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首页智能交通系统高级技术参考系列:第139卷
"Advanced Technologies for Intelligent Transportation Systems (ITS) 是一本深入探讨智能交通系统领域最新进展和技术的专业书籍,作为'Intelligent Systems Reference Library'系列的一部分。该系列旨在发布一套全面且易于理解的参考文献库,汇集了智能系统在工程、计算机科学、航空电子、商业、电子商务、环境、医疗保健、物理学和生命科学等多个领域的创新成果和发展动态。其目标是提供结构化的知识框架,使读者能够获取到理论、应用和设计方法方面的最新信息。 该书由Marco Picone、Stefano Busanelli、Michele Amoretti、Francesco Zanichelli和Gianluigi Ferrari等专家编撰,编辑团队包括Janusz Kacprzyk(波兰科学院)和Lakhmi C. Jain(澳大利亚堪培拉大学),他们共同确保了内容的权威性和前沿性。书中内容涵盖了广泛的学科交叉,不仅探讨了智能系统的核心理论,还详尽介绍了这些技术如何应用于实际交通系统的优化,如流量管理(traffic flow)、城市规划(HCM,即交通设施评价系统,用于评估道路设计对交通效率的影响)等。 通过阅读这本书,读者不仅可以了解到当前最先进的交通信息技术,还能掌握如何将这些技术融入到设计和决策过程中,以提高交通运输的效率、安全性和可持续性。无论是科研人员、工程师、政策制定者还是行业从业者,都能从中受益匪浅,获取宝贵的知识和洞见,推动智能交通系统的未来发展。"
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DFE Distance From Event
DGT Distributed Geographic Table
DHT Distributed Hash Table
DIFS Distributed InterFrame Space
DK DGT Kernel
DMH DGT Message Handler/Dispacher
DSRC Dedicated Short-Range Communications
DSSS Direct Sequence Spread Spectrum
EC European Commission
ECDA Enhanced Distributed Channel Access
ECU Engine Control Unit
EG Event Generator
EIFS Extended IFS
EMDV Emergency Message for Vehicular Environments
ERP Extended Rate PHY
ETSI European Telecommunications Standards Institute
EU European Union
FCC Federal Communications Commission
FCS Frame Control Sequence
FHSS Frequency Hopping Spread Spectrum
FHWA Federal Highway Administration
FNTP Fast Networking and Transport Layer protocol
FSAP Fast Service Advertisement Protocol
FTM Fluid Traffic Model
GB GeoBucket
GDP Gross Domestic Product
GM Geo-Bucket Manager
GP Global Position
GPA Global Positioning System
HCCA HCF Controlled Channel Access
HCF Hybrid Coordination Function
HR/DSSS High Rate Direct Sequence Spread Spectrum
I2V Infrastructure-to-Vehicle
IBSS Independent BSS
ICT Information Comm unication Technologies
IDM-LC Intelligent Driver Motion with Lane Changes
IF Irresponsible Forwarding
ITS Intelligent Transportation System
ITSC ITS Communication
IVC Inter-Vehicular Communication
IVCES In-Vehicle Communications and Entertainment System
LM Location Manager
LocHNESs Localizing and Handling Network Event System
LTE-A LTE Advanced
M2M Machine-to-Machine
xviii Acronyms
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MAC Medium Access Control
MAF Mass Air Flow
MANET Mobile Ad-hoc NETworks
MBMS Multimedia Broadcast Multicast Service
MCDS Minimum Connected Dominant Set
MIMO Multiple-Input Multiple-Output
ML Map Loader
MM Mobility Model
NHTSA National Highway Traffic Safety Administration
NPE Node Position Error
OBU On-Board Unit
OFDM Orthogonal Frequency Division Multiplexing
OIS On-board Infotainment System
P2P Peer-to-Peer
PAF Probability Assignment Function
PATH Partners for Advanced Transportation TecHnology
PCF Point Coordination Function
PDF Probability Density Function
PER Packet Error Rate
PMF Probability Mass Function
PP Probe Packet
PU Primary User
QoS Quality of Service
RB Resource Block
RE REachability
RITA Research and Innovative Technology Administration
RSU Road Side Unit
RTD Round-Trip Delay
RTS/CTS Ready-To-Send/Clear-To-Send
RTSM Real-Time Simulation Monitoring
SB Smart Broadcast
SDO Standards Development Organizations
SIFS Short Inter Frame Space
SL Sip2Peer Layer
SM Subscription Manager
SNR Signal to Noise Ratio
SS Switch Station
SSWE Switch Station Web Editor
TD Transmission Domain
TE Transmission Efficiency
TSF Timing Synchronization Function
TTL Time To Live
TXOP Transmission Opportunity
UDP User Data Protocol
UI User Interface
Acronyms xix
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UMB Urban Multihop Broadcast
USA United States of America
USDOT United States Department of Transportation
V2I Vehicle-to-Infrastructure
V2V Vehicle-to-Vehicle
V2X Vehicle-to-X
