adverse conditions, which is challenging but indispensable for formulating effective traffic management strategies. One limitation of existing radar-camera perception systems is that they focus on integrating multi-source information without directly considering scene information, leading to difficulties in achieving scene adaptive fusion. How to establish the connection between scene information and multi-source information is the key challenge to solving this problem. In this article, we propose a Scene adaptive Sensor Fusion (SSF) framework that characterizes scene information and integrates it into radar-camera fusion schemes, aiming to achieve high-quality roadside traffic perception. Specifically, we introduce a multi-source object association method that accurately associates multi-source sensor information on the roadside. We then utilize coding techniques to characterize the scene information, including visibility characterization regarding lighting and weather conditions, and road characterization regarding sensor viewpoint. By incorporating sensor and scene information into the fusion model, the SSF framework effectively establishes the connection between them. We evaluate the SSF framework on the Roadside Radar and Video Dataset (RRVD) and the Traffic flow Parameter Estimation Dataset (TPED), both collected from real-world traffic scenarios. Experiments demonstrate that SSF significantly improves vehicle detection accuracy under various adverse conditions compared to traditional single-source sensing methods and other state-of-the-art fusion techniques. Furthermore, vehicle trajectories based on SSF detection results enable accurate traffic parameter estimation, such as volume, speed, and density, in complex and dynamic environments.</div></div>","PeriodicalId":54792,"journal":{"name":"Journal of Intelligent Transportation Systems","volume":"29 6","pages":"Pages 698-718"},"PeriodicalIF":2.8,"publicationDate":"2025-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142179723","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Transfer learning for cross-modal demand prediction of bike-share and public transit 共享单车和公共交通跨模式需求预测的迁移学习

IF 2.8 3区工程技术

Journal of Intelligent Transportation Systems Pub Date : 2025-11-02 Epub Date: 2024-06-24 DOI: 10.1080/15472450.2024.2371913

Mingzhuang Hua , Francisco Camara Pereira , Yu Jiang , Xuewu Chen , Junyi Chen

{"title":"Transfer learning for cross-modal demand prediction of bike-share and public transit","authors":"Mingzhuang Hua , Francisco Camara Pereira , Yu Jiang , Xuewu Chen , Junyi Chen","doi":"10.1080/15472450.2024.2371913","DOIUrl":"10.1080/15472450.2024.2371913","url":null,"abstract":"<div><div>The urban transportation system is a combination of multiple transport modes, and the interdependencies across those modes exist. This means that the travel demand across different travel modes could be correlated as one mode may receive/create demand from/for another mode, not to mention natural correlations between different demand time series due to general demand flow patterns across the network. It is expected that cross-modal ripple effects will become more prevalent with Mobility as a Service. Therefore, by propagating demand data across modes, a better demand prediction could be obtained. To this end, this study explores various transfer learning strategies and machine learning models for cross-modal demand prediction. The trip data of bike-share, metro, and taxi are processed as the station-level passenger flows, and then the proposed prediction method is tested in the large-scale case studies of Nanjing and Chicago. The results suggest that prediction models with transfer learning perform better than unimodal prediction models. Fine-tuning without freezing strategy performs the best among all transfer learning strategies, and the split-brain strategy can handle the data missing problem. Furthermore, the 3-layer stacked Long Short-Term Memory model performs particularly well in cross-modal demand prediction. These results verify our deep transfer learning method’s forecasting improvement over existing benchmarks and demonstrate the good transferability for cross-modal demand prediction in multiple cities.</div></div>","PeriodicalId":54792,"journal":{"name":"Journal of Intelligent Transportation Systems","volume":"29 6","pages":"Pages 640-653"},"PeriodicalIF":2.8,"publicationDate":"2025-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141613115","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A reinforcement learning based autonomous vehicle control in diverse daytime and weather scenarios 基于强化学习的自主车辆控制，适用于不同的白天和天气情况

IF 2.8 3区工程技术

Journal of Intelligent Transportation Systems Pub Date : 2025-11-02 Epub Date: 2024-06-21 DOI: 10.1080/15472450.2024.2370010

