Transportation Research Part C-Emerging Technologies最新文献

{"title":"Vessel traffic flow prediction through multi-scale spatiotemporal attention in dual-graph networks","authors":"Haowen Lei ,&nbsp;Ruoxue Liu ,&nbsp;Jiajing Chen ,&nbsp;Haijiang Li ,&nbsp;Shuai Jia","doi":"10.1016/j.trc.2026.105529","DOIUrl":"10.1016/j.trc.2026.105529","url":null,"abstract":"<div><div>Accurate forecasting of vessel traffic flow is vital for intelligent maritime operations, yet it is challenged by complex spatiotemporal dependencies and a mix of deterministic and stochastic influences. To address these challenges, this study proposes the Parallel Spatiotemporal Attention (PSTA) framework which introduces the following three key innovations. First, in terms of architectural design, PSTA employs a parallel temporal backbone that couples multi-view Temporal Convolutional Networks (TCNs) with Long Short-Term Memory (LSTM) units and a dual-graph spatial module that captures complex topology through geographic proximity and functional similarity. Second, this study proposes a constraint-aware fusion mechanism that utilizes a temporal-to-spatial cross-attention module with a masking strategy to embed waterway connectivity and AIS data quality, ensuring that the integration of spatiotemporal features follows the actual layout of the water network. Finally, this study provides domain-specific insights through cross-port validation on two distinct typologies (Zhoushan and Shanghai ports), revealing how modeling requirements shift across different port environments. Extensive experiments demonstrate that PSTA consistently outperforms state-of-the-art benchmarks. The results highlight its potential to support data-driven decision-making in maritime traffic management.</div></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":"184 ","pages":"Article 105529"},"PeriodicalIF":7.6,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146072512","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":"Large language model guided deep reinforcement learning for safe autonomous vehicle decision making","authors":"Hao Pang,&nbsp;Zhenpo Wang,&nbsp;Guoqiang Li","doi":"10.1016/j.trc.2025.105511","DOIUrl":"10.1016/j.trc.2025.105511","url":null,"abstract":"<div><div>Deep reinforcement learning (DRL) has shown promising potential for decision-making in autonomous driving. However, it requires extensive interaction with the environment and generally has low learning efficiency. To address these challenges, this paper proposes a novel large language model (LLM) guided deep reinforcement learning (LGDRL) framework for the decision-making problem in autonomous driving. Leveraging the powerful reasoning capabilities of LLMs, an LLM-based driving expert is designed to provide intelligent guidance in the DRL learning process. Subsequently, an innovative expert policy constrained algorithm and a novel LLM-intervened interaction mechanism are developed to efficiently integrate the guidance from the LLM expert to enhance the performance of DRL decision-making policies. Extensive experiments are conducted to evaluate the performance of the proposed LGDRL method. The results demonstrate that our proposed method effectively leverages expert guidance to enhance both learning efficiency and performance of DRL, achieving superior driving performance. Moreover, it enables the DRL agent to maintain consistent and reliable performance in the absence of LLM expert guidance, which is promising for real-world applications. The supplementary videos are available at <span><span>https://bitmobility.github.io/LGDRL/</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":"184 ","pages":"Article 105511"},"PeriodicalIF":7.6,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145961770","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":"Robotic sorting systems: Robot management and layout design optimization","authors":"Tong Zhao ,&nbsp;Xi Lin ,&nbsp;Fang He ,&nbsp;Hanwen Dai","doi":"10.1016/j.trc.2025.105500","DOIUrl":"10.1016/j.trc.2025.105500","url":null,"abstract":"<div><div>In the contemporary logistics industry, automation plays a pivotal role in enhancing production efficiency and expanding industrial scale. In particular, autonomous mobile robots have become integral to modernization efforts in warehouses. One noteworthy application in robotic warehousing is the robotic sorting system (RSS), which is distinguished by its cost-effectiveness, simplicity, scalability, and adaptable throughput control. Previous research on RSS efficiency often assumed an ideal robot management system, ignoring potential traffic delays and assuming constant travel times. To address this gap, we introduce a novel robot traffic management method, named Rhythmic Control for the Sorting Scenario (RC-S), for RSS operations, along with an analytical estimation formula that establishes the quantitative relationship between system performance and configurations. Simulations validate that RC-S reduces average service time by 10.3 % compared to the classical cooperative A* algorithm, while also improving throughput and runtime. Based on the performance analysis of RC-S, we develop a layout optimization model that considers system configurations, desired throughput, and costs to minimize expenses and determine the optimal layout. Numerical studies show that facility costs dominate at lower throughput levels, while labor costs prevail at higher throughput levels. Additionally, due to traffic efficiency limitations, an RSS is well-suited for small-scale operations like end-of-supply-chain distribution centers.