陈龙彪南强青年拔尖人才、副教授，博士生导师详情

科研团队

陈龙彪
南强青年拔尖人才、副教授，博士生导师
longbiaoch@xmu.edu.cn

陈龙彪是厦门大学南强青年拔尖人才计划研究员、信息学院副教授，福建省高层次引进人才、厦门市高层次留学人才。2016年师从潘纲教授获得浙江大学博士学位，2018年师从张大庆教授获得法国索邦大学（原巴黎第六大学）博士学位，回国前曾任法国国立电信研究院助理研究员。主要研究方向为群智感知、普适计算、城市计算。连续两次以第一作者获得CCF-A类会议UbiComp最佳论文提名奖（国内高校首次），荣获2022年度ACM SIGSPATIAL中国分会新星奖（本年度全国唯一）。累计发表论文50篇，其中CCF-A类论文9篇（一作及通讯），CCF-B类和中科院二区论文14篇（一作及通讯），3篇论文他引大于100次；授权发明专利5项，主持国家自然科学基金面上项目、青年项目、国防基础科研项目、国家高分遥感项目等6项。担任ACM中国SIGSPATIAL分会执行委员、CCF普适计算专委会执行委员、CCF YOCSEF厦门分论坛主席、CCF高级会员、金砖国家青年科学家论坛成员。担任ACM UbiComp 2021社会活动主席，以及多届CCF和谐人机环境会议（HHME）技术论坛联合主席；担任AAAI, IJCAI等国际会议程序委员会委员，以及FSC, Vehicles等国际期刊编委。主持教育部“智能基座”产教融合协同育人基地项目1项，指导学生获得第八届中国国际“互联网+”大学生创新创业大赛福建省赛区金奖、第二届CrowdOS移动群智感知平台开源创新大赛一等奖。

主持的科研项目

国家自然科学基金面上项目，基于深度强化学习的群智数据标注方法研究，2022-2025
国家自然科学基金青年项目，基于群智感知的城市出行需求匹配与共享交通调度优化研究，2019-2021
国防基础科研项目，DRFM干扰动态行为学习与建模，2019-2021
国家高分遥感项目，某XX系统-图像XX软件，2020-2021
福建省自然科学基金面上项目，城市共享交通资源优化调度关键技术研究，2018-2021
厦门市产学研协同创新项目，2020-2021

获奖荣誉

2022年度ACM SIGSPATIAL中国分会新星奖（本年度全国唯一）
ACM UbiComp 2015, 2016 两次最佳论文提名奖（一作，国内高校首次）
华为云计算先锋教师
2018年度福建省科技进步奖一等奖：城市交通多源感知与智能计算的研究和推广（排序第9）
厦门大学信息学院本科生创新创业导师

学术任职

金砖国家青年科学家论坛成员、人工智能分论坛主持人（2022）
中国计算机学会青年计算机科技论坛（CCF YOCSEF）厦门主席（2021）、副主席（2020）、学术秘书（2019）
中国计算机学会普适计算专委会执行委员、ACM SIGSPATIAL 中国分会执行委员会委员
厦门市欧美同学会理事（2020-至今）
社会活动主席：2021年ACM UbiComp Social Activity Chair（CCF-A类会议）
论坛主席：2019年第三届CCF智能感知与城市计算前沿论坛（参会规模200余人）
论坛主席：2017年起连续五年成功举办CCF和谐人机环境联合会议技术讲座（HHME Tutorials）（累计参会近1000人）
国际期刊编委：Frontiers in Sustainable Cities (FSC), Journal of Intelligent Learning Systems and Applications (JILSA), MDPI Vehicles
国际会议程序委员会委员：AAAI 2020, IJCAI 2020等

