Transportation Research Part C-Emerging Technologies最新文献

{"title":"Developing and validating an adaptive multi-layer vehicle trajectory reconstruction method for outlier removal","authors":"Ruijie Li ,&nbsp;Zuduo Zheng ,&nbsp;Dong Ngoduy ,&nbsp;Linbo Li","doi":"10.1016/j.trc.2024.104946","DOIUrl":"10.1016/j.trc.2024.104946","url":null,"abstract":"<div><div>Trajectory data is vital for traffic flow studies, and aerial photography-based methods are increasingly used to collect such data. However, these datasets often contain errors from various sources, which can be exacerbated by numerical derivative processes. Previous efforts have not fully addressed some of these issues such as consistency, varying length of outlier sequences, and unknown ground truth trends. Moreover, existing validation methods are often indirect and problematic. To address these limitations, we propose an adaptive multi-layer vehicle trajectory reconstruction method, which consists of three modules: the Initial Window Arrangement module to ensure the precise alignment between the reconstruction window and detected outlier fragments, maintaining internal consistency at boundary points; the Window Size Feasibility Test module to adaptively determine the window size according to varying length of outlier sequences, and XGBoost-based Ground Truth Estimation module to be combined with a least-square-based objective function to significantly improve reconstruction accuracy and more closely replicate the underlying trend. Additionally, we introduce the jerk-based reconstruction, which outperforms the acceleration-based reconstruction. To reliably assess and select the best ground truth estimation scheme and objective function, a novel synthetic dataset containing both the ground truth and realistic outlier fragments is proposed. Subsequently, a comparative evaluation of six different outlier removal methods was conducted using Zen Traffic Data. The validation results, utilizing both the synthetic dataset and Zen Traffic Data, demonstrate the exceptional performance of the proposed method across various evaluation perspectives. The good performance of the proposed outlier removal method is further demonstrated by comparing the IDM calibration results using the trajectories with and without outliers being removed. With just one parameter (jerk anomaly threshold), the parameter settings of our method are more objective and generalizable.</div></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":"171 ","pages":"Article 104946"},"PeriodicalIF":7.6,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142757495","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":"No time for stopping: A Stop-Less Autonomous Modular (SLAM) bus service","authors":"Zaid Saeed Khan ,&nbsp;Mónica Menéndez","doi":"10.1016/j.trc.2024.104888","DOIUrl":"10.1016/j.trc.2024.104888","url":null,"abstract":"<div><div>We leverage the in-motion transfer capability of autonomous modular buses to propose SLAM bus, a novel bus service paradigm that gives passengers a nearly stop-less travel experience from their origin to their destination bus stop. It does this by using supplementary modular units that detach and attach from the main bus at bus stops to serve boarding and alighting passengers, while the main bus traverses the route without stopping. The result is a stop-less operation that eliminates the need for passengers to stop at bus stops where they do not wish to alight. For busier bus stops that cannot be effectively served by the stop-less operation, the whole bus makes a stop instead. The service makes both pre-determined and real-time choices between these operating modes based on the expected and actual demand of alighting and non-alighting passengers. The SLAM bus service thus significantly reduces travel times since passengers experience fewer stops between their desired origin and destination bus stops, making its travel time more competitive with private vehicles while still providing the economies of scale of public transport. Our proof-of-concept simulation results show that, compared to an equivalent conventional bus service, the proposed service can reduce passengers’ average travel cost by about <span><math><mrow><mn>15</mn><mo>−</mo><mn>20</mn><mtext>%</mtext></mrow></math></span> for a realistic bus route.</div></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":"171 ","pages":"Article 104888"},"PeriodicalIF":7.6,"publicationDate":"2024-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142746205","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":"Real-time executable platoon formation approach using hierarchical cooperative motion planning framework","authors":"Hanyu Zhang,&nbsp;Lili Du","doi":"10.1016/j.trc.2024.104942","DOIUrl":"10.1016/j.trc.2024.104942","url":null,"abstract":"<div><div>While connected and automated vehicle (CAV) platooning holds promise for enhancing traffic efficiency and reducing energy consumption, we still lack efficient algorithms for guiding the local movements of CAVs to form a platoon on a road due to significant computational and control challenges. This study addresses this gap by designing a real-time executable Hierarchical and Recursive Platoon Formation (HR-PF) framework tailored to mixed flow traffic conditions that encompass both Human-Driven Vehicles (HDV) and CAVs. The HR-PF framework comprises three hierarchical mathematical models (modules) designed to optimize platoon formation while considering both macroscopic traffic conditions and microscopic traffic safety. Module-I formulates a mixed integer quadratic program to determine the timing, location, and state of platoon formation. It is further extended to a mixed integer nonlinear program so that we can also select the optimal size of the target platoon. Module-II designs a Hybrid State-Lattice Motion planner to generate optimal trajectory references for CAVs to approach the target platoon state, ensuring microscopic traffic safety. Module-III develops longitudinal and lateral controllers to enable CAVs to track trajectory references accurately. These models function recursively at varying frequencies to balance mathematical rigor with practical application. Numerical experiments demonstrate that HR-PF facilitates efficient platoon formation in real-time across diverse traffic scenarios and road geometries while sustaining traffic efficiency. Furthermore, the performance of platoon formation is affected by surrounding traffic density and CAV penetrations, with prompt formation observed under LOS C and D traffic environments and slightly more traffic impacts under LOS E and F. These findings provide robust support for exploring advanced platoon formation and platooning strategies for CAVs under complicated traffic environments.