Transportation Research Part C-Emerging Technologies最新文献

{"title":"Is cooperative always better? Multi-Agent Reinforcement Learning with explicit neighborhood backtracking for network-wide traffic signal control","authors":"Yilong Ren ,&nbsp;Yizhuo Chang ,&nbsp;Zhiyong Cui ,&nbsp;Xiao Chang ,&nbsp;Haiyang Yu ,&nbsp;Xiaosong Li ,&nbsp;Yinhai Wang","doi":"10.1016/j.trc.2025.105265","DOIUrl":"10.1016/j.trc.2025.105265","url":null,"abstract":"<div><div>Multi-Agent Reinforcement Learning (MARL) has been empirically demonstrated as a highly promising paradigm for the Cooperative Traffic Signal Control (CTSC) of urban road networks. A review of recent MARL-based literature reveals a counter-intuitive finding: several sophisticated approaches have been outperformed by simpler independent control schemes when applied across multiple intersections. This paper analyzes the phenomenon and proposes a hypothesis that <em>the setting of surveillance zone length may determine whether a MARL-based CTSC algorithm is effective or not.</em> We prove this hypothesis qualitatively and quantitatively and find that the intersection interactions are time-lagged. Faced with the incomplete surveillance zone, we model the CTSC process as a decentralized partially observable Markov decision process (Dec-POMDP). Further, we propose ENB-RL, a MARL model with explicit neighborhood backtracking to handle the lag in impacts from neighbors. The core of our proposal is an ENB module, which consists of a neighborhood backtracking stack to store and update neighborhood intersections’ historical throughput in a segmented weighted way, and a multi-head attention model for spatio-temporal differentiated input. Such explicit and precise inputs can improve the agent’s observations in incomplete perceptual environments. Considering that historical backtracking information may lead to convergence instability, we introduce random Gaussian noise for Double Deep Q-Network (DDQN) to generate uncertainty and improve the efficiency and stability of exploration. Experimental results show that ENB-RL has the best convergence performance on both synthetic and real-world datasets, and outperforms other state-of-the-art MARL models. Ablation experiments confirm the efficacy of each component in the framework. Moreover, the proposed ENB module can also be plugged and played in mainstream RL-based models.</div></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":"179 ","pages":"Article 105265"},"PeriodicalIF":7.6,"publicationDate":"2025-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144704136","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 integrated method based on wavelet modulus maxima and local Holder exponents for automatic phase detection and labelling of lane-changing execution","authors":"Zhuo Cao ,&nbsp;Zuduo Zheng ,&nbsp;Mehmet Yildirimoglu ,&nbsp;Shimul (Md. Mazharul) Haque","doi":"10.1016/j.trc.2025.105285","DOIUrl":"10.1016/j.trc.2025.105285","url":null,"abstract":"<div><div>This paper presents a novel automated method for detecting and labelling lane change (LC) execution phases using trajectory data. By integrating Wavelet modulus maxima lines with the local Holder exponents (WTMM-LHE), WTMM-LHE accurately identifies the commencement of LC execution. This methodology addresses the generalization challenges faced by traditional fixed-interval and rule-based approaches across different datasets. Furthermore, it improves upon the previous Wavelet transform modulus-based methods by effectively eliminating confounding results, thereby enhancing its robustness even with challenging trajectory profiles. Experiments on both synthetic and naturalistic trajectories were conducted to test this method’s performance. Results show that the proposed approach significantly enhances the reliability and accuracy of LC phase identification, improving data availability for calibrating, training, and modeling LC behaviors. Additionally, this study demonstrates the application of the proposed automatic labelling methods on machine learning-based LC prediction models, highlighting its ability to improve the accuracy of training data labelling, with potential implications for advanced driver assistance systems (ADAS) and connected and autonomous vehicles (CAVs).</div></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":"179 ","pages":"Article 105285"},"PeriodicalIF":7.6,"publicationDate":"2025-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144696969","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 spatial–Temporal Large Language Model with Denoising Diffusion Implicit for predictions in centralized multimodal transport systems","authors":"Zhiqi Shao ,&nbsp;Haoning Xi ,&nbsp;Haohui Lu ,&nbsp;Ze Wang ,&nbsp;Michael G.H. Bell ,&nbsp;Junbin Gao","doi":"10.1016/j.trc.2025.105249","DOIUrl":"10.1016/j.trc.2025.105249","url":null,"abstract":"<div><div>Centralized multimodal transport systems face significant challenges due to data isolation, missing values, and heterogeneous spatial–temporal features, which hinder accurate prediction in traffic flow and travel demand. To address these challenges, we propose Spatial–Temporal Large Language Model with Denoising Diffusion Implicit (STLLM-DF), an innovative which integrates a Spatial–Temporal Denoising Diffusion Implicit Model (ST-DDIM) with a Spatial–Temporal Large Language Model (ST-LLM) to improve the predictions in traffic flow and travel demand in multimodal transport systems. The ST-DDIM effectively learns data distributions to recover noisy and incomplete data, while the ST-LLM captures complex spatial–temporal dependencies across multimodal networks, eliminating manual feature engineering. Extensive experiments conducted on ten real-world datasets from Sydney demonstrate that STLLM-DF consistently outperforms baseline models in both single-task and multi-task predictions (e.g., ), while consistently excelling in short-term and long-term predictions. On average, STLLM-DF achieves improvements in Mean Absolute Error (MAE) by 2.40%, Root Mean Square Error (RMSE) by 4.50%, and Mean Absolute Percentage Error (MAPE) by 1.51%. Furthermore, we evaluate the noise tolerance of STLLM-DF, demonstrating its robust performance under data imperfections. This paper presents a scalable, data-driven solution for managing multimodal transport systems, offering actionable insights for transport regulators.