检索结果-内蒙古大学图书馆

SSRN 2022年

作者： Chen, Shuiwang Hu, Lu Yao, Zhihong Zhu, Juanxiu Zhao, Bin Jiang, Yangsheng School of Transportation and Logistics Southwest Jiaotong University Chengdu610031 China National Engineering Laboratory of Integrated Transportation Big Data Application Technology Chengdu610031 China School of Management Xihua University Sichuan Chengdu610039 China School of Automobile & Transportation Engineering Xihua University Sichuan Chengdu610039 China

The emergence of connected autonomous vehicles (CAVs) provides a possibility to develop an efficient and sustainable mobility option. To foster the adoption of CAVs, the dedicated CAV lanes have been widely discussed in terms of optimal planning and allocating infrastructures for CAVs. Meanwhile, a sensible operation manner of dedicated lanes is also crucial to reap the maximum benefits. In this paper, we propose a novel idea, i.e., the intermittent dedicated CAV lanes (IDCLs), which dynamically provide a separate right-of-way for human-driven vehicles (HVs) to maximize traffic efficiency and minimize environmental costs. To this end, we first tailor two multi-class queuing models as fundamental tools to evaluate the traffic state. A traffic emission model is proposed based on the proposed queuing models. Then, a queuing-based bi-objective optimization model, which aims to maximize the traffic throughput and minimize the green tax simultaneously, is developed for the optimal operation of IDCL. Subsequently, the non-dominated sorting genetic algorithm-II (NSGA-II) is employed to find the Pareto optimal solutions. Finally, numerical experiments are conducted under different market penetration rates (MPRs) of CAVs and various traffic intensities. The results reported in this paper help policymakers and authorities draw insights into the IDCL policy. © 2022, The Authors. All rights reserved.

关键词： Autonomous vehicles

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NSGA II for Configuration Optimization of Cold Chain Logistic Network with Fully Shared Facilities and Equipments

Journal of Network Intelligence

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Journal of Network Intelligence 2023年第4期8卷 1497-1516页

作者： Yu, Qu-Bo An, Le-Yao Yu, Qing-Feng Liu, Xiao-Wei Pu, Fang-Zhu Ni, Shao-Quan School of Transportation and Logistics Southwest Jiaotong University Chengdu610031 China New Corridor Logistics Group Co. Ltd. of Sichuan Port and Shipping Investment Chengdu610000 China National Engineering Laboratory of Integrated Transportation Big Data Application Technology Southwest Jiaotong University Chengdu610031 China

In the face of multiple issues such as unreasonable allocation of cold chain logistics resources, environmental pollution, and energy crises, better resource integration, optimization, and upgrading are needed. One of the methods to address this is fully shared facilities and equipment of cold chain logistics network. This paper proposes the construction of a cold chain logistics resource sharing platform, which includes five main entities: supply nodes, demand nodes, demand node logistics companies, 4PL and 3PLs, aiming to achieve timely response to cold chain logistics demands. A unified optimization method for facility and equipment configuration throughout the cold chain logistics process is selected. A dual-objective programming model is constructed to minimize logistics companies’ operational costs and carbon emissions. The optimization is performed on various aspects such as the types and quantities of facility and equipment configurations. To solve this dual-objective programming model, the NSGA-II intelligent algorithm is employed. The results show that compared to the sharing of single types of cold chain logistics resources such as shared delivery vehicles or shared cold storage, full sharing is an effective approach to reducing logistics companies’ operational costs, carbon emissions, and idle cold chain logistics resources. It optimizes the internal allocation of cold chain logistics resources within the cold chain logistics network. © 2023.

