Pub Date : 2023-11-01DOI: 10.1109/mits.2023.3301877
Qiwei Xie, Kun Shi, Xiao Wu, Wuling Huang, Xiaolong Zheng, Yongjun Li
The transportation industry is considered the foundation and bridge of national economic development, enabling the growth of the social economy. However, it also consumes a considerable amount of energy, resulting in high levels of carbon dioxide (CO2) emissions. As a component of China’s vigorous promotion of energy-saving and emissions-reducing policies in recent years, it is crucial to maximize transportation efficiency without increasing transportation energy consumption and CO2 emissions. In this article, the transportation process in China is divided into two stages while maintaining the same total energy consumption and CO2 emissions. Additionally, the generalized equilibrium efficient frontier data envelopment analysis (GEEFDEA) model is enhanced to achieve this. The improved model extends the single-stage GEEFDEA model to a two-stage process, allowing for a more detailed analysis of the internal dynamics within the transportation system. Furthermore, in this article, the fixed inputs and outputs of the original model are further extended to include fixed undesired outputs, expanding the applicability of the model. This also enables the possibility of energy conservation and emissions reduction while promoting development and enhancing efficiency. Based on the improved model, the transport efficiency, energy consumption adjustment, and CO2 emissions adjustment of 30 provinces in China are measured. Finally, the transportation situation and characteristics of three regions, consisting of 30 provinces, are analyzed, and reasonable suggestions for the development of transportation in each region are presented. Furthermore, this article utilizes spatial econometric methods to analyze the impact factors of transportation economic efficiency and their corresponding spatial spillover effects by taking into consideration the intricate interrelationships among regions. The results indicate several findings. First, there is a significant positive spatial correlation in the transportation economic efficiency among Chinese provinces. Second, an increase in per capita gross domestic product, highway transportation, and the proportion of secondary industry have negative effects on transportation economic efficiency. Moreover, the increase in the proportion of secondary industry is negatively correlated with the efficiency of neighboring provinces. Finally, the improvement of energy-saving technology significantly promotes an increase in transportation economic efficiency.
{"title":"Transportation Efficiency Evaluation Under the Policies of Energy Savings and Emissions Reduction","authors":"Qiwei Xie, Kun Shi, Xiao Wu, Wuling Huang, Xiaolong Zheng, Yongjun Li","doi":"10.1109/mits.2023.3301877","DOIUrl":"https://doi.org/10.1109/mits.2023.3301877","url":null,"abstract":"The transportation industry is considered the foundation and bridge of national economic development, enabling the growth of the social economy. However, it also consumes a considerable amount of energy, resulting in high levels of carbon dioxide (CO2) emissions. As a component of China’s vigorous promotion of energy-saving and emissions-reducing policies in recent years, it is crucial to maximize transportation efficiency without increasing transportation energy consumption and CO2 emissions. In this article, the transportation process in China is divided into two stages while maintaining the same total energy consumption and CO2 emissions. Additionally, the generalized equilibrium efficient frontier data envelopment analysis (GEEFDEA) model is enhanced to achieve this. The improved model extends the single-stage GEEFDEA model to a two-stage process, allowing for a more detailed analysis of the internal dynamics within the transportation system. Furthermore, in this article, the fixed inputs and outputs of the original model are further extended to include fixed undesired outputs, expanding the applicability of the model. This also enables the possibility of energy conservation and emissions reduction while promoting development and enhancing efficiency. Based on the improved model, the transport efficiency, energy consumption adjustment, and CO2 emissions adjustment of 30 provinces in China are measured. Finally, the transportation situation and characteristics of three regions, consisting of 30 provinces, are analyzed, and reasonable suggestions for the development of transportation in each region are presented. Furthermore, this article utilizes spatial econometric methods to analyze the impact factors of transportation economic efficiency and their corresponding spatial spillover effects by taking into consideration the intricate interrelationships among regions. The results indicate several findings. First, there is a significant positive spatial correlation in the transportation economic efficiency among Chinese provinces. Second, an increase in per capita gross domestic product, highway transportation, and the proportion of secondary industry have negative effects on transportation economic efficiency. Moreover, the increase in the proportion of secondary industry is negatively correlated with the efficiency of neighboring provinces. Finally, the improvement of energy-saving technology significantly promotes an increase in transportation economic efficiency.","PeriodicalId":48826,"journal":{"name":"IEEE Intelligent Transportation Systems Magazine","volume":"1 1","pages":"192-211"},"PeriodicalIF":3.6,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"62345958","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}