部分论文列表 Selected Publications

2023

Tieqi Shou, Zhuohan Ye, Yayao Hong, Zhiyuan Wang, Hang Zhu, Zhihan Jiang, Dingqi Yang, Binbin Zhou, Cheng Wang, Longbiao Chen
CrowdQ: Predicting the Queue State of Hospital Emergency Department Using Crowdsensing Mobility Data-Driven Models
UbiComp

bibtex

@article{10.1145/3610875, author = {Shou, Tieqi and Ye, Zhuohan and Hong, Yayao and Wang, Zhiyuan and Zhu, Hang and Jiang, Zhihan and Yang, Dingqi and Zhou, Binbin and Wang, Cheng and Chen, Longbiao}, title = {CrowdQ: Predicting the Queue State of Hospital Emergency Department Using Crowdsensing Mobility Data-Driven Models}, year = {2023}, issue_date = {September 2023}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, volume = {7}, number = {3}, url = {https://doi.org/10.1145/3610875}, doi = {10.1145/3610875}, abstract = {Hospital Emergency Departments (EDs) are essential for providing emergency medical services, yet often overwhelmed due to increasing healthcare demand. Current methods for monitoring ED queue states, such as manual monitoring, video surveillance, and front-desk registration are inefficient, invasive, and delayed to provide real-time updates. To address these challenges, this paper proposes a novel framework, CrowdQ, which harnesses spatiotemporal crowdsensing data for real-time ED demand sensing, queue state modeling, and prediction. By utilizing vehicle trajectory and urban geographic environment data, CrowdQ can accurately estimate emergency visits from noisy traffic flows. Furthermore, it employs queueing theory to model the complex emergency service process with medical service data, effectively considering spatiotemporal dependencies and event context impact on ED queue states. Experiments conducted on large-scale crowdsensing urban traffic datasets and hospital information system datasets from Xiamen City demonstrate the framework's effectiveness. It achieves an F1 score of 0.93 in ED demand identification, effectively models the ED queue state of key hospitals, and reduces the error in queue state prediction by 18.5\%-71.3\% compared to baseline methods. CrowdQ, therefore, offers valuable alternatives for public emergency treatment information disclosure and maximized medical resource allocation.}, journal = {Proc. ACM Interact. Mob. Wearable Ubiquitous Technol.}, month = {sep}, articleno = {122}, numpages = {28}, keywords = {Hospital queue state modeling, Mobile trajectory mining, Spatiotemporal crowdsensing data, Urban computing} }

2022

Hang Zhu, Tieqi Shou, Ruiying Guo, Zhihan Jiang, Zeyu Wang, Zhiyuan Wang, Zhiyong Yu, Weijie Zhang, Cheng Wang, Longbiao Chen
RedPacketBike: A graph-based demand modeling and crowd-driven station rebalancing framework for bike sharing systems
TMC

bibtex

Zhihan Jiang, Xin He, Chenhui Lu, Binbin Zhou, Xiaoliang Fan, Cheng Wang, Xiaojuan Ma, Edith C.H. Ngai, Longbiao Chen
Understanding Drivers’ Visual and Comprehension Loads in Traffic Violation Hotspots Leveraging Crowd-Based Driving Simulation
TITS

bibtex

Longbiao Chen, Xin He, Xiantao Zhao, Han Li, Yunyi Huang, Binbin Zhou, Wei Chen, Yongchuan Li, Chenglu Wen, Cheng Wang
GoComfort: Comfortable Navigation for Autonomous Vehicles Leveraging High-Precision Road Damage Crowdsensing
TMC

bibtex

2021

Zhihan Jiang, Hang Zhu, Binbin Zhou, Chenhui Lu, Mingfei Sun, Xiaojuan Ma, Xiaoliang Fan, Cheng Wang, Longbiao Chen
CrowdPatrol: A mobile crowdsensing framework for traffic violation hotspot patrolling
TMC

bibtex

Longbiao Chen, Chenhui Lu, Fangxu Yuan, Zhihan Jiang, Leye Wang, Daqing Zhang, Ruixiang Luo, Xiaoliang Fan, Cheng Wang
UVLens: urban village boundary identification and population estimation leveraging open government data
UbiComp

