1
Towards the Transportation Internet: Concepts, Architectures and
Requirements
Hui Li a,b, Yongquan Chen a,b, Bokui Chen b,c,d, Chong Wang c
a Shenzhen Institute of Artificial Intelligence and Robotics for Society, China
b Robotics and Intelligent Manufacturing & School of Science and Engineering, The Chinese University of Hong
Kong, Shenzhen, China
c Tsinghua Shenzhen International Graduate School, Tsinghua University, China
d Artificial Intelligence Research Center, Peng Cheng Laboratory, China
Abstract
The Internet has become a general network design paradigm. The Energy Internet is a
successful case applied in the energy field. Disruptive transportation changes have been driven
by the development of technologies such as cloud computing, 5G, AI, autonomous driving, and
mobility services. We propose the Transportation Internet by introducing the paradigm into the
transportation field. The Transportation Internet brings dynamics, efficiency, intelligence,
scalability, and openness to transportation. It also provides a valuable reference system for the
future transportation automation. By comparing with the internet, this paper draws out concepts,
proposes architectures and gives requirements of the Transportation Internet. A small-scale
verification has been carried out.
1. Introduction
Transportation is the infrastructure of human society. It allows people or things to move
from one location to another. Traffic accidents and congestion increase rapidly due to the
increase of vehicles. This gives a negative impact on the economy, environment and people's
quality of life. Kalra and Paddock (2016) mentioned that approximately 8 million traffic
accidents occur each year, resulting in 7 million injuries and approximately 1.3 million deaths.
According to the technical report of NHTSA (the National Highway Traffic Safety
Administration), 94% of road accidents are caused by human error. Traffic congestion wastes
about 90 billion hours, which have led to a 2% drop in GDP. In addition, Bagloee et al. (2016)
mentioned that the carbon emissions generated by automobiles are approximately 220 million
tons. Safer and more efficient transportation is urgently needed around the world.
With the maturity of technologies such as 5G, cloud computing, automation, and AI, it
brings opportunities to solve transportation problems. The European Commission’s Joint
Research Center believes that these technology drivers are shaping four major changes that have
started gaining momentum in the last decade and promise to disrupt the century-old mobility
concept in the future: automation, connectivity, decarburization and sharing. The first trend is
the change to vehicles. Based on autonomous driving and new energy technologies, Ciuffo et al.
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(2019) proposed that the human-oriented transportation system will be transformed into a
machine-oriented transportation system. The second is the change to the road. ERTRAC
Working Group (2019) updated and released “Connected Automated Driving Roadmap”. The
road infrastructure is based on intelligence and connectivity , supports the autonomous driving,
and realizes the automation of transportation. The third is the change to mobility services.
Mobility services can be accessed anytime and anywhere, and everyone can be a consumer or a
provider of mobility services. Adler et al. (2019) predicted by 2040, most cars will be electric,
connected and capable of autonomous driving, and potential ownership sharing is a necessary
prerequisite. The expected impact of these disruptive technologies and services may greatly
contribute to the realization of an efficient, safe, sustainable and inclusive transportation system
in the future. Coupled with data governance, infrastructure, network security and other factors,
the transportation changes will have a significant impact on society.
In order to respond to changes in the transportation system, we need to rethink the
architecture of the transportation system. This article proposes the Transportation Internet as a
solution for the future transportation. The content discussed includes basic concepts, core
architecture and key requirements.
2. Literature Review
Strawn G (2014) and Glowniak J (1998) summarized that the Internet (hereinafter referred
to as the Information Internet in order to distinguish the concept) is a packet switching network
developed by "ARPA" developed by Defense Advanced Research Projects Agency. Bob Kahn
invented the TCP protocol, and together with Vint Cerf invented the IP protocol, which
constituted the cornerstone of Information Internet. Routers transmit information between
multiple different networks through standardized communication protocols. These protocols
support routing, naming, and commonly used services on the Information Internet. The
Information Internet becomes a huge network infrastructure and open ecology based on the
automatic switching of IP packets. Typical internet applications include search, instant
messaging, e-commerce, cloud computing, etc.
After years of practice in the Information Internet, the problems in traditional routers have
been founded such as complex architecture, low flexibility, and difficult management.
McKeown N (2011) proposed the SDN architecture. Blial et al. (2016) proposed that through
the introduction of the OpenFlow interface, the router's control logic (control plane) is separated
from the underlying switch (data plane), which breaks the vertical integration of equipment.
Figure 1 shows the architecture of SDN. SDN realizes network programming through well-
defined programming interfaces, which simplify policy implementation and network
management. Based on SDN technology, Kim and Feamster (2013) found out the problems of
the current state-of-the-art network configuration and management mechanism, then they
introduced mechanisms to improve various aspects of network management. Sendra et al. (2017)
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proposed a routing protocol based on artificial intelligence, which selects the best data
transmission path according to the best standards and network status. Network function
virtualization (NFV) make network device functions no longer depend on dedicated hardware
based on virtualization technology. NFV can reduce expensive network equipment costs.
Besides, Kreutz et al. (2014) proposed that resources can be fully and flexibly shared to achieve
rapid development and deployment of network services. The network operating system (NOS)
is a novel paradigm of developing next-generation network management and operating platforms.
Lopez et al. (2014) thought that NOS not only surpasses the concept of SDN, but also constitutes
the basic promoter of NFV.
Figure 1: The Architecture of SDN (Fundation Open Networking 2012)
Success of the Information Internet has made it a general technological paradigm. On the
one hand, the technologies of the Information Internet extend in the information field from the
Desktop Internet to the Mobile Internet, the Internet of Things, the Internet of Vehicles, etc. On
the other hand, the technologies of the Information Internet extend to the outside of information
field. Hu et al. (2017) mentioned that the Energy Internet draws on the paradigm in the energy
field, forms a series of innovations such as energy routers, and becomes an effective solution for
complex energy systems.
