Transportation Research Part C-Emerging Technologies最新文献

{"title":"A parameter privacy-preserving strategy for mixed-autonomy platoon control","authors":"Jingyuan Zhou,&nbsp;Kaidi Yang","doi":"10.1016/j.trc.2024.104885","DOIUrl":"10.1016/j.trc.2024.104885","url":null,"abstract":"<div><div>It has been demonstrated that leading cruise control (LCC) can improve the operation of mixed-autonomy platoons by allowing connected and automated vehicles (CAVs) to make longitudinal control decisions based on the information provided by surrounding vehicles. However, LCC generally requires surrounding human-driven vehicles (HDVs) to share their real-time states, which can be used by adversaries to infer drivers’ car-following behavior, potentially leading to financial losses or safety concerns. This paper aims to address such privacy concerns and protect the behavioral characteristics of HDVs by devising a parameter privacy-preserving approach for mixed-autonomy platoon control. First, we integrate a parameter privacy filter into LCC to protect sensitive car-following parameters. The privacy filter allows each vehicle to generate seemingly realistic pseudo states by distorting the true parameters to pseudo parameters, which can protect drivers’ privacy in behavioral parameters without significantly influencing the control performance. Second, to enhance the reliability and practicality of the privacy filter within LCC, we first introduce an individual-level parameter privacy preservation constraint to the privacy filter, focusing on the privacy level of each individual parameter pair. Subsequently, we extend the current approach to accommodate continuous parameter spaces through a neural network estimator. Third, analysis of head-to-tail string stability reveals the potential impact of privacy filters in degrading mixed traffic flow performance. Simulation shows that this approach can effectively trade off privacy and control performance in LCC. We further demonstrate the benefit of such an approach in networked systems, i.e., by applying the privacy filter to a preceding vehicle, one can also achieve a certain level of privacy for the following vehicle.</div></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":null,"pages":null},"PeriodicalIF":7.6,"publicationDate":"2024-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142531891","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 vulnerability envelope of urban rail transit networks under simultaneous disruptions of stations and line sections","authors":"Yu Gu ,&nbsp;Anthony Chen ,&nbsp;Yingying Xu ,&nbsp;Songyot Kitthamkesorn","doi":"10.1016/j.trc.2024.104887","DOIUrl":"10.1016/j.trc.2024.104887","url":null,"abstract":"<div><div>This paper develops a modeling approach to analyze the vulnerability of an urban rail transit network (URTN) under disruptions of rail stations, line sections, and their various combinations. The recently developed concept of vulnerability envelope is adapted to understand the possible performances of URTN under different severities of disruptions. Single-level optimization models are developed to derive the upper and lower bounds of URTN vulnerability envelope with consideration of rail transit route redundancy, which reflects the required level of service quality from the perspective of rail transit passengers. The difference between upper and lower URTN vulnerability bounds captures the range of URTN performance losses among all possible disruption scenarios. Numerical examples based on the Nanjing and Hong Kong metro networks are conducted to illustrate the properties and verify the applicability of the developed models. The results show that the proposed URTN vulnerability envelope is effective to analyze the possible range of URTN with different operability requirements under combined multi-infrastructure disruptions. Outcome of the developed method is useful for vulnerability assessment and important infrastructure identification, which may facilitate the vulnerability-oriented decision-making in URTNs.</div></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":null,"pages":null},"PeriodicalIF":7.6,"publicationDate":"2024-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142531889","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":"Eco-driving of connected autonomous vehicles in urban traffic networks of mixed autonomy with cut-in and escape lane-changes of manually-driven vehicles","authors":"Yonghui Hu ,&nbsp;Yibing Wang ,&nbsp;Jingqiu Guo ,&nbsp;Lihui Zhang ,&nbsp;Qirong Lu ,&nbsp;Hao Liu ,&nbsp;Yongfu Li","doi":"10.1016/j.trc.2024.104889","DOIUrl":"10.1016/j.trc.2024.104889","url":null,"abstract":"<div><div>Urban eco-driving of connected autonomous vehicles (CAVs) aims to optimize CAVs’ speed trajectories to avoid sharp accelerations/decelerations and stops at signalized intersections for the minimization of energy consumption of mixed traffic of CAVs and manually-driven vehicles (MVs). Existing eco-driving studies rarely considered lane changes of MVs. Besides ordinary lane changes that usually take place in traffic flow, eco-driving CAVs tend to trigger specific types of lane changes of MVs, i.e. cut-in from adjacent lanes to the front of CAVs, or escape from behind CAVs to adjacent lanes. It is significant to investigate the interplay between such extraordinary lane changes of MVs and eco-driving endeavors. This paper has developed a generic and deployable eco-driving strategy for CAVs that can deal with both lateral disturbances (e.g. cut-in and escape lane changes of MVs) and longitudinal disturbances (e.g. MVs moving in front and vehicle queues