bibtex

@article{10.1145/3463495, author = {Chen, Longbiao and Lu, Chenhui and Yuan, Fangxu and Jiang, Zhihan and Wang, Leye and Zhang, Daqing and Luo, Ruixiang and Fan, Xiaoliang and Wang, Cheng}, title = {UVLens: Urban Village Boundary Identification and Population Estimation Leveraging Open Government Data}, year = {2021}, issue_date = {June 2021}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, volume = {5}, number = {2}, url = {https://doi.org/10.1145/3463495}, doi = {10.1145/3463495}, abstract = {Urban villages refer to the residential areas lagging behind the rapid urbanization process in many developing countries. These areas are usually with overcrowded buildings, high population density, and low living standards, bringing potential risks of public safety and hindering the urban development. Therefore, it is crucial for urban authorities to identify the boundaries of urban villages and estimate their resident and floating populations so as to better renovate and manage these areas. Traditional approaches, such as field surveys and demographic census, are time consuming and labor intensive, lacking a comprehensive understanding of urban villages. Against this background, we propose a two-phase framework for urban village boundary identification and population estimation. Specifically, based on heterogeneous open government data, the proposed framework can not only accurately identify the boundaries of urban villages from large-scale satellite imagery by fusing road networks guided patches with bike-sharing drop-off patterns, but also accurately estimate the resident and floating populations of urban villages with a proposed multi-view neural network model. We evaluate our method leveraging real-world datasets collected from Xiamen Island. Results show that our framework can accurately identify the urban village boundaries with an IoU of 0.827, and estimate the resident population and floating population with R2 of 0.92 and 0.94 respectively, outperforming the baseline methods. We also deploy our system on the Xiamen Open Government Data Platform to provide services to both urban authorities and citizens.}, journal = {Proc. ACM Interact. Mob. Wearable Ubiquitous Technol.}, month = {jun}, articleno = {57}, numpages = {26}, keywords = {urban village, urban computing, population estimation, heterogeneous data} }

2020

Longbiao Chen, Thi-Mai-Trang Nguyen, Dingqi Yang, Michele Nogueira, Cheng Wang, Daqing Zhang
Data-driven C-RAN optimization exploiting traffic and mobility dynamics of mobile users
TMC

bibtex

2018

Longbiao Chen, Xiaoliang Fan, Leye Wang, Daqing Zhang, Zhiyong Yu, Jonathan Li, Thi-Mai-Trang Nguyen, Gang Pan, Cheng Wang
RADAR: road obstacle identification for disaster response leveraging cross-domain urban data
UbiComp

bibtex

@article{10.1145/3161159, author = {Chen, Longbiao and Fan, Xiaoliang and Wang, Leye and Zhang, Daqing and Yu, Zhiyong and Li, Jonathan and Nguyen, Thi-Mai-Trang and Pan, Gang and Wang, Cheng}, title = {RADAR: Road Obstacle Identification for Disaster Response Leveraging Cross-Domain Urban Data}, year = {2018}, issue_date = {December 2017}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, volume = {1}, number = {4}, url = {https://doi.org/10.1145/3161159}, doi = {10.1145/3161159}, abstract = {Typhoons and hurricanes cause extensive damage to coast cities annually, demanding urban authorities to take effective actions in disaster response to reduce losses. One of the first priority in disaster response is to identify and clear road obstacles, such as fallen trees and ponding water, and restore road transportation in a timely manner for supply and rescue. Traditionally, identifying road obstacles is done by manual investigation and reporting, which is labor intensive and time consuming, hindering the timely restoration of transportation. In this work, we propose RADAR, a low-cost and real-time approach to identify road obstacles leveraging large-scale vehicle trajectory data and heterogeneous road environment sensing data. First, based on the observation that road obstacles may cause abnormal slow motion behaviors of vehicles in the surrounding road segments, we propose a cluster direct robust matrix factorization (CDRMF) approach to detect road obstacles by identifying the collective anomalies of slow motion behaviors from vehicle trajectory data. Then, we classify the detected road obstacles leveraging the correlated spatial and temporal features extracted from various road environment data, including satellite images and meteorological records. To address the challenges of heterogeneous features and sparse labels, we propose a semi-supervised approach combining co-training and active learning (CORAL). Real experiments on Xiamen City show that our approach accurately detects and classifies the road obstacles during the 2016 typhoon season with precision and recall both above 90\%, and outperforms the state-of-the-art baselines.}, journal = {Proc. ACM Interact. Mob. Wearable Ubiquitous Technol.}, month = {jan}, articleno = {130}, numpages = {23}, keywords = {urban computing, disaster response, cross-domain data, Mobility data mining} }

