Parallel Transportation Systems: Toward IoT-Enabled Smart Urban Traffic Control and Management

智能交通系统 先进的交通管理系统 计算机科学 智能决策支持系统 过程(计算) 分布式计算 智能代理 运输工程 系统工程 工程类 人工智能 操作系统
作者
Fenghua Zhu,Yisheng Lv,Yuanyuan Chen,Xiao Wang,Gang Xiong,Fei‐Yue Wang
出处
期刊:IEEE Transactions on Intelligent Transportation Systems [Institute of Electrical and Electronics Engineers]
卷期号:21 (10): 4063-4071 被引量:257
标识
DOI:10.1109/tits.2019.2934991
摘要

IoT-driven intelligent transportation systems (ITS) have great potential and capacity to make transportation systems efficient, safe, smart, reliable, and sustainable. The IoT provides the access and driving forces of seamlessly integrating transportation systems from the physical world to the virtual counterparts in the cyber world. In this paper, we present visions and works on integrating the artificial intelligent transportation systems and the real intelligent transportation systems to create and enhance “intelligence” of IoT-enabled ITS. With the increasing ubiquitous and deep sensing capacity of IoT-enabled ITS, we can quickly create artificial transportation systems equivalent to physical transportation systems in computers, and thus have parallel intelligent transportation systems, i.e. the real intelligent transportation systems and artificial intelligent transportation systems. The evolution process of transportation system is studied in the view of the parallel world. We can use a large number of long-term iterative simulation to predict and analyze the expected results of operations. Thus, truly effective and smart ITS can be planned, designed, built, operated and used. The foundation of the parallel intelligent transportation systems is based on the ACP theory, which is composed of artificial societies, computational experiments, and parallel execution. We also present some case studies to demonstrate the effectiveness of parallel transportation systems.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
更新
PDF的下载单位、IP信息已删除 (2025-6-4)

科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
刚刚
痛苦啊完成签到,获得积分10
1秒前
极品男大完成签到,获得积分10
1秒前
Akim应助土豆丝炒姜丝采纳,获得10
1秒前
栗栗栗子发布了新的文献求助10
2秒前
antinomy完成签到,获得积分10
2秒前
新洸发布了新的文献求助10
2秒前
lionel发布了新的文献求助10
3秒前
整齐的冰珍完成签到,获得积分10
3秒前
所所应助Zert采纳,获得10
4秒前
善学以致用应助SmallBamboo采纳,获得10
4秒前
玄易发布了新的文献求助10
5秒前
科研通AI6应助yiyi采纳,获得10
5秒前
xinlinwang发布了新的文献求助10
5秒前
5秒前
5秒前
NexusExplorer应助tf采纳,获得10
5秒前
7秒前
小恶于发布了新的文献求助10
7秒前
专注雨珍发布了新的文献求助30
7秒前
罗luoluo发布了新的文献求助20
7秒前
量子星尘发布了新的文献求助10
7秒前
deng完成签到 ,获得积分10
8秒前
8秒前
刘研完成签到,获得积分10
9秒前
祺号花店发布了新的文献求助10
9秒前
江鑫楷发布了新的文献求助10
10秒前
所所应助李小明采纳,获得30
10秒前
欢呼的初蓝完成签到,获得积分10
11秒前
11秒前
Luckqi6688发布了新的文献求助80
11秒前
刘研发布了新的文献求助10
11秒前
大脸猫完成签到 ,获得积分10
12秒前
12秒前
12秒前
专注雨珍完成签到,获得积分10
13秒前
华仔应助小手拉大手采纳,获得10
13秒前
dew发布了新的文献求助10
13秒前
14秒前
无极微光应助伶俐的黑猫采纳,获得20
14秒前
高分求助中
(应助此贴封号)【重要!!请各用户(尤其是新用户)详细阅读】【科研通的精品贴汇总】 10000
List of 1,091 Public Pension Profiles by Region 1001
On the application of advanced modeling tools to the SLB analysis in NuScale. Part I: TRACE/PARCS, TRACE/PANTHER and ATHLET/DYN3D 500
L-Arginine Encapsulated Mesoporous MCM-41 Nanoparticles: A Study on In Vitro Release as Well as Kinetics 500
Haematolymphoid Tumours (Part A and Part B, WHO Classification of Tumours, 5th Edition, Volume 11) 400
Virus-like particles empower RNAi for effective control of a Coleopteran pest 400
Unraveling the Causalities of Genetic Variations - Recent Advances in Cytogenetics 400
热门求助领域 (近24小时)
化学 材料科学 医学 生物 工程类 有机化学 生物化学 物理 纳米技术 计算机科学 内科学 化学工程 复合材料 物理化学 基因 遗传学 催化作用 冶金 量子力学 光电子学
热门帖子
关注 科研通微信公众号,转发送积分 5465271
求助须知:如何正确求助?哪些是违规求助? 4569649
关于积分的说明 14320326
捐赠科研通 4496051
什么是DOI,文献DOI怎么找? 2463064
邀请新用户注册赠送积分活动 1452084
关于科研通互助平台的介绍 1427253