Transportation Research Part B-Methodological最新文献

{"title":"Liner fleet deployment and empty container repositioning under demand uncertainty: A robust optimization approach","authors":"Xi Xiang ,&nbsp;Xiaowei Xu ,&nbsp;Changchun Liu ,&nbsp;Shuai Jia","doi":"10.1016/j.trb.2024.103088","DOIUrl":"10.1016/j.trb.2024.103088","url":null,"abstract":"<div><div>This paper investigates a robust optimization problem concerning the integration of fleet deployment and empty container repositioning in a shipping line network, where a fleet of vessels is dispatched to transport both laden and empty containers, aiming to fulfill a predetermined set of requests over a defined time horizon. The sizes of customer demands are uncertain and are characterized by a budgeted uncertainty set. This study aims to ascertain the vessel types assigned to each shipping route, the routing of laden containers, and the repositioning of empty containers in a manner that minimizes the total cost. Simultaneously, it ensures the feasibility of all transportation plans for any realization of demand within the uncertainty set. We introduce a path-based two-stage robust formulation for addressing the problem. In the first stage, the assignment of vessel types to each shipping route is determined, and the second stage focuses on establishing the routing of laden containers and repositioning of empty containers under a worst-case scenario. We propose the Column-and-Constraint Generation algorithm for solving the proposed robust formulation. To address large-scale size instances, we propose an acceleration technique, i.e., the piece-wise affine policy, which reduces the dimensions of the uncertainty set while maintaining a bounded compromise in solution quality. Comprehensive numerical experiments derived from real-world industries, such as the Shanghai port and CMA CGM, are conducted to validate the proposed formulation and solution methodologies.</div></div>","PeriodicalId":54418,"journal":{"name":"Transportation Research Part B-Methodological","volume":"190 ","pages":"Article 103088"},"PeriodicalIF":5.8,"publicationDate":"2024-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142417310","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":"Robust convoy movement problem under travel time uncertainty","authors":"Byung Jun Ju,&nbsp;Byung Do Chung","doi":"10.1016/j.trb.2024.103091","DOIUrl":"10.1016/j.trb.2024.103091","url":null,"abstract":"<div><div>A convoy represents a collection of vehicles traveling with a spacing of 50–100 m between them for tactical purposes. The convoy movement problem is a variant of the vehicle routing problem, an NP-hard problem aimed at determining the paths and schedules of convoys. Given the uncertainties in travel times during wartime, attributable to various factors such as road conditions and enemy threats, it is essential to consider uncertain travel times when determining convoy paths and schedules. Therefore, this study introduces a robust convoy movement problem under travel time uncertainty. A polyhedral set for uncertain travel times is used to derive a robust counterpart for the problem. To solve the proposed problem, we establish an exact algorithm that determines optimal solutions by iteratively generating and integrating multiple paths of convoys. This algorithm involves four steps: generation of <em>k</em>-th robust shortest paths, construction of path combinations, adjustment of departure times, and conduction of optimality check. These steps are iterated sequentially until the optimal solution is obtained. In computational experiments, the exact algorithm demonstrates superior performance and reduced computation time compared with the commercial solver CPLEX on both real instances and randomly generated instances. In addition, we conduct a sensitivity analysis for several parameters related to the problem, providing valuable managerial insights for decision-makers.</div></div>","PeriodicalId":54418,"journal":{"name":"Transportation Research Part B-Methodological","volume":"190 ","pages":"Article 103091"},"PeriodicalIF":5.8,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142417311","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 iterative method for integrated hump sequencing, train makeup, and classification track assignment in railway shunting yard","authors":"Bojian Zhang ,&nbsp;Jun Zhao ,&nbsp;Andrea D’Ariano ,&nbsp;Yongxiang Zhang ,&nbsp;Tao Feng ,&nbsp;Qiyuan Peng","doi":"10.1016/j.trb.2024.103087","DOIUrl":"10.1016/j.trb.2024.103087","url":null,"abstract":"<div><div>In a railway shunting yard, the transformation of inbound trains into properly composed outbound trains is a complex task because it involves decisions of multiple operations processes. This study addresses the integrated optimization of hump sequencing, train makeup, and classification