Computers begin to be applied to transportation in the 1970s, which brings the beginning
of Intelligent Transportation Systems (ITS). An et al. (2011) summarized that ITS integrating
information and communication technologies provides a series of application subsystems, such
as electronic toll collection system (ETC), electronic police system, signal control system, traffic
information system, etc. Smart city transportation is a new stage in the development of intelligent
transportation. Torre-Bastida et al. (2018) mentioned that advanced technologies such as sensing
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technology, communication technology, cloud computing and automatic control technology are
integrated into each node of the transportation system to form an intelligent transportation pattern
of efficient management and control of transportation facilities. It can guarantee the driving
efficiency and safety in transportation and reduce the environmental pollution caused by traffic
congestion, which is the development direction of urban transportation. Zantalis et al. (2019)
believed that intelligent transportation includes route optimization, parking, traffic light, accident
prevention and detection, road anomalies and infrastructure applications. Rizwan et al. (2016)
proposed a low-cost real-time intelligent transportation management system, which quickly
acquires transportation data through the Internet of Things and sent it to the processor for big
data analysis, and provided solutions through predictive analysis. Javaid et al. (2018) proposed
an intelligent transportation management system based on the Internet of things, which can
intelligently change signal timing according to the traffic density at specific road sides, and
regulate traffic flow more effectively through communication with local servers. Pandit et al.
(2014) proposed a system that uses image processing technology to realize traffic signal control.
Sukhadiaetal.(2020)used artificial intelligence to manage intelligent transportation system and
realized automatic management and implementation, so as to deal with the travel scenarios of
major traffic problems. Ossman and Amer (2019) proposed an intelligent transportation
management system based on dynamic timing to control road traffic conditions more effectively.
Zhou et al. (2010) proposed the use of a wireless sensor network for adaptive control of traffic
light in intelligent transportation systems. Ibanez et al. (2015) proposed and discussed the
problems and challenges in the combination of intelligent transportation with cloud computing
and the Internet of Things.
According to the research by Gerla et al. (2014), it can be seen that the realization of smart
city transportation cannot be separated from the Information Internet, while the development of
the Internet of Vehicles technology can combine vehicles with the Information Internet and
promote the development of smart city transportation. On the whole, intelligent transportation
and smart city transportation focus on solving existing problems and lack of integrated solutions
for future transportation automation.
As an extension of the Internet of Things (IoT), the Internet of Vehicles (IoV) is a
communication system based on vehicles. Contreras-Castillo et al. (2017) summarized that the
Internet of Vehicles provided information services for vehicles through cellular networks such
as 3G/4G in the beginning. With the gradual maturity of V2X technology, the Internet of
Vehicles includes V2X technology. V2X is a cooperative ITS subsystem, which is called
DSRC/WAVE in IEEE, C-ITS in ETSI, and C-V2X (LTE-V2X/5G-V2X) in 3GPP. V2X
includes vehicle-to-vehicle (V2V), vehicle-to-infrastructure (V2I), vehicle-to-network (V2N),
vehicle-to-pedestrian equipment (V2P) and other communication modes. Through
communication and cooperation among participants, the Internet of Vehicles has greatly
changed the way of road travel and improved road safety and traffic efficiency. Typical vehicle
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networking scenarios can be classified into the following categories: collaborative perception,
collaborative sensing, collaborative manipulation, perception of vulnerable road users (VRU),
platooning driving, remote driving, etc. Currently, the Internet of Vehicles based on software
defined network (SDN) technology is seen as a key driver for the development of IoV in cost-
effective and flexible network management, as well as efficient resource orchestration. Bagloee
et al. (2016) proposed the architecture for big data analysis of real-time intelligent transportation
system in the IoV environment. Dimitrakopoulos G (2011) considered the connection between
vehicle-based networks and social networks was contributing to more efficient transportation.
Gasmi and Aliouat (2019) compared the differences between traditional VANET and IoV in
communication architecture and network technology. Zhang et al. (2017) introduced the
complete architecture of IoV, and introduces the layered architecture, protocol stack, network
model, challenges and future of IoV. Zhou et al. (2020) introduced the development of V2X
technology and demonstrates its application in IoV. Zhuang et al. (2019) proposed that
SDN/NFV technology can enhance the performance of IoV and support various scenarios and
applications of IoV. Salahuddin et al. (2014) proposed a roadside unit cloud (RSU) that supports
car networking, and its architecture is dynamically instantiated by using a software-defined
network (SDN). Lee and Park (2012) proposed a CVIC algorithm that does not need traffic
signals in the context of the Internet of Vehicles to realize the cooperation between vehicles and
infrastructure when all vehicles are fully automated, so as to achieve effective intersection
operation and management[33]. On the whole, the Internet of Vehicles is a communication
solution between vehicles and equipment rather than a solution to the transportation system.
Table1: The classification of referred literature
Literature review Information
Internet
Intelligent
Transportation
System
Internet
of
Vehicles
Topic
Strawn G (2014) √ History of
Information Internet
Glowniak J (1998) √ History of
Information Internet
Blial et al. (2016) √ Architecture of
Information Internet
Kim and Feamster
(2013)
√ SDN and network
management
Sendra et al. (2017) √ SDN and AI
Kreutz et al. (2014) √ NFV
Lopez et al. (2014) √ NOS
Hu et al. (2017) √ Energy Internet
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An et al. (2011) √ Overview of ITS
Torre-Bastida et al.
(2018)
√ Overview of ITS
Zantalis et al. (2019) √ Machine learning, IoT
and ITS
Rizwan et al. (2016) √ IoT, Big data and ITS
Javaid et al. (2018) √ IoT and ITS
Pandit et al. (2014) √ Image recognition and
ITS
Salahuddin et al.
(2014)
√ AI and ITS
Ossman and Amer
(2019)
√ Dynamic timing and
ITS
Zhou et al. (2010) √ Traffic light control in
ITS
Ibanez et al. (2015) √ √ ITS, cloud computing
and IoT
Gerla et al. (2014) √ Overview of IoV
Contreras-Castillo et
al. (2017)
√ Overview of IoV
Bagloee et al. (2016) √ √ Big data, ITS and IoV
Dimitrakopoulos G
(2011)
√ √ IoT and IoV
Gasmi and Aliouat
(2019)
√ √ Information Internet
and IoV
Zhang et al. (2017) √ The architecture of
IoV
Zhou et al. (2020) √ V2X and IoV
Zhuang et al. (2019) √ √ SDN, NFV and IoV
Salahuddin et al.
(2014)
√ √ SDN, RSU and IoV
Lee and Park (2012) √ Vehicle control and
IoV
3. Transportation Internet
Based on IP technology, the Information Internet has built a network infrastructure of
automatic switching and formed a huge open ecology and innovation environment. Packets are
transmitted over the communication network from host address A to host address B. The
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transportation network is similar to the Information Internet and vehicles are similar to IP packets
on the Internet. Vehicles are exchanged through the road network. A trip is a process of moving
vehicles from address A to address B. Therefore, the road network can be regarded as a switching
system of vehicles. We further draw on the concept of the Information Internet and put forward
the concept of the Transportation Internet to form a transportation system similar to the Internet.