at downstream intersections), without assuming communications between CAVs and MVs. The eco-driving task was formulated as an optimal control problem with safety constraints, and tackled under a unified rolling-horizon framework, with each cut-in lane change treated as a newly emerging longitudinal disturbance to CAVs. The eco-driving performance was thoroughly evaluated for an urban multilane road network based on SUMO. The eco-driving strategy was demonstrated capable of tackling various disturbances of MVs and effectively achieving the eco-driving purpose. For the eco-driving effects on lane changes of MVs, the numbers of cut-in and escape lane changes ascended until the market penetration rate (MPR) of CAVs reached 30% and then kept decreasing, while the number of ordinary lane changes dropped monotonically with the MPR increase. As to the impact of cut-in and escape lane changes of MVs on eco-driving, the energy saving benefits of all CAVs and MVs grew with the MPR increase, despite the disturbances of MV lane changes. Similar results were not reported before.</div></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":null,"pages":null},"PeriodicalIF":7.6,"publicationDate":"2024-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142531890","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":"Analytical Eco-Driving for electric and conventional vehicles: A unified computational approach","authors":"Luis Alfredo Wulf Ribelles ,&nbsp;Kristan Gillet ,&nbsp;Guillaume Colin ,&nbsp;Antoine Simon ,&nbsp;Yann Chamaillard ,&nbsp;Cédric Nouillant","doi":"10.1016/j.trc.2024.104879","DOIUrl":"10.1016/j.trc.2024.104879","url":null,"abstract":"<div><div>This paper presents a methodology to generate energy efficient speed profiles for electric and conventional vehicles using analytical Eco-Driving solutions. In recent years, the energy savings that can be achieved by adjusting the speed of road vehicles have motivated the development of ecological driving strategies. From this perspective, different Eco-Driving strategies are formulated as free final time Optimal Control Problems aiming at minimizing the energy consumed during a trip subject to input and speed constraints. These Eco-Driving problems are solved using Pontryagin’s Minimum Principle to derive the closed-form expressions composing the (un-)constrained solutions. Moreover, the performance of the proposed strategies is compared against two reference cycles and the global optimal savings given by a Dynamic Programming strategy. The simulation results show that up to 26.43% and 32.18% of energy savings can be obtained for electrified and conventional vehicles, respectively, while keeping the computation time in a millisecond range.</div></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":null,"pages":null},"PeriodicalIF":7.6,"publicationDate":"2024-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142446265","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":"City metro network expansion based on multi-objective reinforcement learning","authors":"Liqing Zhang ,&nbsp;Leong Hou U ,&nbsp;Shaoquan Ni ,&nbsp;Dingjun Chen ,&nbsp;Zhenning Li ,&nbsp;Wenxian Wang ,&nbsp;Weizhi Xian","doi":"10.1016/j.trc.2024.104880","DOIUrl":"10.1016/j.trc.2024.104880","url":null,"abstract":"<div><div>This manuscript focuses on investigating the metro network expansion problem, which is formulated as a Markov Decision Process and addressed using a sequential station selection methodology. To identify an effective expansion strategy, we introduce a multi-objective reinforcement learning framework, which encompasses objectives such as traffic demands, social equity, and network accessibility. The proposed method can explore the entire city area without limiting the search space, by leveraging reward calculations to fine-tune the policy during the learning process To effectively address the challenges posed by multiple objectives and the curse of dimensionality, the proposed method utilizes an actor-critic framework. The actor is responsible for selecting actions, specifically determining the next metro station to be added to the network. The critic evaluates the performance of the given policy, providing feedback on the quality of the expanded metro network. Furthermore, by integrating the Tchebycheff decomposition method into the actor-critic framework, the proposed method enhances the exploration and optimization of the non-convex metro network expansion problem. Our method has been validated through experiments utilizing real-world data and outperforms traditional heuristic algorithms by over 30%. These results compellingly illustrate the superior effectiveness of our proposed method.</div></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":null,"pages":null},"PeriodicalIF":7.6,"publicationDate":"2024-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142446266","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":"Multimodal traffic assignment considering heterogeneous demand and modular operation of shared autonomous vehicles","authors":"Ting Wang ,&nbsp;Sisi Jian ,&nbsp;Chengdong Zhou ,&nbsp;Bin Jia ,&nbsp;Jiancheng Long","doi":"10.1016/j.trc.2024.104881","DOIUrl":"10.1016/j.trc.2024.104881","url":null,"abstract":"<div><div>This study proposes a solution to address the lack of consideration for personalized needs in complex multimodal transportation systems by formulating and solving a heterogeneous demand traffic assignment problem (HD-TAP). The HD-TAP takes into account the varying preferences of travelers when selecting travel modes and the common occurrence of multiple people traveling