Transportation Research Part C-Emerging Technologies最新文献

{"title":"Understanding bus delay patterns under different temporal and weather conditions: A Bayesian Gaussian mixture model","authors":"Xiaoxu Chen ,&nbsp;Saeid Saidi ,&nbsp;Lijun Sun","doi":"10.1016/j.trc.2025.105000","DOIUrl":"10.1016/j.trc.2025.105000","url":null,"abstract":"<div><div>In public transit systems, bus delays significantly impact service reliability and passenger satisfaction. Causal delays, consisting of link running and stop dwell delays, are critical factors contributing to overall bus delay patterns. This paper develops a Bayesian probabilistic model to analyze bus delay patterns with a focus on causal delays under varying weather and temporal conditions, which can help to understand how the underlying causal delay patterns contribute to arrival delay patterns. Employing a Gaussian mixture model integrated with a topic model approach, the study analyzes causal delays as multivariate random variables, capturing the influence of temporal and weather conditions on bus service reliability. For model inference, we propose a Markov Chain Monte Carlo (MCMC) sampling method to estimate the model parameters. The analysis is conducted using real-world data from a bus route in Calgary, Canada. We categorize the identified delay patterns into four on-time categories: extreme earliness, moderate earliness, extreme lateness, and moderate lateness. Results indicate that adverse weather significantly influences extreme delay patterns in particular, suggesting the necessity for transit agencies to consider these factors in schedule optimization. Beyond pattern identification, the proposed model offers probabilistic delay estimation, enabling accurate forecasting of future delays based on current conditions and observations. Validation results demonstrate that our probabilistic estimates align closely with observed data, proving the model’s practical applicability in real-time operations and offering actionable insights to enhance the punctuality and efficiency of urban bus services.</div></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":"171 ","pages":"Article 105000"},"PeriodicalIF":7.6,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143096398","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":"Delay predictive analytics for airport capacity management","authors":"Nuno Antunes Ribeiro ,&nbsp;Jordan Tay ,&nbsp;Wayne Ng ,&nbsp;Sebastian Birolini","doi":"10.1016/j.trc.2024.104947","DOIUrl":"10.1016/j.trc.2024.104947","url":null,"abstract":"<div><div>Local delay predictions are crucial for optimizing airport capacity management, enhancing overall resilience, efficiency, and effectiveness of airport operations. This paper delves into the development and comparison of state-of-the-art predictive analytics techniques—spanning rule-based simulations, queuing models, and data-driven approaches—and demonstrates how they can empower informed decision-making toward mitigating the impact of potential delays across the whole spectrum of capacity management initiatives—from long-term strategic capacity planning to near real-time air traffic flow management. Using real-world data for four major airports in Southeast Asia, we comprehensively assess the performance of different methods and highlight the improved predictive capabilities achievable through data-driven methods and the incorporation of sophisticated features. Results show that (i) embedding queuing model features into machine learning models effectively captures congestion dynamics and nonlinear patterns, resulting in an improvement in predictive accuracy; (ii) incorporating advanced day-of features – lightning strikes, wind conditions, and propagated delays from prior hours – further enhances prediction accuracy, yielding <span><math><mrow><mi>M</mi><mi>A</mi><mi>E</mi></mrow></math></span> gains ranging from 15% to 30%, contingent on the specific airport; (iii) in cases where limited information is available (years to months in advance of operations), conventional simulation and queuing models emerge as robust alternatives. Ultimately, we conceptualize and validate a delay prediction framework for airport capacity management, characterizing the different planning phases based on their specific delay prediction requirements and identifying appropriate methods accordingly. This framework offers practical guidance to airport authorities, enabling them to effectively leverage delay predictions into their airport capacity management practices.</div></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":"171 ","pages":"Article 104947"},"PeriodicalIF":7.6,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143096502","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":"An advanced learning environment and a scalable deep reinforcement learning approach for rolling stock circulation on urban rail transit line","authors":"Yuhua Yang ,&nbsp;Haoyang Huo ,&nbsp;Nikola Bešinović ,&nbsp;Yichen Sun ,&nbsp;Shaoquan Ni","doi":"10.1016/j.trc.2024.104976","DOIUrl":"10.1016/j.trc.2024.104976","url":null,"abstract":"<div><div>Rolling stock circulation is the process of assigning rolling stocks to a set of predetermined train trips with fixed departure and arrival times. This paper considers mathematical models, solving approaches, and numerical experiments for hypothesized and real-world cases of rolling stock circulation, in which two end-point depots of an urban rail transit line are involved. The objective aims to minimize the total number of rolling stocks in utilization, to balance the workload of the utilized rolling stocks, and to balance the numbers of rolling stocks available at each depot at the beginning and the