社会交通中的社会信号分析与感知

陈虹宇; 艾红; 王晓; 吕宜生; 陈圆圆; 王飞跃

doi:10.16383/j.aas.c200055

社会交通中的社会信号分析与感知

doi: 10.16383/j.aas.c200055 cstr: 32138.14.j.aas.c200055

陈虹宇^{1, 2,},
艾红^1,,
王晓^{2, 3,},
吕宜生^2,,
陈圆圆^2,,
王飞跃^{2, 3,}

1.
哈尔滨理工大学自动化学院哈尔滨 150080
2.
中国科学院自动化研究所复杂系统管理与控制国家重点实验室北京 100190
3.
青岛智能产业技术研究院青岛 266109

基金项目: 国家自然科学基金青年项目(61702519), 中国科协青年人才托举工程(2017QNRC001), 北京市自然科学基金项目(8172018)资助

详细信息

作者简介:
陈虹宇：中国科学院自动化研究所与哈尔滨理工大学自动化学院联合培养硕士研究生. 2017年获得长安大学电子控制与工程学院自动化专业学士学位. 主要研究方向为交通数据分析, 社会交通, 智能交通. E-mail: chenhongyu2017@ia.ac.cn

艾红：哈尔滨理工大学自动化学院教授. 2003年获得哈尔滨理工大学硕士学位. 主要研究方向为网络智能控制, 多传感器数据融合. E-mail: aihong@hrbust.edu.cn

王晓：中国科学院自动化研究所复杂系统管理与控制国家重点实验室副研究员. 2016年获得中国科学院大学社会计算专业博士学位. 主要研究方向为社会交通, 动态网群组织, 人工智能和社交网络分析. 本文通信作者. E-mail: x.wang@ia.ac.cn

吕宜生：中国科学院自动化研究所复杂系统管理与控制国家重点实验室副研究员. 主要研究方向为交通数据分析, 动态交通建模, 平行交通管理与控制系统. E-mail: yisheng.lv@ia.ac.cn

陈圆圆：中国科学院自动化研究所复杂系统管理与控制国家重点实验室助理研究员. 2018年获得中国科学院大学控制理论与控制工程专业博士学位. 主要研究方向为交通数据分析, 社会交通, 平行交通管理与控制系统. E-mail: yuanyuan.chen@ia.ac.cn

王飞跃：中国科学院自动化研究所复杂系统管理与控制国家重点实验室主任, 中国科学院大学中国经济与社会安全研究中心主任, 青岛智能产业技术研究院院长. 主要研究方向为平行系统的方法与应用, 社会计算, 平行智能以及知识自动化. E-mail: feiyue.wang@ia.ac.cn

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出版历程
- 收稿日期: 2020-02-05
- 录用日期: 2020-07-02
- 网络出版日期: 2021-06-10
- 刊出日期: 2021-06-10

Analysis and Perception of Social Signals in Social Transportation

CHEN Hong-Yu^{1, 2
,},
AI Hong^1
,,
WANG Xiao^{2, 3
,},
LV Yi-Sheng^2
,,
CHEN Yuan-Yuan^2
,,
WANG Fei-Yue^{2, 3
,}

1.
School of Automation, Harbin University of Science and Technology, Harbin 150080
2.
State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190
3.
Qingdao Academy of Intelligent Industries, Qingdao 266109

Funds: Supported by National Natural Science Foundation of China (61702519), the Young Elite Scientists Sponsorship Program of China Association of Science and Technology (2017QNRC001), and Beijing Natural Science Foundation (8172018)

More Information

Author Bio:
CHEN Hong-Yu　Master student at the Institute of Automation, Chinese Academy of Sciences, and the School of Automation, Harbin University of Science and Technology. She received her bachelor degree in automation from Chang＇ an University in 2017. Her research interest covers traffic data analysis, social transportation, and intelligent transportation

AI Hong　Professor at the School of Automation, Harbin University of Science and Technology. She received her master degree from Harbin University of Science and Technology in 2003. Her research interest covers intelligent network control and multi sensor data fusion

WANG Xiao　Associate professor at the State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences. She received her Ph.D. degree in social computing from University of Chinese Academy of Sciences in 2016. Her research interest covers social transportation, cyber movement organizations, artificial intelligence, and social network analysis. Corresponding author of this paper

LV Yi-Sheng　Associate professor at the State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences. His research interest covers traffic data analysis, dynamic traffic modeling, and parallel traffic management and control systems