Table 2 is a basic comparison of the concepts of the Information Internet and the Transportation
Internet.
Table2: Information Internet VS Transportation Internet
Information Internet Transportation Internet
Host Address:
1. The address information that describes
communication requirements
2. Host Address includes IP address, domain
name and other forms
T-Address (Transportation Address):
1. T-Address is the location information that
describes transportation requirements (such
as hospitals, logistics centers, parks, etc.)
2. The current format of transportation
address is string. The IP-like address can be
used as a unified form for the automatic
routing.
Information Network:
1. A system that consists of the host,
communication equipment and the
connections
2. The communication equipment that
includes switch and router equipment
T-Network (Transportation Network):
1. T-Network is a system consisting of origin,
destination, connecting roads and ancillary
equipment
2. At present, the equipment mainly includes
signal machines. T-Network can form
transportation routing equipment in the next
step.
Data Packets:
1. Packet is the object (IP packet) transmitted
in the information network, which is
forwarded based on the standardized message
format
T-Vehicle (Transportation Vehicle):
1. T-Vehicle includes all kinds of objects that
are transported in the transportation network,
including vehicles and pedestrians, etc., The
unified transportation technology is needed.
Figure 2 is the model based on concepts of the Transportation Internet:
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Figure 2: Model of the Transportation Internet
Then, we discuss each part of the Transportation Internet separately.
3.1. T-Address
The T-Address is a location used to indicate the origin and destination. Existing -
transportation addresses are typically stored as strings in a map database. Due to a large number
of addresses, diverse description languages, different string lengths, and multiple names, the
transportation address is inefficient to query, slow to update, and complicated to maintain, which
makes it difficult to adapt to the automatic routing requirements for automated vehicles. Due to
the similarity between transportation address and host address, we can refer to IP protocol and
introduce the Transportation Protocol (TP) technologies to describe it as the T-Address. Table 3
is the comparison of the host address and the T-Address.
Table3: Host Address VS T-Address
Host Address T-Address
IP Address:
1. IP address is the address that the host
communicates with the outside world based
on IP protocol.
2. IP addresses have both addressing and
addressing functions.
TP-Address (Transportation Protocol
Address):
1. TP-Address is used to indicate the location
of the origin and destination.
2. TP-Address can be encoded and addressed
through IP-like address technology.
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IP Port:
IP Port is used to distinguish different
application interfaces under the same IP
address.
TP-Port (Transportation Protocol Port):
TP-port is used to distinguish different
application interfaces under the same TP-
Address (corresponding to parking space,
house number, etc.).
DN (Domain Name):
1. DN is a name introduced for the readability
of IP address.
2. DN realizes the mapping of host domain
name and IP address through DNS service.
TP-DN (Transportation Protocol Domain
Name):
1. TP-DN is a name introduced for the
readability and uniqueness of TP-Address.
2. TP-DN realizes the mapping of
transportation domain name and TP-Address
through naming service.
3.2. T-Network
The T-Network is a network system composed of various roads, intersections, and their
auxiliary equipment, including stations, parking lots, and so on. Due to the similarity between
the transportation network and the information network, we can refer to the information network
to introduce T-Node and T-Connection to describe the T-Network and propose a new device
form of transportation router. Table 4 shows the comparison between the information network
and the transportation network.
Table4: Information Network VS T-Network
Information Network T-Network
Network Connection:
1. Network connection is mainly composed
of optical fiber and transmission equipment.
2. The transmission time of packets in the
network connection is almost negligible, and
the number of packets transmitted is limited
by bandwidth.
T-Connection (Transportation
Connection):
1. T-Connection is mainly composed of
various roads.
2. The driving time of vehicles on the T-
Connection needs to be considered. Traffic
flow is affected by the number of lanes,
natural environment and other factors.
Network Node:
1. Network nodes are mainly composed of
intersections of network connections.
Network nodes deploying information
routing equipment such as switches and
routers which are responsible for packet
T-Node (Transportation Node):
1. T-Node is mainly composed of various road
intersections (including special two-fork
intersections). T-Node deploys various
transportation routing equipment, which is
responsible for the passage of vehicles
between T-Connections.
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forwarding between multiple network
connections.
2. The main functions of information routing
equipment are as follows:
A. Collect network information: receiving IP
packets (communication destination address,
priority, etc.) and collecting router
information (routing information such as link
information and queue status).
B. Compute network routing: select the
appropriate transmission line according to
routing algorithm based on routing
information and IP packet header.
C. Control message forwarding: send IP
packets according to appropriate policies
according to the selected transmission line.
2. The main functions of transportation
routing equipment are as follows:
A. Collect transportation information: collect
vehicle information (speed, location, priority,
etc.) and road information (vehicle density,
traffic flow).
B. Calculate transportation routing: based on
road information and vehicle information,
select appropriate control and guidance
strategies according to path algorithm.
C. Control the passing vehicle: use the
transportation terminal device (signal light
devices, guidance devices, etc.) to display the
inducing information and use the wireless
network to send control information to the
vehicle.
3.2.1. Transportation Routing Equipment
The network nodes of the Information Internet are equipped with switches, routers and other
equipment. According to the complexity of T-Nodes, we divide them into similar equipment
forms. Table 5 is the comparison between the information routing equipment and the
transportation routing equipment.
Table 5: Information Routing Equipment VS Transportation Routing Equipment
Information Routing Equipment Transportation Routing Equipment
Switch:
Node devices that handle two- tier switching.
T-Switch (Transportation Switch ):
The equipment is responsible for controlling
the passage of simple nodes (such as the node
without signal light).
Router:
Node devices that handle three-tier switching.
T-Router (Transportation Router ):
The equipment is responsible for controlling
the passage of complex nodes (such as the
node with signal light).
For the sake of convenience, these two types of equipment are collectively known as the T-
Router without a special declaration.
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According to the research by Abdella et al. (2018), as it can be seen from the functions of
T-Node, the T-Router is the core of the Transportation Internet. Compared with information
routers, transportation routers have the following characteristics:
First, T-Router is required to monitor and control the -T-Network. T-Router through wired
or wireless communication, connects all transportation equipment (such as signal light, camera,
radar, etc.) nearby the T-Node, collects transportation status (such as the density of vehicles,
traffic events, etc.), and controls the transportation equipment (such as traffic signal, dynamic
speed limits, traffic induction, etc.).