</div></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":"179 ","pages":"Article 105249"},"PeriodicalIF":7.6,"publicationDate":"2025-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144672686","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":"PE-RLHF: Reinforcement Learning with Human Feedback and physics knowledge for safe and trustworthy autonomous driving","authors":"Zilin Huang,&nbsp;Zihao Sheng,&nbsp;Sikai Chen","doi":"10.1016/j.trc.2025.105262","DOIUrl":"10.1016/j.trc.2025.105262","url":null,"abstract":"<div><div>In the field of autonomous driving, developing safe and trustworthy autonomous driving policies remains a significant challenge. Recently, Reinforcement Learning with Human Feedback (RLHF) has attracted substantial attention due to its potential to enhance training safety and sampling efficiency. Nevertheless, existing RLHF-enabled methods in the autonomous driving domain often falter when faced with imperfect human demonstrations, potentially leading to training oscillations or even worse performance than rule-based approaches. Inspired by the human learning process, we propose <strong>Physics-enhanced Reinforcement Learning with Human Feedback (PE-RLHF)</strong>. This novel framework synergistically integrates human feedback (e.g., human intervention) and physics knowledge (e.g., traffic flow model) into the training loop of reinforcement learning. The key advantage of PE-RLHF is that the learned policy will perform at least as well as the given physics-based policy, even when human feedback quality deteriorates, thus ensuring trustworthy safety improvements. PE-RLHF introduces a Physics-enhanced Human-AI (PE-HAI) collaborative paradigm for dynamic action selection between human and physics-based actions, employs a reward-free approach with a proxy value function to capture human preferences, and incorporates a minimal intervention mechanism to reduce the cognitive load on human mentors. Extensive experiments across diverse driving scenarios demonstrate that PE-RLHF significantly outperforms traditional methods, achieving state-of-the-art (SOTA) performance in safety, efficiency, and generalizability, even with varying quality of human feedback. The philosophy behind PE-RLHF not only advances autonomous driving technology but can also offer valuable insights for other safety-critical domains. Demo video and code are available at: <span><span>https://zilin-huang.github.io/PE-RLHF-website/</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":"179 ","pages":"Article 105262"},"PeriodicalIF":7.6,"publicationDate":"2025-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144662138","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":"TRIP: Transport reasoning with intelligence progression — A foundation framework","authors":"Zhiyuan Liu ,&nbsp;Zhen Zhou ,&nbsp;Ziyuan Gu ,&nbsp;Shaoweihua Liu ,&nbsp;Pan Liu ,&nbsp;Yujie Zhang ,&nbsp;Yiliu He ,&nbsp;Kangyu Zhang","doi":"10.1016/j.trc.2025.105260","DOIUrl":"10.1016/j.trc.2025.105260","url":null,"abstract":"<div><div>The rapid evolution of intelligent transportation systems faces significant challenges, including incomplete traffic state representation, ineffective fusion of multi-source heterogeneous knowledge, and difficulties in hierarchical decision optimization. Traditional methods often isolate physical dynamics from semantic contexts, leading to fragmented reasoning and suboptimal control. To address these limitations, we propose TRIP (Transport Reasoning with Intelligence Progression), a novel framework grounded in dual state space theory. TRIP decomposes traffic system states into a physical state space and a semantic state space, interconnected via learnable mappings that ensure bidirectional, Lipschitz-continuous alignment. Leveraging advancements in large language models and world models, TRIP employs a hierarchical reinforcement learning approach to enable progressive reasoning—mimicking human expertise by transitioning from semantic understanding to physical prediction and action. Key innovations include cross-modal alignment to bridge data-driven and knowledge-based paradigms, scalable dual state space modeling for efficient long-sequence processing, and theoretical guarantees for stability and robustness. By unifying physical and semantic intelligence, TRIP lays the theoretical foundation for interpretable, real-time transportation systems capable of navigating complex, dynamic environments while balancing global optimization with local constraints. This work bridges a critical gap in ITS, offering a pathway toward adaptive, human-centric urban mobility solutions.