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6th International Conference on Transportation Information and Safety, ICTIS 2021

作者： Zhou, Xinan Lu, Xiaoqian Song, Yicheng Yang, Hongtai Southwest Jiaotong University School of Transportation and Logistics National United Engineering Laboratory of Integrated and Intelligent Transportation National Engineering Laboratory of Integrated Transportation Big Data Application Technology Chengdu China

ISBN: (纸本)9781665497138

At the beginning of 2020, with the rapid spread of COVID-19 around the world, the passenger flow of subway has suffered from a serious impact. Based on the subway passenger flow data in Chicago, this article analyzes the impact of COVID-19 on rail transit passenger flow. ArcGIS is used to visualize the spatial-temporal distribution of the passenger flow of different stations during different time periods. Based on the fluctuation characteristics of passenger flow before and after the outbreak of COVID-19, one of the deep learning methods, the LSTM (Long-Short Term Memory) neural network model, is constructed to predict the passenger flow of each station in the scenario of no virus. The decline of passenger flow is calculated for each station. Stepwise regression model is constructed to determine factors that explain the decline in passenger flow, and significant factors are obtained: the original passenger flow, number of houses and jobs within 800m buffer zone, number of bus stops within 800m buffer zone, whether the station is a transfer station, distance from the station to the city center, and the number of low-income people. The results of the study show that after the outbreak of COVID-19, the passenger flow of the subway in Chicago experience a 'cliff-like' decline in the short term. The passenger flow in most areas dropped by more than 80%, and the passenger flow of some severely impacted stations dropped by more than 90%. Characteristics of the station and built environment factors of different stations influence the decline of passenger flow. © 2021 IEEE.

关键词： COVID-19

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A Method for Identifying the Timeliness of Manufacturing data Based on Weighted Timeliness Graph 9th

A Method for Identifying the Timeliness of Manufacturing Dat...

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19th International Conference on Advanced data Mining and applications, ADMA 2023

作者： Liu, Zehua Ding, Xuefeng Jiang, Yuming Hu, Dasha College of Computer Science Sichuan University Chengdu610065 China Big Data Analysis and Fusion Application Technology Engineering Laboratory of Sichuan Province Chengdu610065 China

ISBN: (纸本)9783031466601

Timeliness is one of the important indicators of data quality. In industrial production processes, a large amount of dependent data is generated, often resulting in unclear timestamps. Therefore, this article combines the conclusion dependency graph into a process dependency graph to determine the identification order of the timeliness of each process data;By constructing a weighted timeliness graph (WTG) and path single flux, a data timeliness identification method that does not completely rely on timestamps is proposed. Finally, a time-effectiveness identification method based on weighted time-effectiveness graph was discussed through an example and 9 dependency rules, and the effectiveness of the method was verified through a set of experiments. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： big data

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A Framework for Multiclass Contour Visualization

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IEEE Transactions on Visualization and Computer Graphics 2023年第1期29卷 353-362页

作者： Li, Sihang Yu, Jiacheng Li, Mingxuan Liu, Le Zhang, Xiaolong Luke Yuan, Xiaoru Laboratory of Machine Perception Ministry of Education School of AI Peking University China Center for Data Science Academy for Advanced Interdisciplinary Studies Peking University China National Engineering Laboratory for Big Data Analysis and Application Peking University China School of Computer Science Northwestern Polytechnical University China College of Information Sciences and Technology Pennsylvania State University United States

Multiclass contour visualization is often used to interpret complex data attributes in such fields as weather forecasting, computational fluid dynamics, and artificial intelligence. However, effective and accurate representations of underlying data patterns and correlations can be challenging in multiclass contour visualization, primarily due to the inevitable visual cluttering and occlusions when the number of classes is significant. To address this issue, visualization design must carefully choose design parameters to make visualization more comprehensible. With this goal in mind, we proposed a framework for multiclass contour visualization. The framework has two components: a set of four visualization design parameters, which are developed based on an extensive review of literature on contour visualization, and a declarative domain-specific language (DSL) for creating multiclass contour rendering, which enables a fast exploration of those design parameters. A task-oriented user study was conducted to assess how those design parameters affect users' interpretations of real-world data. The study results offered some suggestions on the value choices of design parameters in multiclass contour visualization. © 2022 IEEE.

