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20th Chinese Materials Conference, CMC 2019

作者： Chen, Hualong Liu, Yu Gong, Xianzheng Hao, Liwei Sun, Boxue Li, Xiaoqing National Engineering Laboratory for Industrial Big-data Application Technology Beijing100124 China Beijing University of Technology the Key Laboratory of Advanced Functional Materials Ministry of Education of China Beijing100124 China Beijing Building Materials Academy of Sciences Research State Key Laboratory of Solid Waste Reuse for Building Materials Beijing100041 China

ISBN: (纸本)9783035715668

The scarcity of water resource has become one of major issues that constrain economic development and urbanization process in China. The water footprint is a comprehensive indicator used to measure water consumption and pollution that is widely used in global or regional studies. The previous practices showed that water footprint analysis was an effective tool to achieve sustainable utilization of water resources by guiding the development of water-saving technology and product. This paper reviewed the progress of water footprint research in materials industry including related theory, method and application. Firstly, the basic concept of water footprint was introduced. Secondly, the current accounting and assessment methods of water footprint and their applicable fields were summarized. Thirdly, the case studies on the water footprint of metallic materials, nonmetallic materials and chemical materials were reviewed to analyze its guidance significance on the sustainable development of water resources. At last, some suggestions for future research on the water footprint of materials were proposed. © 2020 Trans Tech Publications Ltd, Switzerland.

关键词： Chemical analysis

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Recognition Method of Urban Residents’ Travel Mode based on GPS data

Recognition Method of Urban Residents’ Travel Mode based on...

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3rd International Conference on Mechatronics engineering and Information technology(ICMEIT 2019)

作者： Huabin Liu Chunfu Shao MOE Key Laboratory for Urban Transportation Complex Systems Theory and Technology Beijing Jiaotong University Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport Beijing Jiaotong University

The rising of big data and the Internet has brought about tremendous changes in travel. The rapid development of computer technology, network technology, wireless communication technology, portable devices, and location-based services provides an opportunity for the application of GPS technology to travel behavior survey. GPS technology has become a new technology to study urban residents’ travel behavior and to identify urban residents’ travel modes. This paper delivery a travel mode recognition method for urban residents’ GPS travel data. Through the process of GPS data preprocessing, trajectory recognition and feature extraction, the recognition algorithm is designed to identify seven urban common travel modes, which are walking, bicycle, car, bus, taxi, subway and urban rail. In this paper, a trajectory recognition algorithm based on transition points is used to segment the trajectory of a single travel mode by identifying the transition points and pedestrian sections. The accuracy of the trajectory recognition process is about 79.8% by validating the open data set. For the extracted single trajectory, the Bagged Trees combined model is used to identify the travel mode with an accuracy of about 76.2%.

关键词： GPS travel mode mode recognition trip recognition

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Dynamic Origin-Destination Demand Prediction with Improved LSTM Model

Dynamic Origin-Destination Demand Prediction with Improved L...

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IEEE International Conference on Intelligent transportation engineering (ICITE)

作者： Wei Tan Yang Yang Pengfa Song Yanning Huang Jing Liu Fangfang Zheng Southwest municipal engineering design & research institute of China Chengdu China School of Transportation and Logistics Southwest Jiaotong University Chengdu China National Engineering Laboratory of Integrated Transportation Big Data Application Technology Chengdu China

As the basis of dynamic traffic distribution, traffic demand prediction needs to address the complicated coupling correlations within the road network structure and the spatial and seasonal variation of traffic demand. Dynamic origin-destination (OD) demand prediction can present the correlation between nodes in the road network pattern and the time-varying and periodicity of traffic data. In this paper, we propose a dynamic OD prediction model considering the spatial-temporal and environmental aspects (STE) of the network (STE-CNN-LSTM) based on the Long-Short-Term-Memory (LSTM) algorithm. The proposed model consists of three modules including the spatial, temporal and external environment modules. The spatial module extracts the spatial correlation information of OD data. The temporal module extracts the time correlation information of OD data, and the external environment module deals with the impact of the external factors (e.g., weather, accidents) on OD demand. To verify the effectiveness of the proposed model, we conducted experiments using the OD dataset collected by the Caltrans Performance Measurement System (PeMS) of California in the United States, and the results show that the proposed STE-CNN-LSTM model performs better than the ARIMA, general LSTM and CNN-LSTM models in most respect in most respects.

