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arXiv 2022年

作者： Xu, Xincao Liu, Kai Dai, Penglin Xie, Ruitao Cao, Jingjing Luo, Jiangtao The College of Computer Science Chongqing University Chongqing400040 China The School of Computing and Artificial Intelligence Southwest Jiaotong University Chengdu611756 China The National Engineering Laboratory of Integrated Transportation Big Data Application Technology Chengdu611756 China The College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China The School of Transportation and Logistics Engineering Wuhan University of Technology Hubei430063 China The Electronic Information and Networking Research Institute Chongqing University of Posts and Telecommunications Chongqing400065 China

Cooperative sensing and heterogeneous information fusion are critical to realize vehicular cyber-physical systems (VCPSs). This paper makes the first attempt to quantitatively measure the quality of VCPS by designing a new metric called Age of View (AoV). Specifically, we first present the system architecture where heterogeneous information can be cooperatively sensed and uploaded via vehicle-to-infrastructure (V2I) communications in vehicular edge computing (VEC). Logical views are constructed by fusing the heterogeneous information at edge nodes. Further, we formulate the problem by deriving a cooperative sensing model based on the multi-class M/G/1 priority queue, and defining the AoV by modeling the timeliness, completeness and consistency of the logical views. On this basis, a multi-agent deep reinforcement learning solution is proposed. In particular, the system state includes vehicle sensed information, edge cached information and view requirements. The vehicle action space consists of the sensing frequencies and uploading priorities of information. A difference-reward-based credit assignment is designed to divide the system reward, which is defined as the VCPS quality, into the difference reward for vehicles. Edge node allocates V2I bandwidth to vehicles based on predicted vehicle trajectories and view requirements. Finally, we build the simulation model and give a comprehensive performance evaluation, which conclusively demonstrates the superiority of the proposed solution. Copyright © 2022, The Authors. All rights reserved.

关键词： Reinforcement learning

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Railway Network Delay Evolution: A Heterogeneous Graph Neural Network Approach

SSRN

引用

SSRN 2022年

作者： Li, Zhongcan Huang, Ping Wen, Chao Rodrigues, Filipe National Engineering Laboratory of Integrated Transportation Big Data Application Technology Southwest Jiaotong University Chengdu610031 China Kgs. Lyngby2800 Denmark Institute for Transport Planning and Systems ETH Zurich Zurich8093 Switzerland High-speed Railway Research Centre University of Waterloo WaterlooN2L3G1 Canada

This paper develops a heterogeneous graph neural network (HetGNN) model, which can dispose of dissimilarly attributed nodes (i.e., nodes with different input features), to investigate the evolution of railway network delay by predicting the delays of running trains (RTs). A graph architecture combining the HetGNN model and the GraphSAGE homogeneous graph neural network (HomoGNN), called SAGE-Het, is proposed. This architecture considers four kinds of nodes, namely RTs, terminated trains (TTs), passing stations (PSs), and terminated stations (TSs), and can capture the effects of the interactions between trains and other trains (e.g., the train headways), trains and stations (e.g., the scheduled remaining train running times to the downstream station), and stations and other stations (e.g., the minimum running times) on delay evolution. In contrast to the traditional machine learning methods that require the inputs to have the same formats (e.g., in rectangular or grid-like arrays) and the HomoGNNs that do not allow for dissimilarly attributed nodes in the graph, SAGE-Het allows for flexible inputs. The data from two areas of the China railway network, namely the Guangzhou South network (GZS-Net) and the Changsha South network (CSS-Net), are applied to test the performance and robustness of the proposed SAGE-Het model. The experimental results show that SAGE-Het exhibits better performance than the existing delay prediction methods and some advanced HetGNNs used for other prediction tasks for both short and long delays. Subsequently, the influences of train interactions on delay propagation are investigated. The results show that train interactions are insignificant when the train headways are long (e.g., when the headways are over 20 min, cutting the edges does not decrease the prediction performance). Ultimately, it is found that the predictive performances of SAGE-Het under different prediction time horizons (10/20/30 min ahead) all outperform the performances of o

