Transportation Research Part C-Emerging Technologies最新文献

{"title":"Traffic dynamics modeling with an extended S3 car-following model","authors":"Zelin Wang ,&nbsp;Yuqian Lin ,&nbsp;Zhiyuan Liu ,&nbsp;Yu Dong ,&nbsp;Yuan Zheng ,&nbsp;Pan Liu ,&nbsp;Qixiu Cheng","doi":"10.1016/j.trc.2025.105494","DOIUrl":"10.1016/j.trc.2025.105494","url":null,"abstract":"<div><div>In recent decades, there has been a notable surge in research attention on microscopic car-following modeling, reflecting its indispensable role in investigating and simulating human driving behavior. Breakthroughs in operations research and machine learning have reinvigorated scholarly interest in addressing intricate traffic flow issues encountered in real-world scenarios. This study advances the current state of traffic dynamics modeling by presenting a novel extended S-shaped three-parameter (ES3) car-following model, achieving a harmonious balance between empirical accuracy and mathematical simplicity while enabling macroscopic-microscopic integration. Both local and string stability conditions of the ES3 model are derived and corroborated by numerical experiments. Utilizing real-world and autonomous vehicle trajectory data, we calibrate and validate the ES3 model using three distinct methodologies: single-vehicle microscopic model calibration, multi-vehicle microscopic model calibration, and integrated self-consistent macroscopic-microscopic model calibration. These methodologies target different aspects, including individual vehicle behavior, platoon trajectories, and the integration of macroscopic and microscopic traffic flows. Comparative analyses against existing physics-based models demonstrate the exceptional performance of the proposed ES3 model in microscopic traffic flow modeling. Overall, the experimental results indicate its capability to accurately reproduce human drivers’ and autonomous vehicles’ car-following behaviors while elucidating the underlying mechanisms governing the observed macroscopic traffic phenomena.</div></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":"183 ","pages":"Article 105494"},"PeriodicalIF":7.6,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145823187","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":"Transferability in data poisoning attacks on spatiotemporal traffic forecasting models","authors":"Xin Wang ,&nbsp;Feilong Wang ,&nbsp;Yuan Hong ,&nbsp;Xuegang (Jeff) Ban","doi":"10.1016/j.trc.2025.105501","DOIUrl":"10.1016/j.trc.2025.105501","url":null,"abstract":"<div><div>Machine learning and deep learning models have been shown to be sensitive to adversarial perturbations within training data, where specially crafted examples can lead to compromised model performance. With the widespread adoption of deep learning models, such as graph neural networks (GNNs) and Transformers, for spatiotemporal traffic forecasting, the vulnerability of their training processes has emerged as a critical area for research. In this paper, we investigate the vulnerability of deep learning-based spatiotemporal traffic forecasting models during their training phase. We propose a practical, black-box poisoning attack framework to demonstrate the <em>transferability</em> of adversarial perturbations in data poisoning attacks on these forecasting models. Specifically, we craft adversarial perturbations against a public deep learning model on a public dataset and then apply these perturbations to other deep learning-based traffic forecasting models. Our transferred perturbations can universally compromise multiple traffic forecasting models without requiring access to any internal information or structure of the target models. Numerical experiments are conducted on six traffic state estimation and forecasting models across four traffic datasets. The revealed transferability highlights a substantial vulnerability in traffic forecasting models that are widely used in intelligent transportation applications.</div></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":"183 ","pages":"Article 105501"},"PeriodicalIF":7.6,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145823188","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":"Optimized control method for cooperative on-ramp merging of connected vehicles based on discretized spatial feature modeling","authors":"Chaofan Lei,&nbsp;Xiaojun Tan,&nbsp;Jie Zhang,&nbsp;Nanwei Xiao,&nbsp;Kunxiang Deng,&nbsp;Jun Li","doi":"10.1016/j.trc.2025.105459","DOIUrl":"10.1016/j.trc.2025.105459","url":null,"abstract":"<div><div>Vehicle-to-everything (V2X) technology can enable cooperative multivehicle control at traffic bottlenecks, such as ramp merging areas, to achieve increased safety and efficiency. However, the existing methods still face limitations in terms of optimizing efficiency, reducing complexity, and enhancing scalability in multilane scenarios. A discretized spatial feature modeling method for on-ramp merging areas is proposed in this study. By constructing occupancy flow features, uneven traffic features, and ramp flow radiation features, valuable information in space can be quantified. This information is then stored in discretized, equally sized rectangular segments and concatenated into a fixed-dimensional feature matrix. A multivehicle cooperative control method is developed on the basis of this model, incorporating a rolling optimization strategy with priority sequencing to evaluate and select the optimal actions for each vehicle. The control algorithm was comprehensively evaluated in mixed-traffic scenarios and demonstrated effectiveness. Compared with the benchmark algorithms, it achieved optimal and stable traffic efficiency, reducing the total completion time by at least 7.60 % while ensuring safe vehicle spacing. Field tests further