track assignment problem in a railway shunting yard. Several key practical yard operation constraints are considered, including train formulation constraints, hump sequencing constraints, and limitations of the maximum number and capacity of classification tracks. By introducing a new representation of block flow, the integrated problem, which adopts the extended single-stage strategy and the train-to-track policy, is formulated as a unified 0-1 integer linear programming model. The objective of the proposed model is to minimize the weighted-sum of the total dwell time of all railcars and the formulation deviation penalties of all outbound trains. Then, an iterative two-phase decomposition approach is developed to reduce the complexity of solving the integrated problem. The first phase aims to explore all feasible humping sequences using a Branch-and-Bound (B&amp;B) algorithm. Each time a new humping sequence is generated in the first phase, the second phase containing a Branch-and-Price (B&amp;P) algorithm is applied to solve the integrated train makeup and classification track assignment problem with the known humping sequence found in the first phase. In addition, greedy heuristics and lower bounding techniques are designed in both phases to improve computational efficiency. Comprehensive experiments are investigated based on a set of real-life instances. The results show that exact approaches provide optimal solutions, whereas heuristic approaches yield satisfactory solutions within a shorter computation time. Moreover, sensitivity analyses on the number of classification tracks and the effects of different deviation penalties are also performed to gain more managerial insights.</div></div>","PeriodicalId":54418,"journal":{"name":"Transportation Research Part B-Methodological","volume":"190 ","pages":"Article 103087"},"PeriodicalIF":5.8,"publicationDate":"2024-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142329777","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":"Freelance drivers with a decline choice: Dispatch menus in on-demand mobility services for assortment optimization","authors":"Yue Yang ,&nbsp;Seeun William Umboh ,&nbsp;Mohsen Ramezani","doi":"10.1016/j.trb.2024.103082","DOIUrl":"10.1016/j.trb.2024.103082","url":null,"abstract":"<div><div>With the prosperity of sharing economy, more part-time and freelance suppliers (i.e., drivers) join on-demand mobility services. Because of suppliers’ autonomy and behavioural heterogeneity, the platform cannot ensure that suppliers will accept a dispatch order. One approach to mitigate this supply uncertainty is to provide suppliers with personalized menus of dispatch recommendations. A key issue then is to determine which dispatch orders (that can be passenger or goods services) should be allocated into the assortment menu of each supplier. This paper probabilistically models the suppliers’ order acceptance and choice behaviour, including a <em>decline</em> option. We propose two assortment optimization problems, disjoint and joint menus, to maximize the expected number of matches. We show that the objective function of the disjoint menu assortment problem is monotone non-decreasing submodular. In contrast, the objective function of the joint menu assortment problem is non-monotone and non-submodular. Accordingly, we present a standard greedy (SG) algorithm to solve the disjoint assortment problem, and <span><math><msup><mrow><mi>γ</mi></mrow><mrow><mo>∗</mo></mrow></msup></math></span>-greedy and local search (LS) algorithms for the joint assortment problem. By bundling orders into consolidated routes, this paper extends the proposed menu assortment methods to the context of meal delivery services. A case study is presented based on the real-world demand in the Manhattan road network. The results show that drivers’ autonomy to decline the dispatch orders creates substantial coexistence of idle drivers and unmatched orders in the market. The proposed menu assortment methods curb such matching friction. Moreover, the numerical results demonstrate that the proposed algorithms significantly outperform the traditional dispatching policies applied in practice, e.g., one-to-one matching, in terms of platform efficiency, e.g., achieving more matches, customers’ experiences, e.g., reducing waiting time, and benefits for drivers, e.g., tapering off the income inequality among drivers.