关键词： Graph neural networks

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Railway Network Delay Evolution: A Heterogeneous Graph Neural Network Approach

arXiv

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arXiv 2023年

Railway operations involve different types of entities (stations, trains, etc.), making the existing graph/network models with homogenous nodes (i.e., the same kind of nodes) incapable of capturing the interactions between the entities. This paper aims to develop a heterogeneous graph neural network (HetGNN) model, which can address different types of nodes (i.e., heterogeneous nodes), to investigate the train delay evolution on railway networks. To this end, a graph architecture combining the HetGNN model and the GraphSAGE homogeneous GNN (HomoGNN), called SAGE-Het, is proposed. The aim is to capture the interactions between trains, trains and stations, and stations and other stations on delay evolution based on different edges. In contrast to the traditional methods that require the inputs to have constant dimensions (e.g., in rectangular or grid-like arrays) or only allow homogeneous nodes in the graph, SAGE-Het allows for flexible inputs and heterogeneous nodes. The data from two sub-networks of the China railway network, namely the Guangzhou South network (GZS-Net) and the Changsha South network (CSS-Net), are applied to test the performance and robustness of the proposed SAGE-Het model. The experimental results show that SAGE-Het exhibits better performance than the existing delay prediction methods and some advanced HetGNNs used for other prediction tasks;the predictive performances of SAGE-Het under different prediction time horizons (10/20/30 min ahead) all outperform other baseline methods;the accuracies are over 90% under the permissible 3-minute errors for the three prediction time horizons. Specifically, the influences of train interactions on delay propagation are investigated based on the proposed model. The results show that train interactions become subtle when the train headways increase (e.g., when the train headways are over 20 min, canceling the edges does not decrease the prediction performance). This finding directly contributes to decision-ma

关键词： Graph neural networks

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A Novel Approach of Evaluating the Safety Awareness of Elderly Pedestrians in China and U.S

SSRN

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SSRN 2022年

作者： Guo, Manze Yuan, Zhenzhou Yue, Rui Peng, Yongxin Janson, Bruce Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport School of Traffic and Transportation Beijing Jiaotong University Beijing Jiaotong University No.3 Shangyuancun Haidian District Beijing100044 China Department of Civil Engineering University of Colorado Denver P.O. Box 173364 DenverCO80217-3364 United States

Elderly pedestrians are one of the groups that are most vulnerable to traffic accidents among all ages. Exploring the safety awareness of elderly pedestrians is of significant importance in enacting countermeasures for their safety. Therefore, this study aims to examine the safety awareness assessments of elderly groups. The critical point of assessing the cognition and trends in the behavioral patterns was to explore their "knowledge," "confidence," and "behavior" performing in road traffic safety. A composite model that combines exploratory factor analysis (EFA), entropy weight method (EWM), gray correlation analysis (GRA), and fuzzy comprehensive evaluation model (FSE) was proposed for evaluating the indices system algorithm, namely, the EEGF method. The elderly safety awareness evaluation system focuses on three first-level indices: level of safety knowledge, self-confidence in safety assessment, and safety attitudes, all supported by an additional ten second-level indices. The output of the EEFG method generates a composite safety awareness score for the different groups studied, which indicates the safety awareness level. The trends observed in the scores of all elderly pedestrian groups were compared. Accordingly, an online questionnaire was designed to collect data from two countries. Results showed that respondents aged >90 had the lowest composite traffic safety awareness scores, and their self-assessments and behavior were inconsistent. This information can be used by the transportation authorities to implement plans, policies, and programs to improve the safety awareness of elderly pedestrians. © 2022, The Authors. All rights reserved.

