Transportation Research Part C-Emerging Technologies最新文献

{"title":"Joint optimization for crowd evacuation and vehicle scheduling at multimodal transportation hubs","authors":"Qiyu Tang ,&nbsp;Yunchao Qu ,&nbsp;Haodong Yin ,&nbsp;Wei Zhang ,&nbsp;Jianjun Wu","doi":"10.1016/j.trc.2025.105117","DOIUrl":"10.1016/j.trc.2025.105117","url":null,"abstract":"<div><div>Transportation hubs are critical nodes that accommodate substantial passenger flows, which will lead to significant congestion during peak hours, predictable events (e.g., holiday, extreme weather) or emergencies (e.g., operational disruption). It is crucial to design a collaborative evacuation strategy that fully utilizes the multimodal transportation capacities at hubs. Considering the impact of various transportation operations on crowd evacuation, this paper proposes a mixed-integer linear programming model that integrates pedestrian flow assignment and multimodal vehicle scheduling to efficiently evacuate the crowd. In the model, a demand-switching strategy among modes is incorporated, and various operational characteristics of transportation modes including departure times and fleet sizes are optimized for vehicle scheduling. Throughout the evacuation process, pedestrian dynamics are formulated by the cell transmission model (CTM). To solve the large-scale problems, a tailored Variable Neighborhood Search (VNS) algorithm based on decomposition is developed, where the subproblem is reconstructed on a time-expanded network to accelerate the solution process. The effectiveness of the proposed method model and algorithm are validated through a series of numerical experiments. The results show that the tailored VNS algorithm can effectively solve large-scale problems within a reasonable timeframe. The case study also demonstrates that the demand-switching strategy could optimize the use of available transportation resources, reducing the clearance time for taxis by 17.2%. Furthermore, the findings highlight the importance of adapting evacuation strategies to different emergency scenarios. This approach can be potentially applied to enhance emergency crowd management responses at transportation hubs.</div></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":"174 ","pages":"Article 105117"},"PeriodicalIF":7.6,"publicationDate":"2025-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143724361","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":"Optimal road facility spare parts location with continuum approximation","authors":"Daijiro Mizutani ,&nbsp;Shunichi Fukuyama ,&nbsp;Koki Satsukawa","doi":"10.1016/j.trc.2025.105109","DOIUrl":"10.1016/j.trc.2025.105109","url":null,"abstract":"<div><div>This study presents a new methodology for optimizing the location of spare parts depots for expressway facilities using a continuum approximation (CA) approach. The increasing importance of expressway facility asset management necessitates efficient strategies for minimizing both user impact during failures and the costs of maintaining spare parts depots. In this study, we first formulate a model within the framework of dynamic facility location planning (DFLP), a type of integer programming (IP), to optimize the spare parts location plan, taking into account the failure processes of expressway facilities. Traditional IP models are computationally intensive when applied to large-scale networks. To address this, we adapt the CA approach, traditionally used for facility location problems in Euclidean spaces, to handle network distances by embedding the road network into a new Euclidean space using the Isomap algorithm. The proposed methodology was then applied to spare parts location optimization problems of electronic toll collection (ETC) systems in a real-world expressway network in Japan. The results demonstrate that the proposed methodology significantly reduced the optimization computation time by 85.6% to 97.9% compared with an existing method, showcasing a substantial improvement in computational efficiency while also obtaining near-optimal solutions.</div></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":"174 ","pages":"Article 105109"},"PeriodicalIF":7.6,"publicationDate":"2025-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143705197","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":"A benchmark for cycling close pass detection from video streams","authors":"Mingjie Li ,&nbsp;Ben Beck ,&nbsp;Tharindu Rathnayake ,&nbsp;Lingheng Meng ,&nbsp;Zijue Chen ,&nbsp;Akansel Cosgun ,&nbsp;Xiaojun Chang ,&nbsp;Dana Kulić","doi":"10.1016/j.trc.2025.105112","DOIUrl":"10.1016/j.trc.2025.105112","url":null,"abstract":"<div><div>Cycling is a healthy and sustainable mode of transport. However, interactions with motor vehicles remain a key barrier to increased cycling participation. The ability to detect potentially dangerous interactions from on-bike sensing could provide important information to riders and policymakers. A key influence on rider comfort and safety is close passes, <em>i.e</em>., when a vehicle narrowly passes a cyclist. In this paper, we introduce a novel benchmark, called Cyc-CP, towards close pass (CP) event detection from video streams. The task is formulated into two problem categories: scene-level and instance-level. Scene-level detection ascertains the presence of a CP event within the provided video clip. Instance-level detection identifies the specific vehicle within the scene that precipitates a CP event. To address these challenges, we introduce four benchmark models, each underpinned by advanced deep-learning methodologies. For training and evaluating those models, we have developed a synthetic dataset alongside the acquisition of a real-world dataset. The benchmark evaluations reveal that the models achieve an accuracy of 88.13% for scene-level detection and 84.60% for instance-level detection on the real-world dataset. We envision this benchmark as a test-bed to accelerate CP detection and facilitate interaction between the fields of road safety, intelligent transportation systems and artificial intelligence. Both the benchmark datasets and detection models will be available at <span><span>https://github.com/SustainableMobility/cyc-cp</span><svg><path></path></svg></span> to facilitate experimental reproducibility and encourage more in-depth research in the field.