关键词： Carbon

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AE-Net: Autonomous Evolution Image Fusion Method Inspired by Human Cognitive Mechanism

arXiv

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arXiv 2020年

作者： Fang, Aiqing Zhao, Xinbo Yang, Jiaqi Cao, Shihao Zhang, Yanning National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology School of Computer Science Northwestern Polytechnical University Xian710072 China

In order to solve the robustness and generality problems of the image fusion task, inspired by the human brain cognitive mechanism, we propose a robust and general image fusion method with autonomous evolution ability, and is therefore denoted with AE-Net. Through the collaborative optimization of multiple image fusion methods to simulate the cognitive process of human brain, unsupervised learning image fusion task can be transformed into semi-supervised image fusion task or supervised image fusion task, thus promoting the evolutionary ability of network model weight. Firstly, the relationship between human brain cognitive mechanism and image fusion task is analyzed and a physical model is established to simulate human brain cognitive mechanism. Secondly, we analyze existing image fusion methods and image fusion loss functions, select the image fusion method with complementary features to construct the algorithm module, establish the multi-loss joint evaluation function to obtain the optimal solution of algorithm module. The optimal solution of each image is used to guide the weight training of network model. Our image fusion method can effectively unify the cross-modal image fusion task and the same modal image fusion task, and effectively overcome the difference of data distribution between different datasets. Finally, extensive numerical results verify the effectiveness and superiority of our method on a variety of image fusion datasets, including multi-focus dataset, infrared and visible dataset, medical image dataset and multi-exposure dataset. comprehensive experiments demonstrate the superiority of our image fusion method in robustness and generality. In addition, experimental results also demonstate the effectiveness of human brain cognitive mechanism to improve the robustness and generality of image fusion. Keywords: Image fusion, deep learning, non-linear characteristics, feature selection characteristics, knowledge synergy. Copyright © 2020, The Authors.

关键词： Image fusion

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How Do Age Compositions Affect Pedestrian Dynamics on Stairways? Findings from laboratory Experiments

SSRN

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SSRN 2024年

作者： Shi, Dongdong Liu, Jiahui Ma, Jian Lovreglio, Ruggiero Wang, Qiao Chen, Juan School of Transportation and Logistics National Engineering Laboratory of Integrated Transportation Big Data Application Technology Southwest Jiaotong University Chengdu610031 China School of Built Environment MasseyUniversity Auckland0632 New Zealand Faculty of Geosciences and Environmental Engineering Southwest Jiaotong University Chengdu610031 China

Understanding pedestrian dynamics for different age compositions is paramount for enhancing their safety level on stairways in normal and evacuation conditions. In this study, we illustrate the findings of a series of single-file pedestrian flow experiments on stairways. This experiment was carried out with 131 participants having different age compositions, including preschool students (4-5 years), primary school students (8-10 years), middle school students (12-14 years), middle-aged pedestrians (27-57 years), and the elderly pedestrians (63-81 years). We collect data using three experimental settings: individual experiments, single-file pedestrian experiments with age homogeneity, and single-file pedestrian experiments with age-mixed heterogeneity. Our findings show that age plays a substantive non-linear impact on individual unconstrained ascending and descending speeds. In addition, we developed a linear regression model that predicts the pedestrians’ free speed of different ages on stairways. Then, the effects of age compositions on pedestrian dynamics with fundamental diagrams (i.e., speed-density and specific flow-density relations) are analyzed, and the maximum specific flow of age homogeneity is higher than that of age-mixed heterogeneity crowds. Finally, we illustrate how the headway distance impacts participants’ ascending and descending speeds in both homogeneity and heterogeneous age conditions. As such, these new experimental results can provide the foundation for future calibration and validation of pedestrian evacuation models as well as insight into safety guidelines. © 2024, The Authors. All rights reserved.

关键词： Dynamics

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The impact of information on system operations 6

The impact of information on system operations

引用

6th Annual International Conference on Network and Information Systems for Computers, ICNISC 2020

作者： Guan, Tianchao Zhou, Shumin Li, Tongfei Xu, Duo Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport Beijing Jiaotong University Beijing100044 China China Railway Fifth Survey And Design Institute Group CO. LTD. Beijing102600 China Beijing Key Laboratory of Traffic Engineering Beijing University of Technology Beijing100124 China Postgraduate School People's Public Security University of China Beijing100038 China

The operating system is a complex system that includes supply and demand. Changes in external information have an impact on the operation of the system, especially on both supply and demand. This paper analyzes the supply and demand and establishes the nonlinear programming problem of system operation under the social optimal goal. With the increase of external information intervention, there will be more suitable travel fares for the demand side. However, the supply side's related operating indicators (for example, frequency, bus fleet, and vehicle capacity, etc.) have shown a trend of first increasing and then decreasing due to existing resource constraints. It can be seen that under the same information interference, there are differences in the law of change between the supply and demand sides. Besides, an example is used to verify the effectiveness of the model. Through the model and calculation examples, we can know that the supplier and the demander can meet their own needs under the social optimal goal. This provides certain theoretical support for future research information on system operation. © 2020 Journal of Physics: Conference Series.

