Transportation Research Part C-Emerging Technologies最新文献

{"title":"Prediction-failure-risk-aware online dial-a-ride scheduling considering spatial demand correlation via approximate dynamic programming and scenario approach","authors":"Weitiao Wu ,&nbsp;Honghui Zou ,&nbsp;Ronghui Liu","doi":"10.1016/j.trc.2024.104801","DOIUrl":"10.1016/j.trc.2024.104801","url":null,"abstract":"<div><p>The dial-a-ride (DAR) service is a precursor to emerging shared mobility. Service providers expect efficient management of fleet resources to improve service quality without degrading economic viability. Most existing studies overlook possible future demands that could yield better matching opportunities and scheduling benefits, and therefore have short-sighted limitations. Moreover, the effects of correlated demand and potential prediction errors were ignored. To address these gaps, this paper investigates prediction-failure-risk-aware online DAR scheduling with spatial demand correlation. Request selection and cancellation are explicitly considered. We formulate the problem as a Markov decision process (MDP) and solve it by approximate dynamic programming (ADP). We further develop a demand prediction model that can capture the characteristics of DAR travel demand (uncertainty, sparsity, and spatial correlation). Deep quantile regression is adopted to estimate the marginal distribution of each OD pair. These marginals are combined into a joint demand distribution by constructing a Gaussian Copula to capture the spatial demand correlation. A prediction error correction mechanism is proposed to eliminate prediction errors and rectify policies promptly. Based on the model properties, several families of customized pruning strategies are devised to improve the computational efficiency and solution quality of ADP. We solve policies over time in the dynamic environment mixed with actual and stochastic future demands via the ADP algorithm and scenario approach. We propose the value function rolling method and multi-scenario exploration method, to address the deviation of the value function and identify the optimal policy from multiple future demand scenarios. Numerical results demonstrate the importance and benefits of incorporating demand forecasting and spatial correlation into the DAR operation. The improvement due to prediction is significant even when the prediction is imperfect, while the demand prediction can hedge against the negative effects of request cancellation. The real-world application result shows that compared to state-of-the-practice, the overall delivery efficiency can be substantially improved, along with better service quality and fleet size savings.</p></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":"169 ","pages":"Article 104801"},"PeriodicalIF":7.6,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142173200","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 novel framework of the alternating direction method of multipliers with application to traffic assignment problem","authors":"Honggang Zhang ,&nbsp;Yu Dong ,&nbsp;Xiangyang Xu ,&nbsp;Zhiyuan Liu ,&nbsp;Pan Liu","doi":"10.1016/j.trc.2024.104843","DOIUrl":"10.1016/j.trc.2024.104843","url":null,"abstract":"<div><p>This paper proposes a novel algorithmic framework to enhance the convergence efficiency of the alternating direction method of multipliers (ADMM) by incorporating the successive over relaxation (SOR) splitting method. The proposed framework holds applicability across various research fields for improving convergence efficiency. Currently, there exist two main methods for decomposing the separate optimization problems: Gauss-Seidel (GS) and Jacobi methods. The SOR method introduced in this paper offers a more efficient alternative. Following the original ADMM algorithm’s framework, we provide a detailed procedure for incorporating the SOR method into the ADMM framework in place of the GS splitting method. This development gives rise to a new method called ADMM-SOR, and then we apply this newly proposed algorithm to solve the deterministic user equilibrium (DUE) problem. Subsequently, to ensure the reliability of the proposed algorithm, we rigorously prove its convergence by leveraging some properties of variational inequalities. Additionally, the impact of the relaxation factor on the efficiency of the ADMM-SOR method is conducted, and we also explore a novel method to self-adjust the relaxation factor during each iteration. The new algorithm is verified based on numerical experiments, revealing that the novel ADMM-SOR framework achieves faster convergence in comparison to the original one, all the while maintaining exceptional parallel performance.</p></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":"169 ","pages":"Article 104843"},"PeriodicalIF":7.6,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142167835","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 time-embedded attention-based transformer for crash likelihood prediction at intersections using connected vehicle data","authors":"B M Tazbiul Hassan Anik,&nbsp;Zubayer Islam,&nbsp;Mohamed Abdel-Aty","doi":"10.1016/j.trc.2024.104831","DOIUrl":"10.1016/j.trc.2024.104831","url":null,"abstract":"<div><p>The real-time crash likelihood prediction model is an essential component of the proactive traffic safety management system. Over the years, numerous studies have attempted to construct a crash likelihood prediction model in order to enhance traffic safety, but mostly on freeways. In the majority of the existing studies, researchers have primarily used a deep learning-based framework to identify crash potential. Lately, Transformers have emerged as a potential deep neural network that fundamentally operates through attention-based mechanisms. Transformers exhibit distinct functional benefits over established deep learning models like Recurrent Neural Networks (RNNs), Long Short-Term Memory networks (LSTMs), and Convolutional Neural Networks (CNNs). First, they employ attention mechanisms to accurately weigh the significance of different parts of input data, a dynamic functionality that is not available in RNNs, LSTMs, and CNNs. Second, they are well-equipped to handle dependencies over long-range data sequences, a feat RNNs typically struggle with. Lastly, unlike RNNs, LSTMs, and CNNs, which process data in sequence, Transformers can parallelly process data elements during training and inference, thereby enhancing their efficiency. Apprehending the immense possibility of Transformers, this paper proposes inTersection-Transformer (inTformer), a time-embedded attention-based Transformer model that can effectively predict intersection crash likelihood in real-time. The inTformer is basically a binary prediction model that predicts the occurrence or non-occurrence of crashes at intersections in the near future (i.e., next 15 min). The proposed model was developed by employing traffic data extracted from connected vehicles. Acknowledging the complex traffic operation mechanism at intersection, this study developed zone-specific models by dividing the intersection region into two distinct zones: within-intersection and approach zones, each representing the intricate flow of traffic unique to the type of intersection (i.e., three-legged and four-legged intersections). In the ‘within-intersection’ zone, the inTformer models attained a sensitivity of up to 73%, while in the ‘approach’ zone, the sensitivity peaked at 74%. Moreover, benchmarking the optimal zone-specific inTformer models against earlier studies on crash likelihood prediction at intersections and several established deep learning models trained on the same connected vehicle dataset confirmed the superiority of the proposed inTformer. Further, to quantify the impact of features on crash likelihood at intersections, the SHAP (SHapley Additive exPlanations) method was applied on the best performing inTformer models. The most critical predictors were average and maximum approach speeds, average and maximum control delays, average and maximum travel times, split failure percentage and count, and percent arrival on green.</p></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":"169 ","pages":"Article 104831"},"PeriodicalIF":7.6,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142167838","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 role of individual compensation and acceptance decisions in crowdsourced delivery","authors":"Alim Buğra Çınar,&nbsp;Wout Dullaert,&nbsp;Markus Leitner,&nbsp;Rosario Paradiso,&nbsp;Stefan Waldherr","doi":"10.1016/j.trc.2024.104834","DOIUrl":"10.1016/j.trc.2024.104834","url":null,"abstract":"<div><p>High demand, rising customer expectations, and government regulations are forcing companies to increase the efficiency and sustainability of urban (last-mile) distribution. Consequently, several new delivery concepts have been proposed that increase flexibility for customers and other stakeholders. One of these innovations is crowdsourced delivery, where deliveries are made by occasional drivers who wish to utilize their surplus resources (unused transport capacity) by making deliveries in exchange for some compensation. The potential benefits of crowdsourced delivery include reduced delivery costs and increased flexibility (by scaling delivery capacity up and down as needed). The use of occasional drivers poses new challenges because (unlike traditional couriers) neither their availability nor their behavior in accepting delivery offers is certain. The relationship between the compensation offered to occasional drivers and the probability that they will accept a task has been largely neglected in the scientific literature. Therefore, we consider a setting in which compensation-dependent acceptance probabilities are explicitly considered in the process of assigning delivery tasks to occasional drivers. We propose a mixed-integer nonlinear model that minimizes the expected delivery costs while identifying optimal assignments of tasks to a mix of professional and occasional drivers and their compensation. We propose an exact two-stage solution algorithm that allows to decompose compensation and assignment decisions for generic acceptance probability functions and show that the runtime of this algorithm is polynomial under mild conditions. Finally, we also study a more general case of the considered problem setting, show that it is NP-hard and propose an approximate linearization scheme of our mixed-integer nonlinear model. The results of our computational study show clear advantages of our new approach over existing ones. They also indicate that these advantages remain in dynamic settings when tasks and drivers are revealed over time and in which case our method constitutes a fast, yet powerful heuristic.</p></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":"169 ","pages":"Article 104834"},"PeriodicalIF":7.6,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0968090X24003553/pdfft?md5=a3cf9eb6d9094ae5aa67e8b3ad1ac966&pid=1-s2.0-S0968090X24003553-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142163108","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":"Copula-based transferable models for synthetic population generation","authors":"Pascal Jutras-Dubé ,&nbsp;Mohammad B. Al-Khasawneh ,&nbsp;Zhichao Yang ,&nbsp;Javier Bas ,&nbsp;Fabian Bastin ,&nbsp;Cinzia Cirillo","doi":"10.1016/j.trc.2024.104830","DOIUrl":"10.1016/j.trc.2024.104830","url":null,"abstract":"<div><p>Population synthesis involves generating synthetic yet realistic representations of a target population of micro-agents for behavioral modeling and simulation. Traditional methods, often reliant on target population samples, such as census data or travel surveys, face limitations due to high costs and small sample sizes, particularly at smaller geographical scales. We propose a novel framework based on copulas to generate synthetic data for target