User-Driven Cloud Transportation System for Smart Driving

ABSTRACT

Traffic congestion is a major problem worldwide affecting millions of peoples daily

activities. Intelligent transportation system (ITS) has emerged as an effective way to

improving the performance of transportation systems, aiming to relieve the congestion

and maximize total social benefit. Key challenges of its in recent years include the per-

vasive data collection, data security, privacy preserving, large volume data processing,

and intelligent analytics. These challenges lead to a revolution in ITS development by

leveraging the crowd sourcing scheme and cloud computing architecture. A user-driven

Cloud Transportation system (CTS) employs a scheme of user-driven crowd sourcing

to collect user data for traffic model construction and congestion prediction including

data collection, filtering, modeling, intelligent computation and publish.

Contents

1 INTRODUCTION 1

2 INTELLIGENT TRANSPORTATION SYSTEM (ITS) 2

2.1 Intelligent transport technologies . . . . . . . . . . . . . . . . . . . . . . 3

2.1.1 Wireless communications . . . . . . . . . . . . . . . . . . . . . . . 3

2.1.2 Computational technologies . . . . . . . . . . . . . . . . . . . . . 4

2.1.3 Floating car data/floating cellular data . . . . . . . . . . . . . . . 4

2.1.4 Sensing technologies . . . . . . . . . . . . . . . . . . . . . . . . . 6

2.1.5 Inductive loop detection . . . . . . . . . . . . . . . . . . . . . . . 6

2.1.6 Video vehicle detection . . . . . . . . . . . . . . . . . . . . . . . . 6

2.1.7 Bluetooth detection . . . . . . . . . . . . . . . . . . . . . . . . . . 7

2.2 Intelligent transport applications . . . . . . . . . . . . . . . . . . . . . . 7

2.2.1 Emergency vehicle notification systems . . . . . . . . . . . . . . . 7

2.2.2 Automatic road enforcement . . . . . . . . . . . . . . . . . . . . . 8

2.2.3 Variable speed limits . . . . . . . . . . . . . . . . . . . . . . . . . 9

2.3 Challenges . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10

3 USER-DRIVEN CLOUD TRANSPORTATION

SYSTEM 11

3.1 Cloud computing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

3.2 Application Scenario . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

3.3 Overview of User-Driven CTS . . . . . . . . . . . . . . . . . . . . . . . . 12

3.4 Core CTS Services . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15

4 SYSTEM DESIGN AND IMPLEMENTATION 17

4.1 System Architecture . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17

4.2 Prototype Implementation . . . . . . . . . . . . . . . . . . . . . . . . . . 23

4.3 Preliminary User Feedback . . . . . . . . . . . . . . . . . . . . . . . . . 24

5 CONCLUSION AND FUTURE WORK 25

References 26

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List of Figures

1 RFID E-ZPass reader by using vehicle re-identification method . . . . . . 5

2 Saw cut loop detectors for vehicle detection . . . . . . . . . . . . . . . . 7

3 Automatic speed enforcement gantry . . . . . . . . . . . . . . . . . . . . . 9

4 Variable speed limit sign in the United States . . . . . . . . . . . . . . . . 9

5 Application Scenario and Workflow . . . . . . . . . . . . . . . . . . . . . 12

6 User-Driven CTS Review . . . . . . . . . . . . . . . . . . . . . . . . . . 13

7 User-Driven CTS Structure . . . . . . . . . . . . . . . . . . . . . . . . . 14

8 User-Driven CTS Service Model . . . . . . . . . . . . . . . . . . . . . . . 15

9 CTS Server Architecture . . . . . . . . . . . . . . . . . . . . . . . . . . . 18

10 Components of IaaS Model . . . . . . . . . . . . . . . . . . . . . . . . . . 19

11 MapReduce Computing Model . . . . . . . . . . . . . . . . . . . . . . . . 19

12 System Interaction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22

13 Screenshot of Mobile Application Prototype . . . . . . . . . . . . . . . . . 23

14 Screenshot of Background Management System Prototype . . . . . . . . . 24

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User-Driven Cloud Transportation System for Smart Driving