</div></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":"171 ","pages":"Article 104942"},"PeriodicalIF":7.6,"publicationDate":"2024-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142746207","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":"FAIRLANE: A multi-agent approach to priority lane management in diverse traffic composition","authors":"Rohit K. Dubey ,&nbsp;Damian Dailisan ,&nbsp;Javier Argota Sánchez–Vaquerizo ,&nbsp;Dirk Helbing","doi":"10.1016/j.trc.2024.104919","DOIUrl":"10.1016/j.trc.2024.104919","url":null,"abstract":"<div><div>The rise of autonomous driving technologies prompts a reevaluation of traditional urban traffic control and lane management. Dedicated lanes for connected and autonomous vehicles (CAVs), with intermittent access for other vehicles, have been proven to enhance road capacity and reduce underutilization moderately. However, this assumes all non-CAVs are smart vehicles, which is different from the current baseline for the street vehicle mix. Presently, our streets feature a mix of CAVs, smart vehicles, and human-driven vehicles, and the research on dedicated lanes using the realistic mixed traffic environment is missing. In this paper, we investigate the enhancement of road utilization using realistic mixed traffic combinations and identify the penetration rate of CAVs and smart vehicles necessary to improve baseline utilization. Previous studies have focused on lane-management strategies in single-vehicle settings, neglecting the interaction of CAVs with neighboring CAVs and smart vehicles. Therefore, we propose a multi-agent reinforcement learning-based framework to facilitate fair utilization of priority lanes, considering driving comfort, traffic efficiency, and safety during lane-changing. Through multiple experiments on a realistic network, our results demonstrate that the proposed framework significantly improves traffic efficiency, particularly when the penetration rate of CAVs is below 40% and Semi-Autonomous Vehicles (SAVs) constitute 50% of the remaining vehicles. The framework outperforms traditional lane management strategies, reducing mean waiting time and increasing average speed. This study provides nuanced information on different vehicle penetration rates, enabling more informed decisions on when to install priority lanes. This highlights the importance of considering mixed traffic environments in designing autonomous vehicle infrastructure and sets the stage for future advancements in urban traffic management.</div></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":"171 ","pages":"Article 104919"},"PeriodicalIF":7.6,"publicationDate":"2024-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142746206","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":"Integrating Energy-Efficient Train Control in railway Vertical Alignment Optimization: A novel Mixed-Integer Linear Programming approach","authors":"Yichen Sun ,&nbsp;Shaoquan Ni ,&nbsp;Dingjun Chen ,&nbsp;Qing He ,&nbsp;Shuangting Xu ,&nbsp;Yan Gao ,&nbsp;Tao Chen","doi":"10.1016/j.trc.2024.104943","DOIUrl":"10.1016/j.trc.2024.104943","url":null,"abstract":"<div><div>Incorporating train control into the railway design process enables a practical and comprehensive evaluation of the lifecycle utility of a track profile. This paper proposes a novel integrated approach, termed EETC-VAO, which combines railway track Vertical Alignment Optimization (VAO) and Energy-Efficient Train Control (EETC). Initially formulated as a Mixed-Integer Nonlinear Programming (MINLP) problem, EETC-VAO aims to meet various geometric constraints and simultaneously minimize construction costs, traction energy consumption, and section running times in both directions. The model is subsequently reformulated into an equivalent Mixed-Integer Linear Programming (MILP) model using linearization methods and is further enhanced with valid inequalities, logic cuts, and a warm start algorithm with random velocity generation. The model has been extensively tested across a variety of case studies and train types, from synthetic small-scale scenarios to challenging real-world cases spanning from 3 to 71.2 km. Our findings demonstrate that operational costs can be significantly reduced with only marginal increases in construction costs. The integrated approach achieves reductions in total lifecycle costs of up to 40%, revealing a critical trade-off between construction and operational expenses. Notably, our results also indicate that lower construction costs do not inherently conflict with reduced operational costs, emphasizing the critical importance of integrating the train control scheme into the VAO problem.