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Research on Equipment State Correlation Analysis Based on Digital Model of High-speed Railway Public Works Equipment

Research on Equipment State Correlation Analysis Based on Di...

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Digital Twins and Parallel Intelligence (DTPI), IEEE International Conference on

作者： Yanhua Wu Ping Li Zhibo Cheng Wenya Xu Chenkun Jin Hairong Dong National Engineering Laboratory of Comprehensive Transportation Big Data Application Technology China Academy of Railway Sciences Corporation Limited Beijing China China Academy of Railway Sciences Corporation Limited Beijing China Beijing Jiaotong University Beijing China

One of the key factors in the safe operation of high-speed railways is the operational status of public works equipment. Constructing the digital model of public works equipment, fusing basic attribute information with detection and monitoring status information, and characterizing the current operating status of public works equipment, will provide more intuitive support for the change law of state change analysis. The correlation analysis between TQI and settlement based on digital model will provide auxiliary decision support for equipment maintenance.

关键词： Analytical models Correlation Digital twin Conferences Maintenance engineering Rail transportation Monitoring

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Unique in the Metro System: The Likelihood to Re-Identify a Metro User with Limited Trajectory Points

SSRN

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SSRN 2023年

作者： Yang, Hongtai Ping, An Wei, Hongmin Zhai, Guocong School of Transportation and Logistics Institute of System Science and Engineering National Engineering Laboratory of Integrated Transportation Big Data Application Technology National United Engineering Laboratory of Integrated and Intelligent Transportation Southwest Jiaotong University Chengdu611756 China Guangdong–Hong Kong-Macau Joint Laboratory for Smart Cities Shenzhen518060 China School of Transportation and Logistics National Engineering Laboratory of Integrated Transportation Big Data Application Technology National United Engineering Laboratory of Integrated and Intelligent Transportation Southwest Jiaotong University Chengdu611756 China Department of Civil & Environmental Engineering Old Dominion University 129C Kaufman Hall NorfolkVA23529 United States

Though the collection of metro smart card data could help improve the operations of the metro system, the release of such data might lead to privacy issues. Few studies have quantified the probability to re-identify a user from the smart card data using very limited trajectory points. Thus, this study investigates this topic by analyzing eight-day metro smart card data of Chengdu, China. Results reveal that, on the macro level, three random trajectory points with a temporal resolution of one minute and one hour are enough to identify over 90% and 67% of the users. Even when the resolution is reduced to one day, 20% of the users could be still be identified by three points. On the individual level, three carefully selected points with a temporal resolution of one minute, one hour, and one day could lead to a re-identification risk no less than 0.5 for 99%, 89%, and 52% of the users. The effects of number of points, number of users, and other temporal resolutions are also thoroughly evaluated. These findings emphasize the great privacy issues involved in the release of metro smart card data and remind metro operators to take proactive measures to enhance privacy protection. © 2023, The Authors. All rights reserved.

关键词： Smart cards

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Urban Water Sprinkler Routing: A Multi-Depot Mixed Capacitated Arc Routing Problem Incorporating Real-Time Demands

arXiv

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arXiv 2024年

作者： Yang, Hongtai Liu, Luna Han, Ke Lei, Boyi School of Transportation and Logistics National Engineering Laboratory of Integrated Transportation Big Data Application Technology National United Engineering Laboratory of Integrated and Intelligent Transportation Institute of System Science and Engineering Southwest Jiaotong University Chengdu611756 China School of Economics and Management Southwest Jiaotong University Chengdu611756 China

