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SSRN 2022年

作者： Meng, Dongli Song, Guohua Lu, Hongyu Wu, Yizheng Zhai, Zhiqiang Yu, Lei Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport Beijing Jiaotong University 3 Shangyuancun Haidian District Beijing100044 China School of Civil and Environmental Engineering Georgia Institute of Technology 790 Atlantic Drive AtlantaGA30332 United States Department of Transportation Studies Texas Southern University 3100 Cleburne Avenue HoustonTX77004 United States Xuchang University Henan Xuchang461000 China

In the application of microscopic traffic simulations for vehicle emissions modeling, the reality and platoon stability of vehicle trajectories derived from the car-following component has been questioned. This study compared the platoon stability of the Gipps car-following model, the full velocity difference model (FVDM), the intelligent driver model (IDM), and the Wiedemann model for emissions modeling, followed by a parameter sensitivity analysis and a stochasticity impact investigation. The results indicated the superior performance of the FVDM and IDM for emissions estimation compared with the Wiedemann model, and the emissions estimation errors were stable along the platoon for the above models. Gipps model generated realistic vehicle dynamics of the first following vehicle, however, the emissions estimation errors increased along the platoon. This study found that it is difficult to improve the platoon stability of the Gipps model by parameter calibration. While taking into account the driving behavior heterogeneity and the human factors, the Gipps model was modified by incorporating the stochastic parameters to reproduce representative platoon trajectories for accurate emissions estimations. © 2022, The Authors. All rights reserved.

关键词： Stochastic systems

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Predictability of real temporal networks

arXiv

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arXiv 2020年

作者： Tang, Disheng Du, Wenbo Shekhtman, Louis Wang, Yijie Havlin, Shlomo Cao, Xianbin Yan, Gang School of Electronic and Information Engineering Beihang University Beijing100191 China School of Physics Science and Engineering Tongji University Shanghai200092 China National Engineering Laboratory of Big Data Application Technologies of Comprehensive Transportation Beijing100191 China Network Science Institute Northeastern University BostonMA02115 United States Department of Physics Bar Ilan University Ramat Gan5290002 Israel Shanghai Institute of Intelligence Science and Technology Tongji University Shanghai200092 China CAS Center for Excellence in Brain Science and Intelligence Technology Chinese Academy of Sciences Shanghai200031 China

Links in most real networks often change over time. Such temporality of links encodes the ordering and causality of interactions between nodes and has a profound effect on network dynamics and function. Empirical evidence has shown that the temporal nature of links in many real-world networks is not random. Nonetheless, it is challenging to predict temporal link patterns while considering the entanglement between topological and temporal link patterns. Here, we propose an entropy-rate-based framework, based on combined topological–temporal regularities, for quantifying the predictability of any temporal network. We apply our framework on various model networks, demonstrating that it indeed captures the intrinsic topological–temporal regularities whereas previous methods considered only temporal aspects. We also apply our framework on 18 real networks of different types and determine their predictability. Interestingly, we find that, for most real temporal networks, despite the greater complexity of predictability brought by the increase in dimension, the combined topological–temporal predictability is higher than the temporal predictability. Our results demonstrate the necessity for incorporating both temporal and topological aspects of networks in order to improve predictions of dynamical processes. Copyright © 2020, The Authors. All rights reserved.

关键词： Topology

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Unsupervised evaluation of airspace complexity based on kernel principal component analysis

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Hangkong Xuebao/Acta Aeronautica et Astronautica Sinica 2019年第8期40卷

作者： Zhang, Zhuxi Zhu, Xi Zhu, Shaochuan Zhang, Mingyuan Du, Wenbo National Engineering Laboratory of Big Data Application Technologies of Comprehensive Transportation Beihang University Beijing100083 China Unit 32751 Force of PLA China Research Institute of Frontier Science Beihang University Beijing100083 China School of Transportation Science and Engineering Beihang University Beijing100083 China School of Electronic and Information Engineering Beihang University Beijing100083 China

Airspace complexity evaluation is a key means to measure the airspace operational situation and the controller workload, providing the basis for the operation optimization. Its accurate evaluation is a challenging problem in the aviation domain due to numerous influencing factors, the complex correlations between factors, and the high difficulty of collecting labelled samples. This paper proposes an unsupervised evaluation method for airspace complexity. Firstly, the kernel principal component analysis is utilized to mine the nonlinear correlations in different sample dimensions, and extract several principal components in which the airspace complexity information is maximized. Furthermore, the principal component clustering which can be customized according to user requirements is designed. The proposed method achieves accurate complexity evaluation capacity under the unsupervised condition, providing effective technical support for air traffic management like airspace configuration and traffic management. © 2019, Press of Chinese Journal of Aeronautics. All right reserved.