Badr Ben Elallid , Miloud Bagaa , Nabil Benamar , Nabil Mrani

{"title":"A reinforcement learning based autonomous vehicle control in diverse daytime and weather scenarios","authors":"Badr Ben Elallid , Miloud Bagaa , Nabil Benamar , Nabil Mrani","doi":"10.1080/15472450.2024.2370010","DOIUrl":"10.1080/15472450.2024.2370010","url":null,"abstract":"<div><div>Autonomous driving holds significant promise for substantially reducing road fatalities. Unlike traditional machine learning methods that have conventionally been applied to enhance the motion control of Autonomous Vehicles (AVs), recent attention has shifted toward the utilization of Deep Learning (DL) and Deep Reinforcement Learning (DRL) techniques. These advanced approaches have the potential to greatly improve AV vehicle control and empower vehicles to learn from their surroundings. However, the majority of existing research has concentrated on straightforward scenarios, often neglecting the intricate challenges posed by vulnerable road users such as pedestrians, cyclists, and motorcyclists, as well as the influence of varying weather conditions. In this study, we propose a novel model founded on DRL, specifically leveraging Deep-Q Networks (DQN), to effectively manage AVs in complex scenarios characterized by heavy traffic, diverse road users, and diverse weather conditions. Our approach involves training the model in diverse weather conditions, encompassing clear daytime and nighttime as well as challenging weather conditions like heavy rainfall during both the day and sunset. Through this comprehensive training, the AV becomes proficient in navigating safely through intersections and reaching its destination without any accidents. To rigorously evaluate and validate our proposed approach, extensive testing was conducted employing the CARLA simulator. The simulation results unequivocally demonstrate that our model not only reduces travel delays but also minimizes the occurrence of collisions, marking a significant step forward in achieving safer and more efficient autonomous driving.</div></div>","PeriodicalId":54792,"journal":{"name":"Journal of Intelligent Transportation Systems","volume":"29 6","pages":"Pages 626-639"},"PeriodicalIF":2.8,"publicationDate":"2025-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141737117","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Distributed coordinated control of mixed expressway and urban roads: a sensitivity-based approach 混合快速路和城市道路的分布式协调控制：基于敏感性的方法

IF 2.8 3区工程技术

Journal of Intelligent Transportation Systems Pub Date : 2025-11-02 Epub Date: 2024-04-22 DOI: 10.1080/15472450.2024.2345348

Ben Zhai , Bing Wu , Yanli Wang , Bin Ran

{"title":"Distributed coordinated control of mixed expressway and urban roads: a sensitivity-based approach","authors":"Ben Zhai , Bing Wu , Yanli Wang , Bin Ran","doi":"10.1080/15472450.2024.2345348","DOIUrl":"10.1080/15472450.2024.2345348","url":null,"abstract":"<div><div>The inherent interdependency between mixed expressway and urban road networks calls for an effective coordinated traffic control approach. This study introduces a sensitivity-based distributed method to coordinate ramp metering and traffic signal control. Firstly, the quantitative relationships between network nodes are established through a developed traffic flow model for mixed networks. The network-level sensitivity information is analyzed to assess the influence of controllers on the traffic state. Additionally, a dynamic subproblems decomposition algorithm is proposed, considering the network’s spatial structure and dynamic traffic characteristics. By employing community detection techniques, tightly connected controllers are clustered to enable efficient and adaptive distributed control. The optimization problem is then decomposed into several subproblems, each formulated as a bi-level programming problem within a model predictive control framework. Numerical experiments demonstrate that the proposed strategy enhances the traffic efficiency of mixed networks with a 14.5% reduction in total travel time during peak hours, which shows better performance compared with the other three existing control strategies.</div></div>","PeriodicalId":54792,"journal":{"name":"Journal of Intelligent Transportation Systems","volume":"29 6","pages":"Pages 579-592"},"PeriodicalIF":2.8,"publicationDate":"2025-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141023115","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Performance evaluation of path choice set generation algorithms for route choice modelling 用于路径选择建模的路径选择集生成算法性能评估

IF 2.8 3区工程技术

Journal of Intelligent Transportation Systems Pub Date : 2025-11-02 Epub Date: 2024-07-05 DOI: 10.1080/15472450.2024.2373866

Raghav Malhotra , Chintan Advani , Ashish Bhaskar

引用次数: 0

A platoon formation algorithm for intersections with blue phase control in mixed traffic 混合交通中采用蓝色相位控制的交叉路口的排队编队算法

IF 2.8 3区工程技术

Journal of Intelligent Transportation Systems Pub Date : 2025-11-02 Epub Date: 2024-05-08 DOI: 10.1080/15472450.2024.2352390

Ruochen Hao , Sa Gao , Xinwei Wang , Wanjing Ma , Bart van Arem , Meng Wang

{"title":"A platoon formation algorithm for intersections with blue phase control in mixed traffic","authors":"Ruochen Hao , Sa Gao , Xinwei Wang , Wanjing Ma , Bart van Arem , Meng Wang","doi":"10.1080/15472450.2024.2352390","DOIUrl":"10.1080/15472450.2024.2352390","url":null,"abstract":"<div><div>Increasing attention is being paid to intersection signal control with cooperative platoons. Assuming platoons being formed, such platoons cannot only improve the intersection capacity but also minimize the number of control units, especially when dedicated connected and automated vehicle (CAV) lanes are considered. However, the platoon formation process is often neglected, especially for lane-changing and overtaking maneuvers in mixed traffic. This may jeopardize the potential of signal control with platoons. This article proposes a platoon formation algorithm that computes the optimal lane, platoon sequence, and speed profiles of CAVs under the requirement of the central traffic controller. The algorithm is designed for mixed traffic conditions and hence the performance of human-driven vehicles is also considered. A mixed integer linear program model is formulated to minimize the deviation from the desired platoon configuration and the disturbance to overall traffic under any arbitrary initial condition. Numerical experiments are designed to test the effectiveness and the computational performance of the proposed algorithm. Results show that CAVs with signal control can form platoons with rational motion. Besides, the platoon penetration significantly affects platooning feasibility, while the platoon length does not. This suggests that CAVs can form long platoons at intersections to improve traffic throughput.</div></div>","PeriodicalId":54792,"journal":{"name":"Journal of Intelligent Transportation Systems","volume":"29 6","pages":"Pages 593-611"},"PeriodicalIF":2.8,"publicationDate":"2025-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141367146","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