</div></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":"184 ","pages":"Article 105500"},"PeriodicalIF":7.6,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145842859","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":"From voice to safety: Language AI powered pilot-ATC communication understanding for airport surface movement collision risk assessment","authors":"Yutian Pang,&nbsp;Andrew Paul Kendall,&nbsp;Alex Porcayo,&nbsp;Mariah Barsotti,&nbsp;Anahita Jain,&nbsp;John-Paul Clarke","doi":"10.1016/j.trc.2026.105540","DOIUrl":"10.1016/j.trc.2026.105540","url":null,"abstract":"&lt;div&gt;&lt;div&gt;Surface movement collision risk is critical for airport safety. These models play a vital role in identifying and mitigating potential hazards during airport ground operations by providing warnings of near-miss incidents, thereby reducing the risk of accidents that could jeopardize human lives and financial assets. However, existing models, developed decades ago, have not fully integrated recent advancements in machine intelligence, where incorporating additional functionalities presents promising opportunities for improved risk assessment. This work provides a feasible solution to the existing airport surface safety monitoring capabilities (i.e., Airport Surface Surveillance Capability (ASSC)), namely language AI-based voice communication understanding for collision risk assessment. The proposed framework consists of two major parts, (a) rule-enhanced Named Entity Recognition (NER); (b) surface collision risk modeling. NER module generates information tables by processing voice communication transcripts, which serve as references for producing potential taxi plans and calculating the surface movement collision risk. We first collect and annotate our dataset based on open-sourced video recordings and safety investigation reports. Additionally, we refer to FAA Order JO 7110.65W and FAA Order JO 7340.2N to get the list of heuristic rules and phase contractions of communication between the pilot and the Air Traffic Controller (ATCo). Then, we propose the novel ATC Rule-Enhanced NER method, which integrates the heuristic rules into the model training and inference stages, resulting in a hybrid rule-based NER model. We show the effectiveness of this hybrid approach by comparing different setups with different token-level embedding models. For the risk modeling, we adopt the node-link airport layout graph from NASA FACET and model the aircraft taxi speed at each link as a log-normal distribution and derive the total taxi time distribution. Then, we propose a spatiotemporal formulation of the risk probability of two aircraft moving across potential collision nodes during ground movement. Furthermore, we propose the real-time implementation of such a method to obtain the lead time, with a comparison with a Petri-Net based method. We show the effectiveness of our approach through case studies, (a) the Haneda airport runway collision accident happened in January 2024; (b) the KATL taxiway collision happened in September 2024; (c) the Tenerife airport disaster in March 1977. We show that, by understanding the pilot-ATC communication transcripts and analyzing surface movement patterns, the proposed model estimates the surface movement collision probability within machine processing time, thus enabling proactive measures to possible collisions at a certain node, which improves airport safety. A study on validating the log-normal assumption of aircraft taxi speed distributions is also given. We provide the link to code and data repository &lt;span&gt;&lt;span","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":"184 ","pages":"Article 105540"},"PeriodicalIF":7.6,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146072502","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 on-demand delivery with spatial divisions of labor","authors":"Yue Yang ,&nbsp;André van Renssen ,&nbsp;Mohsen Ramezani","doi":"10.1016/j.trc.2025.105509","DOIUrl":"10.1016/j.trc.2025.105509","url":null,"abstract":"<div><div>The rapid evolution of on-demand delivery services has significantly influenced traditional logistics, particularly in urban areas where there is a surge in customer demand for timely and efficient delivery of small to medium-sized parcels. This paper investigates the concept of <em>spatial divisions of labor</em> in on-demand deliveries where the delivery network is partitioned into several operational regions, each managed by designated couriers. To facilitate parcel transshipment between regions, a set of accessible lockers is strategically placed at the shared borders of adjacent regions. We introduce and address a multi-hop delivery with spatial divisions of labor (MDSDL) problem, which involves dynamic parcel-courier dispatching and routing to minimize total operational costs. Within a rolling-horizon decision framework, the MDSDL problem is decomposed into two interdependent subproblems: (i) Region-Level Path Optimization (RLPO) that determines the coarse-grained, multi-hop path each parcel should take through the network, from its pickup region to its destination region, via the lockers, with the objective of minimizing delivery lateness. This path specifies the sequence of service regions a parcel must traverse to reach its final destination. (ii) Courier Route Optimization (CRO) that manages fine-grained, intra-region dispatching and routing by assigning incoming pickup and drop-off tasks to local couriers, who each has a continuously updated schedule. Subsequently, we develop a novel heuristic approach to dynamically solve RLPO and CRO in real time considering a rolling-horizon formulation. Extensive comparative experiments are conducted to demonstrate the advantages of the proposed approach. Implementing <em>spatial divisions of labor</em> not only enhances system efficiency and reduces operational costs but also improves the customer experience by reducing lateness and shortening ready-to-pickup times.