confirmed its feasibility. The related test videos and code can be obtained from <span><span>https://github.com/Lei-CF/MergeControl</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":"183 ","pages":"Article 105459"},"PeriodicalIF":7.6,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145593127","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 branch-and-cut algorithm for routing a heterogeneous drone-ground vehicle fleet to deliver time-sensitive products","authors":"Sayed Hamid Hosseini Dolatabadi ,&nbsp;Tanveer Hossain Bhuiyan ,&nbsp;Waquar Kaleem ,&nbsp;Anirudh Subramanyam ,&nbsp;Taner Cokyasar","doi":"10.1016/j.trc.2025.105454","DOIUrl":"10.1016/j.trc.2025.105454","url":null,"abstract":"<div><div>The growing demand for timely delivery of time-sensitive products, such as pharmaceuticals, prepared food, and other healthcare items, has underscored the need for faster last-mile delivery solutions. This paper explores combining the high speed and energy efficiency of drones with the longer delivery range and larger carrying capacity of ground vehicles (GVs) to deliver time-sensitive products. We consider a mixed fleet of drones comprising different drone types with distinct characteristics (cost, power consumption, battery capacity, payload capacity, speed, and weather sensitivity) performing direct out-and-back deliveries and a homogeneous fleet of GVs performing multiple deliveries per trip. We present a novel mixed-integer linear programming model for unsynchronized routing of this heterogeneous fleet to deliver packages having distinct release times and time windows to geographically distributed customers. We develop an efficient exact branch-and-cut (B&amp;C) algorithm and a B&amp;C-integrated clustering-based heuristic algorithm for efficiently solving large problems. Numerical results based on a real-life case study of prepared food delivery data from the San Francisco Bay Area, United States, and actual drone data from drone flight tests demonstrate that our exact B&amp;C algorithm is 8.5 times faster than commercial solvers, whereas the B&amp;C-integrated clustering-based algorithm can solve real-life problems of 207-node networks. Results also reveal that using only drones reduces energy consumption by 96 % compared to a heterogeneous fleet of drones and GVs, but increases overall costs by 141.4 %. Moreover, using only GVs increases the total cost and energy consumption by 19.6 % and 13.2 %, respectively, compared to a heterogeneous fleet of drones and GVs.</div></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":"183 ","pages":"Article 105454"},"PeriodicalIF":7.6,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145599049","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":"Early detection of traffic flow breakdown at highway bottleneck using section-based transitional instability","authors":"Hyeyoung Tak,&nbsp;Hwasoo Yeo","doi":"10.1016/j.trc.2025.105483","DOIUrl":"10.1016/j.trc.2025.105483","url":null,"abstract":"<div><div>Traffic flow breakdowns refer to abrupt transitions from free-flow to congested conditions, typically occurring at freeway bottlenecks. Due to their sudden onset and the complex interactions driving them, early detection remains a challenging task in traffic operations. These events are often preceded by a transitional state—referred to as the pre-breakdown state—during which traffic conditions exhibit growing instability. Most existing studies define breakdowns using threshold-based criteria such as a sudden shift in speed at a single detector. Although such point-based methods can capture local changes, they often fail to reflect broader spatial interactions across adjacent sections. This study proposes a section-based framework that jointly analyzes upstream and downstream conditions to capture the spatial heterogeneity within a segment. A composite metric, the Dynamic Instability Score (DIS), is introduced to quantify sectional instability based on short-term temporal responsiveness and spatial imbalance. The methodology includes traffic state identification, DIS computation, and early warning detection. The DIS is calibrated using ROC-based thresholding at the boundary between free-flow and transitional states, ensuring sensitivity to early instability while minimizing false alarms. Empirical evaluations on freeway segments with recurrent bottlenecks demonstrate that DIS serves as a reliable leading indicator. Compared to point-based measures, it achieves higher accuracy and lower false-alarm rates, while preserving practical lead times. These results highlight the importance of capturing transitional instability during the pre-breakdown state and support the use of section-based indicators for proactive congestion management.</div></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":"183 ","pages":"Article 105483"},"PeriodicalIF":7.6,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145689765","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":"Real-time traffic simulation and management for large-scale urban air mobility: Integrating route guidance and collision avoidance","authors":"Canqiang Weng ,&nbsp;Can Chen ,&nbsp;Jingjun Tan ,&nbsp;Tianlu Pan ,&nbsp;Renxin Zhong","doi":"10.1016/j.trc.2025.105477","DOIUrl":"10.1016/j.trc.2025.105477","url":null,"abstract":"<div><div>With a vision to expand transportation supply using low-altitude airspace, urban air mobility (UAM) has emerged as a promising alternative to provide point-to-point travel in congested areas. The rapid development of electric vertical take-off and landing vehicles is expected to make UAM a viable and sustainable transportation mode. Given the spatial heterogeneity of land use patterns in most cities, large-scale UAM deployments will likely focus on specific