Pub Date : 2023-11-01DOI: 10.1109/mits.2023.3295376
Yongkui Sun, Yuan Cao, Peng Li, S. Su
Railway point machines are the key equipment that controls the train route and affects the safety of train operation. Complex and harsh working environments lead to frequent failures, accounting for 40% of the total failures of the railway signaling system. Thus, it is an urgent task to present an intelligent fault diagnosis approach. Considering the easy acquisition and anti-interference characteristics of vibration signals, this article develops a vibration signal-based diagnosis approach. First, variational mode decomposition (VMD) is utilized for nonstationary vibration signal preprocessing, which is verified as a more effective tool than empirical mode decomposition. Then, to comprehensively characterize nonlinear fault characteristics, five kinds of entropy are extracted. To eliminate the redundant information of high-dimensional features, kernel principal component analysis is utilized for multientropy feature fusion. Experiment comparisons demonstrate the superiority of the proposed VMD preprocessing and multientropy fusion method. The diagnosis accuracies of normal-to-reverse and reverse-to-normal switching directions reach 96.57% and 99.43%, respectively, which provides theoretical support for onsite operation and maintenance staff.
{"title":"Entropy Feature Fusion-Based Diagnosis for Railway Point Machines Using Vibration Signals Based on Kernel Principal Component Analysis and Support Vector Machine","authors":"Yongkui Sun, Yuan Cao, Peng Li, S. Su","doi":"10.1109/mits.2023.3295376","DOIUrl":"https://doi.org/10.1109/mits.2023.3295376","url":null,"abstract":"Railway point machines are the key equipment that controls the train route and affects the safety of train operation. Complex and harsh working environments lead to frequent failures, accounting for 40% of the total failures of the railway signaling system. Thus, it is an urgent task to present an intelligent fault diagnosis approach. Considering the easy acquisition and anti-interference characteristics of vibration signals, this article develops a vibration signal-based diagnosis approach. First, variational mode decomposition (VMD) is utilized for nonstationary vibration signal preprocessing, which is verified as a more effective tool than empirical mode decomposition. Then, to comprehensively characterize nonlinear fault characteristics, five kinds of entropy are extracted. To eliminate the redundant information of high-dimensional features, kernel principal component analysis is utilized for multientropy feature fusion. Experiment comparisons demonstrate the superiority of the proposed VMD preprocessing and multientropy fusion method. The diagnosis accuracies of normal-to-reverse and reverse-to-normal switching directions reach 96.57% and 99.43%, respectively, which provides theoretical support for onsite operation and maintenance staff.","PeriodicalId":48826,"journal":{"name":"IEEE Intelligent Transportation Systems Magazine","volume":"1 1","pages":"96-108"},"PeriodicalIF":3.6,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"62345398","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}
Pub Date : 2023-11-01DOI: 10.1109/mits.2023.3274787
Hai-rong Dong, Jinxing Wang, Xingtang Wu, Min Zhou, Jinhu Lü
Accurately predicting delays for high-speed railways (HSRs) is a challenging yet significant task. The historical operation data of the HSRs, implicating delay derivation rules under the dispatchers’ rescheduling strategies, have sparsity characteristics, resulting in heterogeneous prediction performances under different scenarios. This article proposes a Gaussian noise data augmentation-based delay prediction method to cope with the sparsity. Specifically, the Gaussian noise is added to the original data based on the train operation data characteristics. Then, the delay data rather than the full-state dataset are selected as the training data for different designed machine learning prediction models. Numerous studies based on real HSR operational data from the Beijing Railway Bureau show that the proposed method could significantly improve the prediction accuracy under different scenarios with different machine learning models, verifying the effectiveness of the performance improvement. The relevant results could be significantly helpful for real-time train rescheduling and passenger management, thus improving the emergency response capabilities of HSRs.