</div></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":"174 ","pages":"Article 105112"},"PeriodicalIF":7.6,"publicationDate":"2025-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143705196","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 integrated multi-objective model for demand-capacity balancing and strategic de-confliction under autonomous aircraft flight","authors":"Ziang Liu ,&nbsp;Gang Xiao ,&nbsp;Jizhi Mao","doi":"10.1016/j.trc.2025.105102","DOIUrl":"10.1016/j.trc.2025.105102","url":null,"abstract":"<div><div>To support the continued growth of the air transportation industry, Air Traffic Management (ATM) systems are evolving towards Trajectory Based Operations (TBO). Under TBO, ATM components at different levels transcend traditional spatial–temporal boundaries and become interdependent; and the capability of autonomous aircraft flight is developed and integrated into ATM systems. However, these components have been addressed in isolation, and trajectory uncertainty under autonomous aircraft flight has not been fully considered. In this paper, we first consider the impact of trajectory uncertainty on strategic de-confliction under autonomous aircraft flight, present a conflict detection approach and associated concepts for modeling trajectory conflicts to ensure the robustness of strategic de-confliction. Next, we propose a multi-objective integer programming model for tactical planning in high-density en-route airspace. This model synchronizes demand-capacity balancing and strategic de-confliction, while simultaneously optimizing the three key ATM performance metrics: the operating cost of airspace users, the service cost of air navigation service provider and the number of trajectory conflicts. This multi-objective model is solved using an exact tri-objective integer programming algorithm. We conduct several sets of stochastic numerical experiments in a high-density, complex en-route airspace to test the robustness, performance benefits and computational efficiency of the proposed approach. The results demonstrate that this approach ensures the robustness of strategic de-confliction under autonomous aircraft flight in an environment with wind disturbances. It also simultaneously enhances the optimized performance metrics, yielding considerable potential benefits. Additionally, about 20 Pareto-optimal solutions can be obtained within 10 min. Finally, we analyze the interactions between these performance metrics and derive valuable managerial insights.</div></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":"174 ","pages":"Article 105102"},"PeriodicalIF":7.6,"publicationDate":"2025-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143714555","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":"Scaling from macro to micro: A novel approach to bridging gaps in multiple pavement texture scales using generative neural networks","authors":"Lintao Yang,&nbsp;Huizhao Tu,&nbsp;Hongren Gong,&nbsp;Hao Li,&nbsp;Lijun Sun","doi":"10.1016/j.trc.2025.105108","DOIUrl":"10.1016/j.trc.2025.105108","url":null,"abstract":"<div><div>Both pavement macrotexture and microtexture impact skid resistance, which is vital to driving safety. Current laser-based texture measurement methods struggle to balance efficiency and accuracy in large-scale surveys. Static laser scanners offer highly precise texture data but slow in operation, while vehicle-mounted 3D lasers work at traffic speeds but are inferior in precision. To address this trade-off, a series of generative neural networks called Pavement Texture Scaling Networks (PTSNs) are introduced to scale pavement texture across both macro and micro scales. PTSNs feature a multi-layer invertible architecture where each layer doubles or halves the texture resolution, progressively upscaling lower-resolution data to the desired level. The model was trained on texture data from four asphalt surface types at ten resolutions and evaluated with six texture descriptors and wavelet coherence (WTC). At scaling factors of 8×, 64×, and 512×, PTSNs achieved mean profile depth errors of 2.98 %, 3.91 %, and 4.99 %, respectively. The actual and predicted texture power spectral densities coincide at macrotexture scales but diverge at finer microtexture scales (wavelength <span><math><mrow><mi>q</mi><mo>&gt;</mo><msup><mrow><mn>10</mn></mrow><mn>5</mn></msup></mrow></math></span> m⁻¹). Additionally, PTSNs’ performance varied over polishing levels, with the highest errors observed on unpolished surfaces and the lowest on highly polished surfaces. The WTC analysis found that actual and predicted textures correlated strongly across the lane at frequencies below 32 kHz. Overall, PTSNs effectively reconstruct multi-resolution texture across scales, bridging the resolution gap and offering a fast, cost-effective alternative for high-precision pavement texture measurement.