Second, T-Router is required to monitor and control T-Vehicles. T-Router through via
wireless communication (e.g. 5G), connects to T-Vehicles nearby the T-Node. T-Router through
wireless communication collects the status of the vehicle (e.g. identification, position, speed,
throttle, brake, steering wheel, driving intention, event, etc.) and sends the control information
to the vehicle.
Third, T-Router needs to control T-Vehicles continuously. When a T-Vehicle moves from
the one control area of a T-Router to another, it needs to realize the control handover just like a
cellular network handover. Furthermore, drawing on the idea of Autonomous System (AS) of
the Information Internet, the regional management of T-Routers can be realized.
3.2.2. Transportation Routing Algorithm
One of the most important characteristics of an information router is its dynamic routing
capability and has formed mature routing algorithm. Because of the complexity of the T-Network,
T-Router can learn from the information router to form a transportation routing algorithm. Table
6 is the comparison between information routing algorithm and transportation routing algorithm.
Table 6: Information Routing Algorithm VS Transportation Routing Algorithm
Information Routing Algorithm Transportation Routing Algorithm
Static Routing Algorithm:
1. The static routing algorithm is pre-
configured by the administrator and does not
change with the network state.
2. Simple and efficient, but heavy manual
maintenance workload, unable to dynamically
adapt to dynamic network changes.
Static Routing Algorithm:
1. Static routing algorithm carries out path
planning and transportation control through
static data such as static map and signal
configuration.
2. The maintenance workload of static
routing is heavy, and it is difficult to adapt to
the dynamic changes of traffic congestion,
traffic accidents and vehicle failures. It is
also difficult to support automatic routing for
automated driving.
Dynamic Routing Algorithm: Dynamic Routing Algorithm:
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1. The routers collect real-time network status
information, automatically calculates it
according to the operation of the network
system, adjusts the routing strategy, and
selects the best path.
2. The routing algorithm integrates calculation
of multiple network characteristics, including
router hop count, network transmission cost,
bandwidth, delay, load, reliability, maximum
transmission unit, etc.
1. The router collects real-time transportation
dynamic information, automatically learns
and remembers network operation, and
automatically calculates optimal routing
strategy based on graph theory, game theory,
artificial intelligence etc. to realize automatic
transportation routing.
2. The transportation routing algorithm
integrates multiple metrics for calculation,
including hops (number of transportation
routers passing through), connection length
(road length), channel number (number of
lanes), speed, cost, load, etc.
According to dynamic routing algorithm, T-Router issues control strategies to
transportation infrastructure and vehicles to realize transportation routing and scheduling. The
main contents of the transportation control strategies are as follows:
(1) Basic transportation information: According to the collected data from the
transportation equipment and vehicles, T-Router issues the basic transportation data to the
vehicle, including meteorological data, road conditions, position of vehicles, traffic signal, traffic
events, etc., to provide driving assistance information for the vehicle. Typical scenarios include
severe weather warnings, safety warnings for vulnerable groups, intersection collision warnings,
adaptive signal control, etc.
(2) Dynamic map information: T-Router continues to update the map based on the
collected data from the transportation equipment and vehicles, and issues the updated map data
to relevant equipment and vehicles. Typical scenarios include dynamic map updating, dynamic
speed limits, etc.
(3) Dynamic perception information: T-Router collects perception data from vehicles and
roadside sensors. T-Router makes fusion analysis and issues the perception data to relevant
vehicles. Typical scenarios include cooperative perception, etc.
(4) Dynamic planning information: T-Router collects data (origination and destination,
driving intention, driving preference, etc.) from the transportation equipment and vehicles,
carries out traffic planning, route planning and driving planning based on dynamic routing
algorithms, then sends the planning data to the transportation equipment and vehicles in real time.
Typical scenarios include dynamic tidal lanes, cooperative vehicles merging and shunting, etc.
(5) Driving control information: According to the remote operation, T-Route carries out
the lateral and longitudinal control of the vehicle, and sends the instruction data to the vehicle.
Typical scenarios include remote driving.
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3.2.3. Transportation Quality of Services
Tomovic et al. (2014) mentioned that in terms of service quality, the Transportation Internet
is similar to the Information Internet in providing services based on the "best effort" model. For
some special needs, such as ambulances and fire trucks, the Transportation Internet also needs
to provide high-priority services. The Transportation Internet can learn from the quality of
services (QoS) model of the Information Internet to provide service guarantee. Table 7 shows
the comparison between information quality of services and transportation quality of services.
Table 7: Information Quality of Services VS Transportation Quality of Services
Information Quality of Services Transportation Quality of Services
BestEffort:
BestEffort is used to send messages as much
as possible without any guarantee of delay,
reliability, etc.
T-BestEffort (Transportation BestEffort):
T-Besteffort is used to transport as much as
possible without any guarantee of delay,
reliability, etc.
IntServ:
Integrated-Services (IntServ) is an end-to-end
QoS technology. Before sending a message,
the network should be confirmed has reserved
resources, including bandwidth, delay, etc.
T-IntServ (Transportation IntServ):
T-IntServ is an end-to-end QoS technology.
Before the vehicle leaves, T-Network should
be confirmed that resources have been
reserved.
DiffServ:
Distinguishing-Service (DiffServ) is a simple
classification of QoS requirements. Routers
implement different forwarding
characteristics according to different
categories.
T- DiffServ (Transportation DiffServ ):
T-DiffServ is a simple classification of
transportation QoS demand. T-Router
realizes different control characteristics
according to different categories.
3.3. T-Vehicle
T-Vehicle includes all kinds of means of transport running on the T-Network, including
vehicles, pedestrians and other objects. T-Vehicle is similar to data packet of the Information
Internet, but also have great differences. Table 8 shows the comparison of packets and vehicles.
Table 8: Data Packet VS T-Vehicle
Data Packet T-Vehicle
1. Data packet is transmitted in the
information network, including the source
address, destination address, priority and
other information.
1. T-Vehicle includes all kinds of vehicles and
pedestrians transmitted in the traffic network
include departure address, destination
address, priority and other information.
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2. Data packets can be copied, compressed
and discarded, and the transmission time
between nodes is negligible. IP packets have
the nature of passive routing.
2. T-Vehicle cannot be copied,
incompressible, or discarded, and may break
down or be damaged; the travel time and
process of the vehicle need to be considered;
vehicles have active routing capability.
3. T-Vehicle have identification (license
plate) and can be recognized and tracked.