关键词： Principal component analysis

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A Certification Scheme for Realizing the Value of Future Accounts Receivable Claims

SSRN

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SSRN 2023年

作者： He, Juan Li, Zhengbo Xianglin, Jiang Ren, Jun Xue, Qian School of Transportation and Logistics Southwest Jiaotong University Sichuan Chengdu611756 China Institute For Supply Chain Finance Studies National Engineering Laboratory of Application Technology of Integrated Transportation Big Date Southwest Jiaotong University Sichuan Chengdu611756 China Institute for Financial Studies Fudan University Shanghai200433 China China State Railway Group Company Limited Beijing100080 China

There exists a certain cycle of workload data such as volume of engineering, supply, service, etc. between the buyer and seller while the transaction is not completed. The workload data value belongs to the enterprise's future receivables, existing a huge intrinsic economic value. However, due to difficulty in activating the value of workload data, as well as the authenticity and uniqueness of transactions and goods are not guaranteed, resulting in the financing service based on future receivables is not promoted. Therefore, this paper proposes a certification scheme for realizing the value of future receivables claims. The scheme uses pseudo-anonymous identifiers to symmetrically encrypt workload data on the chain. It also embeds cryptographic promises to strengthen the privacy protection of the data, as well as digital evidence detailing the protocols and conditions for certifying the value of future receivables claims with the help of smart contracts. The study shows that the proposed authentication mechanism can ensure the measurable, observable, and authenticated value of future receivable claims, and avoid the same workload data from being authenticated twice or falsely. At the same time, the scheme also has certain security attributes, dispute resolution and auditing functions. In addition, the research will accelerate the realization of the flow of future receivables and open a brand-new channel for product innovation based on future receivables under data credit empowerment. © 2023, The Authors. All rights reserved.

关键词： Smart contract

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Structure and Evolution of Urban Heavy Truck Mobility Networks

SSRN

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SSRN 2022年

作者： Yang, Yitao Jia, Bin Liu, Erjian Yan, Xiao-Yong de Bok, Michiel Tavasszy, Lóránt A. Gao, Ziyou Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport Ministry of Transport Beijing Jiaotong University Beijing100044 China Institute of Transportation System Science and Engineering Beijing Jiaotong University Beijing100044 China Department of Transport & Planning Faculty of Civil Engineering and Geosciences Delft University of Technology Stevinweg1 Delft2628 CN Netherlands Instituto de Física Interdisciplinar y Sistemas Complejos IFISC CSIC UIB Palma de Mallorca07122 Spain

Revealing the structural properties and understanding the evolutionary mechanisms of the urban heavy truck mobility network (UHTMN) provide insights in assessment of freight policies to manage and regulate the urban freight system, and are of vital importance for improving the livability and sustainability of cities. Although massive urban heavy truck mobility data become available in recent years, in-depth studies on the structure and evolution of UHTMN are still lacking. Here we use massive urban heavy truck GPS data in China to construct the UHTMN and reveal its a wide range of structure properties. We further develop an evolving network model that simultaneously considers weight, space and system element duplication. Our model reproduces the observed structure properties of UHTMN and helps us understand its underlying evolutionary mechanisms. Our model also provides new perspectives for modeling the evolution of many other real-world networks, such as citation networks and air transportation networks. © 2022, The Authors. All rights reserved.

关键词： Complex networks

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Reconstruction of hidden representation for robust feature extraction

arXiv

引用

arXiv 2017年

作者： Zeng, Y.U. Tianrui, L.I. Ning, Y.U. Yi, P.A.N. Chen, Hongmei Bing, L.I.U. Southwest Jiaotong University School of Information Science and Technology National Engineering Laboratory of Integrated Transportation Big Data Application Technology Chengdu611756 China College at Brockport State University of New York Department of Computing Sciences BrockportNY14420 United States Georgia State University Department of Computer Science AtlantaGA30302 United States University of Illinois at Chicago Department of Computer Science ChicagoIL60607 United States

