Transportation Research Part C-Emerging Technologies最新文献

{"title":"Optimal pricing and vehicle allocation in local ride-sharing markets with user heterogeneity","authors":"Wen-Xiang Wu ,&nbsp;Rui Sun ,&nbsp;Xiao-Ming Liu ,&nbsp;Hai-Jun Huang ,&nbsp;Li-Jun Tian ,&nbsp;Hua-Yan Shang","doi":"10.1016/j.trc.2025.105084","DOIUrl":"10.1016/j.trc.2025.105084","url":null,"abstract":"<div><div>A ride-sharing platform (RSP) typically provides both solo and pooled ride services to passengers. Passengers opting for pooled rides pay a lower fare but generally experience longer travel times. Pooled ride services gain from improving occupancy per car, thereby serving more passengers, but this comes at the cost of a lower profit margin per passenger compared to solo ride services. We develop a stochastic queueing model to characterize the user equilibrium in a local on-demand market for solo and pooled ride services. In this model, passengers have heterogeneous values of time (VOTs), and drivers operate as independent agents. We find that in equilibrium, the VOT threshold value regulated by the set trip fares for solo and pooled ride services determines passengers’ travel mode choices. Specifically, passengers with lower VOTs than the threshold value choose to pool, while the others choose to ride alone. Built upon the user equilibrium, we then design customized optimal pricing and vehicle allocation strategies to maximize the total expected revenue of the RSP. This approach adapts the revenue-maximizing pricing and vehicle allocation strategies to a specific local ride-sharing market. It achieves this customization by considering factors such as users’ VOTs, supply and demand levels, spatial distances, and prevailing traffic conditions. Numerical results demonstrate that, in situations of high demand but limited supply, our proposed optimal pricing and vehicle allocation strategy effectively maximizes the total expected revenue of the RSP in the presence of spatial–temporal variations in ride-sharing demand. In such scenarios, solo ride prices are set at higher levels, and a majority of idle vehicles are allocated to solo passengers. Conversely, when demand is low but supply is sufficient, combining the optimal pricing strategy with a proportional vehicle allocation strategy also nearly maximizes the total expected revenue. In this case, the optimal vehicle allocation strategy is deemed non-critical due to the surplus supply. Solo ride prices are adjusted differently than those in high-demand situations to incentivize solo ride selection while discouraging pooled rides, ultimately resulting in the highest total expected revenue.</div></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":"174 ","pages":"Article 105084"},"PeriodicalIF":7.6,"publicationDate":"2025-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143637682","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 MPC approach for virtually coupled train set with non-analytic safety distance","authors":"Xiaolin Luo ,&nbsp;Dongming Wang ,&nbsp;Tao Tang ,&nbsp;Hongjie Liu","doi":"10.1016/j.trc.2025.105087","DOIUrl":"10.1016/j.trc.2025.105087","url":null,"abstract":"<div><div>Resorting to the emerging virtual coupling technology, multiple train units can operate as a virtually coupled train set (VCTS) to improve the flexibility and efficiency of train operations. To strictly guarantee collision avoidance, the space–time separation principle should be employed, where a non-analytic safety distance is used to safely separate units among VCTS. Thus, the formation control of VCTS is challenging, since it lacks analytic models to tune controllers with tracking accuracy and computational efficiency. To solve this problem, this paper proposes a data-driven model predictive control (DDMPC) approach. Based on a database with previously measured VCTS trajectories, we present a linear data-driven model to describe the non-analytic VCTS formation, such that the controller of DDMPC is yielded by solving a quadratic programming problem in a computationally efficient way. Next, to improve tracking accuracy, we optimize the modeling accuracy in the cost function of DDMPC, and bound the uncertainties from data-driven modeling and coupled states of VCTS. Furthermore, sufficient conditions are derived to guarantee constraint satisfaction and stability for VCTS. Finally, the advantages of the proposed DDMPC approach are demonstrated by comparing with several approaches in tracking accuracy and computational efficiency.