</div></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":"174 ","pages":"Article 105108"},"PeriodicalIF":7.6,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143680486","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 data-driven preference learning approach for multi-objective vehicle routing problems in last-mile delivery","authors":"Zahra Nourmohammadi ,&nbsp;Bohan Hu ,&nbsp;David Rey ,&nbsp;Meead Saberi","doi":"10.1016/j.trc.2025.105101","DOIUrl":"10.1016/j.trc.2025.105101","url":null,"abstract":"<div><div>Last-mile delivery service providers and drivers often choose routes deviating from the shortest distance, influenced by personal preferences and various business-level factors. This study introduces an innovative data-driven optimization approach for learning preferences of decision-makers (DMs) in multi-objective vehicle routing problems. Utilizing real-world historical data from a last-mile delivery logistics platform, we develop a machine-learning framework to learn DMs’ preferences in designing delivery routes. To design our approach we focus on a multi-objective capacitated vehicle routing problem with time windows and develop an integrated framework that combines supervised learning models, sampling techniques, and optimization methods to determine preference weights for objective functions based on selected features. We conduct extensive numerical experiments to test the proposed data-driven optimization approach. Our findings suggest that analyzing historical planned and actual routes reveals DMs’ preferences, such as prioritizing workload balance and minimizing fleet usage over travel distance alone. Furthermore, this study offers insights into key factors shaping last-mile delivery logistics, including workload distribution and deviations from pre-planned routes, enabling more informed and human-centered decision-making in logistics optimization.</div></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":"174 ","pages":"Article 105101"},"PeriodicalIF":7.6,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143680490","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":"TMS-GNN: Traffic-aware Multistep Graph Neural Network for bus passenger flow prediction","authors":"Asiye Baghbani ,&nbsp;Saeed Rahmani ,&nbsp;Nizar Bouguila ,&nbsp;Zachary Patterson","doi":"10.1016/j.trc.2025.105107","DOIUrl":"10.1016/j.trc.2025.105107","url":null,"abstract":"<div><div>Bus network plays a critical role in urban transportation affecting the use of private vehicles, traffic congestion, and urban accessibility. The accurate prediction of bus passenger flow is key to improving transit passenger experience and increasing the efficiency of bus network operations. In line with recent advances in deep learning for passenger flow prediction, graph neural networks (GNNs) have become increasingly popular due to their ability to account for the network structure between stops. Existing GNN-based models for bus passenger flow prediction, however, face several limitations. First, they do not take into account some distinctive characteristics of bus networks, such as their coexistence with vehicular traffic and their high sensitivity to urban traffic conditions. Moreover, sequence prediction models that have been widely applied to multistep passenger flow prediction suffer from a critical issue, called “exposure bias.” This results in the propagation and accumulation of errors through prediction steps while making predictions for farther time horizons. To address these issues, this study presents the Traffic-Aware multistep Graph Neural Network (TMS-GNN) model with Scheduled Sampling, a graph-based deep-learning framework designed to forecast multistep bus passenger flows at individual stops across a bus network. The model takes into account factors such as bus stop connectivity, urban traffic impacts, and multi-dimensional temporal patterns; and addresses exposure bias by employing a curriculum learning strategy called Scheduled Sampling. The comparison between the proposed model and other popular baseline models on two real-world networks with different geographical and urban patterns in Canada and USA shows that TMS-GNN outperforms the baselines in both the overall network-wide task, as well as multistep prediction. Also, to verify the contribution of the proposed components of the model, an ablation study is conducted. The results of the ablation study validate the design choices as well.</div></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":"174 ","pages":"Article 105107"},"PeriodicalIF":7.6,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143680489","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":"Intelligent testing environment generation for autonomous vehicles with implicit distributions of traffic behaviors","authors":"Kun Ren,&nbsp;Jingxuan Yang,&nbsp;Qiujing Lu,&nbsp;Yi Zhang,&nbsp;Jianming Hu,&nbsp;Shuo Feng","doi":"10.1016/j.trc.2025.105106","DOIUrl":"10.1016/j.trc.2025.105106","url":null,"abstract":"<div><div>The advancement of autonomous vehicles hinges significantly on addressing safety concerns and obtaining reliable evaluation results. Testing the safety of autonomous vehicles is challenging due to the complexity of the high-dimensional traffic environment and the rarity of safety-critical events, often requiring billions of miles to achieve comprehensive validation, which is inefficient and costly. Current approaches, such as accelerated testing using importance sampling, aim to provide unbiased estimates of the performance of autonomous vehicles by generating a new distribution of background vehicles’ behaviors based on an initial nominal distribution. However, these methods require knowledge of the original distribution of traffic behaviors, which is often difficult to obtain in practice. In response to these challenges, we introduce a novel methodology termed implicit importance sampling (IIS). Unlike traditional methods, IIS is designed to generate intelligent driving environments based on implicit distributions of traffic behaviors where the true distributions are unknown or not explicitly defined. IIS method leverages accept-reject sampling to construct an unnormalized proposal distribution, which increases the likelihood of sampling adversarial cases. Through applying importance sampling technique with unnormalized proposal distribution, IIS enhances testing efficiency and obtains reliable and representative evaluation results as well. The bias caused by unnormalization is also proved to be controlled and bounded.