CHEN Yuan-Yuan　Assistant professor at the State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences. He received his Ph.D. degree in control theory and control engineering from University of Chinese Academy of Sciences in 2018. His research interest covers traffic data analysis, social transportation, and parallel traffic management and control systems

WANG Fei-Yue　State specially appointed expert and director of the State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, director of China Economic and Social Security Research Center in University of Chinese Academy of Sciences, dean of Qingdao Academy of Intelligent Industries. His research interest covers methods and applications for parallel systems, social computing, parallel intelligence, and knowledge automation

摘要

摘要: 近年来, 智能便携设备和移动互联网的迅速发展促使网络空间中积累了海量的数据信息, 从而影响了众多领域的研究与发展. 本文针对在以社会信号为主的数据对交通领域的影响下产生的跨学科领域−社会交通, 从数据及相应技术方法和研究应用方面对物理和网络空间信息的感知、挖掘、分析与利用的研究成果进行综述, 并分析、总结与展望该领域的未来研究趋势.
- 社会交通 /
- 智能交通系统 /
- 平行系统 /
- ACP理论 /
- 跨媒体数据
Abstract: In recent years, the rapid development of smart portable devices and mobile Internet has led to the accumulation of massive data information in cyberspace, and has affected research and development in many fields. This paper focuses on an emerging interdisciplinary research field named social transportation, which derived from transportation under the impact of data based mainly on social signals. The perception, mining, analysis and utilization upon information of physical and cyber space in related work are introduced according to data, corresponding technical methods, and research applications. The research trends of social transportation is also analyzed and summarized.
- Social transportation /
- intelligent transportation system /
- parallel system /
- ACP theory /
- cross-media data

HTML全文

图 1 常规数据研究流程及相应典型技术方法

Fig. 1 Routine data research process and corresponding typical technical methods

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图 2 一种用于交通预测的GAN架构示例^[111]

Fig. 2 An example framework of the conditional GAN for traffic prediction^[111]

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图 3 已完成出租车需求量统计结果^[35]

Fig. 3 The statistical result of fulfilled taxi demands^[35]

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图 4 特大城市大数据的可视化融合^[24]

Fig. 4 Visual fusion of mega-city big data^[24]

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图 5 社会交通研究与应用的发展趋势及分类

Fig. 5 The development trend and classification of social transportation research and application

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图 6 基于社交媒体大数据的交通感知分析与可视化系统^[75]

Fig. 6 A traffic sensing and analyzing system using social media data^[75]

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图 7 社会交通领域研究的基本要素

Fig. 7 Basic elements of research in the field of social transportation

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表 1 影响城市交通状况变化的因素

Table 1 Factors affecting the change of urban traffic conditions

既定因素	非既定因素
天气	实时路况
节假日	交通舆情
体育赛事、文艺演出等大型集会活动	交通拥堵、事故等非既定交通事件
道路施工、管制等既定交通事件	其他突发特殊事件
交通方式及其运行安排	$\vdots $
环境、经济、土地、人口等相关信息
$\vdots $

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表 2 可用真实数据的类别、来源、类型、信息及研究实例

Table 2 The categories, sources, data types, information and examples of available data

类别	来源		主要数据类型及信息	典型研究实例
物理空间	传感设备、浮动车和移动通讯终端以及共享单车、公交巴士和地铁等媒介		1) 数值: 日期、时间、 GPS定位、速度、加速度等	文献 [8, 10-11, 13-14, 17-19, 21, 23-35]
			2) 文本: 方向、起点、终点、地址、站点、车牌号码、基站编号等	文献 [17, 21-23, 25-27]
			3) 图像/视频/语音: 图像、视频等	文献 [18]
网络空间	非社交类	在线地图服务提供商、签到网站、政府部门和公共场所或科研组织机构等官方机构或组织的公开信息发布网站	1) 数值: 时间、气温、速度、流量、GPS定位等	文献 [19, 21, 31, 33-43]
			2) 文本: 地址、站点、事件类型及描述、天气情况等	文献 [19, 21-22, 28-30, 32, 38-39, 41, 45-50]
			3) 图像/视频/语音: 图像、视频等	文献 [12, 27]
	社交类	Twitter、微博、贴吧、论坛和出行服务等媒介	1) 数值: 时间、GPS定位、手机号码等	文献 [12, 15, 18, 40, 51-61]
			2) 文本: 用户名称、出行方式、地址、事件及描述、评论、天气情况等	文献 [1, 4, 12, 24, 28, 31, 36, 38-39, 41-43, 48-50, 52-58, 60, 62-78]
			3) 图像/视频/语音: 图像、视频等	文献 [79]

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