</div></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":"179 ","pages":"Article 105260"},"PeriodicalIF":7.6,"publicationDate":"2025-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144655206","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":"Optimizing roadside LiDAR beam distribution to enhance vehicle detection performance considering dynamic vehicle occlusion effects","authors":"Yongjiang He ,&nbsp;Dajiang Suo ,&nbsp;Peng Cao ,&nbsp;Xiaobo Liu","doi":"10.1016/j.trc.2025.105268","DOIUrl":"10.1016/j.trc.2025.105268","url":null,"abstract":"<div><div>The distribution of LiDAR beams is the primary factor determining the density and coverage of point clouds over target vehicles, thus it has the most significant impact on vehicle detection performance. However, quantifying the influence of beam configurations on perception outcomes in complex traffic environments is challenging due to dynamic occlusions between vehicles and varying traffic densities. This study provides an effective framework for optimizing LiDAR beam distribution in dynamic traffic environments. Firstly, we proposed a dynamic occlusion model to calculate expected occlusion effects of target vehicles under various traffic flow densities. Based on this, an analytical model is developed to quantify the relationship between LiDAR beam distribution and vehicle detection performance. Furthermore, we developed a highly efficient optimization model for LiDAR beam distribution to enhance vehicle detection performance. Real-world experiments were conducted to collect vehicle point clouds using two types of LiDAR across six scenarios to validate the proposed models. Additionally, a series of simulation-based experiments demonstrated that the LiDAR beam distributions obtained from the optimization model achieved superior vehicle detection performance compared to SOTA methods. Notably, the optimization results for the 16-beam LiDAR are particularly significant, enhancing vehicle detection <em>Recall</em> by up to 63.7% (a 4.4-fold increase) compared to the baseline beam distribution. In addition, the optimization for an 80-beam LiDAR is completed in just 45 s, representing a speed improvement of 1,000 times compared to SOTA methods. This work provides both theoretical and practical contributions to the design of LiDAR sensing systems, which can be applied to intelligent transportation systems and autonomous driving infrastructure.</div></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":"179 ","pages":"Article 105268"},"PeriodicalIF":7.6,"publicationDate":"2025-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144655207","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":"Electric vehicles shared charging and parking: A reservation-based dynamic rolling allocation framework considering unpunctuality","authors":"Hongyang Zhang ,&nbsp;Jinrui Gong ,&nbsp;Wei Tang ,&nbsp;Zhenyu Mei ,&nbsp;Chi Feng","doi":"10.1016/j.trc.2025.105259","DOIUrl":"10.1016/j.trc.2025.105259","url":null,"abstract":"<div><div>Installing charging stations in public parking lots is a widely adopted strategy to meet the growing demand for electric vehicle (EV) charging. However, in high-demand areas, repurposing conventional parking spaces as charging spaces can increase parking cruising time. Additionally, idle periods in charging spaces may reduce overall space utilization efficiency. This paper proposes a reservation-based dynamic rolling allocation framework that incorporates a novel charging space-sharing mechanism, aiming to enhance space utilization and maximize revenue in hybrid parking lots. To account for user unpunctuality, this paper models arrival and leave time uncertainty using a probability distribution and incorporate it into a robust optimization formulation, which is then transformed into a mixed-integer programming (MIP) model. This approach enables the estimation of a theoretical time buffer required to minimize scheduling conflicts between consecutive users, given a predefined maximum probability of parking or charging conflict. A case study based on simulated data derived from large-scale real-world records validates the effectiveness of the proposed model. Results show that the dynamic rolling allocation and sharing mechanisms significantly improve total revenue and reduce user conflicts compared to baseline approaches. Additionally, comprehensive parameter sensitivity analyses offer practical insights for managing hybrid parking lots and optimizing operational performance.</div></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":"179 ","pages":"Article 105259"},"PeriodicalIF":7.6,"publicationDate":"2025-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144655205","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":"Berth allocation in dry bulk export terminals with channel restrictions","authors":"Zhen Cao ,&nbsp;Qinghe Sun ,&nbsp;Wenyuan Wang ,&nbsp;Shuaian Wang","doi":"10.1016/j.trc.2025.105263","DOIUrl":"10.1016/j.trc.2025.105263","url":null,"abstract":"<div><div>Efficient berth allocation (BA) is critical to port management, as berthing time and location directly impact operational efficiency. In dry bulk export terminals, the BA problem becomes more complex due to deballasting delays and pre-deballasting procedures, particularly under restrictive channel conditions. Terminal operators must balance pre-deballasting requirements with timely berthing to minimize delays. To address these challenges, we formulate the BA problem as a dynamic program, enabling sequential decision-making for each ship at every stage. To address the extensive state-action space, we propose a hierarchical decision framework that divides each stage into four planning-level substages and one scheduling-level substage, each handled by a dedicated agent. The planning level determines berthing positions and ship sequence, while the scheduling level coordinates berthing, channel access, and deballasting timelines based on planning outcomes. We propose a Planning by Reinforcement Learning and Scheduling by Optimization (PRLSO) approach, where agents employ either reinforcement learning (RL) or optimization, depending on substage characteristics. By confining RL-based agents to a reduced decision space, we significantly reduce training complexity. Following this, the remaining scheduling problem is solved on a reduced scale free from computational challenge. Experimental results show that the proposed method generates high-quality solutions in near real-time, even for large-scale instances. The framework also improves training efficiency and supports industrial-scale implementation.