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Characterizing Flight Delay Profiles with a Tensor Factorization Framework

engineering 2021年第4期7卷 465-472页

作者： Mingyuan Zhang Shenwen Chen Lijun Sun Wenbo Du Xianbin Cao School of Electronic and Information Engineering Beihang UniversityBeijing 100191China National Engineering Laboratory for Comprehensive Transportation Big Data Application Technology Beijing 100191China Department of Civil Engineering and Applied Mechanics McGill UniversityMontrealQC H3A 0C3Canada

In air traffic and airport management,experience gained from past operations is crucial in designing appropriate strategies when facing a new ***,this paper uses massive spatiotemporal flight data to identify similar traffic and delay patterns,which become critical for gaining a better understanding of the aviation system and relevant ***,as the datasets imply complex dependence and higher-order interactions between space and time,retrieving significant features and patterns can be very *** this paper,we propose a probabilistic framework for highdimensional historical flight *** apply a latent class model and demonstrate the effectiveness of this framework using air traffic data from 224 airports in China during 2014–*** find that profiles of each dimension can be clearly divided into various patterns representing different regular *** prove the effectiveness of these patterns,we then create an estimation model that provides preliminary judgment on the airport delay *** outcomes of this study can help airport operators and air traffic managers better understand air traffic and delay patterns according to the experience gained from historical scenarios.

关键词： Air traffic management Flight delay Latent class model Tensor decomposition

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Finding robust and consistent space-time delivery paths for multi-day vehicle routing problem

Finding robust and consistent space-time delivery paths for ...

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2019 IEEE Intelligent transportation Systems Conference, ITSC 2019

作者： Zhuge, Lijuan Tong, Lu Carol Wu, Hailong Chen, Yiheng Zhou, Xuesong Beijing Jiaotong University School of Traffic and Transportation Haidian Beijing100044 China Beihang University School of Electronic and Information Engineering Beijing100091 China National Engineering Laboratory for Comprehensive Transportation Big Data Application Technology Beijing100091 China Beijing Jingdong Zhenshi Information Technology Co. Ltd. Logistics RandD Department China Arizona State University School of Sustainable Engineering and the Built Environment TempeAZ85281 United States

ISBN: (纸本)9781538670248

Facing uncertain and dynamic demands over different days, logistics companies need to adopt adaptive solutions for the last-mile delivery. Meanwhile, one of the major barriers for implementing vehicle routing problem (VRP) algorithms in real world dispatching systems is the stability of delivery driver's routes and schedules. To establish a theoretically sound and practically useful solution framework, this paper aims to optimize robust and consistent space-time paths for the multi-day VRP by providing daily schedules with limited variations from the master schedule. A multi-commodity network flow-based optimization model is proposed to minimize generalized transportation costs and the daily deviation of day-dependent space-time paths among demand analysis zones. Lagrange relaxation (LR) methods and alternating direction method of multipliers (ADMM) are used to handle complex side constraints. Experiments on illustrative networks and Beijing delivery network are developed to demonstrate the effectiveness of the proposed method. © 2019 IEEE.

关键词： Vehicle routing

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Identifying Flight Trajectory Patterns via a Density-Aided Hierarchical Clustering Algorithm 5th

Identifying Flight Trajectory Patterns via a Density-Aided H...

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5th China Aviation Science and technology Conference, 2021

作者： Zhang, Zhuxi Chen, Yichong Fang, Jing Zhou, Xueyang An, Yuhang Zhu, Xi National Engineering Laboratory for Comprehensive Transportation Big Data Application Technology Beihang University Beijing100191 China Unit 32751 Beijing China School of Electronics and Information Engineering Beihang University Beijing100191 China Aviation Data Communication Corporation Beijing100191 China School of Computer Science and Engineering Beihang University Beijing100191 China Research Institute for Frontier Science Beihang University Beijing100191 China