关键词： data visualization

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The difference between customers and subscribers in Boston tourists using shared bicycles under COVID-19: Trip frequency and its determinants 6

The difference between customers and subscribers in Boston t...

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6th International Conference on Transportation Information and Safety, ICTIS 2021

作者： Jiang, Lanzhen Yang, Hongtai Zheng, Rong Huo, Jinghai Southwest Jiaotong University School of Transportation and Logistics National United Engineering Laboratory of Integrated and Intelligent Transportation National Engineering Laboratory of Integrated Transportation Big Data Application Technology Chengdu China

ISBN: (纸本)9781665497138

The COVID-19 pandemic has changed the frequency of tourists using shared bicycles to a certain extent. This paper uses the cycling data of Boston, USA from June 2020 to December 2020, taking users in areas without bicycle sites as tourists. Using customers and subscribers among tourists to share bicycle trip data on a daily basis, taking into account the weather, the built environment of bicycle sites, and the number of COVID-19 cases per week, a negative binomial regression model is used to explore these factors during the epidemic. The relationship between regional customers and subscribers using shared bicycle trips and influencing factors. The results show that, first of all, during the pandemic, there is a double-peak tide phenomenon in the usage of shared bicycles by subscribers among tourists, and customers use more in the evening peak. Secondly, the population density and employment density around bicycle sites have a significant positive impact on the shared bicycle use of customers and subscribers. For every 1% increase in the population, subscriber usage increases by 4.55%, and every 1 % increase in employment Density, customer usage increased by 4.22 %. In addition, the number of bus stations and subway stations has a restraining effect on the use of shared bicycles. Finally, the number of epidemics will increase the usage of customers and subscribers to a certain extent, with subscribers increasing by 0.25% and customer usage increasing by 2.92 %. The research results can timely analyze the usage of bicycles shared by different users among tourists during the pandemic, and can help transportation agencies adjust services in different regions to better control the spread of the virus. © 2021 IEEE.

关键词： Bicycles

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Determine Lane Function of Variable Approach Lane at the Signalized Intersection Based on Real-time Traffic Status 6

Determine Lane Function of Variable Approach Lane at the Sig...

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6th International Conference on Transportation Information and Safety, ICTIS 2021

作者： Tang, Liying Liu, Yugang National United Engineering Laboratory of Integrated and Intelligent Transportation Southwest Jiaotong University School of Transportation and Logistics National Engineering Laboratory of Integrated Transportation Big Data Application Technology Chengdu China

ISBN: (纸本)9781665497138

How to determine the lane function of Variable Approach Lane (VAL) is critical for satisfying the turning traffic demand and decreasing vehicle delay. Though a lot of scholars have studied this topic, most of them ignore the cycle-by-cycle traffic demand fluctuation at a signalized intersection. This paper proposes a dynamic offline lane assignment scheme to determine the lane function of VAL based on real-time traffic status identification. Firstly, the Fuzzy C- Means Algorithm is utilized to identify the traffic status based on three indicators: volume, occupancy, and speed in a cycle. Then, a rapid traffic status identification system focusing on through and left-turn traffic is built. A dynamic lane assignment model is established to determine the lane function of VAL in a real-time manner. An example is introduced to better illustrate how the method works. The result shows that the number of vehicles released increases by 9.8% while the delay per vehicle is reduced by 9.2%. In addition, the performance of dynamic lane, fixed left-turn lane, and fixed through lane under different traffic volumes is compared and results show dynamic lane has the best performance. Finally, this paper compares the proposed dynamic lane optimization method with two other optimization methods. Results reveal that the proposed method works better. © 2021 IEEE.

关键词： Vehicles

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Decrease of Taxi Ridership due to the Impact of COVID-19: A Case Study of New York City 6

Decrease of Taxi Ridership due to the Impact of COVID-19: A ...