</div></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":"179 ","pages":"Article 105263"},"PeriodicalIF":7.6,"publicationDate":"2025-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144633704","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":"Benders decomposition for traveling salesman problem with drone: A space-time-state network perspective","authors":"Chenhao Zhang ,&nbsp;Yaoxin Wu ,&nbsp;Tao Feng ,&nbsp;Yibei Zhang ,&nbsp;Maocan Song ,&nbsp;Lin Cheng","doi":"10.1016/j.trc.2025.105255","DOIUrl":"10.1016/j.trc.2025.105255","url":null,"abstract":"<div><div>As technology advances, the integration of diverse transportation modes is accelerating, with truck-drone collaboration emerging as a promising solution to urban last-mile delivery challenges. While prior research has extensively covered spatial synchronization in customer service networks, studies on temporal synchronization and time-dependence in real transportation networks remains limited, as does the consideration of candidate rendezvous points. To address this gap, we propose a novel modeling approach to the Traveling Salesman Problem with Drone, utilizing a Space-Time-State network framework, termed TSPD-STS. An arc-based integer linear programming (ILP) model is presented for single-truck single-drone delivery, incorporating time-dependent travel time influenced by varying speeds, candidate rendezvous, drone capacity, payload-dependent endurance and time windows. We solve the problem by a branch-and-Benders-cut approach based on generalized Benders decomposition, dividing it into an assignment problem (i.e., the master problem) and a coupled scheduling problem (i.e., the subproblem). The master problem is solved only once, while the subproblem dynamically provides cuts along the branching. To improve computational efficiency, we design five classes valid inequalities to strengthen the relaxed master problem, and propose a dynamically tightened optimal cut and a novel valid cut which, unlike traditional no-good cut, can handle clusters of variables in a single step. Extensive computational experiments on 170 instances with 4 to 20 space nodes, tested under four time-dependent patterns and compared against ten algorithms using branch-and-bound and operation-based dynamic programming as baselines, demonstrate the superior performance of the proposed method. Our approach reduces computation time by 38.15% to 79.43% and yields smaller optimality gaps, with an average of 0.46%. Sensitivity analyses provide practical insights for logistics planning and strategic decision-making.</div></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":"179 ","pages":"Article 105255"},"PeriodicalIF":7.6,"publicationDate":"2025-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144623398","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":"Enhancing spatiotemporal demand prediction in transportation systems through region generation using soft clustering","authors":"Kyoungok Kim ,&nbsp;Peter Zhang","doi":"10.1016/j.trc.2025.105258","DOIUrl":"10.1016/j.trc.2025.105258","url":null,"abstract":"<div><div>Accurate spatiotemporal demand prediction is crucial for the effective service management of various transportation platforms, such as ride-hailing and micromobility services. While existing research primarily focuses on developing better prediction algorithms for demand, the relatively overlooked process of region generation is also important. In particular, predictions based on improperly defined regions can lead to poor predictive performance due to the modifiable areal unit problem. Therefore, it is essential to generate regions that reflect actual spatiotemporal demand patterns before training prediction models. This study proposes a region generation technique using a soft clustering approach, allowing spatial atomic units to belong to multiple clusters, unlike the conventional hard clustering method where each unit belongs to only one cluster. The proposed method selects spatial atomic units located at cluster boundaries and allows them to be part of adjacent clusters, thereby maintaining the geographic continuity of each cluster while overcoming the limitations of fixed boundaries that fail to capture the influence between neighboring clusters. Using three real-world datasets, the performance in demand prediction is evaluated based on regions generated by the proposed method and several comparison methods. The results show that the proposed method not only achieves the highest prediction accuracy but also exhibits the lowest variance in prediction accuracy across clusters.</div></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":"179 ","pages":"Article 105258"},"PeriodicalIF":7.6,"publicationDate":"2025-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144623396","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}