One-stage estimation of cyclic arrival rates using license plate recognition data 基于车牌识别数据的循环到达率单阶段估计

IF 2.8 3区工程技术

Journal of Intelligent Transportation Systems Pub Date : 2025-11-02 Epub Date: 2024-08-14 DOI: 10.1080/15472450.2024.2392720

Chengchuan An , Yang He , Jiawei Lu , Zhenbo Lu , Jingxin Xia

{"title":"One-stage estimation of cyclic arrival rates using license plate recognition data","authors":"Chengchuan An , Yang He , Jiawei Lu , Zhenbo Lu , Jingxin Xia","doi":"10.1080/15472450.2024.2392720","DOIUrl":"10.1080/15472450.2024.2392720","url":null,"abstract":"<div><div>Traffic arrival on urban roads usually reveals time-varying rates due to the effects of signal control, motivating us to investigate the distribution of arrival rates within the signal cycle. However, such information is not readily available as only a portion of arrival traffic can be observed in the field. Previous studies have utilized different data sources and proposed probability-based models to solve the estimation problem. However, these studies rely heavily on the assumption that the observed vehicles are evenly distributed in the arrival traffic flow and exact signal timings are known. This study uses license plate recognition (LPR) data collected at adjacent signalized intersections to estimate cyclic arrival rates in a historical period. A probability-based model is formulated by parameterizing the vehicle arrival distribution as a mixture of circular distributions. This facilitates us to describe similar traffic arrival patterns over different signal cycles and capture multiple arrival platoons from different signal phases within the signal cycle. The proposed model is validated in a field experiment with different arrival patterns and traffic conditions. Model performance under both full and partial monitoring of LPR sensors at the upstream intersection is analyzed. The robustness of the proposed model with unevenly sampled observations in arrival traffic flow is also investigated.</div></div>","PeriodicalId":54792,"journal":{"name":"Journal of Intelligent Transportation Systems","volume":"29 6","pages":"Pages 719-732"},"PeriodicalIF":2.8,"publicationDate":"2025-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145475639","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Integrating vehicle trajectory planning and arterial traffic management to facilitate eco-approach and departure deployment 整合车辆轨迹规划和干道交通管理，促进生态进场和离场部署

IF 2.8 3区工程技术

Journal of Intelligent Transportation Systems Pub Date : 2025-11-02 Epub Date: 2024-06-17 DOI: 10.1080/15472450.2024.2369988

Hao Liu , Alex A. Kurzhanskiy , Wanshi Hong , Xiao-Yun Lu

{"title":"Integrating vehicle trajectory planning and arterial traffic management to facilitate eco-approach and departure deployment","authors":"Hao Liu , Alex A. Kurzhanskiy , Wanshi Hong , Xiao-Yun Lu","doi":"10.1080/15472450.2024.2369988","DOIUrl":"10.1080/15472450.2024.2369988","url":null,"abstract":"<div><div>Eco-approach and departure (EAD) enable continuous vehicle motion in urban signalized corridors. Since such a motion can extend to the EAD vehicles’ followers, it makes EAD a promising technology to benefit the traffic flow where automated vehicles and conventional vehicles coexist. Most existing EAD studies envision an ideal setting that neglects real-world operational conditions such as lane changes, multi-movement intersection configuration, partially automated fleet, and/or limited traffic state awareness. This study aims to fill the gap by designing an EAD algorithm considering real-world traffic operation constraints. The proposed algorithm uses a model predictive controller to minimize vehicle speed reduction and variation based on the real-time traffic signal control plan and measured queues at the intersection. The required inputs are readily available at many modern intersections. We observed that the proposed controller’s performance might degrade because of lane-changing maneuvers and lead-left turn traffic signals. These observations motivated our development of a lane change management strategy and a signal control implementation strategy to facilitate the EAD implementation. The lane change management strategies separate the EAD operations and lane-changing maneuvers in time and space. The signal control implementation strategy applies lag-left turn signals to enable EAD operation for both the through and left-turn vehicles. Compared to the non-EAD case, our EAD approach produces 2.5% to 7.8% energy savings while keeping similar intersection mobility. Notably, this approach brings about 2.5% to 3.6% energy savings in a 2% CAV case. This result demonstrates the feasibility of deploying EAD at low connected automated vehicle penetration rates.</div></div>","PeriodicalId":54792,"journal":{"name":"Journal of Intelligent Transportation Systems","volume":"29 6","pages":"Pages 612-625"},"PeriodicalIF":2.8,"publicationDate":"2025-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141511945","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0