This paper aims to develop a new and robust approach to feature representation. Motivated by the success of Auto-Encoders, we first theoretically analyze and summarize the general properties of all algorithms that are based on traditional Auto-Encoders: 1) The reconstruction error of the input can not be lower than a lower bound, which can be viewed as a guiding principle for reconstructing the input. Additionally, when the input is corrupted with noises, the reconstruction error of the corrupted input also can not be lower than a lower bound. 2) The reconstruction of a hidden representation achieving its ideal situation is the necessary condition for the reconstruction of the input to reach the ideal state. 3) Minimizing the Frobenius norm of the Jacobian matrix of the hidden representation has a deficiency and may result in a much worse local optimum value. We believe that minimizing the reconstruction error of the hidden representation is more robust than minimizing the Frobenius norm of the Jacobian matrix of the hidden representation. Based on the above analysis, we propose a new model termed Double Denoising Auto-Encoders (DDAEs), which uses corruption and reconstruction on both the input and the hidden representation. We demonstrate that the proposed model is highly flexible and extensible and has a potentially better capability to learn invariant and robust feature representations. We also show that our model is more robust than Denoising Auto-Encoders (DAEs) for dealing with noises or inessential features. Furthermore, we detail how to train DDAEs with two different pre-training methods by optimizing the objective function in a combined and separate manner, respectively. Comparative experiments illustrate that the proposed model is significantly better for representation learning than the state-of-the-art models. Copyright © 2017, The Authors. All rights reserved.

关键词： Jacobian matrices

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Research on short-term traffic flow prediction model based on threshold autoregression

Advances in Transportation Studies

引用

Advances in transportation Studies 2018年第SPECIAL ISSUE 2018期3卷 79-84页

作者： Zhang, J.J. Shao, C.F. Wang, F. MOE Key Laboratory for Urban Transportation Complex Systems Theory and Technology Beijing Jiaotong University No.3 Shangyuancun Road Haidian District Beijing100044 China Arcplus Group PLC Number 258 Shi Men Er Road Shanghai China Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport Beijing Jiaotong University No.3 Shangyuancun Road Haidian District Beijing100044 China Henan Police College No. 1 East Road lung Zi lake Zhengzhou450046 China

Traffic congestion is a problem faced by cities, and it is urgent for solving this issue. The real-time and accuracy of traffic flow prediction directly affect the efficiency of traffic flow guidance system, which is a hot issue of intelligent transportation system research. The time series of the same traffic flow has different characteristics in different time buckets, so the traffic flow sequence is divided into subsequences with different characteristics by the accumulative departure method. A prediction model of short-time traffic flow sequence based on threshold autoregression (TAR) is established by taking the above time as the threshold value. The real-time traffic flow time series of city expressway in Beijing is taken as the object of empirical study. The results show that the average error and mean absolute error of the model are -0.38% and 7.38% that are lower than 3.08% and 7.73% of ARIMA prediction model when the prediction step size is 1. When the prediction step size is 5, the average error and mean absolute error are -0.71% and 14.78% that are lower than 5.40% and 17.44% of ARIMA prediction model. The simulation results demonstrate that the algorithm has better prediction accuracy, which can verify the feasibility and effectiveness of the algorithm in the prediction realm. © 2018, Gioacchino Onorati Editore. All rights reserved.

关键词： Forecasting

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Understanding and Detecting Privacy Leakage Vulnerabilities in Hyperledger Fabric Chaincodes

Understanding and Detecting Privacy Leakage Vulnerabilities ...

引用

International Symposium on Software Reliability engineering (ISSRE)

作者： Ziming Chen Yue Li Jianbo Gao Jiashuo Zhang Ke Wang Jianbin Hu Zhi Guan Zhong Chen School of Computer Science Peking University Beijing China College of Computer Science and Technology Taiyuan University of Technology Taiyuan China Beijing Key Laboratory of Security and Privacy in Intelligent Transportation Beijing Jiaotong University Beijing China Peking University Chongqing Research Institute of Big Data Chongqing China National Engineering Research Center for Software Engineering Peking University Beijing China

ISBN: (数字)9798350353884

ISBN: (纸本)9798350353891

The application on a blockchain cannot maintain secrecy because its data is replicated across all peers in the network. To remedy this problem, Hyperledger Fabric introduces private data collection (PDC) into its smart contract (i.e. chaincode) to facilitate applications that require privacy. However, recent studies have revealed that PDC is too complex for chaincode developers to fully understand and use correctly, leading to privacy leaks vulnerabilities. In this paper, we present an empirical study on the prevalence of PDC misuse in chaincodes by extracting privacy leakage cases from StackOverflow posts and Hyperledger Fabric repositories on GitHub. Subsequently, we systematically categorize the misuse of PDC into three categories of vulnerabilities resulting in the leakage of private data and provide formal definitions for them. Furthermore, we develop PDChecker, an automated security analysis framework for identifying the privacy and security vulnerabilities in Fabric chaincodes. We evaluated PDChecker on 956 real-world chaincodes applying PDC and found that 67.78% of them contain at least one privacy leakage vulnerability. In addition, PDChecker uncovered 10 zero-day vulnerabilities documented by the China national Vulnerability database.