Fugitive road dust (FRD), as one of the major pollutants in the city, poses great harm to the environment and the physical health of citizens. A common countermeasure adopted by government agencies is employing on-road water trucks (sprinklers) to spray water (sprinkle) on urban streets to reduce the FRD. Currently, the traveling routes of sprinklers are usually planned based on drivers’ experience, which may lead low operation efficiency and could not respond to the real-time sprinkling demands. To address these issues, this study formulates the routes planning of sprinklers as a multi-depot mixed capacitated arc routing problem with real-time demands with the aim of minimizing the sprinklers’ travel distance. We develop an improved adaptive large neighborhood search (ALNS) algorithm that incorporates a tabu-list and a perturbation mechanism to solve this problem. Furthermore, a problem-specific acceleration mechanism is designed to reduce unnecessary search domains to improve the efficiency of the algorithm. Empirical experiments are conducted based on various scenarios and the results demonstrate that the proposed algorithm generates solutions that are superior or at least comparable to the solutions generated by the traditional ALNS algorithm but with significantly lower computation time. Sensitivity analysis is conducted to explore the effects of relevant parameters on the results. This study is the first to incorporate real-time FRD pollution information, gathered through multiple data sources via IoT technology, into urban sprinkling operations, extending the traditional CARP from a tactical planning to a real-time operational environment. A real-world implementation case is also presented. Copyright © 2024, The Authors. All rights reserved.

关键词： Efficiency

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A novel semi-supervised multi-view clustering framework for screening Parkinson’s disease

arXiv

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arXiv 2020年

作者： Zhang, Xiaobo Zhai, Donghai Yang, Yan Zhang, Yiling Wang, Chunlin School of Information Science and Technology Southwest Jiaotong University Chengdu611756 China National Engineering Laboratory of Integrated Transportation Big Data Application Technology Southwest Jiaotong University Chengdu611756 China

In recent years, there are many research cases for the diagnosis of Parkinson's disease (PD) with the brain magnetic resonance imaging (MRI) by utilizing the traditional unsupervised machine learning methods and the supervised deep learning models. However, unsupervised learning methods are not good at extracting accurate features among MRIs and it is difficult to collect enough data in the field of PD to satisfy the need of training deep learning models. Moreover, most of the existing studies are based on single-view MRI data, of which data characteristics are not sufficient enough. In this paper, therefore, in order to tackle the drawbacks mentioned above, we propose a novel semi-supervised learning framework called Semi-supervised Multi-view learning Clustering architecture technology (SMC). The model firstly introduces the sliding window method to grasp different features, and then uses the dimensionality reduction algorithms of Linear Discriminant Analysis (LDA) to process the data with different features. Finally, the traditional single-view clustering and multi-view clustering methods are employed on multiple feature views to obtain the results. Experiments show that our proposed method is superior to the state-of-art unsupervised learning models on the clustering effect. As a result, it may be noted that, our work could contribute to improving the effectiveness of identifying PD by previous labeled and subsequent unlabeled medical MRI data in the realistic medical environment. Copyright © 2020, The Authors. All rights reserved.

关键词： Magnetic resonance imaging

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Takeover Performance and Workload under Varying Automation Levels, Time Budget and Road Curvature

Takeover Performance and Workload under Varying Automation L...

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2022 IEEE Asia-Pacific Conference on Image Processing, Electronics and Computers, IPEC 2022

作者： Xu, Chengliang Li, Penghui Li, Yibing Merat, Natasha Lu, Ziwang Guo, Xing Tsinghua University State Key Laboratory of Automotive Safety and Energy School of Vehicle and Mobility Beijing China Beijing Jiaotong University Key Lab. of Transport Industry of Big Data Application Technologies for Comprehensive Transport School of Traffic and Transportation Beijing China University of Leeds Institute for Transport Studies Leeds United Kingdom Beijing Information Science and Technology University School of Mechanical and Electrical Engineering Beijing China