areas, such as intertransfer traffic between suburbs and city centers. However, large-scale UAM operations connecting multiple origin-destination pairs raise concerns about air traffic safety and efficiency due to potential conflict movements, particularly at major conflict points analogous to roadway junctions. To meet the safety and efficiency requirements of future UAM operations, this work proposes an air traffic management framework that integrates route guidance and collision avoidance. The route guidance mechanism optimizes aircraft distribution across both spatial and temporal dimensions by regulating their paths (composed of waypoints). Given the optimized paths, the collision avoidance algorithm generates collision-free aircraft trajectories between waypoints in the 3D space. To enable large-scale applications, we develop fast approximation methods for centralized path planning and adopt the velocity obstacle model for distributed collision avoidance. To our knowledge, this work is one of the first to integrate route guidance and collision avoidance for UAM. Simulation results demonstrate that the proposed framework enables efficient and flexible UAM operations, including air traffic assignment, local congestion mitigation, and dynamic no-fly zone management. Compared with a collision-free baseline strategy, the proposed framework achieves considerable improvements in traffic safety and efficiency, with increases in the average minimum separation (+98.2 %), the average travel speed (+70.2 %), and the trip completion rate (+130 %), along with a reduction in the energy consumption (-23.0 %). The proposed framework demonstrates its potential for real-time traffic simulation and management in large-scale UAM systems.</div></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":"183 ","pages":"Article 105477"},"PeriodicalIF":7.6,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145689766","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":"Which type of backpressure is more stable? – Comparative analysis based on two-movement intersections","authors":"Dianchao Lin ,&nbsp;Li Li","doi":"10.1016/j.trc.2025.105478","DOIUrl":"10.1016/j.trc.2025.105478","url":null,"abstract":"<div><div>Stability, which indicates that queues do not grow infinitely over time, is a key concept in control policies such as BackPressure (BP). However, its abstract nature and diverse definitions make its comparative analysis difficult both theoretically and experimentally. As a result, simulations in existing studies often use alternative metrics, such as average delay, to evaluate the performance of different control policies. Little research directly compares different stabilities through theory or experiments. In this paper, we compare seven common stability definitions and theoretically demonstrates that they are equivalent in simulations and applications. Furthermore, we propose a <em>t</em>-test method for identifying whether a queue is stable based on the sequence of queueing differences. This method allows us to classify any sampled demand as stable or unstable based on simulated queues for a given control policy. Therefore, if the network’s dimension, i.e., the number of movements, does not exceed three, we can directly draw the stability region (SR) for all policies and compare their sizes. To accurately reproduce various BP theories, ensure fair comparisons, and facilitate the visualization of SRs, we use simulation codes to simulate a two-movement intersection scenario and discuss its extension to networks. Six distinct types of BP policies are compared, along with analysis for fixed-time and actuated controls. We obtain many insights that are difficult to achieve through purely theoretical analysis and delay-based simulations, including: 1) variability in BP’s SR: the SR typically varies when the BP changes its queue status weight or efficiency weight; 2) size hierarchy of SR: BPs generally outperform actuated controls in terms of SR, and actuated controls tend to outperform fixed-time controls; 3) non-cyclic vs. cyclic BP: non-cyclic BP usually has a larger SR than cyclic BP; 4) effect of real-time supply information: using real-time supply increases the SR of BP, even under the assumption of fixed saturation headway; and 5) SR degradation phenomenon: longer cycle lengths in cyclic BP may cause its SR to degenerate into a rectangular shape typical of fixed-time control.</div></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":"183 ","pages":"Article 105478"},"PeriodicalIF":7.6,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145657758","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":"Learning to retrieve containers: A scale-diverse deep reinforcement learning approach for the container retrieval problem","authors":"Woo-Jin Shin,&nbsp;Inguk Choi,&nbsp;Sang-Hyun Cho,&nbsp;Hyun-Jung Kim","doi":"10.1016/j.trc.2025.105496","DOIUrl":"10.1016/j.trc.2025.105496","url":null,"abstract":"<div><div>This study addresses the container retrieval problem (CRP), a key challenge in the storage yards of automated container terminals where operational efficiency directly affects vessel turnaround time and yard congestion. In storage yards, containers are stacked vertically to maximize space utilization; however, accessing one located below others requires relocating the blocking containers, leading to additional crane movements and delays. The CRP involves retrieving containers from multiple bays in a specified order while minimizing the total working time of the yard crane, with relocation position decisions being critical. The CRP poses several practical challenges: despite being <span><math><mi>NP</mi></math></span>-hard, real-world instances often involve hundreds of containers, requiring high-quality