ISBN: (纸本)9789811674228

Identifying flight trajectory patterns is a vital task that helps controllers better understand the flight operation mechanism, so as to effectively recognize flight anomalies and manage traffic flow, etc. However, flight operation is sensitively affected by the weather and instant airspace regulation, making the flight trajectory pattern too intertwined to be easily distinguished. In this work, we propose a trajectory pattern identification method based on a density-aided hierarchical clustering algorithm. This method employs a weighted trajectory clustering mechanism to keep the minor trajectory patterns from being improperly "swallowed" by other large trajectory patterns. Experimental results show that the proposed method can explicitly distinguish different trajectory patterns and achieve more accurate results than existing approaches. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Clustering algorithms

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Travel Characteristics and Combination Choice Behavior Modeling of Passengers under the Influence of Epidemic Prevention Policies 24

Travel Characteristics and Combination Choice Behavior Model...

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24th COTA International Conference of transportation Professionals: Resilient, Intelligent, Connected, and Lowcarbon Multimodal transportation, CICTP 2024

作者： Zhang, Yue Dong, Chunjiao Li, Penghui Dong, Qianru Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport School of Traffic and Transportation Beijing Jiaotong Univ. Beijing China State Key Laboratory of Intelligent Vehicle Safety Technology China Automotive Engineering Research Institute Co. Ltd. Chongqing China

ISBN: (数字)9780784485484

ISBN: (纸本)9780784485484

The intermodal travel characteristics and combination selection behaviors of passengers are key issues to providing reasonable, efficient, and safe solutions, ensuring the traffic order, and improving quality. With the Beijing Winter Olympics as a background, by combining the Stated Preference and Revealed Preference surveys, the research develops a decision-making behavior model based on nested logit to investigate the passenger travel choices. The impacts of individual attributes, epidemic prevention policies, and preferential measures on the choice of intermodal travel, as well as the combination of departure time and travel mode, are studied. The results show that cultural and creative products have a significant impact on the combined travel choices. The cultural and creative products and consumer vouchers have a significant impact on the choice of intermodal travel. The research results can provide insights into traffic organization and management policies during large-scale events. © ASCE.

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Monotonicity-Induced Health Indicator for Axle-Box Bearings of Urban Rail Vehicles 6th

Monotonicity-Induced Health Indicator for Axle-Box Bearings ...

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6th International Conference on Electrical engineering and Information Technologies for Rail transportation, EITRT 2023

作者： Wang, Yiran Xin, Ge An, Guoping Li, Yilei School of Traffic and Transportation Beijing Jiaotong University Beijing100044 China Center of Safety Technology National Railway Administration Beijing100160 China School of Mechanical Electronic and Control Engineering Beijing Jiaotong University Beijing100044 China Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport Beijing Jiaotong University Beijing100044 China

ISBN: (纸本)9789819993147

Health indicators of axle-box bearings play an important role in failure prediction for urban rail vehicles. Vibration-based signal processing technology is a promising tool to reveal the degradation process with fruitful time-frequency information. However, the processes usually have non-stationary features caused by complex working conditions, which may be confused with the unknown temporal variations under different frequency bands. To address this issue, this paper proposes a new index, named monotonicity-induced health indicator, which is adaptive to weight the optimal frequency bands. First, frequency domain analysis is used to extract features from vibration signals. The monotonicity of these features is then estimated and their weights are optimized by means of maximizing the monotonicity. Eventually, all the features and weights are fused together to obtain a health indicator. The experimental result proves that the proposed health indicator shows its superiority in terms of monotonicity while accurately characterizing the degradation process of the bearings. © 2024, Beijing Paike Culture Commu. Co., Ltd.