3.4. Summary
As a new concept arising from the intersection of fields, the Transportation Internet can
smoothly transfer a large number of technologies and absorb the good genes from the
Information Internet. In addition to the automatic routing, the Transportation Internet can further
inherit the capacity of mass access, flexible architecture, metering and charging, network
operation, open ecology, etc. And it can also solve the existing transportation interface closure,
data isolation, integration difficulties, and other problems to support the future transportation
automation.
4. Architectures of Transportation Internet
Due to the similarities between the Transportation Internet and the Information Internet, the
Transportation Internet can learn from the latest technological achievements of the Information
Internet, such as SDN and NFV, and form its own advanced architecture.
4.1. SDT (Software Defined Transportation)
Software Defined Network (SDN) has become an emerging network architecture paradigm.
Combining with the function model of T-Router, we propose the software defined transportation
(SDT) architecture.
For convenience, SDT is divided into two parts: Software Defined Infrastructure (SDI) and
Software Defined Vehicle (SDV).
4.1.1. SDI (Software Defined Infrastructure)
The functions of transportation equipment include signal control, electronic police, traffic
induction, ETC, energy control, etc. Considering the similarities of the architecture, we take the
signal control function as an example to analyze SDI.
We refer to the OpenFlow and OpenAPI interfaces of SDN and introduce OpenTraffic and
OpenTAPI open interfaces to decouple the signal control part from signal machine to form two
equipment: signal controller and intelligent signal light. The signal controller is responsible for
generating signal control information and sending the control information to the intelligent signal
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light. The intelligent signal light reports the status to the signal controller and receives the control
information to display the light color. The control information is composed of signal cycles and
phases. This is similar to the flow table of the OpenFlow switch in the Information Internet.
Figure 3: Model of Signal Machine Decoupling
After decoupling the signal, the intelligent signal light is flexibly connected to the signal
controller through the IP network. As a software function, signal controllers can be deployed to
general-purpose servers far away, greatly reducing system cost and management complexity.
Several signal controllers can be further centralized deployed to realize regional cooperative
scheduling of signal controllers (e.g., green wave band).
Other transportation functions, such as electronic police, have the similar software-defined
architecture. The existing transportation equipment can be decoupled into two parts: T-
Controller (transportation controller) and T-Infrastructure (transportation infrastructure).
4.1.2. SDV (Software Defined Vehicle)
In Chapter 3.2, we have sorted out various control strategies issued by T-Routers to vehicles.
SDV includes two parts: one is the intelligent vehicle implemented based on software; the other
is the vehicle controller decoupled based on an open interface. Now SDV often refer to the
former part in industry. This paper mainly discusses the relationship between the two parts. After
the intelligent vehicle is started, relevant information such as the level and capability of
autonomous driving will be reported to the vehicle controller. During driving, the vehicle
continuously reports its status (such as position, speed, acceleration, turn signal, etc.) and
environmental perception data to the controller. According to the requirements of different
scenarios, the controller issues control strategies to the vehicle. The control information issued
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by the controller based on the wireless network (such as 5G) with low delay includes dynamic
map, traffic signal, path planning, driving control, etc.
Figure 4: Decoupling Model of Vehicles
Because of the different characteristics of vehicles, we separate the layer of vehicles and
call it the transport layer. The architecture of SDT combining SDI and SDV is as follows:
Figure 5: The Architecture of Software Defined Transportation
Table 9 shows the comparison between SDN in the Information Internet and SDT in the
Transportation Internet.
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Table 9: SDN VS SDT
SDN SDT
Infrastructure Layer:
1. It is mainly composed of SDN switches.
2. The switch device collects devices status
and network status (network topology,
transportation statistics, and network usage)
and send it to the controller.
3. The switch transmits packets according to
strategies issued by the controller.
Infrastructure Layer:
1. The layer is mainly composed of T-
Infrastructures such as intelligent signal
light, camera etc.
2. T-Infrastructure collects transportation
status data and reports to T-Controller.
3. T-Infrastructure receives control strategies
from T-Controllers and implement control
strategies.
Transport Layer (not required):
1. Since the router can directly read the IP
packet address and the IP packet is a passive
route, there is no need for an independent
transport layer.
Transport Layer:
1. The layer is mainly composed of T-
Vehicles.
2. T-Vehicle collects status data and then
sends T-Controllers.
3. T-Vehicle receives control strategies from
T-Controllers and executes control strategies.
Control Layer:
1. It is mainly composed of SDN controllers.
2. The controller receives the status data
reported by switches through the southward
interface and issue control strategies to
switches.
3. The controller realizes network
programming through the OpenFlow protocol
of the south-facing interface and provide
various service access points through
OpenAPI through the north-facing interface.
Control Layer:
1. The layer is mainly composed of T-
Controllers.
2. T-Controller receives the status data
reported by T-Infrastructures and T-Vehicles
through the southbound interface, and issues
control strategies to T-Infrastructures and T-
Vehicles.
3. T-Controller realizes transportation
programming through the south-facing
interface OpenTraffic and provide various
transportation service access points through
the north-facing interface OpenTAPI.
Application Layer:
1. It is composed of various SDN applications.
2. SDN applications access network services
through the Open API interface provided by
the control layer.
Application Layer:
1. T-App (Transportation Application) is
composed of various SDT applications.
2. T-App accesses various transportation
services provided by the controller by calling
the OpenTAPI interface.
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3. SDN application examples include dynamic
access control, server load balancing, network
virtualization, etc.
3. T-App includes: MaaS, logistics service,
etc. It is also called TaaS (Transportation as a
Service).
4.2. TFV (Transportation Function Virtualization)
Referring to the network function virtualization (NFV) technology, we propose the
transportation function virtualization (TFV) technology to realize the centralized deployment
of different types of transportation functions, the management and scheduling of transportation
resources. Based on SDT, the controller software can be centrally deployed to the server to
realize multi-function data sharing and collaborative scheduling and reduce deployment costs.
Table 10 compares NFV and TFV technologies.
Table 10: NFV VS TFV
NFV TFV
1. Based on SDN technology, the controller
does not rely on the special hardware
anymore.
2. Based on virtual network function (VNF)
technology, network functions are centralized
deployment and hardware resources are
shared.
3. Based on network Functional Orchestration
(NFVO), the orchestration and scheduling of
network services can be realized.
1. Based on SDT technology, the controller
does not depend on the special hardware
anymore.
2. Based on virtual transportation function
(VTF) technology, signal control, electronic
police, energy control, vehicle control and
other functions are centralized deployment
and hardware resources are shared.
3. Based on functional orchestration
technology, the orchestration and scheduling
of transportation services can be realized.