populations where only empirical marginal distributions are known. This method utilizes samples from different populations with similar marginal dependencies, introduces a spatial component into population synthesis, and considers various information sources for more realistic generators. Concretely, the process involves normalizing the data and treating it as realizations of a given copula, and then training a generative model before incorporating the information on the marginals of the target population. Utilizing American Community Survey data, we assess our framework’s performance through standardized root mean squared error (SRMSE) and so-called sampled zeros. We focus on its capacity to transfer a model learned from one population to another. Our experiments include transfer tests between regions at the same geographical level as well as to lower geographical levels, hence evaluating the framework’s adaptability in varied spatial contexts. We compare Bayesian Networks, Variational Autoencoders, and Generative Adversarial Networks, both individually and combined with our copula framework. Results show that the copula enhances machine learning methods in matching the marginals of the reference data. Furthermore, it consistently surpasses Iterative Proportional Fitting in terms of SRMSE in the transferability experiments, while introducing unique observations not found in the original training sample.</p></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":"169 ","pages":"Article 104830"},"PeriodicalIF":7.6,"publicationDate":"2024-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142163236","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":"Space-Time adaptive network for origin-destination passenger demand prediction","authors":"Haoge Xu ,&nbsp;Yong Chen ,&nbsp;Chuanjia Li ,&nbsp;Xiqun (Michael) Chen","doi":"10.1016/j.trc.2024.104842","DOIUrl":"10.1016/j.trc.2024.104842","url":null,"abstract":"<div><p>Short-term origin–destination passenger demand prediction involves modeling spatial and temporal characteristics of urban traffic, such as periodicity in demand rate and directionality in flow path. Meanwhile, spatial and temporal heterogeneities often lead to constantly evolving dynamics in in passenger demand, e.g., passengers may exhibit different mobility patterns at different periods or in different regions. Many models fail to capture these heterogeneities and adjust parameters adaptively, leading to suboptimal prediction results. In this paper, we propose a novel space–time adaptive network (STAN) to address these issues. Spatially, an edge-based backbone with a global receptive field is devised. Edge embeddings directly represent pair-wise relations between regions, preserving more fine-grained information and directional interactions. The backbone adaptively updates edge embeddings by fusing static and dynamic information from origin and destination regions, enabling the model to learn intricate spatial relations from simple input data (i.e., basic relation graphs and historical OD matrices). Temporally, a prompter mechanism is proposed to inject temporal information into model parameters, making them time-dependent. The parameter values exhibit periodicity and continuity for all periods, meanwhile, they can be adjusted for each specific period. It makes the model time-aware and enables it to identify similar periods and differentiate dissimilar ones during training. Extensive experiments are conducted on two real-world datasets (i.e., ten-month taxi trips in New York and one-month ride-hailing trips in Ningbo), and the results demonstrate that our model outperforms baseline models and automatically learns certain spatial and temporal semantics. With its simple yet highly scalable structure, our model proves beneficial for implementations and can assist related tasks such as driver-passenger matching and surge pricing.</p></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":"167 ","pages":"Article 104842"},"PeriodicalIF":7.6,"publicationDate":"2024-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142147970","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":"Roadside LiDAR placement for cooperative traffic detection by a novel chance constrained stochastic simulation optimization approach","authors":"Yanzhan Chen ,&nbsp;Liang Zheng ,&nbsp;Zhen Tan","doi":"10.1016/j.trc.2024.104838","DOIUrl":"10.1016/j.trc.2024.104838","url":null,"abstract":"<div><p>Light Detection and Ranging (LiDAR) plays a pivotal role in localization, thereby meeting the imperative to accurately discern vehicle positions and road states for enhanced services in Intelligent Transportation Systems (ITS). As the cooperative perception among multiple LiDARs is necessitated by localization applications spanning extensive road networks, the strategic placement of LiDARs significantly impacts localization outcomes. This research proposes a chance constrained stochastic simulation-based optimization (SO) model for Roadside LiDAR (RSL) placement to maximize the expected value of mean Average Precision (mAP) subject to a budgeted number of RSLs and a chance constraint of ensuring a specific recall value under traffic uncertainties. Importantly, the assessment of a specific RSL placement plan employs a data-driven deep learning approach based on a high-fidelity co-simulator, which is inherently characterized by black-box nature, high computational costs and stochasticity. To address these challenges, a novel Gaussian Process Regression-based Approximate Knowledge Gradient (GPR-AKG) sampling algorithm is designed. In numerical experiments on a bi-directional eight-lane highway, the RSL placement plan optimized by GPR-AKG attains an impressive mAP of 0.829 while ensuring compliance with the chance constraint, and outperforms empirically designed alternatives. The cooperative vehicle detection and tracking under the optimized plan can effectively address false alarms and missed detections caused by heavy vehicle occlusions, and generate highly complete and smooth vehicle trajectories. Meanwhile, the analyses of detection coverage and average effective work duration validate the reasonability of prioritizing the center-mounted RSLs in the optimized plan. The balance analysis of mAP and the number of deployed RSLs confirms the scientific validity of deploying 20 RSLs in the optimized plan. In conclusion, the GPR-AKG algorithm exhibits promise in resolving chance constrained stochastic SO problems marked by black-box evaluations, high computational costs, high dimensions, stochasticity, and diverse decision variable types, offering potential applicability across various engineering domains.