关键词： Frequency domain analysis

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面向排放测算的随机性对跟驰模型车队稳定性的影响研究

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Journal of Central South University 2023年第8期30卷 2772-2789页

作者：孟冬利宋国华鲁洪语吴亦政翟志强于雷 Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport Beijing Jiaotong UniversityBeijing 100044China School of Civil and Environmental Engineering Georgia Institute of TechnologyAtlantaGA 30332USA Department of Transportation Studies Texas Southern UniversityHoustonTX 77004USA

在基于微观交通仿真模型进行交通能耗排放评估时,跟驰模型生成的车辆轨迹无法代表实际交通环境的驾驶特性,从而导致显著的排放测算误差,同时车辆排放在车队中的稳定性未被充分考虑。本文基于真实的车队轨迹比较了Gipps跟驰模型、全速差... 详细信息

在基于微观交通仿真模型进行交通能耗排放评估时,跟驰模型生成的车辆轨迹无法代表实际交通环境的驾驶特性,从而导致显著的排放测算误差,同时车辆排放在车队中的稳定性未被充分考虑。本文基于真实的车队轨迹比较了Gipps跟驰模型、全速差模型(FVDM)、智能驾驶员模型(IDM)和Wiedemann模型的面向排放测算的车队稳定性,分析了参数敏感性和随机性对跟驰模型的影响,提出了考虑随机参数的Gipps跟驰模型。结果表明,与Wiedemann模型相比,FVD模型和IDM模型在排放测算方面的误差较小,并且上述三个模型的排放测算误差沿车队是稳定的。Gipps模型能够真实地刻画车队中第一辆跟驰车的车辆动力学,但排放测算误差沿车队逐渐增大。参数敏感性分析表明模型参数标定难以提高Gipps模型的车队稳定性。结合随机参数对Gipps跟驰模型进行优化后,加速度分布均方根误差、VSP分布均方根误差和排放因子相对误差分别降低了5.00%、2.52%和11.04%,车队误差的标准差分别为0.18%、0.08%和0.86%,这表明随机参数能够提高Gipps跟驰模型面向排放测算的车队稳定性,提升仿真轨迹用于车辆能耗排放评估的准确性。

关键词：跟驰模型车队稳定性随机性排放测算 VSP分布

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Research on freeway travel speed prediction based on commercial vehicle data 12

Research on freeway travel speed prediction based on commerc...

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12th International Conference on Measuring technology and Mechatronics Automation, ICMTMA 2020

作者： Cheng, Genyuan Xie, Dekun Cheng, Xiaochun Chen, Qin Zhao, Jiandong Tangshan Municipal Transportation Bureau Hebei Province Tangshan063000 China Tandon School of Engineering New York University BrooklynNY11201 United States Beijing National Day School Beijing100039 China Beijing Jiaotong University 100044 China Key Lab. of Transport Industry of Big Data Application Technologies for Comprehensive Transport School of Traffic and Transportation Beijing Jiaotong University Beijing100044 China

ISBN: (纸本)9781728170817

With the rapid development of China's economy and the continuous improvement of people's living standards, the public's travel demand is increasing. However, rapid traffic demand growth is bound to bring problems such as traffic congestion and traffic accidents. Accurate travel speed prediction can provide a basis for traveler route selection and traffic guidance. This study uses the Beidou positioning navigation system data to take the Shanghai-Kunming freeway section in Jiangxi Province as a test section to study the travel speed prediction. The contents include:(1) Summarizing the main methods of short-term traffic flow prediction. (2) Design the preprocessing process for Beidou system data. (3) A travel speed prediction model based on support vector regression is established. (4) The SVR model is used to conduct empirical prediction research on different traffic flow states. © 2020 IEEE.

关键词： Support vector machines

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Real-time detection of vehicle speed based on video image 12

Real-time detection of vehicle speed based on video image

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12th International Conference on Measuring technology and Mechatronics Automation, ICMTMA 2020

作者： Cheng, Genyuan Guo, Yubin Cheng, Xiaochun Wang, Dongliang Zhao, Jiandong Tangshan Municipal Transportation Bureau Hebei Province Tangshan063000 China School of Traffic and Transportation Beijing Jiaotong University Beijing100044 China Beijing National Day School Beijing100039 China Tangshan NolMiddle School Hebei Province Tangshan063000 China Key Lab. of Transport Industry of Big Data Application Technologies for Comprehensive Transport School of Traffic and Transportation Beijing Jiaotong University Beijing100044 China