</div></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":"174 ","pages":"Article 105106"},"PeriodicalIF":7.6,"publicationDate":"2025-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143680485","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":"Harmonizing recurring patterns and non-recurring trends in traffic datasets for enhanced estimation of missing information","authors":"Shubham Sharma ,&nbsp;Richi Nayak ,&nbsp;Ashish Bhaskar","doi":"10.1016/j.trc.2025.105083","DOIUrl":"10.1016/j.trc.2025.105083","url":null,"abstract":"<div><div>Traffic datasets commonly comprise missing information due to sensor malfunctions, environmental conditions, security concerns, and technical/data quality issues. These challenges are inherent in real-world traffic data collection systems. Despite numerous imputation algorithms proposed in the literature, concerns persist about selecting a reliable algorithm that consistently performs well across diverse missing data scenarios. This is crucial for two main reasons. Firstly, real-world traffic datasets often exhibit a range of missing gaps with varying temporal durations, encompassing both short and long gaps within a single dataset. Secondly, in spatio-temporal traffic datasets, both recurring and non-recurring traffic conditions coexist. Since different data imputation principles reported in the literature suit either type of missing data (short or long gaps) and traffic conditions (recurring or non-recurring) better than others, algorithms often output sub-optimal estimates for network-wide datasets characterized by multiple types of missing gaps and traffic conditions.</div><div>To address the issue, this paper proposes a tensor decomposition algorithm named SINTD (Stochastic Informed Non-Negative Tensor Decomposition) and logically integrates it with a spline regression model in a novel data imputation framework called SPRINT (Spline-powered Informed Non-negative Tensor Decomposition). Where SINTD mines dominant patterns in the traffic datasets, effective in estimating missing gaps under recurring traffic conditions, integration of spline with tensor decomposition helps <em>a) capturing the time-localized trends unaccounted by tensor decomposition, aiding in approximating better the non-recurring component of the traffic states</em>, and <em>b) complementing SINTD for improved mining of recurring patterns in the subsequent iterations</em> of SPRINT. Although the two algorithms have distinct limitations when used separately, their harmonization allows us to effectively utilize their respective strengths and overcome individual limitations. This paper, through extensive experimentation on six traffic datasets and benchmarking against nine baseline algorithms, demonstrates the efficacy of SPRINT in consistently producing high-accuracy missing data estimates across five diverse missing data scenarios. These include a) experiments on datasets exhibiting a mix of short and long-duration missing gaps—mimicking the intricate missing data structure of real-world traffic datasets, and b) a Logan City (Australia) case study highlighting the imputation of missing data under potential non-recurring traffic conditions resulting from road incidents.</div></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":"174 ","pages":"Article 105083"},"PeriodicalIF":7.6,"publicationDate":"2025-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143680483","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 container drayage problem for electric trucks with charging resource constraints","authors":"Liyang Xiao ,&nbsp;Luxian Chen ,&nbsp;Peng Sun ,&nbsp;Gilbert Laporte ,&nbsp;Roberto Baldacci","doi":"10.1016/j.trc.2025.105100","DOIUrl":"10.1016/j.trc.2025.105100","url":null,"abstract":"<div><div>Amidst the ongoing green transformation in transportation, the electrification of trucks has emerged as a pivotal strategy to address climate-related issues. This paper introduces the container drayage problem for electric trucks, considering the charging resource constraints. Electric trucks are assigned to serve a series of origin–destination tasks between terminals and customers. Each truck can opt between battery swapping and two charging modes: normal and fast, each featuring a nonlinear charging process. The paper addresses the charging queueing problem arising from limitations in charging resources, presenting a novel mixed integer programming model tailored to container drayage challenges for electric trucks. To tackle this challenging problem, we propose an enhanced adaptive large neighborhood search algorithm that integrates an exact method. In the first stage, routes are generated based on customized procedures without considering queueing charging to minimize overall operation costs. The second stage is triggered by the call frequency and condition coefficient, utilizing CPLEX to optimize further queueing charging strategies. The algorithm is applied to instances based on real-world task data obtained from logistics companies. A series of comparative experiments are conducted to validate the efficacy and ascertain the parameter configuration of the algorithm. Furthermore, we examine the influence of charge levels and numbers of replaceable batteries on overall expenses and conduct a comprehensive analysis of the application influence of electric trucks compared to conventional fuel trucks in terms of cost and emissions.</div></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":"174 ","pages":"Article 105100"},"PeriodicalIF":7.6,"publicationDate":"2025-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143680484","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}