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6th International Conference on Transportation Information and Safety, ICTIS 2021

作者： Li, Juan Song, Yicheng Zhou, Xinan Yue, Jinjin Yang, Hongtai 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 Chengdu China

ISBN: (纸本)9781665497138

The outbreak of COVID-19 has greatly impacted all industries of many countries in the world. As an important part of people's daily life, transportation is one of the most severely impacted industries. Taking New York City as an example, this paper explores the decline of taxi ridership due to the COVID-19. The decreased ratio of the actual taxi ridership to the taxi ridership predicted for the no COVID-19 scenario based on historical data is calculated as the dependent variable. The fractional response model is used to study the effect of built environment factors including demographic and socioeconomic, land use, and road-related on the decline of ridership. One model is constructed for each of the four periods, to explore the influence of influencing factors on the dependent variables in different periods. The model results show that the percentage of taxi trips decline is associated with the proportion of high-income people living in the area. The reason could be that these people have more flexible working hours and working places. They can choose to telecommute or travel by private cars to avoid contacting other people during transportation. The analysis of the other factors shows that industrial jobs are related to the low percentage of decline. The model results reveal to us the problem of equity exposed in New York City during the pandemic: limited by jobs(race/income), a portion of citizens are not fully free to choose their travel mode during the pandemic. According to the findings, this paper gives traffic management some policy suggestions. As a result, this study could provide an important reference for policymakers to develop appropriate measures to control the epidemic. © 2021 IEEE.

关键词： COVID-19

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KVSAgg: Secure Aggregation of Distributed Key-Value Sets 39

KVSAgg: Secure Aggregation of Distributed Key-Value Sets

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39th IEEE International Conference on data engineering, ICDE 2023

作者： Wu, Yuhan Dong, Siyuan Zhou, Yi Zhao, Yikai Fu, Fangcheng Yang, Tong Niu, Chaoyue Wu, Fan Cui, Bin Peking University School of Computer Science National Engineering Laboratory for Big Data Analysis Technology and Application Beijing China Peng Cheng Laboratory Shenzhen China Shanghai Jiao Tong University Department of Computer Science and Engineering Shanghai China

ISBN: (纸本)9798350322279

In global data analysis, the central server needs the global statistic of the user data stored in local clients. In such cases, an Honest-but-Curious central server might put user privacy at risk in trying to collect individual statistics of each user. In response, the secure aggregation provides a solution for calculating global statistics without revealing users' privacy data. However, existing secure aggregation protocols only focus on the data in the form of vectors or common sets, which limits their application scope. We formalize a general problem - key-value set secure aggregation - that not only includes secure vector aggregation and private set union but also supports more applications. To address the proposed problem, we devise our solution (called the KVSAgg framework) that promises satisfactory performance in security, efficiency, and accuracy. Our key technique is a homomorphic transform algorithm (called HyperIBLT) that is not only capable of bidirectionally transforming data between key-value sets and vectors, but also able to transform sum operation of sets to addition of vectors. We implement KVSAgg on both CPU and GPU platforms and perform the evaluation on three use cases including federated learning, distributed data counting, and finding global hot items. Compared with our baselines, KVSAgg simultaneously achieves the best security, efficiency higher by orders of magnitude, and zero-error in nearly all cases. All codes are open-source anonymously. © 2023 IEEE.

关键词： Vectors

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Distribution Vehicle Routing Optimization Based on 3D Loading 4th

Distribution Vehicle Routing Optimization Based on 3D Loadin...

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4th International Conference on Smart Vehicular Technology, Transportation, Communication and applications, VTCA 2021

作者： Luo, Xuan Zhang, Jin Yin, Tingyu Zhu, Hongxing Qiu, Mingyue School of Transportation and Logistics Southwest Jiaotong University Chengdu611756 China National Engineering Laboratory of Integrated Transportation Big Data Application Technology Southwest Jiaotong University Chengdu611756 China Comprehensive Transportation Big Data Application Technology Laboratory Chengdu611756 China