end of the planning horizon. To achieve the goals, a multi-commodity flow model and a deep reinforcement learning framework for the rolling stock circulation problem are proposed, accommodating the use of multiple types of rolling stocks, of which the former is a non-linear integer programming model. The multi-commodity flow model is solved by the CP Optimizer embedded in ILOG CPLEX and a custom-developed Ant Colony Optimization algorithm, serving as the exact and heuristic benchmarks respectively. The rolling stock circulation problem is innovatively modeled as a Markov decision process within the deep reinforcement learning framework, incorporating an advanced learning environment. This environment is designed by embedding state definition, constraint detection, and reward assignment, enabling effective interaction with the agent. A proximal policy optimization algorithm with a proximal policy update mechanism and adaptive policy-learning rates is adopted to solve the proposed problem. Numerical experiments on hypothesized and real-world cases illustrate the effectiveness of the proposed deep reinforcement learning method for rolling stock circulation. Compared to the benchmark approach, deep reinforcement learning can improve the solution quality with the problem scale increasing, which proves the adaptiveness to applications with complex environments and large state spaces and shows the strong potential to generalize across problems with different scales.</div></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":"171 ","pages":"Article 104976"},"PeriodicalIF":7.6,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143096503","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":"Shipping map: An innovative method in grid generation of global maritime network for automatic vessel route planning using AIS data","authors":"Lei Liu ,&nbsp;Mingyang Zhang ,&nbsp;Cong Liu ,&nbsp;Ran Yan ,&nbsp;Xiao Lang ,&nbsp;Helong Wang","doi":"10.1016/j.trc.2025.105015","DOIUrl":"10.1016/j.trc.2025.105015","url":null,"abstract":"<div><div>Considering the challenges faced by current global grid-based route planning methods, including vessel navigability underestimation and high computational demands for fine grid configuration, this study introduces an innovative approach to the grid generation of a global maritime network for automatic vessel route planning. By leveraging global Automatic Identification System (AIS) data, the methodology focuses on advanced trajectory segmentation, waypoint detection, clustering algorithms, and route searching. A novel spatiotemporal approach is proposed to facilitate effective trajectory segmentation despite data discontinuities. The Pruned Exact Linear Time (PELT) algorithm is employed to identify waypoints, managing their quantity during heading instability. To recognize crucial berthing areas in ports and strategic waypoint zones at sea, a customized KNN-block adaptive Density-Based Spatial Clustering of Applications with Noise (CKBA-DBSCAN) is developed to address the challenges of varying density clustering parameters and high computational costs. Lastly, the double-layer network matching technique, which starts with grid-based route planning and refines to the final navigable and smoothed route, uniquely integrates data-driven and model-based strategies. Rigorous testing with a year’s worth of global AIS data demonstrates high efficiency in planning navigable routes for various vessel types on worldwide voyages. The results underscore the practicality of the proposed approach in real-world route planning and maritime shipping network development. Remarkably, the methodology achieves a minimum 17.08 % reduction in time for global route generation. This hybrid approach, which integrates the strengths of both data-driven and model-based methods, significantly enhances vessel scheduling and routing efficiencies, showcasing its superior performance in comparative studies and its potential for widespread adoption in the maritime industry.</div></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":"171 ","pages":"Article 105015"},"PeriodicalIF":7.6,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143096519","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":"Decoding pedestrian stress on urban streets using electrodermal activity monitoring in virtual immersive reality","authors":"Mohsen Nazemi,&nbsp;Bara Rababah,&nbsp;Daniel Ramos,&nbsp;Tangxu Zhao,&nbsp;Bilal Farooq","doi":"10.1016/j.trc.2024.104952","DOIUrl":"10.1016/j.trc.2024.104952","url":null,"abstract":"<div><div>The pedestrian stress level is shown to significantly influence human cognitive processes and, subsequently, decision-making, e.g., the decision to select a gap and cross a street. This paper systematically studies the stress experienced by a pedestrian when crossing a street under different experimental manipulations by monitoring the Electrodermal Activity (EDA) using the Galvanic Skin Response (GSR) sensor. To fulfil the research objectives, a dynamic and immersive virtual reality (VR) platform was used, which is suitable for eliciting and capturing pedestrian’s emotional responses in conjunction with monitoring their EDA. A total of 171 individuals participated in the experiment, tasked to cross a two-way street at mid-block with no signal control. Mixed effects models were employed to compare the influence of socio-demographics, social influence, vehicle technology, environment, road design, and traffic variables on the stress levels of the participants. The results indicated that having a street median in the middle of the road operates as a refuge and significantly reduced stress. Younger participants (18–24 years) were calmer than the relatively older participants (55–65 years). Arousal levels were higher when it came to the characteristics of the avatar (virtual pedestrian) in the simulation, especially for those avatars with adventurous traits. The pedestrian location influenced stress since the stress was higher on the street while crossing than waiting on the sidewalk. Significant causes of arousal were fear of accidents and an actual accident for pedestrians. The estimated random effects show a high degree of physical and mental learning by the participants while going through the scenarios.