关键词： Privacy data privacy Distributed ledger databases Smart contracts Fabrics Blockchains Software reliability Security Software development management

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MedUniSeg: 2D and 3D Medical Image Segmentation via a Prompt-driven Universal Model

arXiv

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arXiv 2024年

作者： Ye, Yiwen Chen, Ziyang Zhang, Jianpeng Xie, Yutong Xia, Yong The National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology School of Computer Science and Engineering Northwestern Polytechnical University Xi’an710072 China The College of Computer Science and Technology Zhejiang University Zhejiang China The University of Adelaide Australia Research & Development Institute of Northwestern Polytechnical University in Shenzhen Shenzhen518057 China The Ningbo Institute of Northwestern Polytechnical University Ningbo315048 China

Universal segmentation models offer significant potential in addressing a wide range of tasks by effectively leveraging discrete annotations. As the scope of tasks and modalities expands, it becomes increasingly important to generate and strategically position task- and modal-specific priors within the universal model. However, existing universal models often overlook the correlations between different priors, and the optimal placement and frequency of these priors remain underexplored. In this paper, we introduce MedUniSeg, a prompt-driven universal segmentation model designed for 2D and 3D multi-task segmentation across diverse modalities and domains. MedUniSeg employs multiple modal-specific prompts alongside a universal task prompt to accurately characterize the modalities and tasks. To generate the related priors, we propose the modal map (MMap) and the fusion and selection (FUSE) modules, which transform modal and task prompts into corresponding priors. These modal and task priors are systematically introduced at the start and end of the encoding process. We evaluate MedUniSeg on a comprehensive multi-modal upstream dataset consisting of 17 sub-datasets. The results demonstrate that MedUniSeg achieves superior multi-task segmentation performance, attaining a 1.2% improvement in the mean Dice score across the 17 upstream tasks compared to nnUNet baselines, while using less than 1/10 of the parameters. For tasks that underperform during the initial multi-task joint training, we freeze MedUniSeg and introduce new modules to re-learn these tasks. This approach yields an enhanced version, MedUniSeg*, which consistently outperforms MedUniSeg across all tasks. Moreover, MedUniSeg surpasses advanced self-supervised and supervised pre-trained models on six downstream tasks, establishing itself as a high-quality, highly generalizable pre-trained segmentation model. The code and model will be available at https://***/yeerwen/UniSeg. Copyright © 2024, The Authors.

关键词： Image segmentation

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Assessment of the Fuel Consumption Gap between In-laboratory and Real-World Driving Conditions

SSRN

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SSRN 2022年

作者： Pengfei, Fan Yin, Hang Lu, Hongyu Wu, Yizheng Zhai, Zhiqiang Yu, Lei Song, Guohua Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport Beijing Jiaotong University 3 Shangyuancun Haidian District Beijing100044 China Chinese Research Academy of Environmental Sciences Dayang Fang #8 Chaoyang District Beijing100012 China School of Civil and Environmental Engineering Georgia Institute of Technology 790 Atlantic Drive AtlantaGA30332 United States College of Science and Technology Texas Southern University 3100 Cleburne Avenue HoustonTX77004 United States

A widening fuel consumption gap has been found between in-laboratory and real-world driving conditions, which can bias policy-making concerning energy saving and greenhouse gas reduction. An independent assessment of the gap's contributions is significant to narrowing this gap. This study confirmed an average gap of 42% based on 0.95 billion records of second-by-second vehicle operating (speed, acceleration, etc.) and fuel consumption data collected from 395 light-duty vehicles through On-board Diagnostics (OBD) devices. The impact of fuel consumption rate (FCR)-related (e.g., vehicle mass, tire pressure, temperature, etc.), engine load (or driving cycle)-related (e.g., congested, aggressive driving, etc.), and road grade discrepancies between the in-laboratory test procedure vs. real-world driving conditions were independently assessed by setting the other impact factors as control variables. Results indicated that the FCR-related, engine load-related, and road grade discrepancies contributed 20.7%, 17.0%, and 3.2% to the gap, respectively (only1.0% of the gap has not been explained). © 2022, The Authors. All rights reserved.

关键词： Greenhouse gases

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