关键词： Economics

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Modeling and simulation of vehicle group lane-changing behaviors in upstream segment of ramp areas under a connected vehicle environment

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Smart and Resilient transportation 2019年第1期1卷 17-29页

作者： Haijian Li Zhufei Huang Lingqiao Qin Shuo Zheng Yanfang Yang Beijing Engineering Research Center of Urban Transport Operation Guarantee Beijing University of Technology Beijing China Traffic Operations and Safety Laboratory University of Wisconsin Madison Madison Wisconsin USA School of Electronic and Information Engineering Beijing Jiaotong University Beijing China Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport China Academy of Transportation Sciences Beijing China

Purpose The purpose of this study is to effectively optimize vehicle lane-changing behavior and alleviate traffic congestion in ramp area through the study of vehicle lane-changing behaviors in upstream segment of ramp areas. Design/methodology/approach In the upstream segment of ramp areas under a connected vehicle environment, different strategies of vehicle group lane-changing behaviors are modeled to obtain the best group lane-changing strategy. The traffic capacity of roads can be improved by controlling group lane-changing behavior and continuously optimizing lane-changing strategy through connected vehicle technologies. This paper constructs vehicle group lane-changing strategies in upstream segment of ramp areas under a connected vehicle environment. The proposed strategies are simulated by VISSIM. Findings The results show that different lane-changing strategies are modeled through vehicle group in the upstream segment of ramp areas, which can greatly reduce the delay of ramp areas. Originality/value The simulation results verify the validity and rationality of the corresponding vehicle group lane-changing behavior model strategies, effectively standardize the driver's lane-changing behavior, and improve road safety and capacity.

关键词： A connected vehicle environment Ramp area Upstream segment Vehicle group lane-changing behavior

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SDGFormer: An Efficient Convolution Network Structurally Similar to Transformer

SDGFormer: An Efficient Convolution Network Structurally Sim...

引用

IEEE International Conference on Multimedia and Expo (ICME)

作者： Chaohao Wen Xun Gong School of Computing and Artificial Intelligence SWJTU Chengdu China Engineering Research Center of Sustainable Urban Intelligent Transportation Ministry of Education China National Engineering Laboratory of Integrated Transportation Big Data Application Technology SWJTU Chengdu China Manufacturing Industry Chains Collaboration and Information Support Technology Key Laboratory of Sichuan Province SWJTU Chengdu China

Deep Neural Networks (DNN) have achieved extraordinary success in many visual recognition tasks. Visual Transformer (ViT), which is derived from Natural Language Processing (NLP), has achieved state-of-the-art (SOTA) results on many tasks due to its capability of capturing long-range dependencies in visual data. However, Existing ViT models are challenging to deploy on devices due to their massive computational consumption, huge memory overhead, and reliance on large datasets. In this work, we address these issues by replacing some computationally expensive and memory-intensive modules in ViT with standard Convolutional Neural Network (CNN) modules. Firstly, we propose an efficient Self-Attention module called SDG-Attention (SDGA) with linear space and time complexity, and an economical FeedForward Network (FFN) composed of group convolution and shuffle channel (SFFN). Then, we develop a lightweight CNN model with SDGA and SFFN, SDGFormer, which embraces several priors of ViT and is LayerNorm-Free. We evaluate SDGFormer on ImageNet-1K and Mini-ImageNet, and the SDGFormer-S achieves a comparable top-1 accuracy of 77.6% on ImageNet-1K with 9.1M parameters and 1.6 GFlops regimes. Moreover, our SDGFormer-T achieves SOTA performance on Mini-ImageNet with 83.3% accuracy, demonstrating good generalization on small datasets without extra data.