</div></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":"171 ","pages":"Article 104952"},"PeriodicalIF":7.6,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143095588","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":"Rolling stock rescheduling in high-speed railway networks via a nested Benders decomposition approach","authors":"Denghui Li ,&nbsp;Jun Zhao ,&nbsp;Qiyuan Peng ,&nbsp;Dian Wang ,&nbsp;Qingwei Zhong","doi":"10.1016/j.trc.2025.105001","DOIUrl":"10.1016/j.trc.2025.105001","url":null,"abstract":"<div><div>This paper addresses a rolling stock rescheduling problem (RSRP) during disruptions in a high-speed railway network, focusing on decisions related to the reassignment of physical rolling stock units to trips, deadheading schedules, and maintenance plans and schedules, while taking into account deadheading trips and maintenance requirements. A mixed integer linear programming (MILP) model is formulated, leveraging a directed acyclic graph to represent all feasible connections for each rolling stock unit. The objective is to minimize the weighted sum of canceled trips, schedule deviations, and various service quality and cost indicators. An exact nested Benders decomposition (NBD) algorithm is developed to solve this model. In the algorithm, the RSRP is first decomposed into an outer integer master problem (OMP) and an outer integer subproblem (OSP). The OMP is then divided into an inner integer master problem (IMP) and an inner integer subproblem (ISP), and is solved using the logic-based Benders decomposition (LBBD) algorithm, where strengthened feasibility cuts and optimality cuts, as well as valid inequalities, are added to the IMP to enhance the algorithm’s performance. The OSP, a feasibility problem, is further decomposed into many easier problems at each depot. Subsequently, three implementations including two branch-and-check type approaches and one LBBD approach are customized to solve the outer decomposition problem. We also propose a three-stage approach to solve the IMP, ISP, and OSP, sequentially. The approaches are tested on a set of instances constructed from the high-speed railway network in China. The results show that the approaches can quickly find (near-)optimal solutions for tested instances within a short computation time of several minutes, making it suitable for real-time rolling stock rescheduling applications.</div></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":"171 ","pages":"Article 105001"},"PeriodicalIF":7.6,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143104802","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":"Pedestrian crossing decisions can be explained by bounded optimal decision-making under noisy visual perception","authors":"Yueyang Wang ,&nbsp;Aravinda Ramakrishnan Srinivasan ,&nbsp;Jussi P.P. Jokinen ,&nbsp;Antti Oulasvirta ,&nbsp;Gustav Markkula","doi":"10.1016/j.trc.2024.104963","DOIUrl":"10.1016/j.trc.2024.104963","url":null,"abstract":"<div><div>This paper presents a model of pedestrian crossing decisions based on the theory of computational rationality. It is assumed that crossing decisions are boundedly optimal, with bounds on optimality arising from human cognitive constraints. While previous models of pedestrian behaviour have been either ‘black-box’ machine learning models or mechanistic models with explicit assumptions about cognitive factors, we combine both approaches. Specifically, we mechanistically model noisy human visual perception and model reward considering human constraints in crossing, but we use reinforcement learning to learn boundedly optimal behaviour policy. The model reproduces a larger number of known empirical phenomena than previous models, in particular: (1) the effect of the time to arrival of an approaching vehicle on whether the pedestrian accepts the gap, the effect of the vehicle’s speed on both (2) gap acceptance and (3) pedestrian timing of crossing in front of yielding vehicles, and (4) the effect on this crossing timing of the stopping distance of the yielding vehicle. Notably, our findings suggest that behaviours previously framed as ’biases’ in decision-making, such as speed-dependent gap acceptance, might instead be a product of rational adaptation to the constraints of visual perception. Our approach also permits fitting the parameters of cognitive constraints and rewards per individual to better account for individual differences, achieving good quantitative alignment with experimental data. To conclude, by leveraging both RL and mechanistic modelling, our model offers novel insights into pedestrian behaviour and may provide a useful foundation for more accurate and scalable pedestrian models.