</div></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":"174 ","pages":"Article 105087"},"PeriodicalIF":7.6,"publicationDate":"2025-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143629205","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":"Advancing lane formation and high-density simulations in bidirectional flow: A humanoid pedestrian model incorporating gait dynamics and body rotation","authors":"Xiaoyun Shang ,&nbsp;Rui Jiang ,&nbsp;S.C. Wong ,&nbsp;Ziyou Gao ,&nbsp;Wenguo Weng","doi":"10.1016/j.trc.2025.105086","DOIUrl":"10.1016/j.trc.2025.105086","url":null,"abstract":"<div><div>Current bidirectional pedestrian flow models face challenges in accurately simulating lane formation and high-density conditions. This study addresses these issues by developing an improved humanoid pedestrian model (HPM), which extends the applicability of the original HPM from one-dimensional to two-dimensional scenarios and offers a more realistic simulation of pedestrian behavior. The improved HPM incorporates two distinct gaits—walking while rotating and walking while turning, which capture the complex dynamics of human walking—and an innovative gait-planning process. Additionally, a novel energy-based heuristic rule that considers factors such as deviation from the target direction, body rotation to navigate gaps, and reduced walking velocity is introduced. The energy expression is designed according to the form of mechanical energy, with no parameters requiring calibration. This design enables our model to demonstrate, to some extent, that pedestrians determine their walking direction by minimizing mechanical energy consumption. Simulations are conducted under conditions replicating previous experiments to validate the improved HPM against both experimental results and two classic models, namely the heuristic-based model and the social force model. The improved HPM shows minimal trajectory deviation; effectively replicates body rotation that facilitates efficient lane formation; and transitions swiftly from a randomized flow to stable, well-ordered flow patterns. Moreover, the improved HPM achieves a maximum density of 7 ped/m², representing a significant advancement in modeling high-density scenarios. Overall, the improved HPM offers deep insights into the crowd dynamics of bidirectional flow and thereby improves the accuracy of simulations in high-density situations.</div></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":"174 ","pages":"Article 105086"},"PeriodicalIF":7.6,"publicationDate":"2025-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143619226","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":"Two-step optimization of train timetables rescheduling and response vehicles on a disrupted metro line","authors":"Hui Wang ,&nbsp;Feng Li ,&nbsp;Jialin Liu ,&nbsp;Hao Ji ,&nbsp;Bin Jia ,&nbsp;Ziyou Gao","doi":"10.1016/j.trc.2025.105078","DOIUrl":"10.1016/j.trc.2025.105078","url":null,"abstract":"<div><div>Metro disruption management is currently one of the hot issues in metro research. Existing research has primarily focused on rescheduling normal train timetables or the design of bus bridging services, with limited consideration of the traffic dynamics. In this paper, we introduce a two-step optimization framework to derive a comprehensive evacuation plan encompassing the rescheduled train timetable and the response vehicle scheduling scheme. In the first step, an integer programming model is proposed to reschedule the normal train timetable. The objective function of this model is to minimize total passenger waiting time while considering various constraints such as the timetable rescheduling strategies (i.e., cancellation and short-turning), train headway, and train capacity. In the second step, the response vehicle scheduling model is established based on the Cell Transmission Model (CTM). This model aims to minimize the total travel time of the response vehicles and is capable of capturing traffic dynamics on the evacuation network. To bridge the gap between the mathematical models of the first and second steps, we establish a demand transformation process, which provides a formula for transforming the stranded passenger demand into the demand for response vehicles. Numerical cases of Beijing Metro Line 9 verify the efficiency and effectiveness of our proposed model, and results show that: (1) the direction with fewer train services experiences a greater impact from the disruption. The disruptions occurring within the central region of the metro line tend to affect a greater number of normal train services during peak hours, whereas disruptions occurring within the terminal areas of the metro line tend to affect a greater number of normal train services during off-peak hours; (2) compared with the static shortest route scheme, the dynamic shortest routes of response vehicles contribute a 7% reduction in total travel time.