together. The use of modular shared autonomous vehicles (SAVs) is also considered in the model, which allows for flexibility in combining the number of modules based on the number of group riders. The HD-TAP is formulated as a multimodal, multiclass, multiple equilibrium principles, combined mode split traffic assignment model, incorporating a cross-nested logit model for private vehicle travelers’ route choice behavior and a multinomial logit user equilibrium model for non-private vehicle travelers’ mode and route choice behavior. To solve the HD-TAP, a gradient projection-based algorithm is developed. Numerical examples demonstrate that the proposed algorithm can efficiently solve large-scale multimodal network problems. Through numerical experiments in real-world networks, the study investigates the impacts of preferred travel modes, the number of group riders, and the modular operation of SAVs on system performance. The findings indicate that providing an excessive number of modular SAVs with a capacity of five passengers or fewer may result in a loss of public transit users. It is important to control the supply of such vehicles to ensure the preservation of public transit usage.</div></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":null,"pages":null},"PeriodicalIF":7.6,"publicationDate":"2024-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142425612","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":"An optimization approach for the terminal airspace scheduling problem","authors":"Wayne Ng ,&nbsp;Nuno Antunes Ribeiro ,&nbsp;Diana Jorge","doi":"10.1016/j.trc.2024.104856","DOIUrl":"10.1016/j.trc.2024.104856","url":null,"abstract":"<div><div>Effective air traffic management within the Terminal Manoeuvring Area (TMA) is imperative for mitigating delays, minimizing fuel consumption, and reducing emissions in the aviation sector. While existing research has predominantly focused on optimizing runway sequencing, the Terminal Airspace Scheduling Problem (TASP) has been relatively understudied. This work addresses this gap by proposing an innovative matheuristic algorithm (TMAOpt) that concurrently optimizes both runway aircraft sequencing and decisions within the TMA, including runway selection, speed control, utilization of holding patterns, vectoring, and point merges. The proposed approach combines a Linear Programming (LP) model with metaheuristic algorithms, providing a unique solution approach that balances rapid generation of feasible solutions (within 1 s of computation) and convergence (within 5 min of computation). Validation of our approach involved extensive evaluations using real-world data from the congested terminal airspace of Changi Airport in Singapore. Comparative analyses with existing methods, including commercial microsimulation models like AirTOP, showcase the superior performance of our algorithm, yielding sequences that reduce delays by up to 27%. A sensitivity analysis, exploring varying degrees of permitted TMA interventions, underscores the benefits of their balanced utilization.</div></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":null,"pages":null},"PeriodicalIF":7.6,"publicationDate":"2024-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142425638","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Large scale multi-GPU based parallel traffic simulation for accelerated traffic assignment and propagation","authors":"Xuan Jiang ,&nbsp;Raja Sengupta ,&nbsp;James Demmel ,&nbsp;Samuel Williams","doi":"10.1016/j.trc.2024.104873","DOIUrl":"10.1016/j.trc.2024.104873","url":null,"abstract":"<div><div>Traffic simulation is a critical tool for congestion analysis, travel time estimation, and route optimization in urban planning, benefiting navigation apps, transportation network companies, and state agencies. Traditionally, traffic micro-simulation frameworks are based on road segments and can only support a limited number of main roads. Efficient traffic simulation on a regional scale remains a significant challenge due to the complexity of urban mobility and the large scale of spatiotemporal data. This paper introduces a Large Scale Multi-GPU Parallel Computing based Regional Scale Traffic Simulation Framework (LPSim), which leverages graphical processing unit (GPU) parallel computing to address these challenges. LPSim utilizes a multi-GPU architecture to simulate extensive and dynamic traffic networks with high fidelity and reduced computation time. Using the parallel processing capabilities of GPUs, LPSim can perform tens of millions of individual vehicle dynamics simulations simultaneously, significantly outperforming traditional CPU-based approaches. The framework is designed to be scalable and can easily accommodate the increasing complexity of traffic simulations. We present the theory behind GPU-based traffic simulation, the architecture of single- and multi-GPU based simulations, and the graph partition strategies that enhance computation resource load balance. Our experimental results demonstrate the effectiveness of LPSim in simulating large-scale traffic scenarios. LPSim is capable of completing simulations of 2.82 million trips in just 6.28 min on a single GPU machine equipped with 5120 CUDA cores (Tesla V100-SXM2). Furthermore, utilizing a Google Cloud instance with two NVIDIA V100 GPUs, which collectively offer 10240 CUDA cores, LPSim successfully simulates 9.01 million trips within 21.16 min. We further tested our simulator with the same demand on dual NVIDIA A100-PCIE-40GB GPUs, which finished the simulation in 0.0398 h, approximately 113 times faster than the same simulation scenario