</div></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":"171 ","pages":"Article 104963"},"PeriodicalIF":7.6,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143095600","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":"i-CLTP: Integrated contrastive learning with transformer framework for traffic state prediction and network-wide analysis","authors":"Ruo Jia ,&nbsp;Kun Gao ,&nbsp;Yang Liu ,&nbsp;Bo Yu ,&nbsp;Xiaolei Ma ,&nbsp;Zhenliang Ma","doi":"10.1016/j.trc.2024.104979","DOIUrl":"10.1016/j.trc.2024.104979","url":null,"abstract":"<div><div>Traffic state predictions are critical for the traffic management and control of transport systems. This study introduces an innovative contrastive learning framework coupled with a transformer architecture for spatiotemporal traffic state prediction, designed to capture the spatio-temporal heterogeneity inherent in traffic. The transformer structure functions as the upper level of the prediction framework to minimize the prediction errors between the input and predicted output. Based on the self-supervised contrastive learning, the lower level in the framework is proposed to discern the spatio-temporal heterogeneity and embed the latent characteristic of traffic flow by regenerating the augmentation features. Then, a soft clustering problem is applied between the upper level and lower level to category the types of traffic flow characteristics by minimizing the joint loss across each cluster. Subsequently, the proposed model is evaluated through a real-world highway traffic flow dataset for bench marking against several latest existing models. The experimental results affirm that the proposed model considerably enhances traffic state prediction accuracy. In terms of precision metrics, the model records a Mean Absolute Error of 13.31 and a Mean Absolute Percentage Error of 7.85%, reflecting marked improvements of 2.0% and 14.5% respectively over the latest and most competitive baseline model. Furthermore, the analysis reveals that capacity of the proposed method to learn the cluster patterns of spatio-temporal traffic dynamics reflected by calibrated fundamental diagrams.</div></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":"171 ","pages":"Article 104979"},"PeriodicalIF":7.6,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143096500","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":"Designing a robust and cost-efficient electrified bus network with sparse energy consumption data","authors":"Sara Momen ,&nbsp;Yousef Maknoon ,&nbsp;Bart van Arem ,&nbsp;Shadi Sharif Azadeh","doi":"10.1016/j.trc.2025.105020","DOIUrl":"10.1016/j.trc.2025.105020","url":null,"abstract":"<div><div>This paper addresses the challenges of charging infrastructure design (CID) for electrified public transport networks using Battery Electric Buses (BEBs) under conditions of sparse energy consumption data. Accurate energy consumption estimation is critical for cost-effective and reliable electrification but often requires costly field experiments, resulting in limited data. To address this issue, we propose two mathematical models designed to handle uncertainty and data sparsity in energy consumption. The first is a robust optimization model with box uncertainty, addressing variability in energy consumption. The second is a data-driven distributionally robust optimization model that leverages observed data to provide more flexible and informed solutions. To evaluate these models, we apply them to the Rotterdam bus network. Our analysis reveals three key insights: (1) Ignoring variations in energy consumption can result in operational unreliability, with up to 55% of scenarios leading to infeasible trips. (2) Designing infrastructure based on worst-case energy consumption increases costs by 67% compared to using average estimates. (3) The data-driven distributionally robust optimization model reduces costs by 28% compared to the box uncertainty model while maintaining reliability, especially in scenarios where extreme energy consumption values are rare and data exhibit skewness. In addition to cost savings, this approach provides robust protection against uncertainty, ensuring reliable operation under diverse conditions.</div></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":"171 ","pages":"Article 105020"},"PeriodicalIF":7.6,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143096521","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":"Human-automated vehicle interactions: Voluntary driver intervention in car-following","authors":"Xinzhi Zhong ,&nbsp;Yang Zhou ,&nbsp;Amudha Varshini Kamaraj ,&nbsp;Zhenhao Zhou ,&nbsp;Wissam Kontar ,&nbsp;Dan Negrut ,&nbsp;John D. Lee ,&nbsp;Soyoung Ahn","doi":"10.1016/j.trc.2024.104969","DOIUrl":"10.1016/j.trc.2024.104969","url":null,"abstract":"<div><div>This paper is concerned with the behavior of voluntary driver interventions in automated vehicles in car-following, initiated by the driver in non-safety-critical situations rather than by the system. Specifically, this study analyzes the dynamic process of voluntary driver intervention through evidence accumulation (EA) modeling, which describes the evolution of the driver’s distrust in automation, ultimately resulting in intervention. The model is calibrated using data from a driving simulator experiment. The experimental data also suggests that driver interventions can instigate substantial traffic disturbances that are amplified through upstream traffic. Based on the findings, we develop a car-following control for AVs by embedding the calibrated EA model in a deep reinforcement learning (DRL) framework. Numerical experiments demonstrate that the proposed control can effectively mitigate unnecessary driver interventions while improving traffic stability. <em>The code supporting the findings of this study are available in</em> <span><span><em>Github page</em></span><svg><path></path></svg></span>.</div></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":"171 ","pages":"Article 104969"},"PeriodicalIF":7.6,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143096050","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}