</div></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":"174 ","pages":"Article 105078"},"PeriodicalIF":7.6,"publicationDate":"2025-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143619225","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":"Coordinating ride-pooling with public transit using Reward-Guided Conservative Q-Learning: An offline training and online fine-tuning reinforcement learning framework","authors":"Yulong Hu ,&nbsp;Tingting Dong ,&nbsp;Sen Li","doi":"10.1016/j.trc.2025.105051","DOIUrl":"10.1016/j.trc.2025.105051","url":null,"abstract":"<div><div>This paper introduces a novel reinforcement learning (RL) framework, termed Reward-Guided Conservative Q-learning (RG-CQL), to enhance coordination between ride-pooling and public transit within a multimodal transportation network. We model each ride-pooling vehicle as an agent governed by a Markov Decision Process (MDP), which includes a state for each agent encompassing the vehicle’s location, the number of vacant seats, and all pertinent information regarding the passengers on board. We propose an offline training and online fine-tuning RL framework to learn the optimal operational decisions of the multimodal transportation systems, including rider-vehicle matching, selection of drop-off locations for passengers, and vehicle routing decisions, with improved data efficiency. During the offline training phase, we develop a Conservative Double Deep Q Network (CDDQN) as the action executor and a supervised learning-based reward estimator, termed the Guider Network, to extract valuable insights into action-reward relationships from data batches. In the online fine-tuning phase, the Guider Network serves as an exploration guide, aiding CDDQN in effectively and conservatively exploring unknown state–action pairs to bridge the gap between the conservative offline training and optimistic online fine-tuning. The efficacy of our algorithm is demonstrated through a realistic case study using real-world data from Manhattan. We show that integrating ride-pooling with public transit outperforms two benchmark cases—solo rides coordinated with transit and ride-pooling without transit coordination—by 17% and 22% in the achieved system rewards, respectively. Furthermore, our innovative offline training and online fine-tuning framework offers a remarkable 81.3% improvement in data efficiency compared to traditional online RL methods with adequate exploration budgets, with a 4.3% increase in total rewards and a 5.6% reduction in overestimation errors. Experimental results further demonstrate that RG-CQL effectively addresses the challenges of transitioning from offline to online RL in large-scale ride-pooling systems integrated with transit.</div></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":"174 ","pages":"Article 105051"},"PeriodicalIF":7.6,"publicationDate":"2025-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143610246","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":"Transitioning to electrification: Optimal strategies for ship fleet replacement","authors":"Ziyu Cui ,&nbsp;Xiaowen Fu ,&nbsp;Huan Jin ,&nbsp;Anthony Chen ,&nbsp;Kun Wang","doi":"10.1016/j.trc.2025.105079","DOIUrl":"10.1016/j.trc.2025.105079","url":null,"abstract":"<div><div>To reduce carbon emissions, some shipping companies are strategically transitioning their fleets from diesel-powered ships to electric ones. However, the question of how to cost-effectively design this fleet conversion and the construction of charging stations for electric ships remains a critical issue. In this paper, we present a Mixed-Integer Nonlinear Programming (MINLP) model to minimize the total cost of designing the ship fleet replacement plan within a given planning horizon. At the planning level, the yearly strategic transition schedules for purchasing and salvaging ships are obtained, while the optimal strategy for charging station construction is also determined to satisfy annual cargo demand and charging demand. Then, our model integrates the operational level to determine optimal fleet deployment strategies and operational decisions. Meanwhile, the impact of real-world factors such as waterway depth and emission cost are discussed, respectively. Moreover, we introduce two subsidy schemes and compare their effectiveness on the fleet electrification process. Finally, the proposed model is applied to the Yangtze River shipping network in China using real data. The results show that the shipping company will initially invest in lower-cost electric ships with a limited budget, followed by the acquisition of larger ships. The charging stations are strategically established in the downstream areas, coinciding with the initial deployment of electric ships. Moreover, the impacts of fuel prices and emission cost of diesel-powered ships on the electrification process are compared, with the results indicating that the increase in emission cost has a more pronounced effect on the total cost compared to the rise in fuel prices. Additionally, the subsidy schemes’ effect is explored, revealing that financial incentives markedly accelerate the fleet electrification process.