solutions in real time; yard configurations also vary widely and change frequently, demanding methods that adapt effectively to arbitrary layouts. We propose a novel deep reinforcement learning approach incorporating (1) a size-agnostic network architecture, enabling a single trained network to handle diverse yard configurations, and (2) a scale-diverse learning framework, which trains on a various yard scales using a normalized loss to improve generalization and scalability. Experiments on well-known benchmarks with several hundred containers show that the proposed method substantially outperforms existing baselines across a wide range of yard sizes. It also scales to instances with thousands of containers and maintains strong performance in dynamic settings where retrieval orders are revealed online. Solutions are produced within a second for realistic instances, confirming its effectiveness and practical applicability in real-world automated container terminals. The implementation and datasets used in this study are publicly available in the GitHub repository: <span><span>https://github.com/operagang/CRP_RL</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":"183 ","pages":"Article 105496"},"PeriodicalIF":7.6,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145785149","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":"Statistical inference of boarding and alighting counts in transit systems with incomplete data","authors":"Xiaoxu Chen ,&nbsp;Marc-Olivier Thibault ,&nbsp;Martin Trépanier ,&nbsp;Lijun Sun","doi":"10.1016/j.trc.2025.105484","DOIUrl":"10.1016/j.trc.2025.105484","url":null,"abstract":"<div><div>Automatic passenger counting (APC) systems have been widely used in public transit systems to collect boarding and alighting counts, which are essential for understanding travel demand, optimizing transit operations, and improving transit service quality. However, missing boarding and alighting counts remain a pervasive problem due to APC deployment, hardware malfunctions, or operational disruptions. The reconstruction of these missing data is particularly challenging because boarding and alighting counts must satisfy real-world constraints, such as balance conditions and onboard passenger limits. To address this issue, we propose a probabilistic framework that integrates passenger travel behavior and operational constraints to estimate missing boarding and alighting counts. The framework builds a time-varying Poisson model to estimate boarding demand and employs a method to infer time-varying alighting probabilities. Further, the alighting counts are derived by assigning estimated boarding counts to downstream stops with time-varying alighting probabilities, ensuring that the reconstructed data meet operational constraints. We validate the proposed framework using real-world transit data. The results demonstrate the method’s accuracy and robustness in estimating missing APC data, while also providing valuable insights into time-varying passenger travel behaviors, including arrival rates and alighting probabilities. This framework offers a practical and interpretable solution for reconstructing incomplete boarding and alighting data, with significant implications for improving transit planning and operational decision-making.</div></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":"183 ","pages":"Article 105484"},"PeriodicalIF":7.6,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145732480","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":"Iterative physics-enhanced residual learning for context-aware traffic assignment under biased inputs","authors":"Yuxin Shi ,&nbsp;Keke Long ,&nbsp;William H.K. Lam ,&nbsp;Xiaopeng Li ,&nbsp;Wei Ma","doi":"10.1016/j.trc.2025.105498","DOIUrl":"10.1016/j.trc.2025.105498","url":null,"abstract":"<div><div>Hybrid approaches combining physics-based models and data-driven methods have shown promise in traffic modeling by leveraging physical structure while enhancing learning flexibility. One representative example is Physics-Enhanced Residual Learning (PERL), which augments physics-based predictions with learned residuals to correct modeling errors. However, the effectiveness of physics-based model can degrade under biased input features. To address this limitation, we propose iterative Physics-Enhanced Residual Learning (iPERL), an end-to-end framework designed to improve the robustness of physics-guided models under biased inputs. We apply iPERL to context-aware traffic assignment, in which explanatory inputs such as OD demand, link and node characteristics (e.g., capacity, free-flow speed), and performance function parameters may be biased due to indirect observations and calibration errors, while traffic conditions simultaneously vary with contextual factors like time and weather. iPERL extends standard PERL by incorporating a residual-based input correction mechanism that iteratively calibrates these biased inputs using feedback from residuals between predicted and observed flows. By integrating contextual features, iPERL further enables adaptive correction strategies under diverse traffic scenarios. We evaluate the framework on both synthetic and real-world networks. Results show that iPERL consistently outperforms baseline methods, including standard PERL, particularly when input bias or data scarcity is present. The proposed framework offers a robust, interpretable, and data-efficient solution for traffic flow estimation, with potential for generalization across networks and practical applications.</div></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":"183 ","pages":"Article 105498"},"PeriodicalIF":7.6,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145823196","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}