running on an Intel(R) Xeon(R) Gold 6326 CPU @ 2.90 GHz, which takes 4.49 h to complete. This performance not only demonstrates its speed and scalability advantages over traditional simulation techniques but also highlights LPSim’s unique position as the first traffic simulation framework that is scalable for both single- and multiple-GPU configurations. Consequently, LPSim provides an invaluable tool for individuals and extensive research teams alike, enabling the acquisition of large-scale traffic simulation results in a time-efficient manner. LPSim code is available at: <span><span>https://github.com/Xuan-1998/LPSim</span><svg><path></path></svg></span></div></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":null,"pages":null},"PeriodicalIF":7.6,"publicationDate":"2024-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142425639","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Towards dynamic flight separation in final approach: A hybrid attention-based deep learning framework for long-term spatiotemporal wake vortex prediction","authors":"Nana Chu ,&nbsp;Kam K.H. Ng ,&nbsp;Xinting Zhu ,&nbsp;Ye Liu ,&nbsp;Lishuai Li ,&nbsp;Kai Kwong Hon","doi":"10.1016/j.trc.2024.104876","DOIUrl":"10.1016/j.trc.2024.104876","url":null,"abstract":"<div><div>The conservative and distance-based static wake vortex-related separation may restrict runway operational efficiency. Recent studies have demonstrated the potential of wake separation reduction under the Re-categorisation scheme of Aircraft Weight (RECAT). Furthermore, dynamic time-based flight separation considering vortex evolution with respect to aircraft pairs and meteorological conditions will be the ultimate objective for improving runway operational capacity without compromising safety. This paper presents a hybrid deep learning framework for aircraft wake vortex recognition, evolution prediction, and preliminary dynamic separation assessment in the final approach. Two-stage Deep Convolutional Neural Networks (DCNNs) are utilised to identify vortex locations and strength from wake images. Subsequently, we propose the Attention-based Temporal Convolutional Networks (ATCNs) for future long-term vortex decay and transport forecasts based on initial vortex information from DCNNs. 17,254 wake sequences generated by arrival flights at Hong Kong International Airport (HKIA) are used in this study. The proposed ATCN models outperform the specific benchmarks. Furthermore, the hybrid DCNN-ATCN model shows great benefits in mining both spatial vortex characteristics and temporal dependencies in vortex evolution, and achieves a computational speed of approximately 7 s per sequence. The final vortex duration assessment demonstrates a significant potential for separation reduction in the final approach when the crosswind speed exceeds 3 m/s. This study provides important implications for online and fast-time wake behaviour monitoring and state estimation. The results of vortex duration analysis conform to the RECAT-EU standards and present an efficient strategy for developing dynamic flight separation systems.</div></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":null,"pages":null},"PeriodicalIF":7.6,"publicationDate":"2024-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142425611","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 conflict risk graph approach to modeling spatio-temporal dynamics of intersection safety","authors":"Tao Wang ,&nbsp;Ying-En Ge ,&nbsp;Yongjie Wang ,&nbsp;Carlo G. Prato ,&nbsp;Wenqiang Chen ,&nbsp;Yuchen Niu","doi":"10.1016/j.trc.2024.104874","DOIUrl":"10.1016/j.trc.2024.104874","url":null,"abstract":"<div><div>Intersections are among the most hazardous roadway spaces due to the complex and conflicting road users’ movements. Spatio-temporal modeling of conflict risks among road users can help to identify strategies to mitigate the exacerbation of safety risks and restore hazardous conditions to normal traffic situations<strong>.</strong> This paper proposes the 'Conflict Risk Graph' as a novel concept to infer real-time conflict risks at intersections at a fine-grained level by mapping conflict-prone locations to nodes within a network characterized by specific topological structures. A significant contribution of this work is the development of a Transformer-based Graph Convolutional Network (Trans-GCN), a model that synergistically combines the Transformer's proficiency in capturing global dependence with the GCN's ability to learn local correlations. The Trans-GCN adeptly models the complex evolution patterns of conflict risks at intersections. The evaluation in this paper against five common state-of-the-art deep learning approaches demonstrates the superior performance of the Trans-GCN in conflict risk inference and adaptability to node changes. Furthermore, extensive experiments with different node configurations reveal a correlation between node setup and model performance, showing that higher spatio-temporal resolution decreases inference accuracy. This insight informs the selection of an optimal node configuration that balances the detailed capture of spatio-temporal dynamics with modeling accuracy, enabling ideal conflict risk inferences at intersections. Ultimately, this work offers significant insights for the enhancement of proactive traffic safety management and the advancement of intelligent traffic systems.</div></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":null,"pages":null},"PeriodicalIF":7.6,"publicationDate":"2024-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142425637","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}