</div></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":"171 ","pages":"Article 104943"},"PeriodicalIF":7.6,"publicationDate":"2024-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142720142","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":"Research on rebalancing of large-scale bike-sharing system driven by zonal heterogeneity and demand uncertainty","authors":"Rui Zhao ,&nbsp;Zihao Tian ,&nbsp;Lixin Tian ,&nbsp;Wenshan Liu ,&nbsp;David Z.W. Wang","doi":"10.1016/j.trc.2024.104933","DOIUrl":"10.1016/j.trc.2024.104933","url":null,"abstract":"<div><div>Bike-sharing, as an open and intricate system, encompass a vast and diverse array of data, and are often affected by various time-varying and uncertain factors. Consequently, employing scientific and appropriate rebalancing in the age of big data is pivotal for the system’s sustained and healthy development. This paper takes the dockless bike-sharing system as the research object, considers regional heterogeneity and time-varying demand uncertainty, and proposes a rebalancing strategy that integrates initial inventory determination and remaining inventory uncertainty. Firstly, this paper considers using of Poisson distribution chi-square tests to assess the borrowing and returning behaviors within a zone, selects a unit time tailored to the zone’s unique circumstances to estimate the parameter rate, and independently establishes a non-stationary Markov chain for each zone to determine the initial expected inventory under dynamic zonal demand conditions. Using big data from bike-sharing operations in Nanjing for empirical validation, the results indicate that borrowing and returning behavior in most zones follows a Poisson distribution, and that zones with higher volumes of traffic have fewer bikes initially deployed. Secondly, we address the uncertainty of demand-driven residual inventory and spatial correlations, formulating a robust two-stage optimization model aimed at minimizing the worst-case scenario. We then transform this model into a computationally tractable form using polyhedral uncertainty sets. By analyzing the model structure and mathematical properties, we develop a column-and-constraint generation algorithm for customizing a two-stage robust optimization model based on residual inventory, and compare it with other traditional algorithms. The numerical experimental results show that the proposed model and algorithm have significant advantages in terms of solution accuracy and efficiency, and can play a role in real-world problems. Finally, the paper discusses the impact of various parameters in the model on the solution, yielding results consistent with our expectations.</div></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":"170 ","pages":"Article 104933"},"PeriodicalIF":7.6,"publicationDate":"2024-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142720537","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 theory-informed multivariate causal framework for trustworthy short-term urban traffic forecasting","authors":"Panagiotis Fafoutellis,&nbsp;Eleni I. Vlahogianni","doi":"10.1016/j.trc.2024.104945","DOIUrl":"10.1016/j.trc.2024.104945","url":null,"abstract":"<div><div>Traffic forecasting using Deep Learning has been a remarkably active and innovative research field during the last decades. However, there are still several barriers to real-world, large-scale implementation of Deep Learning forecasting models, including their data requirements, limited explainability and low efficiency. In this paper, we propose a novel theory-driven framework that is based on a Granger causality-inspired feature selection method and a multitask LSTM to jointly predict two traffic variables. Traffic flow theory intuition is induced in the training process by an enhanced Traffic Flow Theory-Informed loss function (TFTI loss), which includes the divergence of the joint prediction of two traffic variables from the actual fundamental diagram of the corresponding location. The theory-informed, Granger causal, multitask LSTM is trained for one step ahead volume and speed forecasting using loop detector data coming from the extended Athens road network (Greece). Findings indicate that the models trained using the TFTI loss and a reduced input space, which includes only causal information, achieve a significantly improved performance, compared to the models using the classic Mean Squared Error loss function. Moreover, we introduce a dedicated trustworthiness evaluation framework that indicates that the proposed approach enhances the trustworthiness of the predictions, as well as the models’ transparency and resilience to noisy data.</div></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":"170 ","pages":"Article 104945"},"PeriodicalIF":7.6,"publicationDate":"2024-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142706435","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":"Mitigating stop-and-go traffic congestion with operator learning","authors":"Yihuai Zhang ,&nbsp;Ruiguo Zhong ,&nbsp;Huan Yu","doi":"10.1016/j.trc.2024.104928","DOIUrl":"10.1016/j.trc.2024.104928","url":null,"abstract":"<div><div>This paper presents a novel neural operator learning framework for designing boundary control to mitigate stop-and-go congestion on freeways. The freeway traffic dynamics are described by second-order coupled hyperbolic partial differential equations (PDEs), i.e. the Aw–Rascle–Zhang (ARZ) macroscopic traffic flow model. The proposed framework learns feedback boundary control strategies from the closed-loop PDE solution using backstepping controllers, which are widely employed for boundary stabilization of PDE systems. The PDE backstepping control design is time-consuming and requires intensive depth of expertise, since it involves constructing and solving backstepping control kernels. Existing machine learning methods for solving PDE control problems, such as physics-informed neural networks (PINNs) and reinforcement learning (RL), face the challenge of retraining when PDE system parameters and initial conditions change. To address these challenges, we present neural operator (NO) learning schemes for the ARZ traffic system that not only ensure closed-loop stability robust to parameter and initial condition variations but also accelerate boundary