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.