ISBN: (纸本)9781728170817

With the continuous development and improvement of Intelligent transportation System, more and more attention has been paid to the real-time and accurate speed information detection. Video vehicle speed detection based on machine vision has attracted the attention of the researchers due to its practical convenience and other advantages. In this paper, we propose a target tracking method based on video image feature matching vehicle to simplify the algorithm complexity and obtain high accuracy. The background subtraction method based on KNN algorithm is used to identify vehicle targets and obtain good initial vehicle characteristics. Experimental results show that the method has high real-time and the relative error of vehicle speed detection can be controlled at about 5%. © 2020 IEEE.

关键词： Target tracking

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Research on optimization of emergency rescue plan based on risk measurement 3

Research on optimization of emergency rescue plan based on r...

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2023 3rd International Conference on big data, Artificial Intelligence and Risk Management, ICBAR 2023

作者： Bu, Fanbiao Chen, Xuhao Lv, Ying Xu, Hengrui Overall Demonstration Center Qian Xuesen Institute of Military Systems Engineering Beijing100024 China Key Lab. 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 Beijing100044 China Shijiazhuang Land and Space Planning and Design Institute Co. Ltd. Hebei Shijiazhuang050019 China Data Center Agricultural Bank of China Limited Beijing100024 China

ISBN: (纸本)9798400716478

In order to reduce the impact of emergencies, this paper takes emergencies in complex geological environment as the background to study the optimization problem of emergency rescue routes. We measure the casualty consequences caused by the delay of rescue time based on conditional value at risk, and adopt multiple modes of transportation to coordinate rescue. A bi-objective optimization model is established with the total risk and total cost as the objectives, then the constraint method is applied to transform the model into a single objective optimization model for solution. Finally, an example is designed to analyze the relationship between the total risk and the total cost, and the rescue path scheme under the condition that the demand information is determined. The results indicate that the established model can provide a reference basis for emergency rescue of emergencies in the process of major transportation engineering construction. © 2023 ACM.

关键词： Optimization

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A Two-Layer Surrogate-Assisted Fish Migration Optimization for Urban Rail Transit Train Route Optimization

SSRN

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SSRN 2022年

作者： Du, Zhi-Gang Pan, Jeng-Shyang Wang, Xiao-Yang Chu, Shu-Chuan Ni, Shao-Quan School of Transportation and Logistics Southwest Jiaotong University Sichuan Chengdu610031 China College of Computer Science and Engineering Shandong University of Science and Technology Qingdao266590 China National and Local Joint Engineering Lab for Integrated Transportation Intelligence Chengdu610031 China National Engineering Lab of Comprehensive Transportation Big Data Application Technology Chengdu610031 China

Meta-heuristic evolutionary algorithms have been widely used in global optimization problems. Still, the global optimization of meta-heuristic evolutionary algorithms relies on a large number of frequent fitness evaluations, which hinders their application in expensive and time-consuming real-world issues. For example, in a large-scale transportation network, due to the large search space and high computational complexity, the performance of the algorithm is degraded. After years of research, many scholars have proposed the idea of using the Surrogate-assisted model to reduce the number of real evaluations for expensive problems and solve the time-consuming global optimization problem in reality. To solve the high-dimensional and time-consuming problem, in reality, a two-layer Surrogate-assisted Fish Migration Optimization (SA-FMO) is proposed. The global surrogate-assisted model is an accurate fit for the entire search space. The local surrogate-assisted model is to capture the topographic details near the current optimal solution position and improves the accuracy estimation of local optimization. In order to verify the effectiveness of our proposed algorithm, this paper first tests six benchmark functions in 50 dimensions. Finally, The optimization of long and short routes in urban rail transit, also known as the Train Routing Optimization problem, is modeled and optimized using the SA-FMO algorithm to improve operational efficiency and accelerate vehicle turnover in the case of unbalanced passenger flow distribution in urban rail transit interruption surfaces. © 2022, The Authors. All rights reserved.

关键词： Global optimization

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