</div></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":"174 ","pages":"Article 105079"},"PeriodicalIF":7.6,"publicationDate":"2025-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143610245","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":"Variational inference for high-dimensional integrated choice and latent variable (ICLV) models within a Bayesian framework","authors":"Gyeongjun Kim ,&nbsp;Yeseul Kang ,&nbsp;Keemin Sohn","doi":"10.1016/j.trc.2025.105023","DOIUrl":"10.1016/j.trc.2025.105023","url":null,"abstract":"<div><div>The variational Bayes is widely used to deal with high-dimensional models. The present study attempts to apply variational inference (VI) to estimate high-dimensional integrated choice and latent variable (ICLV) models. When utilizing the Maximum Simulated Likelihood (MSL) technique to calibrate an ICLV model with the Gaussian kernel, the log-likelihood function cannot be evaluated if the dimension of latent variables and choice options grows. Addressing this, the present study proposes a conditional variational inference (CVI) method that consistently estimate an ICLV model regardless of the dimensions of choice options and latent variables within a Bayesian framework. Variational models are supplanted by neural embedding, and the mean and variance of the Gaussian probability density are parameterized by a neural network, which is called the reparameterization trick. Furthermore, the Gumbel softmax function approximates the ’argmax’ operation for selecting a choice option of the maximum utility, which bypasses the computationally intensive task of calculating choice probabilities. Collectively, these strategies ensure the scalable ICLV model estimation, as increasing the number of latent variables and choice options. The calibration method succeeded in reproducing parameters of a large-scale ICLV model with 30 latent variables and 30 choice options.</div></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":"174 ","pages":"Article 105023"},"PeriodicalIF":7.6,"publicationDate":"2025-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143610242","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":"Stochastic game-based cross-layer defense scheme for jamming-resistant virtual coupled train sets","authors":"Shuomei Ma ,&nbsp;Xiaozhi Qi ,&nbsp;Zhijiang Lou ,&nbsp;Hongwei Wang ,&nbsp;Li Zhu ,&nbsp;Taiyuan Gong ,&nbsp;Yang Li ,&nbsp;Hairong Dong","doi":"10.1016/j.trc.2025.105028","DOIUrl":"10.1016/j.trc.2025.105028","url":null,"abstract":"<div><div>The railway operation concept of Virtually Coupled Train Sets (VCTS) allows for shorter headways between units in a train convoy, enhancing the current capacity limit imposed by existing Communication-Based Train Control (CBTC) systems by enabling units to operate safely at shorter distances. However, due to the use of open Train-to-Train (T2T) wireless communication through Long-Terms Evolution for Metro (LTE-M), VCTS is vulnerable to various cyber-attacks, including jamming attacks, which have largely been overlooked. To address this issue, this paper proposes a Stochastic Game-Based Cross-Layer Defense (SGCD) scheme. This scheme aims to enhance the safety and stability of VCTS in both the physical and cyber layers, in the presence of uncertain communication failures caused by jamming attacks. This proposed scheme formulates the defense approach and the particularly jamming actions as a stochastic game. A cross-layer control approach is employed to mitigate the impact of jamming attacks on the train convoy. The performance of this cross-layer control is mapped to the frequency domain and quantified using the <span><math><msup><mrow><mi>H</mi></mrow><mrow><mi>∞</mi></mrow></msup></math></span> norm to ensure the stability and safety of the VCTS system. Extensive simulation results demonstrate that the SGCD scheme can effectively ensure the running stability and safety of a train convoy under random jamming attacks in the VCTS. The proposed defense mechanism can enhance the security and reliability of the VCTS system, thereby enabling safer and more efficient train operations with shorter headways.