The Transportation Internet is a fusion of three kinds of systems: the transportation network,
the information network and the energy network. After the adoption of SDT technologies, due
to deployment requirements, the Transportation Internet Controller (T-Controller), the
Information Internet Controller (N-Controller) and the Energy Internet Controller (E-Controller)
can be further deployed centrally to form a larger range of scheduling and scheduling capabilities.
4.3. TOS (Transportation Operation System)
Network operating system (NOS) is a paradigm of network management and operation
platform built based on SDN and NFV. Referring to the research of Gude et al. (2008) and
Anadiotis et al. (2015), we also propose the Transportation Operating System (TOS) to
characterize the heterogeneity of transportation system, and use it to provide unified device
management, resource scheduling and programming interface.
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The proposed architecture of TOS is as follows:
Figure 6: The Architecture of Transportation Operating System
(1) Device layer: it includes all kinds of T-Infrastructure and vehicles, which are connected
to TOS platform through OpenTraffic interface to realize the plug and play of device and accept
TOS management and control. From a business perspective, a large number of non-OpenTraffic
devices will also allow access through other extended protocols.
(2) Platform layer: it includes device management, transportation function management,
resource management, and service API management. Based on centralized function and data,
various AI algorithms can be fully applied.
(3) Application layer: based on the abstraction provided by the platform, it includes various
applications through service API interface. Some typical applications include mobility as a
service, charge as a service, signal as a service, etc.
Another benefit of SDT is that controllers can be deployed in cloud computing. With the
help of cloud computing, SDT gathers transportation functions and data, and realizes
transportation analysis, prediction and scheduling based on algorithms such as artificial
intelligence, and further reduces costs. Cloud computing technology has matured to the point
where many commercial cloud computing services are available.
For some latency-critical tasks (such as autonomous driving, etc.), cloud computing delays
may be unacceptable. In this case, Zhuang et al. (2019) and Zhang et al. (2017) proposed that
some resources can be deployed in edge computing to realize flexible and efficient
Transportation Internet services.
20
By flexibly deploying transportation functions in cloud computing and edge computing to
meet various task requirements and balance the allocation of computing resources, TOS can
enhance its performance in terms of end-to-end quality of service assurance and service
customization. In order to take advantage of potential advantages and integrate cloud/edge
computing architecture, the platform of TOS is divided into two layers to form a distributed
system with the edge layer and the center layer.
Figure 7: The Architecture of Distributed Transportation Operating System
(1) Edge Sublayer: responsible for regional management and control, providing functions
including local transportation infrastructure and vehicles access, transportation function
virtualization (such as virtual signal control function, virtual vehicle function, etc.), as well as
local resource collaborative management and scheduling (such as lane level planning,
holographic intersections, etc.). For real-time requirements, edge layer are typically deployed on
edge cloud.
(2) Central Sublayer: responsible for the overall management and control, providing
transportation resource management and collaborative scheduling (such as transportation data
management, AI training, transportation prediction, etc.), transportation services (such as signal
21
service, toll service, vehicle service, etc.), as well as OpenTAPI. The central layer is typically
deployed on the central cloud.
5. Requirements of Transportation Internet
The Transportation Internet is similar to the Information Internet, so many similar
requirements can be introduced. Listing the entire list of requirements is beyond the scope of
this article. We give some of the most important requirements of the Transportation Internet.
5.1. Requirements of "Plug and Play" for Devices
An important factor for the success of the Information Internet is to support the free access
of a large number of PC, mobile phones and other devices, namely the "plug and play" of
devices. The United States has more than 6.3 million kilometers of roads. There are 64 million
kilometers of roads in the world. Globally, Alam et al. (2016) predicted that the number of
vehicles used for private and commercial purposes is expected to reach 2 billion by 2030. The
Transportation Internet also needs to support that all kinds of transportation facilities and
vehicles can access anytime and anywhere.
An open, standardized interface is fundamental to providing plug-and-play capabilities.
The TOS supports to be accessed by different OpenTraffic devices. First, the OpenTraffic
device establishes a secure channel. Then the security authentication capability of the device is
initiated to realize the binding and registration of the device identity. And then realize the
interoperability of the device. OpenTraffic devices share information and make the most of the
data through a secure, seamless, and transparent standard interface. Interoperable device is the
key to implement the Transportation Internet.
5.2. Requirements of Automatic Routing and Scheduling
The existing vehicle path planning algorithm is to find the appropriate path through the
prior static information such as road data in the map. But it cannot adapt to traffic changes
dynamically due to the heavy workload of map making, high maintenance cost, and long update
cycle.
The Transportation Internet has dynamic routing capability and can be applied to the
transportation network with large scale and complex changes. The T-Controller report the
information and routing protocol, collect the change information of road and intersection
(including topology change, node addition and deletion, transportation status, etc.), and
recalculate the routing algorithm, so as to obtain the latest "routing table" and support the
automatic transportation routing.
22
Routing prediction is another key capability of the T-Controller. The T-Controller has a full
amount of transportation data and can predict the future transportation development trend to a
certain extent through AI algorithm. The T-Controller uses parametric (statistical) or non-
parametric or hybrid methods to make automated transportation scheduling possible.
5.3. Requirements of Operation and Metering
The Transportation Internet forms an integrated transportation system of vehicles and
roads. Therefore, the Transportation Internet, like the Information Internet, can become an
operational business network. We need to construct the management plane of the
Transportation Internet and form a management platform with perfect functions.
The Information Internet can provide comprehensive real-time monitoring of the resources
used for different access users, and it can be measured in terms of the time, quantity and content
of the resources, so as to provide a foundation for the commercial operation of the Information
Internet. The Transportation Internet has a similar structure, which can build an econometric
model of transportation resources. Map each trip to the corresponding transportation resources.
The measurement of transportation resources includes the type of vehicles, route of travel and
time of passage. Typical costs include road maintenance, highway tolls, bridge tolls, tunnel tolls,
congestion charges, carbon emissions, etc. Through the monitoring of transportation devices and
vehicles, the transportation controller can obtain real-time information such as vehicle
identification, location, speed and emission, etc. Based on the measurement model, it can make
an accurate measurement for the use of transportation resources for a trip, so as to obtain the cost
of a trip.