</div></div>","PeriodicalId":54418,"journal":{"name":"Transportation Research Part B-Methodological","volume":"190 ","pages":"Article 103082"},"PeriodicalIF":5.8,"publicationDate":"2024-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142322985","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 online auction-based mechanism for pricing and allocation of instant delivery services","authors":"Jiantao Guo,&nbsp;Lan Deng,&nbsp;Baichuan Gong","doi":"10.1016/j.trb.2024.103086","DOIUrl":"10.1016/j.trb.2024.103086","url":null,"abstract":"<div><div>Recently, instant delivery has been growing rapidly, with numerous platforms emerging to offer such services. Requestors dynamically arrive at the platform to place delivery service requests that detail their pickup locations, recipient locations, package weights, departure times, and willingness-to-pay (WTP). The platform then uses its dedicated riders, scattered in different places, to fulfill these requests. Given the dynamic and fluctuating characteristics of the demand, coupled with limited rider resources and heterogenous pickup costs, the platform faces the critical problem of dynamically pricing the requests and assigning the riders to maximize social welfare. To address this problem, we propose an online auction-based transaction mechanism. Specifically, we first propose a scoring function to evaluate the values of the requests over multi-period operations taking into account the requests’ attributes, riders’ delivery costs, and resource availability. Based on the scoring function, we design a time-varying Vickrey–Clarke–Groves (VCG)-like payment rule that can reflect the impacts of fluctuating supply-demand imbalances. Under this rule, a requestor will pay more during undersupply periods than during oversupply periods. To carve out the different impact degrees of the supply-demand imbalances, we further consider the linear, quadratic, and exponential time-varying resource parameters to construct the payment rule. In addition, we develop an online instant delivery resource allocation model to efficiently assign the riders to fulfill the accepted requests. We show that the proposed mechanism has desirable properties (individual rationality, budget balance, and incentive compatibility) and is computationally efficient. Furthermore, we give a lower bound for the mechanism efficiency. To validate the practicality of our mechanism and get some managerial insights into the operations of the instant delivery platform, we conduct numerical studies to compare the performance of our mechanism to the First-in, first-out (FIFO) allocation mechanism and to investigate the impacts of pricing functions, rolling horizon configurations, and rider numbers on the mechanism's performance.</div></div>","PeriodicalId":54418,"journal":{"name":"Transportation Research Part B-Methodological","volume":"190 ","pages":"Article 103086"},"PeriodicalIF":5.8,"publicationDate":"2024-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142319583","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 aggregate matching and pick-up model for mobility-on-demand services","authors":"Xinwei Li ,&nbsp;Jintao Ke ,&nbsp;Hai Yang ,&nbsp;Hai Wang ,&nbsp;Yaqian Zhou","doi":"10.1016/j.trb.2024.103070","DOIUrl":"10.1016/j.trb.2024.103070","url":null,"abstract":"<div><p>This paper presents an Aggregate Matching and Pick-up (AMP) model to delineate the matching and pick-up processes in mobility-on-demand (MoD) service markets by explicitly considering the matching mechanisms in terms of matching intervals and matching radii. With passenger demand rate, vehicle fleet size and matching strategies as inputs, the AMP model can well approximate drivers’ idle time and passengers’ waiting time for matching and pick-up by considering batch matching in a stationary state. Properties of the AMP model are then analyzed, including the relationship between passengers’ waiting time and drivers’ idle time, and their changes with market thickness, which is measured in terms of the passenger arrival rate (demand rate) and the number of active vehicles in service (supply). The model can also unify several prevailing inductive and deductive matching models used in the literature and spell out their specific application scopes. In particular, when the matching radius is sufficiently small, the model reduces to a Cobb–Douglas type matching model proposed by Yang and Yang (2011) for street-hailing taxi markets, in which the matching rate depends on the pool sizes of waiting passengers and idle vehicles. With a zero matching interval and a large matching radius, the model reduces to Castillo model developed by Castillo et al. (2017) that is based on an instant matching mechanism, or a bottleneck type queuing model in which passengers’ matching time is derived from a deterministic queue at a bottleneck with the arrival rate of idle vehicles as its capacity and waiting passengers as its customers. When both the matching interval and matching radius are relatively large, the model also reduces to the bottleneck type queuing model. The performance of the proposed AMP model is verified with simulation experiments.