VSN Vehicular Sensor Network
WAVE Wireless Access in Vehicul ar Env ironments
WLAN Wireless Local Area Network
WPA WiFi Protected Access
WSA WAVE Service Advertisement
WSMP WAVE Short Message Protocol
X-NETAD Cross-Network Effective Traffic Alert Dissemination
xx Acronyms
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Chapter 1
Introduction
1.1 Principles and Challenges
IntelligentTransportation Systems(ITSs) promiseto hugelyimprovesafety, efficiency
and sustainability of our transportation system, by means of a massive adoption of
Information Communication Technologies (ICTs) [1–3]. Not surprisingly, in last
decades ITSs have attracted the worldwide interest of researchers, automotive com-
panies, and governments. In order to create an economically sustainable ITS ecosys-
tem, a large number of projects have been conducted by institutions from all around
the world [4]. For instance, the Advanced Safety Vehicle (ASV) program in Japan [5],
the IntelliDrive project in the United States [6], and, in Europe, the numerous projects
coordinated by the Car 2 Car Communications Consortium (C2C-CC) [7], strongly
supported by the European Commission [8] and by the European Telecommunica-
tions Standards Institute (ETSI) [9].
In the marketplace, ITSs boast a long series of success histories, carried out by
either car manufacturers (with active safety systems), toll road infrastructures oper-
ators (with Electronic Tolling Systems), insurance companies (with black boxes),
Internet companies (with traffic information systems). However, current ITS hard-
ware, software, and communication technologies are closed, i.e., unable to share
data and cooperate together. In other words, current ITS applications are imple-
mented as “silos”, thus yielding to equipment duplication and no data sharing. Such
a fragmented approach is typical of the first development phase of new technologies,
where innovation is driven by pioneers. Figure 1.1 illustrates some significant ITS
applications, implemented according to the stand-alone or not-cooperative approach.
Next years’ biggest challenge will be toachieve a Cooperative ITS (C-ITS) ecosys-
tem, where secured data are shared across several ITS applications developed by
independent actors, leveraging on a solid basis of international standards, as repre-
sented in Fig. 1.2. Such a C-ITS ecosystem would facilitate actions and decisions
that improve transportation safety, sustainability, efficiency and comfort beyond that
achievable by stand-alone ITS systems.
© Springer International Publishing Switzerland 2015
M. Picone et al., Advanced Technologies for Intelligent Transportation Systems,
Intelligent Systems Reference Library 139, DOI 10.1007/978-3-319-10668-7_1
1
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2 1 Introduction
Fig. 1.1 Stand alone ITS
Fig. 1.2 Cooperative ITS
Future integrated ITSs will exploit vehicles provided with sensorial, cognitive,
decision and communication functionalities. Therefore, such smart vehicles will be
able to perceive the surrounding environment, collecting both public-interest infor-
mation (e.g., air pollution measurements) or obtaining data for the autonomous real-
time management of the vehicle itself [10–13]. Current vehicles are equipped with a
large number of sensors—over 100, but it depends on the maker and model. Examples
are oxygen sensor, crankshaft and camshaft position sensors, mass air flow (MAF)
sensor, coolant temperature sensor, etc. All these sensors send data to the Engine
Control Units (ECUs), which are embedded systems that control one or more of the
electrical systems/subsystems of the vehicle. In a recent interview, Bill Ford, exec-
utive chairman of Ford Motor Company, observed that as computer involvement
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