</div></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":"184 ","pages":"Article 105509"},"PeriodicalIF":7.6,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145927801","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 charging optimization for electric buses under photovoltaic-storage-grid energy supply mode","authors":"Yuting Ji ,&nbsp;Yiming Bie ,&nbsp;Dongfang Ma","doi":"10.1016/j.trc.2026.105539","DOIUrl":"10.1016/j.trc.2026.105539","url":null,"abstract":"<div><div>With the growing emphasis on sustainable transportation, the photovoltaic-storage-grid energy supply (PSG-ES) mode has been adopted in electric bus (EB) systems, showing promising performance and strong potential. However, environmental uncertainties cause random fluctuations in both solar output and energy demand, posing challenges to stable system operation. Most existing studies use static or low-frequency dynamic models based on day-ahead forecasts. These methods struggle to adapt to real-time changes, limiting the economic and environmental benefits of the PSG-ES mode. To address this issue, we propose a minute-level dynamic charging scheduling method for multi-route EB systems under the PSG-ES mode. The problem is formulated as a Markov Decision Process, with a penalty function and an action correction mechanism introduced to handle complex operational constraints. To improve learning and adaptability, we develop a deep reinforcement learning algorithm, featuring multi-head networks and composite experience replay to address distribution shifts and policy conflicts. Experiments using real-world EB data show that the proposed method effectively manages supply–demand uncertainties and generates more cost-efficient and environmentally sustainable charging plans. Compared to static scheduling with day-ahead forecasts, it reduces charging costs by 7.48% and carbon emissions by 2.99%.</div></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":"184 ","pages":"Article 105539"},"PeriodicalIF":7.6,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146072503","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 route redundancy-oriented strategic planning towards resilient transportation networks","authors":"Kai Qu ,&nbsp;Xiangdong Xu ,&nbsp;Anthony Chen","doi":"10.1016/j.trc.2025.105503","DOIUrl":"10.1016/j.trc.2025.105503","url":null,"abstract":"<div><div>Sufficient route redundancy ensures the availability of alternative routes during disruptions for critical trips (e.g., evacuations, relief transportation), which is essential for sustaining transportation network resilience. Existing studies on route redundancy focus mainly on assessment rather than optimization, and most adopt a static perspective that ignores the inherently dynamic nature of resilience. This study introduces the definition, evaluation, and optimization methods for dynamic network redundancy. We propose period-based metrics that account for travelers’ adaptive behavior and network congestion and develop a link-based day-to-day model under uncertainty coupled with Dial counting to consistently measure redundancy. A two-stage stochastic bi-level programming model is then formulated to identify investment strategies that maximize expected dynamic redundancy under uncertain disruptions. In the first stage, planners allocate budgets between link retrofitting and new link construction, while in the second stage, travelers dynamically reroute on a daily basis following disruptions. To solve the resulting non-convex optimization problem, we implement a Bayesian optimization framework with parallel scenario evaluation. Experiments on both test and real-world networks demonstrate the properties, features, and applicability of the proposed methods. Results indicate that accounting for dynamic traffic evolution and congestion can reduce redundancy estimates by up to 40% compared to static assessments in the 16-node test network, particularly under severe disruptions and high congestion levels. The spatial–temporal evolution of congestion patterns, which influences travelers’ perception of alternative routes, is naturally captured by dynamic redundancy but overlooked in static assessments. In the Anaheim network, increasing the budget from $300 million to $1200 million raises dynamic redundancy from 4.5 % to 10.1 %, illustrating diminishing marginal returns. The framework developed in this study provides a decision-support tool for more informed, resilience-oriented network planning.</div></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":"184 ","pages":"Article 105503"},"PeriodicalIF":7.6,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145824128","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":"Deconstructing driving behaviors in interactions with pedestrians at uncontrolled crosswalks: an imitation learning method","authors":"Tao Wang ,&nbsp;Minh Kieu ,&nbsp;Chengmin Li ,&nbsp;Wenqiang Chen ,&nbsp;Ying-En Ge","doi":"10.1016/j.trc.2025.105508","DOIUrl":"10.1016/j.trc.2025.105508","url":null,"abstract":"<div><div>The objective of this paper is to deconstruct driving behaviors in interactions with pedestrians at uncontrolled crosswalks. Trajectory data are used to extract variables describing driver–pedestrian interactions, including position, acceleration, velocity, yaw rate, and interaction risk. Driving behavior is modeled as utility-driven, intelligent, and rational decision-making within the framework of a finite-state Markov decision process (MDP). The vanilla generative adversarial imitation learning (GAIL) framework is improved to reconstruct a human-like driving behavior model where the utility function is defined as the deviation between the agent’s behavior distribution and that of human drivers. Maximizing this utility through a deep reinforcement learning (RL) approach drives agents to progressively clone the behavioral policies of human drivers in the real world. The behavioral policy is formulated as a pre-trained driving behavior model and validated on a simulation platform for its ability in reproducing human driving behavior. Experimental results show that the model successfully reproduces the rationality of human drivers and generates human-like interaction trajectories in the simulation environment. Transfer experiments further demonstrate the generalizability of the pre-tained behavioral model. The interaction policy map and the state-value map are visualized to elucidate the generative mechanisms underlying human-like trajectories by revealing risk- and context-dependent layered patterns and latent behavioral preferences. This work contributes to the advancement of human-like behavioral models, thereby enhancing the fidelity of traffic microsimulation and improving behavior modeling in complex driver–pedestrian interactions.