关键词： Factor analysis

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A fine-grained perspective on the robustness of global cargo ship transportation networks

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Journal of Geographical Sciences 2018年第7期28卷 881-899页

作者：彭澎程诗奋陈金海廖梦迪吴琳刘希亮陆锋 State Key Laboratory of Resources and Environmental Information System Institute of Geographic Sciences and Natural Resources Research CAS University of Chinese Academy of Sciences Navigation Aids Technology Research Center of Jimei University National & Local Joint Engineering Research Center for Marine Navigation Aids Services College of Geomatics Shandong University of Science and Technology Institute of Computing Technology CAS Fujian Collaborative Innovation Center for Big Data Applications in Governments Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application

The robustness of cargo ship transportation networks is essential to the stability of the world trade system. The current research mainly focuses on the coarse-grained, holistic cargo ship transportation network while ignoring the structural diversity of different sub-networks. In this paper, we evaluate the robustness of the global cargo ship transportation network based on the most recent Automatic Identification System（AIS） data available. First, we subdivide three typical cargo ship transportation networks（i.e., oil tanker, container ship and bulk carrier） from the original cargo ship transportation network. Then, we design statistical indices based on complex network theory and employ four attack strategies, including random attack and three intentional attacks（i.e., degree-based attack, betweenness-based attack and flux-based attack） to evaluate the robustness of the three typical cargo ship transportation networks. Finally, we compare the integrity of the remaining ports of the network when a small proportion of ports lose their function. The results show that 1） compared with the holistic cargo ship transportation network, the fine-grain-based cargo ship transportation networks can fully reflect the pattern and process of global cargo transportation; 2） different cargo ship networks behave heterogeneously in terms of their robustness, with the container network being the weakest and the bulk carrier network being the strongest; and 3） small-scale intentional attacks may have significant influence on the integrity of the container network but a minor impact on the bulk carrier and oil tanker transportation networks. These conclusions can help improve the decision support capabilities in maritime transportation planning and emergency response and facilitate the establishment of a more reliable maritime transportation ***： The robustness of cargo ship transportation networks is essential to the stability of the world trade system. The current resear

关键词： complex network fine-grained cargo ship transportation network robustness automatic identification system

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Experimental study and modeling of the lower-level controller of automated vehicle

arXiv

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arXiv 2022年

作者： Liu, Hua-Qing Zheng, Shi-Teng Jiang, Rui Tian, Junfang Yan, Ruidong Zhang, Fang Zhang, Dezhao The Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport Ministry of Transport Beijing Jiaotong University Beijing100044 China The Institute of Systems Engineering College of Management and Economics Tianjin University Tianjin300072 China The Beijing Idriverplus Technology Co. Ltd. Beijing100044 China

Accurate modeling of lower-level controller plays an important role in the traffic flow of automated vehicles (AVs). However, there lacks enough attention with this respect. To address this issue, we conduct a field experiment with two vehicles that are equipped with developable autonomous driving system, where one can customize the upper-level control algorithm. Based on the field experimental data, a new lower-level control model is developed and compared with two widely used ones. The comparison results show that the proposed model outperforms the two previous models in capturing the observed actual acceleration, especially the troughs of the acceleration time series. Furthermore, theoretical analysis indicates that comparing with the proposed model, the two previous models significantly overestimate the stability region of the traffic flow of the AVs and the capacity of stable traffic flow. Our study is expected to further shed light on the importance of accurate lower-level control modeling. Copyright © 2022, The Authors. All rights reserved.

关键词： Level control

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A Novel Approach of Evaluating the Safety Awareness of Elderly Pedestrians in China and U.S

SSRN

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SSRN 2022年

关键词： Factor analysis

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埋入式传感、图像处理技术和机器学习方法在路面监测与分析中应用的最新研究进展

引用

engineering 2021年第6期 309-334页

作者：侯越李秋晗张晨陆国阳叶周景陈逸涵汪林兵曹丹丹 Beijing Key Laboratory of Traffic Engineering Beijing University of Technology National Center for Materials Service Safety University of Science and Technology Beijing School of Transportation Southeast University Department of Civil and Environmental Engineering The Hong Kong Polytechnic University Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport Beijing Jiaotong University Department of Civil and Environmental Engineering Virginia Tech

在现代交通系统中,道路作为车辆和行人使用频率最高的民用基础设施之一,其服役状况和使用寿命直接影响通行体验和效率。因此,在路面发生不可逆损伤之前进行路面健康监测和及时养护,对于保障公共交通服务质量以及通行安全至关重要。通过... 详细信息