</div></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":"174 ","pages":"Article 105028"},"PeriodicalIF":7.6,"publicationDate":"2025-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143610243","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":"Driving towards stability and efficiency: A variable time gap strategy for Adaptive Cruise Control","authors":"Shaimaa K. El-Baklish,&nbsp;Anastasios Kouvelas,&nbsp;Michail A. Makridis","doi":"10.1016/j.trc.2025.105074","DOIUrl":"10.1016/j.trc.2025.105074","url":null,"abstract":"<div><div>Automated vehicle technologies offer a promising avenue for enhancing traffic efficiency, safety, and energy consumption. Among these, Adaptive Cruise Control (ACC) systems stand out as a prevalent form of automation on today’s roads, with their time gap settings holding paramount importance. While decreasing the average time headway tends to enhance traffic capacity, it simultaneously raises concerns regarding safety and string stability. This study introduces a novel variable time gap feedback control policy aimed at striking a balance between maintaining a minimum time gap setting under equilibrium car-following conditions, thereby improving traffic capacity, while ensuring string stability to mitigate disturbances away from the equilibrium flow. Leveraging nonlinear <span><math><msub><mrow><mi>H</mi></mrow><mrow><mi>∞</mi></mrow></msub></math></span> control technique, the strategy employs a variable time gap component as the manipulated control signal, complemented by a constant time gap component that predominates during car-following equilibrium. The effectiveness of the proposed scheme is evaluated against its constant time-gap counterpart calibrated using field platoon data from the OpenACC dataset. Through numerical and traffic simulations, our findings illustrate that the proposed algorithm effectively dampens perturbations within vehicle platoons, leading to a more efficient and safer mixed traffic flow.</div></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":"174 ","pages":"Article 105074"},"PeriodicalIF":7.6,"publicationDate":"2025-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143619227","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":"MoGERNN: An inductive traffic predictor for unobserved locations","authors":"Qishen Zhou ,&nbsp;Yifan Zhang ,&nbsp;Michail A. Makridis ,&nbsp;Anastasios Kouvelas ,&nbsp;Yibing Wang ,&nbsp;Simon Hu","doi":"10.1016/j.trc.2025.105080","DOIUrl":"10.1016/j.trc.2025.105080","url":null,"abstract":"<div><div>Given a partially observed road network, how can we predict the traffic state of interested unobserved locations? Traffic prediction is crucial for advanced traffic management systems, with deep learning approaches showing exceptional performance. However, most existing approaches assume sensors are deployed at all locations of interest, which is impractical due to financial constraints. Furthermore, these methods are typically fragile to structural changes in sensing networks, which require costly retraining even for minor changes in sensor configuration. To address these challenges, we propose MoGERNN, an inductive spatio-temporal graph model with two key components: (i) a Mixture of Graph Experts (MoGE) with sparse gating mechanisms that dynamically route nodes to specialized graph aggregators, capturing heterogeneous spatial dependencies efficiently; (ii) a graph encoder-decoder architecture that leverages these embeddings to capture both spatial and temporal dependencies for comprehensive traffic state prediction. Experiments on two real-world datasets show MoGERNN consistently outperforms baseline methods for both observed and unobserved locations. MoGERNN can accurately predict congestion evolution even in areas without sensors, offering valuable information for traffic management. Moreover, MoGERNN is adaptable to the changes of sensor network, maintaining competitive performance even compared to its retrained counterpart. Tests performed with different numbers of available sensors confirm its consistent superiority, and ablation studies validate the effectiveness of its key modules. The code of this work is publicly available at: <span><span>https://github.com/ZJU-TSELab/MoGERNN</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":"174 ","pages":"Article 105080"},"PeriodicalIF":7.6,"publicationDate":"2025-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143592941","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}