ISBN: (纸本)9789811640384

The rapid development of e-commerce leads to increasingly fierce competition in the logistics industry. Cost reduction and efficiency has become the key issue for logistics enterprises to survive. Loading optimization and path optimization are the two core issues in distribution activities, and they are inseparable and related. The joint optimization of the two is more in line with the actual distribution optimization needs. Based on the analysis of the interaction between routing and loading, this paper proposes a multi-objective optimization model with the minimum distribution cost, the minimum number of vehicles and the maximum customer satisfaction as the objectives. A hybrid nesting algorithm based on NSGAII algorithm is designed, in which the packing module is designed by tree search algorithm combined with the deepest and leftmost algorithm. Finally, the real data of China Railway Express Chengdu branch are taken as a case to verify the feasibility of the model and algorithm. Combined with the results of the algorithm, some suggestions are put forward. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Customer satisfaction

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REncoder: A Space-Time Efficient Range Filter with Local Encoder 39

REncoder: A Space-Time Efficient Range Filter with Local Enc...

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39th IEEE International Conference on data engineering, ICDE 2023

作者： Wang, Ziwei Zhong, Zheng Guo, Jiarui Wu, Yuhan Li, Haoyu Yang, Tong Tu, Yaofeng Zhang, Huanchen Cui, Bin Peking University School of Computer Science National Engineering Laboratory for Big Data Analysis Technology and Application Beijing China Peng Cheng Laboratory Shenzhen China Zte Corporation Beijing China Tsinghua University Beijing China

ISBN: (纸本)9798350322279

A range filter is a data structure to answer range membership queries. Range queries are common in modern applications, and range filters have gained rising attention for improving the performance of range queries by ruling out empty range queries. However, state-of-the-art range filters, such as SuRF and Rosetta, suffer either high false positive rate or low throughput. In this paper, we propose a novel range filter, called REncoder. It organizes all prefixes of keys into a segment tree, and locally encodes the segment tree into a Bloom filter to accelerate queries. REncoder supports diverse workloads by adaptively choosing how many levels of the segment tree to store. We theoretically prove that the error of REncoder is bounded and derive the asymptotic space complexity under the bounded error. We conduct extensive experiments on both synthetic datasets and real datasets. The experimental results show that REncoder outperforms all state-of-the-art range filters. © 2023 IEEE.

关键词： data structures

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RT3DHVC: A Real-time Human Holographic Video Conferencing System with a Consumer RGB-D Camera Array

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IEEE Transactions on Circuits and Systems for Video Technology 2024年第5期35卷 4226-4241页

作者： Jiang, Yuqi Li, Jing Dai, Yanran Qin, Haidong Zhou, Xiaoshi Zhang, Yong Liu, Hongwei Yan, Kefan Yang, Tao The School of Telecommuni cations Engineering Xidian University Xi’an710071 China The National Engineering Laboratory for Integrated AeroSpace-Ground-Ocean Big Data Application Technology SAIIP School of Computer Science Northwestern Polytechnical University Xi’an710129 China

In this paper, we present an end-to-end holographic video conferencing system that enables real-time high-quality free-viewpoint rendering of participants in different spatial regions, placing them in a unified virtual space for a more immersive display. Our system offers a cost-effective, complete holographic conferencing process, including multiview 3D data capture, RGB-D stream compression and transmission, high-quality rendering, and immersive display. It employs a sparse set of commodity RGB-D cameras that capture 3D geometric and textural information. We then remotely transmit color and depth maps via standard video encoding and transmission protocols. We propose a GPU-parallelized rendering pipeline based on an image-based virtual view synthesis algorithm to achieve real-time and high-quality scene rendering. This algorithm uses an on-the-fly Truncated Signed Distance Function (TSDF) approach, which marches along virtual rays within a computed precise search interval to determine surface intersections. We then design a multiweight projective texture mapping method to fuse color information from multiple views. Furthermore, we introduce a method that uses a depth confidence map to weight the rendering results from different views, which mitigates the impact of sensor noise and inaccurate measurements on the rendering results. Finally, our system places conference participants from different spaces into a virtual conference environment with a global coordinate system through coordinate transformation, which simulates a real conference scene in physical space, providing an immersive remote conferencing experience. Experimental evaluations confirm our system’s real-time, low-latency, high-quality, and immersive capabilities. ©1991-2012 IEEE.

关键词： Video conferencing

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