In the Transportation Internet, the observability, controllability and predictability of trips
are three key aspects. Measurement is of great significance in the Transportation Internet. The
Transportation Internet can inform consumers of the current use of transportation resources, and
through the flexible price as a feedback mechanism, induce consumers to evaluate the travel cost,
adjust their travel tools, travel paths and travel time accordingly, so as to realize the control of
traffic flow. With consumers participating in peak load management, there is a way to ensure
optimal use of transportation resources. The powerful information collection and technical
analysis capability of the Transportation Internet can carry out load control accurately, and then
dynamically change the travel price to reflect the actual demand of the transportation market.
The Transportation Internet instructs consumers to prioritize travel and can monitor
transportation behavior in real time, effectively saving costs and creating a more economical
transportation market.
5.4. Requirements of Ecology for Open Application
Another important factor in the success of the Information Internet is the open application
ecosystem. Because the Transportation Internet has a similar architecture to the Information
23
Internet, the Transportation Internet has the natural openness and the integration ability of the
Information Internet.
Based on the openness of the Transportation Internet, transportation will be provided in the
form of on-demand services. Traditional logistics services, taxi services, bus services, as well as
emerging mobility services, etc. will form the transportation as a Service (TaaS) model. Typical
services include logistics as a service, mobility as a service (MaaS), charge as a service (CaaS),
energy management as a service (EMaaS), signal control as a service (SCaaS), etc. Any person
or organization may at any time provide transportation services as a producer or enjoy them as
a consumer. These services are based on a unified platform of the Transportation Internet to form
an open application ecology, and further integration with the Information Internet and the Energy
Internet ecology.
5.5. Requirements of System Trustworthiness
The Transportation Internet is closely related to human life, and even directly affects
human life safety. It is important to build a reliable system so that people can fully use this
technology without any worries. Trustworthiness includes reliability, security, privacy, etc.
(1) Reliability: it describes the performance of the Transportation Internet under normal
conditions. KPI such as bandwidth, wait time, and connectivity can be used to characterize the
reliability of the network. Service providers can sign SLA to prove their reliability. For instance,
the reliability of a vehicle connection shall be 99.9999%. Another example, many vehicle
services (such as autonomous driving) have strict delay requirements. URLLC technology in
5G can be used.
(2) Security: As Lu et al. (2019) mentioned in the paper, due to SDT paradigm, multiple
access and cloud integration, the Transportation Internet security is facing more and more
challenges. Therefore, security measures need to be taken at the terminal, edge and cloud
simultaneously. Terminal security to deal with the vulnerability of the vehicle network. Edge
security handles access security to prevent data transmission from being tampered with and
eavesdropped. Cloud security prevents unauthenticated users from breaking into the cloud and
prevents data stored in the cloud from being abused.
(3) Privacy: because the vehicle will generate a large number of sensitive data, and may
further transfer the data to the nearby peer vehicle or cloud, therefore, the Transportation
Internet needs to pay attention to how to deal with problems such as identity privacy and
location privacy, so that an attacker cannot link data to a specific user. Meet the requirements
of relevant regulations, such as GDPR of the European Union.
5.6. Requirements of Validation and Promotion
24
The development process of SDN shows that the technology forum can better improve and
promote the Transportation Internet through formulating the overall architecture, open interface
and other standards and specifications. Open community and industrial ecology is an effective
means to promote development of the Transportation Internet.
The Transportation Internet, as a new solution, has brought systematic changes to the
traditional transportation. It is necessary to conduct effective verification before scale
implementation. To ensure that the impact on the existing traffic is minimized and the smooth
evolution of the transportation system is realized. The verification methods include small-scale
functional verification and large-scale demonstration verification.
In addition, although this paper mainly describes the Transportation Internet based on road
transportation, in fact, due to the generality of the Internet model, it can be further promoted to
support other transportation forms, including rail, water, aviation, etc. More general
generalizations need to be studied.
6. Verifications and Demonstrations
A small-scale demonstration for the Transportation Internet is currently being carried out
at the Chinese University of Hong Kong's campus in Shenzhen. By installing cameras, radar,
signal lights and other sensors on the roadside and based on 5G network and edge computing,
an experimental system is built. The overall scheme is shown in Figure 8.
The system preliminarily realizes software defined signal machine, software defined
vehicle, transportation operation system, etc., and verified dynamic routing planning, changing
lane by consultation, perception beyond LOS, cooperative merging, cooperative signal control,
remote driving, and automated parking scenarios. The system deployment scheme is shown in
Figure 9, and the user interface is shown in Figure 10.
Figure 8: The Overall Scheme of the Transportation Internet
25
Figure 9: The System Deployment Scheme of the Transportation Internet
Figure 10: A Prototype GUI for the Transportation Operating System
It has been verified that the Transportation Internet system supports various application
scenarios of intelligent connected vehicles and intelligent roads. And the Transportation Internet
system has the online network operation capability similar to the Information Internet. Due to
26
the concentration of data and functions, it is friendly to AI and cloud computing, and can support
existing intelligent transportation applications, as well as future transportation automation. At
the same time, the verification shows that the whole system has good scalability and lower total
cost. In order to better verify the implementation, a more complete and larger scale system is
urgently needed.
7. Conclusions
The advent of autonomous driving, as well as the development requirements of future road
transportation, has brought some urgent challenges to transportation. This paper proposes basic
concepts, architectures and requirements of the Transportation Internet, and provides an
internet-type transportation solution. The Transportation Internet has broken the traditional
static closed transportation system and transformed into a dynamic, open and operable large-
scale distributed network system. Thus, it will bring all aspects of transformation for
transportation production, deployment, transport, management, operation and service, and
enhance customer experience, improve system utilization rate, reduce maintenance complexity,
and form an open transportation market.
This paper is just the beginning, more special research will be carried out. Based on the
paradigm of the Information Internet, as well as the progress of technologies such as 5G, AI
and cloud computing, the Transportation Internet has the conditions for success.
References
Nidhi Kalra, Susan M. Paddock (2016). Driving to safety: How many miles of driving would
it take to demonstrate autonomous vehicle reliability?. Transportation Research Part A:
Policy and Practice, 94, 182-193
Bagloee S A, Tavana M, Asadi M, et al. (2016). Autonomous vehicles: challenges,
opportunities, and future implications for transportation policies. Journal of modern
transportation, 24(4), 284-303.
Alonso Raposo M, Ciuffo B, Ardente F, et al. (2019). The future of road transport. Joint
Research Centre (Seville site).
ERTRAC Working Group (2019). Connected Automated Driving Roadmap. ERTRAC.
Adler M W, Peer S, Sinozic T (2019). Autonomous, connected, electric shared vehicles (ACES)
and public finance: An explorative analysis. Transportation Research Interdisciplinary
Perspectives, 2, 100038.