关键词：

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Modeling the Aberrant Behavior of Nigerian Drivers Using the Manchester Driver Behavior Questionnaire

SSRN

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SSRN 2021年

作者： Labbo, Muwaffaq Safiyanu Xinguo, Jiang School of Transportation and Logistics Southwest Jiaotong University West Park High-Tech District Chengdu611756 China National Engineering Laboratory of Integrated Transportation Big Data Application Technology West Park High-Tech District Chengdu611756 China Department of Civil Engineering Kano University of Science and Technology Wudil Kano Nigeria

The Driver Behavior Questionnaire (DBQ) is one of the most widely used tool for measuring self-reported driving style and behavior. Nigeria, which is among the top countries with high rate of accident is lacking in behavioral and safety studies. Therefore, the aim of this research was to develop a measurement model for the Nigerian Drivers, and to investigate the factor structure of the DBQ in Nigerian context. 741 Nigerian Drivers of age 18 years and above, with and without driving license participated in the questionnaire. The Confirmatory Factor Analysis (CFA) was used for the development of the Model, in which 20 questions out of 30 were used. Three (H1, H2, and H3) models with different factors were hypothesized, in which the second model H2 was found to be the best performing among the three models, with Violation, Lapses and Aggression as factors. The results were in consistent with the usefulness of the DBQ in characterizing risky behaviors in Nigeria, which can be used in improving road safety. © 2021, The Authors. All rights reserved.

关键词： Factor analysis

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Multi-Route Coordination for Bus Systems in Response to Road Disruptions

SSRN

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SSRN 2022年

作者： Zheng, Hankun Sun, Huijun Kang, Liujiang Dai, Peiling Wu, Jianjun Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport Ministry of Transport Institute of Transportation System Science and Engineering School of Traffic and Transportation Beijing Jiaotong University Beijing100044 China State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong University Beijing100044 China

Road disruptions frequently occur in practice, resulting in a productivity loss of bus services and widespread passenger delays. To cope with road disruptions, this paper investigates a multi-route coordination problem to jointly adjust bus routes and optimize bus timetables. Specifically, three adjusting strategies are adopted for bus routes of varying passenger demands: detouring, short-running, and cancellation. Then, a two-step framework is proposed to address the multi-route coordination problem. Concretely, a column generation procedure is developed in the first step to generate candidate adaptive bus routes and passenger paths. Following that, an integrated integer linear programming model is built in the second step to simultaneously determine bus timetables and combinations of those adaptive bus routes. After realizing the low computational efficiency for large-scale problems, this paper designs a customized decomposition algorithm based on set partitioning to solve the above model and obtain near-optimal solutions efficiently. Finally, the proposed methodology is applied to a real-world bus network in Beijing to verify its validity and effectiveness. Moreover, two comparative analyses are conducted to investigate the benefits of incorporating bus timetable optimization and explore the applicability of different adjusting strategies. © 2022, The Authors. All rights reserved.

关键词： Buses

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Identifying intercity freight trip ends of heavy trucks from GPS data

arXiv

引用

arXiv 2021年

作者： Yang, Yitao Jia, Bin Yan, Xiao-Yong Li, Jiangtao Yang, Zhenzhen Gao, Ziyou Key Laboratory of Integrated Transport Big Data Application Technology for Transport Industry Beijing Jiaotong University Beijing100044 China Institute of Transportation System Science and Engineering Beijing Jiaotong University Beijing100044 China

The intercity freight trips of heavy trucks are important data for transportation system planning and urban agglomeration management. In recent decades, the extraction of freight trips from GPS data has gradually become the main alternative to traditional surveys. Identifying the trip ends (origin and destinflation, OD) is the first task in trip extraction. In previous trip end identification methods, some key parameters, such as speed and time thresholds, have mostly been defined on the basis of empirical knowledge, which inevitably lacks universality. Here, we propose a data-driven trip end identification method. First, we define a speed threshold by analyzing the speed distribution of heavy trucks and identify all truck stops from raw GPS data. Second, we define minimum and maximum time thresholds by analyzing the distribution of the dwell times of heavy trucks at stop location and classify truck stops into three types based on these time thresholds. Third, we use highway network GIS data and freight-related points-of-interest (POIs) data to identify valid trip ends from among the three types of truck stops. In this step, we detect POI boundaries to determine whether a heavy truck is stopping at a freight-related location. We further analyze the spatiotemporal characteristics of intercity freight trips of heavy trucks and discuss their potential applications in practice. © 2021, CC BY.

关键词： Global positioning system

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