</p></div>","PeriodicalId":54418,"journal":{"name":"Transportation Research Part B-Methodological","volume":"190 ","pages":"Article 103070"},"PeriodicalIF":5.8,"publicationDate":"2024-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142271500","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":"Kinetic Monte Carlo simulations of 1D and 2D traffic flows: Nonlocal models with generalized look-ahead rules","authors":"Yi Sun","doi":"10.1016/j.trb.2024.103083","DOIUrl":"10.1016/j.trb.2024.103083","url":null,"abstract":"<div><p>This paper presents a study on traffic flow models in one-dimensional (1D) and two-dimensional (2D) lattices. The models incorporate generalized look-ahead rules that consider nonlocal slow-down effects. The proposed cellular automata (CA) models use stochastic rules to determine the movement of cars based on the traffic configuration ahead of each car. Specifically, a look-ahead rule is used that considers both the car density ahead and a generalized interaction function based on the distance between cars. The CA models are simulated using an efficient kinetic Monte Carlo (KMC) algorithm. The numerical results in 1D demonstrate that the flows from the KMC simulations align with the macroscopic averaged fluxes for the look-ahead rule, across various parameter settings. In the 2D results, a sharp phase transition is observed from freely flowing traffic to global jamming, depending on the initial density of cars.</p></div>","PeriodicalId":54418,"journal":{"name":"Transportation Research Part B-Methodological","volume":"190 ","pages":"Article 103083"},"PeriodicalIF":5.8,"publicationDate":"2024-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142271615","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":"Dynamic scheduling of flexible bus services with hybrid requests and fairness: Heuristics-guided multi-agent reinforcement learning with imitation learning","authors":"Weitiao Wu ,&nbsp;Yanchen Zhu ,&nbsp;Ronghui Liu","doi":"10.1016/j.trb.2024.103069","DOIUrl":"10.1016/j.trb.2024.103069","url":null,"abstract":"<div><p>Flexible bus is a class of demand-responsive transit that provides door-to-door service. It is gaining popularity now but also encounters many challenges, such as high dynamism, immediacy requirements, and financial sustainability. Scientific literature designs flexible bus services only for reservation demand, overlooking the potential market for immediate demand that can improve ride pooling and financial sustainability. The increasing availability of historical travel demand data provides opportunities for leveraging future demand prediction in optimizing fleet utilization. This study investigates prediction failure risk-aware dynamic scheduling flexible bus services with hybrid requests allowing for both reservation and immediate demand. Equity in request waiting time for immediate demand is emphasized as a key objective. We model this problem as a multi-objective Markov decision process to jointly optimize vehicle routing, timetable, holding control and passenger assignment. To solve this problem, we develop a novel heuristics-guided multi-agent reinforcement learning (MARL) framework entailing three salient features: 1) incorporating the demand forecasting and prediction error correction modules into the MARL framework; 2) combining the benefits of MARL, local search algorithm, and imitation learning (IL) to improve solution quality; 3) incorporating an improved strategy in action selection with time-related information about spatio-temporal relationships between vehicles and passengers to enhance training efficiency. These enhancements are general methodological contributions to the artificial intelligence and operations research communities. Numerical experiments show that our proposed method is comparable to prevailing benchmark methods both with respect to training stability and solution quality. The benefit of demand prediction is significant even when the prediction is imperfect. Our model and algorithm are applied to a real-world case study in Guangzhou, China. Managerial insights are also provided.</p></div>","PeriodicalId":54418,"journal":{"name":"Transportation Research Part B-Methodological","volume":"190 ","pages":"Article 103069"},"PeriodicalIF":5.8,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142239620","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":"Local detouredness: A new phenomenon for modelling route choice and traffic assignment","authors":"Thomas Kjær Rasmussen ,&nbsp;Lawrence Christopher Duncan ,&nbsp;David Paul Watling ,&nbsp;Otto Anker Nielsen","doi":"10.1016/j.trb.2024.103052","DOIUrl":"10.1016/j.trb.2024.103052","url":null,"abstract":"<div><p>This study introduces the novel concept of local detouredness, i.e. detours on subsections of a route, as a new phenomenon for understanding and modelling route choice. Traditionally, Stochastic User Equilibrium (SUE) traffic assignment models have