</div></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":"184 ","pages":"Article 105508"},"PeriodicalIF":7.6,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146014568","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":"On the applicability of time series anomaly detection methods to real-world traffic volume data","authors":"Iman Taheri Sarteshnizi,&nbsp;Majid Sarvi,&nbsp;Saeed Asadi Bagloee,&nbsp;Neema Nassir","doi":"10.1016/j.trc.2026.105536","DOIUrl":"10.1016/j.trc.2026.105536","url":null,"abstract":"<div><div>Time Series Anomaly Detection (TSAD or TAD) refers to the automatic and data-driven identification of abnormal segments in time series data, a task that has been studied extensively for decades. Despite recent transformative and novel findings revealed by efforts in this field, the literature on traffic anomaly detection has not yet fully reflected on these emerging trends to draw practical conclusions. In this paper, we focus on the applicability of state-of-the-art and well-established TAD methods to road traffic volume data, making contributions in two main ways. First, given the proven and major contribution of evaluation data to TAD outcomes, we argue that existing anomaly-labeled datasets from transportation and traffic systems require substantial enhancements in terms of both data size and label quality. To address this, we propose a new platform to inspect and label large-scale volume data of urban areas based on its unique characteristics and the latest taxonomy of time series anomalies. Second, based on the established framework, we also formulate the TAD problem in traffic volume data and introduce a discord-based, context-embedded, and light-weight traffic anomaly detection method, named Step-isolated Traffic Discords Discovery (Si-TDD), to address this problem. Benefiting from our labeling platform, AnoLT (Anomaly Labeled Traffic) is presented in this paper for the first time as a comprehensive, open-source, and anomaly-labeled spatiotemporal dataset collected from 147 locations across Melbourne, Australia. Comparative results with more than 20 baselines also indicate that Si-TDD considerably outperforms recent TAD solutions when it comes to traffic volume data, achieving a 67% F1 score with the AnoLT dataset. This paper highlights the key role of incorporating context-related information into existing TAD solutions to boost their effectiveness in traffic anomaly detection, a factor that is often overlooked in the current literature.</div></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":"184 ","pages":"Article 105536"},"PeriodicalIF":7.6,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146072518","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 joint, context-aware neural network-based travel demand and scheduling model","authors":"Joel Fredriksson,&nbsp;Anders Karlström","doi":"10.1016/j.trc.2025.105512","DOIUrl":"10.1016/j.trc.2025.105512","url":null,"abstract":"<div><div>Recent advancements in machine learning, and neural networks in particular, have introduced new opportunities for activity-based travel demand modeling and scheduling, providing data-driven alternatives to traditional theory-driven methods. While previous machine learning-based scheduling models have integrated combinations of activity, destination, and mode choice as separate sub-models, none have yet, to the best of our knowledge, unified these components into a single, jointly learned framework.</div><div>This paper introduces Skyline-NNjoint, a novel fully neural network-based scheduling model that jointly predicts an agent’s activity, destination, and mode choice decisions at each discrete time step throughout the day. To capture substitution effects and interdependencies among alternatives, the model introduces a Global Context Module (GCM) that enables each alternative to adjust its attractiveness based on the context of all others. While similar context-based approaches have been used in other domains, this is, to the best of our knowledge, the first application of such a mechanism in travel demand modeling. This provides a data-driven approach to relax the Independence of Irrelevant Alternatives (IIA) assumption inherent in multinomial logit models. The effectiveness of the GCM is evaluated by comparing Skyline-NNjoint to a baseline version without it, isolating its contribution to model performance.</div><div>The model is trained on travel survey data from Stockholm and evaluated using both cross-entropy loss and simulated daily activity–travel trajectories. Cross-entropy loss results confirm that the GCM improves predictive performance. Simulation results show that Skyline-NNjoint produces patterns of activity participation, trip timing, and mode choice that closely match observed data. Notably, the model accurately reproduces mode distributions across activity purposes, highlighting its capacity to capture interdependencies in joint decision-making.</div></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":"184 ","pages":"Article 105512"},"PeriodicalIF":7.6,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145978362","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}