controller computation. The first scheme embeds NO-approximated control gain kernels within a analytical state feedback backstepping controller, while the second one directly learns a boundary control law from functional mapping between model parameters to closed-loop PDE solution. The stability guarantee of the NO-approximated control laws is obtained using Lyapunov analysis. We further propose the physics-informed neural operator (PINO) to reduce the reliance on extensive training data. The performance of the NO schemes is evaluated by simulated and real traffic data, compared with the benchmark backstepping controller, a Proportional Integral (PI) controller, and a PINN-based controller. The NO-approximated methods achieve a computational speedup of approximately 300 times with only a 1% error trade-off compared to the backstepping controller, while outperforming the other two controllers in both accuracy and computational efficiency. The robustness of the NO schemes is validated using real traffic data, and tested across various initial traffic conditions and demand scenarios. The results show that neural operators can significantly expedite and simplify the process of obtaining controllers for traffic PDE systems with great potential application for traffic management.</div></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":"170 ","pages":"Article 104928"},"PeriodicalIF":7.6,"publicationDate":"2024-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142706436","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":"The Heterogeneous-Fleet Electric Vehicle Routing Problem with Nonlinear Charging Functions","authors":"Weiquan Wang ,&nbsp;Yossiri Adulyasak ,&nbsp;Jean-François Cordeau ,&nbsp;Guannan He","doi":"10.1016/j.trc.2024.104932","DOIUrl":"10.1016/j.trc.2024.104932","url":null,"abstract":"<div><div>This paper introduces the Heterogeneous-Fleet Electric Vehicle Routing Problem with Nonlinear Charging Functions (HEVRP-NL). This problem involves routing a heterogeneous fleet of electric vehicles, utilizing multiple charging modes, and accounting for time-dependent waiting time functions at charging stations. The problem is modeled using a path-based mixed-integer linear programming formulation. To solve this problem, we present an algorithmic framework that alternates between two components. The first component is an iterated local search algorithm with a problem-specific route evaluation function, which obtains local optimal solutions and generates a pool of high-quality routes. The second component is a set-partitioning model that combines a subset of routes from the pool, which is constructed based on reduced costs, into a feasible solution. We design HEVRP-NL benchmark instances based on the publicly available electric fleet size and mix vehicle routing problem instances, which are used to evaluate our methods. For small-scale HEVRP-NL instances, the proposed model can be employed in a general-purpose mixed integer programming solver to achieve optimal solutions or find good upper bounds. This exact approach serves as an evaluation of our heuristic algorithm’s ability to attain optimal solutions rapidly. Extensive computational results on large-scale HEVRP-NL instances illustrate the advantages of considering non-linear charging functions and show the impact of waiting time at the charging stations. Finally, we conduct experiments on 120 benchmark instances for the E-VRP-NL and 168 benchmark instances for the E-FSMFTW-PR, which are the special cases of our problem. The results indicate that our algorithm outperforms existing approaches from the literature and identifies 32 new best solutions for the E-VRP-NL and 33 new best solutions for the E-FSMFTW-PR, respectively.</div></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":"170 ","pages":"Article 104932"},"PeriodicalIF":7.6,"publicationDate":"2024-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142706468","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":"Toward real-time operations of modular-vehicle transit services: From rolling horizon control to learning-based approach","authors":"Qingyun Tian ,&nbsp;Yun Hui Lin ,&nbsp;David Z.W. Wang ,&nbsp;Kaidi Yang","doi":"10.1016/j.trc.2024.104938","DOIUrl":"10.1016/j.trc.2024.104938","url":null,"abstract":"<div><div>Recent technological advancements have opened doors for real-time adjustments and controls during public transport operations. In particular, the introduction of modular vehicles has the potential to significantly enhance public transit service quality. This innovative public transit service with modular vehicles, characterized by its flexible schedules and vehicle formations, allows for the dynamic management of transit capacity to meet the fluctuating passenger demands. This paper proposes to schedule the flexible modular-vehicle transit service in real time considering the varying demands. To jointly optimize the service schedule and vehicle formations, we propose the rolling horizon control approach to decompose the complex problem into subproblems that can be solved efficiently during the process. On top of this, we introduce a learning-based optimization proxy to streamline the optimization process within the rolling horizon framework, enabling near-optimal decisions to be made with minimal execution time without directly solving the optimization problem. Through numerical studies, we demonstrate the effectiveness and efficiency of the proposed methods in terms of solution quality and efficiency. Furthermore, our case studies show that modular vehicles can adapt to the changing demand and effectively reduce the total costs in the transit system.</div></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":"170 ","pages":"Article 104938"},"PeriodicalIF":7.6,"publicationDate":"2024-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142706469","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}