ISBN: (纸本)9781665409025

In SAE Level 2 (L2) and Level 3 (L3) automated driving, drivers are required to take over control from automation when reaching system boundaries. The length of time budget is the vital parameter that needs to be determined to ensure a safe takeover manner. Existing literature on this issue rarely considers potential interactions between time budget and other factors, and a variable time budget seemed to be paradoxical in critical unplanned transitions. To this end, this study investigated drivers' takeover performance and workload under varying automation levels, time budget and road curvature. We employed the operational design domain (ODD) exiting event as the transition scenario where the ego car was about to leave the highway. Results showed that drivers in L3 takeovers performed worse and elicited higher workload than L2 when entering a curved ramp with a limited time budget. Takeovers when entering a curved ramp yielded worse performance and higher workload, as compared to entering a straight ramp. Such detrimental effects caused by loss of situation awareness (SA) and change of road curvature can be mitigated by increasing the length of time budget. All these findings suggest that even in simplified ODD exiting scenarios, the length of time budget should vary according to the state of the driver and the environment. © 2022 IEEE.

关键词： Automation

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The Vcg Pricing Policy with Unit Reserve Prices for Ride-Sourcing is 3/4-Individual Compatibility

SSRN

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SSRN 2024年

作者： Li, Ruijie Chen, Haiyuan Liu, Xiaobo Zhang, Kenan School of Transportation and Logistics Southwest Jiaotong University Sichuan Chengdu611756 China National Engineering Laboratory of Integrated Transportation Big Data Application Technology Southwest Jiaotong University Sichuan Chengdu611756 China Switzerland

We consider a customized pricing and matching strategy for ride-sourcing services in which drivers and riders disclose their willingness to pay (WTP) per unit distance and willingness to accept (WTA) per unit distance, respectively. The platform then matches drivers and riders based on their reports to maximize the total social welfare. While this strategy is motivated to promote transparency, fair pricing, and participant satisfaction, it faces a particular challenge to ensure riders and drivers truthfully report their valuations, which are considered private information. To encourage truthful reports, we implement the Vickrey-Clarke-Groves pricing policy augmented with unit reserve prices, referred to as VCG-URP. For the riders, the reserve price is set as the minimum WTP per unit distance, while for the drivers, it is the maximum WTA per unit distance. We demonstrate that VCG-URP ensures that riders provide truthful valuations. However, it does not guarantee the same level of honesty from drivers in reporting their valuations. While drivers cannot enhance their utility by overstating their unit WTA, they may benefit from understating it. These theoretical results are confirmed by numerical experiments with several additional findings: i) although the underreporting strategy of the drivers theoretically has an opportunity to achieve a higher utility, it always leads to a decrease in expected utility over randomly generated matching scenarios;ii) the introduction of reserve prices has larger impacts on riders than drivers and benefits the platform in excluding less profitable trips;and iii) the performance of VCG-URP is reasonably robust towards the market density, while the VCG price is less applied when the market becomes sparse, mainly due to the loss of substitutability among participants. All these findings bring valuable insights into ride-sourcing services in practice. © 2024, The Authors. All rights reserved.

关键词： Costs

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Environmental Impact on Connected and Automated Vehicle in Heterogeneous State Considering Different Penetration Rates

Environmental Impact on Connected and Automated Vehicle in H...

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2022世界交通运输大会(WTC2022)

作者： Yixin Zhang Xumei Chen Xiaoyue Liu Yu Zhang Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport Beijing Jiaotong University School of Traffic and Transportation Xuchang University Department of Civil & Environmental Engineering University of Utah

With the rapid development of Connected and Automated Vehicle(CAV) technology,vehiclesequipped with such technology have gradually entered the commercialization *** a long time to come,road traffic will be *** this environment,whether different mixes of human-driven vehicles(HVs) and CAVs can achieve the expected benefits and whether the overall benefits on the road will improve are important *** main goal of this paper is to quantify the impact of CAV technology on traffic efficiency and traffic environment in complex ***,the driving behavior of HV and CAV is characterized based on the Wiedemann ***,the measurement model of energy consumption and emission is established based on the kinetic ***,a simulation platform is developed based on VISSIM for a case *** traffic efficiency and environmental benefit characteristics of vehicles on an expressway in Beijing are measured at different CAV penetration *** results show that with the increase of CAV penetration,the average operating time,average energy consumption,and average vehicle emissions are significantly *** study will effectively support the formulation of relevant road traffic management policies.

关键词： Wiedemann model Connected and automated vehicle Energy consumption emission Vissim

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