{"title":"Gaussian Noise Data Augmentation-Based Delay Prediction for High-Speed Railways","authors":"Hai-rong Dong, Jinxing Wang, Xingtang Wu, Min Zhou, Jinhu Lü","doi":"10.1109/mits.2023.3274787","DOIUrl":"https://doi.org/10.1109/mits.2023.3274787","url":null,"abstract":"Accurately predicting delays for high-speed railways (HSRs) is a challenging yet significant task. The historical operation data of the HSRs, implicating delay derivation rules under the dispatchers’ rescheduling strategies, have sparsity characteristics, resulting in heterogeneous prediction performances under different scenarios. This article proposes a Gaussian noise data augmentation-based delay prediction method to cope with the sparsity. Specifically, the Gaussian noise is added to the original data based on the train operation data characteristics. Then, the delay data rather than the full-state dataset are selected as the training data for different designed machine learning prediction models. Numerous studies based on real HSR operational data from the Beijing Railway Bureau show that the proposed method could significantly improve the prediction accuracy under different scenarios with different machine learning models, verifying the effectiveness of the performance improvement. The relevant results could be significantly helpful for real-time train rescheduling and passenger management, thus improving the emergency response capabilities of HSRs.","PeriodicalId":48826,"journal":{"name":"IEEE Intelligent Transportation Systems Magazine","volume":"1 1","pages":"8-18"},"PeriodicalIF":3.6,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"62345763","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}
Pub Date : 2023-11-01DOI: 10.1109/mits.2023.3264028
Hang Zhao, Dihua Sun, Min Zhao, Baohui Li, Changchang He, Xinhai Chen
Typical communication and detection issues in a nonideal information environment, such as sensing failure, communication and sensing delay, and packet loss, further aggravate the adverse impacts of human-driven vehicle (HV) uncertainty on a mixed vehicle platoon. To guarantee the performance of the mixed vehicle platoon featuring HVs and connected and automated vehicles under the nonideal information environment, this article proposes a platoon control strategy integrating a combined longitudinal and lateral control and message recovery. Specifically, by building the dataset associated with HV behaviors, a long short-term memory (LSTM) predictor is established to recover the problematic HV messages (i.e., position, velocity, and heading) caused by the nonideal information environment. Furthermore, based on the boundary of the HV states, an evaluation and correction (EC) method is presented to suppress the adverse impacts of prediction failures. Then, a combined longitudinal and lateral controller cooperating with the LSTM predictor and EC method is developed to enhance the stability and safety of the mixed vehicle platoon under the nonideal information environment. In a theoretical analysis, the relatedness between the asymptotic stability and string stability of the platoon and predictor accuracy is strictly proved. Finally, comparative experiments verify the effectiveness of the proposed control strategy by employing driver-in-the-loop simulations.