2017

Longbiao Chen, Jérémie Jakubowicz, Dingqi Yang, Daqing Zhang, Gang Pan
Fine-grained urban event detection and characterization based on tensor cofactorization
THMS

bibtex

2016

Longbiao Chen, Daqing Zhang, Leye Wang, Dingqi Yang, Xiaojuan Ma, Shijian Li, Zhaohui Wu, Gang Pan, Thi-Mai-Trang Nguyen, Jérémie Jakubowicz
Dynamic cluster-based over-demand prediction in bike sharing systems
UbiComp

bibtex

@inproceedings{10.1145/2971648.2971652, author = {Chen, Longbiao and Zhang, Daqing and Wang, Leye and Yang, Dingqi and Ma, Xiaojuan and Li, Shijian and Wu, Zhaohui and Pan, Gang and Nguyen, Thi-Mai-Trang and Jakubowicz, J\'{e}r\'{e}mie}, title = {Dynamic cluster-based over-demand prediction in bike sharing systems}, year = {2016}, isbn = {9781450344616}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, url = {https://doi.org/10.1145/2971648.2971652}, doi = {10.1145/2971648.2971652}, abstract = {Bike sharing is booming globally as a green transportation mode, but the occurrence of over-demand stations that have no bikes or docks available greatly affects user experiences. Directly predicting individual over-demand stations to carry out preventive measures is difficult, since the bike usage pattern of a station is highly dynamic and context dependent. In addition, the fact that bike usage pattern is affected not only by common contextual factors (e.g., time and weather) but also by opportunistic contextual factors (e.g., social and traffic events) poses a great challenge. To address these issues, we propose a dynamic cluster-based framework for over-demand prediction. Depending on the context, we construct a weighted correlation network to model the relationship among bike stations, and dynamically group neighboring stations with similar bike usage patterns into clusters. We then adopt Monte Carlo simulation to predict the over-demand probability of each cluster. Evaluation results using real-world data from New York City and Washington, D.C. show that our framework accurately predicts over-demand clusters and outperforms the baseline methods significantly.}, booktitle = {Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing}, pages = {841–852}, numpages = {12}, keywords = {urban data, over-demand prediction, bike sharing system}, location = {Heidelberg, Germany}, series = {UbiComp '16} }

Longbiao Chen, Daqing Zhang, Xiaojuan Ma, Leye Wang, Shijian Li, Zhaohui Wu, Gang Pan
Container port performance measurement and comparison leveraging ship GPS traces and maritime open data
TITS

bibtex

2015

Longbiao Chen, Daqing Zhang, Gang Pan, Xiaojuan Ma, Dingqi Yang, Kostadin Kushlev, Wangsheng Zhang, Shijian Li
Bike sharing station placement leveraging heterogeneous urban open data
UbiComp

bibtex

2014

Longbiao Chen, Daqing Zhang, Gang Pan, Leye Wang, Xiaojuan Ma, Chao Chen, Shijian Li
Container throughput estimation leveraging ship GPS traces and open data
UbiComp

bibtex