Strawn G (2014). Masterminds of the Arpanet. It Professional, 16(3), 66-68.
27
Glowniak J (1998). History, structure, and function of the Internet. Seminars in Nuclear
Medicine, 28(2): 135-144.
McKeown N (2011). How SDN will shape networking. Open Networking Summit.
Blial O, Ben Mamoun M, Benaini R (2016). An Overview on SDN Architectures with Multiple
Controllers. Journal of Computer Networks and Communications 2016.
Kim H, Feamster N (2013). Improving network management with software defined networking.
IEEE Communications Magazine, 51(2), 114-119.
Sendra S, Rego A, Lloret J, et al. (2017). Including artificial intelligence in a routing protocol
using software defined networks. 2017 IEEE International Conference on
Communications Workshops (ICC Workshops), 670-674.
Kreutz D, Ramos F M V, Verissimo P E, et al. (2014). Software-defined networking: A
comprehensive survey. Proceedings of the IEEE, 103(1): 14-76.
Lopez V, de Dios O G, Fuentes B, et al. (2014). Towards a network operating system. OFC
2014. IEEE, 1-3.
Fundation O N (2012). Software-Defined Networking: The New Norm for Networks.
Hu R, Wang W, Ma W, et al. (2017). Application of power electronic technology to energy
Internet. 2017 12th IEEE Conference on Industrial Electronics and Applications (ICIEA),
1343-1347.
An S, Lee B H, Shin D R. (2011). A survey of intelligent transportation systems. 2011 Third
International Conference on Computational Intelligence, Communication Systems and
Networks, 332-337.
Torre-Bastida AI, Del SerJ, LanaI, et al. (2018). Big Data for transportation and mobility: recent
advances, trends and challenges. IET Intelligent Transport Systems, 12(8): 742-755.
Zantalis F, Koulouras G, Karabetsos S, et al. (2019). A review of machine learning and IoT in
smart transportation. Future Internet, 11(4): 94.
Rizwan P, Suresh K, Babu M R. (2016). Real-time smart traffic management system for smart
cities by using Internet of Things and big data. 2016 international conference on emerging
technological trends (ICETT), 1-7.
Javaid S, Sufian A, Pervaiz S, et al. (2018). Smart traffic management system using Internet of
Things. 2018 20th International Conference on Advanced Communication Technology
(ICACT), 393-398.
28
Pandit V, Doshi J, Mehta D, et al. (2014). Smart traffic control system using image processing.
International Journal of Emerging Trends & Technology in Computer Science (IJETTCS),
2278-6856.
Sukhadia A, Upadhyay K, Gundeti M, et al. (2020). Optimization of smart traffic governance
system using artificial intelligence. Augmented Human Research, 5: 1-14.
Ossman R A, Amer M. (2019). Dynamic Timing Based Smart Traffic Management System for
Urban Cities. IECON 2019-45th Annual Conference of the IEEE Industrial Electronics
Society, 1: 5475-5479.
Zhou B, Cao J, Zeng X, et al. (2010). Adaptive traffic light control in wireless sensor network-
based intelligent transportation system. 2010 IEEE 72nd Vehicular technology
conference-fall, 1-5.
Ibanez J G, Zeadally S, Contreras-Castillo J. (2015). Integration challenges of intelligent
transportation systems with connected vehicle, cloud computing, and internet of things
technologies. IEEE Wireless Communications, 6(22): 122-128.
Gerla M, Lee E K, Pau G, et al. (2014). Internet of vehicles: From intelligent grid to autonomous
cars and vehicular clouds. 2014 IEEE world forum on internet of things (WF-IoT), 241-
246.
Contreras-Castillo J, Zeadally S, Guerrero-Ibanez J A. (2017). Internet of vehicles: architecture,
protocols, and security. IEEE internet of things Journal, 5(5): 3701-3709.
Dimitrakopoulos G. (2011). Intelligent transportation systems based on internet connected
vehicles: Fundamental research areas and challenges. 2011 11th International Conference
on ITS Telecommunications, 145-151.
Gasmi and Aliouat (2019). Vehicular Ad Hoc NETworks versus Internet of Vehicles-A
Comparative View. 2019 International Conference on Networking and Advanced Systems
(ICNAS), 1-6.
Zhang W, Zhang Z, Chao H C. (2017). Cooperative fog computing for dealing with big data in
the internet of vehicles: Architecture and hierarchical resource management. IEEE
Communications Magazine, 55(12): 60-67.
Zhou H, Xu W, Chen J, et al. (2020). Evolutionary V2X technologies toward the Internet of
vehicles: Challenges and opportunities. Proceedings of the IEEE, 108(2): 308-323.
Zhuang W, Ye Q, Lyu F, et al. (2019). SDN/NFV-empowered future IoV with enhanced
communication, computing, and caching. Proceedings of the IEEE, 108(2): 274-291.
Salahuddin M A, Al-Fuqaha A, Guizani M. (2014). Software-defined networking for rsu clouds
in support of the internet of vehicles. IEEE Internet of Things journal, 2(2): 133-144.
29
Lee J, Park B. (2012). Development and evaluation of a cooperative vehicle intersection control
algorithm under the connected vehicles environment. IEEE Transactions on Intelligent
Transportation Systems, 13(1): 81-90.
Abdella J, Shuaib K, Harous S. (2018). Energy Routing Algorithms for the Energy Internet.
2018 International Conference on Intelligent Systems (IS), 80-86.
Tomovic S, Prasad N, Radusinovic I. (2014). SDN control framework for QoS provisioning.
2014 22nd telecommunications forum Telfor (TELFOR), 111-114.
Gude N, Koponen T, Pettit J, et al. (2008). NOX: towards an operating system for networks.
ACM SIGCOMM Computer Communication Review, 38(3): 105-110.
Anadiotis A C G, Galluccio L, Milardo S, et al. (2015). Towards a software-defined Network
Operating System for the IoT. 2015 IEEE 2nd World Forum on Internet of Things (WF-
IoT), 579-584.
Zhang J and Letaief K B. (2015). Mobile edge intelligence and computing for the internet of
vehicles. Proceedings of the IEEE, 108(2): 246-261.
Alam M, Ferreira J, Fonseca J. (2016). Introduction to intelligent transportation systems.
Intelligent Transportation Systems, 1-17.
Lu R, Zhang L, Ni J, et al. (2019). 5G vehicle-to-everything services: Gearing up for security
and privacy. Proceedings of the IEEE, 108(2): 373-389.