在现代交通系统中,道路作为车辆和行人使用频率最高的民用基础设施之一,其服役状况和使用寿命直接影响通行体验和效率。因此,在路面发生不可逆损伤之前进行路面健康监测和及时养护,对于保障公共交通服务质量以及通行安全至关重要。通过路面结构动力响应监测和路面状况评估可有效表征路面损伤状况。埋入式传感器、图像处理和机器学习是目前常用的三种路面结构动力响应监测技术和分析方法。本文综述了近年来上述三种技术在路面工程中的应用现状,并阐述了这些技术在未来路面工程监测与分析中的发展方向。

关键词：路面监测与分析最新的研究进展埋入式传感器图像处理技术机器学习方法

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A Lightweight CNN-Transformer Network with Agent Attention Mechanism for 3D Whole Heart Segmentation

A Lightweight CNN-Transformer Network with Agent Attention M...

引用

Pattern Recognition and Artificial Intelligence (PRAI), International Conference on

作者： Jiatong Li Yifan Wang Dianrong Du Hengfei Cui National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology Northwestern Polytechnical University Xi'an China Research & Development Institute of Northwestern Polytechnical University in Shenzhen Shenzhen China Chongqing Innovation Center Northwestern Polytechnical University Chongqing China

ISBN: (数字)9798350350890

ISBN: (纸本)9798350350906

In recent years, heart diseases have become one of the major global health issues, emphasizing the importance of 3D whole heart segmentation technology for accurate and efficient treatment. Currently, 3D whole heart segmentation algorithms often combine Transformer technologies with 3D Convolutional Neural Networks (3D CNNs), leveraging the self-attention mechanism of Transformers for global information modeling and 3D CNNs for capturing local contextual information. However, the high computational complexity of Transformers leads to huge computational resource wastage. To address this issue, this work proposes a novel whole heart segmentation algorithm named AgentTransBTS, based on an improved 3D Agent Attention mechanism and the TransBTS framework. This algorithm utilizes TransBTS as the baseline model, capitalizing on its deep feature extraction and information integration capabilities to capture global dependencies in images. To reduce computational complexity without sacrificing the model's expressive power, an enhanced 3D Agent Attention mechanism is introduced. comprehensive experiments were conducted on the Multi-Modality Whole Heart Segmentation (MM-WHS) 2017 dataset to evaluate the proposed algorithm. The experimental results demonstrate that our proposed method achieved an average Dice coefficient of 91.9% on the CT dataset and 79.9% on the MRI dataset of the MM-WHS 2017 challenge. Furthermore, our algorithm's FLOPs are 217G, representing an approximate 9.2% improvement in computational efficiency compared to the baseline network.

关键词： Heart Image segmentation Solid modeling Three-dimensional displays Attention mechanisms Computational modeling Magnetic resonance imaging Transformers Computational complexity Biomedical imaging

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Experimental features of emissions and fuel consumption in a car-following platoon

arXiv

引用

arXiv 2023年

作者： Zhou, Shirui Ge, Ying-En Yu, Shaowei Tian, Junfang Jiang, Rui Institute of Systems Engineering College of Management and Economics Tianjin University No. 92 Weijin Road Nankai District Tianjin300072 China Tianjin University Tianjin300072 China College of Transportation Engineering Chang'an University Shaanxi Xi’an710064 China Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport Ministry of Transport Beijing Jiaotong University Beijing100044 China

The paper investigates the features of emissions and fuel consumption (EFC) in a car-following (CF) platoon based on two experimental datasets. Four classical EFC models are employed and a universal concave growth pattern of the EFC along a platoon has been demonstrated. A general framework of coupling EFC and CF models is tested by calibrating and simulating three classical CF models. This work first demonstrates that, at vehicle-pair level, all models perform well on EFC prediction. The intelligent driver model outperforms the other CF models on calibration accuracy, but this is not true on EFC prediction. Second, at platoon level, the predicted EFC is nearly constant along the platoon which qualitatively differs from the experimental observation. The investigation highlights that accurate estimations at vehicle level may be insufficient for analysis at platoon level due to the significant role of oscillation growth and evolution in EFC estimation. Copyright © 2023, The Authors. All rights reserved.

关键词： Calibration

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