</p></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":"167 ","pages":"Article 104838"},"PeriodicalIF":7.6,"publicationDate":"2024-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142136460","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":"Modeling lane changes using parallel learning","authors":"Yu Han,&nbsp;Yan Li,&nbsp;Shixuan Yu,&nbsp;Jiankun Peng,&nbsp;Lu Bai,&nbsp;Pan Liu","doi":"10.1016/j.trc.2024.104841","DOIUrl":"10.1016/j.trc.2024.104841","url":null,"abstract":"<div><p>This paper introduces an innovative approach to model the lane-change (LC) process of vehicles by employing parallel learning, seamlessly integrating conventional physical or behavioral models with data-driven counterparts. The LC process is divided into two distinct steps: the LC decision and the LC implementation, each independently modeled. For the LC decision model, a utility-based model is embedded into a neural network. Simultaneously, the LC implementation model incorporates a conventional car-following model, replicating the behavior of the new follower of the lane-changer, within the training process of a long-short-term memory model. Empirical trajectory data collected from unmanned aerial vehicles, which provides detailed information on the vehicles’ lane-changing process, serves as the basis for training and testing the proposed models. Additionally, data from a different site is employed to assess model transferability. Results demonstrate that the proposed models adeptly predict both LC decisions and implementations, outperforming baseline physical and behavioral models, as well as pure data-driven models, in terms of prediction accuracy and transferability. These findings highlight the significant potential of these models in improving the precision of microscopic traffic simulators.</p></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":"167 ","pages":"Article 104841"},"PeriodicalIF":7.6,"publicationDate":"2024-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142136461","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 network equilibrium model for integrated shared mobility services with ride-pooling","authors":"Xu Chen ,&nbsp;Xuan Di","doi":"10.1016/j.trc.2024.104837","DOIUrl":"10.1016/j.trc.2024.104837","url":null,"abstract":"<div><p>With the growing popularity of transportation network companies (TNC), there remains a gap in network equilibrium models that adequately address the emergence of ride-pooling service that pools two orders into one trip. This challenge arises from the need to enumerate all possible combinations of origin–destination (OD) pooling and sequencing. This paper proposes a network equilibrium framework that integrates ride-hailing platforms’ decision on vehicle dispatching and driver–passenger matching on congested road networks. To facilitate the representation of vehicle and passenger OD flows and ride-pooling options, a layered OD graph is created encompassing ride-sourcing and ride-pooling services over OD nodes. To capture road congestion, a connection between the layered OD graph and road networks is established where the vehicle flows on the OD graph form source demands on road networks. Numerical examples are performed on a toy example and the Sioux Fall network to demonstrate our model and algorithm. The proposed equilibrium framework can efficiently assist policymakers and urban planners to evaluate the impact of TNCs on traffic congestion.</p></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":"167 ","pages":"Article 104837"},"PeriodicalIF":7.6,"publicationDate":"2024-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142136462","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":"Quantifying the vibrancy of streets: Large-scale pedestrian density estimation with dashcam data","authors":"Takuma Oda ,&nbsp;Yuji Yoshimura","doi":"10.1016/j.trc.2024.104840","DOIUrl":"10.1016/j.trc.2024.104840","url":null,"abstract":"<div><p>This paper proposes a new methodology for measuring street-level pedestrian density that combines the strengths of image-based observations with the scalability of drive-by sensing. Despite its importance, existing methods for measuring pedestrian activity have several limitations, including high costs, limited coverage, and privacy concerns. To overcome these issues, our approach exploits operation logs generated by dashboard cameras of moving vehicles to estimate pedestrian density for each street, which is validated with data from approximately 3,000 taxis operating in central Tokyo. We produce vibrancy maps for 292 station areas in central Tokyo by leveraging machine learning to estimate pedestrian density in streets where measurement data is scarce. We also evaluate the reliability and coverage of the measurement and illustrate how the measured pedestrian density data can be utilized for assessing the validity of walkability measures. The paper concludes that this approach could provide valuable data to inform urban planning and city operations.</p></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":"167 ","pages":"Article 104840"},"PeriodicalIF":7.6,"publicationDate":"2024-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142128726","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}