{"title":"Long Short-Term Memory-Assisted Mixed Vehicle Platoon Control Strategy Considering Message Recovery Under Nonideal Information Environment","authors":"Hang Zhao, Dihua Sun, Min Zhao, Baohui Li, Changchang He, Xinhai Chen","doi":"10.1109/mits.2023.3264028","DOIUrl":"https://doi.org/10.1109/mits.2023.3264028","url":null,"abstract":"Typical communication and detection issues in a nonideal information environment, such as sensing failure, communication and sensing delay, and packet loss, further aggravate the adverse impacts of human-driven vehicle (HV) uncertainty on a mixed vehicle platoon. To guarantee the performance of the mixed vehicle platoon featuring HVs and connected and automated vehicles under the nonideal information environment, this article proposes a platoon control strategy integrating a combined longitudinal and lateral control and message recovery. Specifically, by building the dataset associated with HV behaviors, a long short-term memory (LSTM) predictor is established to recover the problematic HV messages (i.e., position, velocity, and heading) caused by the nonideal information environment. Furthermore, based on the boundary of the HV states, an evaluation and correction (EC) method is presented to suppress the adverse impacts of prediction failures. Then, a combined longitudinal and lateral controller cooperating with the LSTM predictor and EC method is developed to enhance the stability and safety of the mixed vehicle platoon under the nonideal information environment. In a theoretical analysis, the relatedness between the asymptotic stability and string stability of the platoon and predictor accuracy is strictly proved. Finally, comparative experiments verify the effectiveness of the proposed control strategy by employing driver-in-the-loop simulations.","PeriodicalId":48826,"journal":{"name":"IEEE Intelligent Transportation Systems Magazine","volume":"1 1","pages":"109-130"},"PeriodicalIF":3.6,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"62345808","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}
Once a freight train is delayed on a busy railway under a quasi-moving-block signaling system, the speed of its following trains may fluctuate, without proper adjustment. Updating the reference speed profile of the delayed freight train is an urgent task to improve the train operation performance and rail utilization ratio. To this end, this article proposes a speed profile optimization method for following delayed trains that is based on the idea of model predictive control (MPC). The state change time of the block section ahead is predicted for each following train according to the speed profile of its preceding train, based on which the distance between the following train and each speed protection curve (DTC) is predicted. The DTC is taken as a key optimization index for the speed profile optimization, and the other optimization indices are train delay and energy consumption. A two-step hierarchical optimization algorithm is proposed in this article. In the upper level, each prediction step is defined as the traveling time of the preceding train in each block section ahead, and the train target cruising speed sequence is calculated, with the dimension decided by the prediction horizon, using particle swarm optimization. In the lower level, the optimal speed profile is calculated based on the rolling optimization algorithm of the train speed curve in the prediction horizon. The proposed algorithm repeats the optimization process with updated train information after each control horizon. Three simulations are presented, which consider a downhill scenario, steep uphill scenario, and temporary speed limit, respectively. Then, the MPC parameters are analyzed and the optimized speed profiles are compared with the other algorithm.
{"title":"Model Predictive Control-Based Speed Profile Optimization of a Freight Train Group With a Hierarchical Algorithm","authors":"Liu Yang, Xubin Sun, Zemin Yao, Weifeng Zhong, Biao Liu, Xianjin Huang","doi":"10.1109/mits.2023.3304412","DOIUrl":"https://doi.org/10.1109/mits.2023.3304412","url":null,"abstract":"Once a freight train is delayed on a busy railway under a quasi-moving-block signaling system, the speed of its following trains may fluctuate, without proper adjustment. Updating the reference speed profile of the delayed freight train is an urgent task to improve the train operation performance and rail utilization ratio. To this end, this article proposes a speed profile optimization method for following delayed trains that is based on the idea of model predictive control (MPC). The state change time of the block section ahead is predicted for each following train according to the speed profile of its preceding train, based on which the distance between the following train and each speed protection curve (DTC) is predicted. The DTC is taken as a key optimization index for the speed profile optimization, and the other optimization indices are train delay and energy consumption. A two-step hierarchical optimization algorithm is proposed in this article. In the upper level, each prediction step is defined as the traveling time of the preceding train in each block section ahead, and the train target cruising speed sequence is calculated, with the dimension decided by the prediction horizon, using particle swarm optimization. In the lower level, the optimal speed profile is calculated based on the rolling optimization algorithm of the train speed curve in the prediction horizon. The proposed algorithm repeats the optimization process with updated train information after each control horizon. Three simulations are presented, which consider a downhill scenario, steep uphill scenario, and temporary speed limit, respectively. Then, the MPC parameters are analyzed and the optimized speed profiles are compared with the other algorithm.","PeriodicalId":48826,"journal":{"name":"IEEE Intelligent Transportation Systems Magazine","volume":"52 1","pages":"64-77"},"PeriodicalIF":3.6,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"62346012","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}
Provides society information that may include news, reviews or technical notes that should be of interest to practitioners and researchers.