been concerned with judging the attractiveness of a route by its total route cost. However, through empirical analysis we show that considering solely the global properties of a route is insufficient. We find that it is important to consider local detouredness both when determining realistic and tractable route choice sets and when determining route choice probabilities. For example, analysis of observed route choice data shows that route usage tends to decay with local detouredness, and that there is an apparent limit on the amount of local detouredness seen as acceptable. No existing models can account for this systematically and consistently, which is the motivation for the new route choice model proposed in this paper: the Bounded Choice Model with Local Detour Threshold (BCM-LDT). The BCM-LDT model incorporates the effect of local detouredness on route choice probability, and has an in-built mechanism that assigns zero probabilities to routes violating a bound on total route costs and/or a threshold on local detouredness. Thereby, the model consistently predicts which routes are used and unused. Moreover, the probability expression is closed-form and continuous. SUE conditions for the BCM-LDT are given, and solution existence is proven. Exploiting the special structure of the problem, a novel solution algorithm is proposed where flow averaging is integrated with a modified branch-and-bound method that iteratively column-generates all routes satisfying local and global bounds. Numerical experiments are conducted on small-scale and large-scale networks, establishing that equilibrated solutions can be found and demonstrating the influence of the BCM-LDT parameters on choice set size and flow allocation.</p></div>","PeriodicalId":54418,"journal":{"name":"Transportation Research Part B-Methodological","volume":"190 ","pages":"Article 103052"},"PeriodicalIF":5.8,"publicationDate":"2024-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0191261524001760/pdfft?md5=138aa793a1feba64b09544765e7cb150&pid=1-s2.0-S0191261524001760-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142228903","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":"Comparing hundreds of machine learning and discrete choice models for travel demand modeling: An empirical benchmark","authors":"Shenhao Wang ,&nbsp;Baichuan Mo ,&nbsp;Yunhan Zheng ,&nbsp;Stephane Hess ,&nbsp;Jinhua Zhao","doi":"10.1016/j.trb.2024.103061","DOIUrl":"10.1016/j.trb.2024.103061","url":null,"abstract":"<div><p>Numerous studies have compared machine learning (ML) and discrete choice models (DCMs) in predicting travel demand. However, these studies often lack generalizability as they compare models deterministically without considering contextual variations. To address this limitation, our study develops an empirical benchmark by designing a tournament model to learn the intrinsic predictive values of ML and DCMs. This novel approach enables us to efficiently summarize a large number of experiments, quantify the randomness in model comparisons, and use formal statistical tests to differentiate between the model and contextual effects. This benchmark study compares two large-scale data sources: a database compiled from literature review summarizing 136 experiments from 35 studies, and our own experiment data, encompassing a total of 6970 experiments from 105 models and 12 model families, tested repeatedly on three datasets, sample sizes, and choice categories. This benchmark study yields two key findings. Firstly, many ML models, particularly the ensemble methods and deep learning, statistically outperform the DCM family and its individual variants (i.e., multinomial, nested, and mixed logit), thus corroborating with the previous research. However, this study also highlights the crucial role of the contextual factors (i.e., data sources, inputs and choice categories), which can explain models’ predictive performance more effectively than the differences in model types alone. Model performance varies significantly with data sources, improving with larger sample sizes and lower dimensional alternative sets. After controlling all the model and contextual factors, significant randomness still remains, implying inherent uncertainty in such model comparisons. Overall, we suggest that future researchers shift more focus from context-specific and deterministic model comparisons towards examining model transferability across contexts and characterizing the inherent uncertainty in ML, thus creating more robust and generalizable next-generation travel demand models.</p></div>","PeriodicalId":54418,"journal":{"name":"Transportation Research Part B-Methodological","volume":"190 ","pages":"Article 103061"},"PeriodicalIF":5.8,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142172339","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}