提供社会信息，可能包括新闻，评论或技术笔记，从业者和研究人员应该感兴趣。
{"title":"IEEE ITSC 2022 [Conference Activities]","authors":"Nan Zhang, Naiqi Wu, Lingxi Li, Yonglin Tian, Xiao Wang, Brendan Morris","doi":"10.1109/mits.2023.3318074","DOIUrl":"https://doi.org/10.1109/mits.2023.3318074","url":null,"abstract":"Provides society information that may include news, reviews or technical notes that should be of interest to practitioners and researchers.","PeriodicalId":48826,"journal":{"name":"IEEE Intelligent Transportation Systems Magazine","volume":"65 5-6","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135455550","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}
Pub Date : 2023-11-01DOI: 10.1109/mits.2023.3314848
Yisheng Lv
Following large language models, the development of large models in industries is becoming more intensified by the day. Without exception, large transportation models, including traffic perception and cognition for large-scale road networks and autonomous driving models, have been issued one after another, kicking off a new round of competition characterized by “big model + big data + big computing power.”
{"title":"Large Transportation Models on the Horizon: Challenges and Issues [Editor’s Column]","authors":"Yisheng Lv","doi":"10.1109/mits.2023.3314848","DOIUrl":"https://doi.org/10.1109/mits.2023.3314848","url":null,"abstract":"Following large language models, the development of large models in industries is becoming more intensified by the day. Without exception, large transportation models, including traffic perception and cognition for large-scale road networks and autonomous driving models, have been issued one after another, kicking off a new round of competition characterized by “big model + big data + big computing power.”","PeriodicalId":48826,"journal":{"name":"IEEE Intelligent Transportation Systems Magazine","volume":"29 11","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135454858","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}
Pub Date : 2023-09-01DOI: 10.1109/MITS.2023.3263890
Keno Garlichs, Maximilian Huber, Lars C. Wolf
Collective perception is one of the key ideas of vehicular networking and allows the exchange of data about perceived objects. However, unlike autonomous driving systems, human drivers cannot screen large numbers of objects to judge their dangerousness. An assistance system in the vehicle, therefore, must do this job. This article shows a concept for a human–machine interface that could be used to warn the driver in case such a system detects an actually dangerous object. A user study in a driving simulator was performed to evaluate its potential to prevent accidents. Eye-tracking glasses were used to analyze the driver’s gaze during different types of situations. Furthermore, the participants’ subjective experience was evaluated with a questionnaire. Results show that drivers trust the system and brake earlier and with more control due to the warnings, and ultimately, the majority of accidents could be avoided thanks to the warnings.
{"title":"How Human Drivers Can Benefit From Collective Perception: A User Study","authors":"Keno Garlichs, Maximilian Huber, Lars C. Wolf","doi":"10.1109/MITS.2023.3263890","DOIUrl":"https://doi.org/10.1109/MITS.2023.3263890","url":null,"abstract":"Collective perception is one of the key ideas of vehicular networking and allows the exchange of data about perceived objects. However, unlike autonomous driving systems, human drivers cannot screen large numbers of objects to judge their dangerousness. An assistance system in the vehicle, therefore, must do this job. This article shows a concept for a human–machine interface that could be used to warn the driver in case such a system detects an actually dangerous object. A user study in a driving simulator was performed to evaluate its potential to prevent accidents. Eye-tracking glasses were used to analyze the driver’s gaze during different types of situations. Furthermore, the participants’ subjective experience was evaluated with a questionnaire. Results show that drivers trust the system and brake earlier and with more control due to the warnings, and ultimately, the majority of accidents could be avoided thanks to the warnings.","PeriodicalId":48826,"journal":{"name":"IEEE Intelligent Transportation Systems Magazine","volume":"15 1","pages":"25-35"},"PeriodicalIF":3.6,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44254352","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}
Pub Date : 2023-09-01DOI: 10.1109/mits.2023.3295393
C. Olaverri-Monreal
{"title":"Driving the Future: Highlights From the 2023 IEEE Intelligent Vehicles Symposium [President's Message]","authors":"C. Olaverri-Monreal","doi":"10.1109/mits.2023.3295393","DOIUrl":"https://doi.org/10.1109/mits.2023.3295393","url":null,"abstract":"","PeriodicalId":48826,"journal":{"name":"IEEE Intelligent Transportation Systems Magazine","volume":"15 1","pages":"4"},"PeriodicalIF":3.6,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"62346304","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}
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