1 INTRODUCTION

Intelligent transportation systems (ITS) are advanced applications which, without

embodying intelligence as such, aim to provide innovative services relating to different

modes of transport and traffic management and enable various users to be better in-

formed and make safer, more coordinated, and ’smarter’ use of transport networks.In

the developing world, the migration from rural to urbanized habitats has progressing

without major development in transportation. Intelligent transportation system (ITS)

has emerged as an effective way to improving the performance of transportation sys-

tems, aiming to relieve the congestion and maximize total social benefit. ITS Intelligent

Transport Systems is a generic term for the integrated application of communications,

control and information processing technologies to the transportation system. The re-

sultant benefits save lives, time, money, energy and the environment. The term ITS is

flexible and capable of being interpreted in a broad or narrow way. Transport telem-

atics is a term used in Europe for the group of technologies that support ITS. ITS

World Congress is a world-wide annual event to promote and showcase ITS technolo-

gies. ERTICO ITS Europe, ITS America and ITS Japan work closely together in the

preparation of the annual ITS World Congress and Exhibition attracting over 8,000

people. Each year the event takes place in a different region (Europe, Americas or Asia-

Pacific) Many of the proposed ITS systems also involve surveillance of the roadways..A

user-driven pattern of ITS based on cloud computing technology named user-driven

cloud transportation system (CTS) to enhance data collection capability. CTS operates

around users, in the least help of external data source, to calculate the gathered data

and provide real time guidance back to them. User data is processed to reconstruct the

traffic model, based on which the prediction algorithm can be performed to guide an

optimized route for each user and the hole transportation system. The central process-

ing system applies the cloud computing architecture to support mass data storage and

distributed computing from user cloud.

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2 INTELLIGENT TRANSPORTATION SYSTEM

(ITS)

ITS is a synergy of related technologies including communications network, sens-

ing equipment and computational computing models. The communication technologies

adopted in ITS include radio frequency, IEEE 802.11p WAVE, WiMAX, GSM and

3G. ITU-T are developing a set of standards named CALM (Continuous Air-interface

for Long- and Medium-range communications), promise to provide the underlying glue

to make all types of communication happen in ITS application. Sensing systems for

ITS are networks of vehicle- and infrastructure-based sensors. For example, inductive

loop detection can be placed in a roadbed to detect vehicles pass through. Traffic flow

measurement and automatic incident detection using video cameras is another form of

vehicle detection. Floating Car Data (FCD) is a method for collecting position and

speed data from vehicles traveling. FCD provides the advantages of less expensive

device, more coverage, less maintenance and high usability. ITS applications can be

classified into four primary categories:

Table 1: ITS Application Classification

ITS Application Category Specific Applications

Travel Information SystemsNavigation SystemReal-Time Traffic InformationPoints of Interest Information

Transportation Management SystemsTraffic Operation CenterTraffic Signal ControlAutomatic Road Enforcement

Transportation Pricing Systems

Electronic Toll CollectionElectronic Road PricingVariable Parking FeesVehicle-Miles Travelled Usage Fees

Public Transportation SystemsReal-Time Public Transit Information SystemAutomatic Vehicle LocationEmergency Vehicle Notification System

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2.1 Intelligent transport technologies

Recent governmental activity in the area of ITS specifically in the United States is

further motivated by an increasing focus on homeland security. Many of the proposed

ITS systems also involve surveillance of the roadways, which is a priority of homeland

security. Funding of many systems comes either directly through homeland security

organisations or with their approval. Further, ITS can play a role in the rapid mass

evacuation of people in urban centres after large casualty events such as a result of a

natural disaster or threat. Much of the infrastructure and planning involved with ITS

parallels the need for homeland security systems.

In the developing world, the migration from rural to urbanized habitats has progressed

differently. Many areas of the developing world have urbanised without significant mo-

torisation and the formation of suburbs. A small portion of the population can afford

automobiles, but the automobiles greatly increase congestion in these multimodal trans-

portation systems. They also produce considerable of air pollution, pose a significant

safety risk, and exacerbate feelings of inequities in the society. High-population den-

sity could be supported by a multimodal system of walking, bicycle transportation,

motorcycles, buses, and trains.

Other parts of the developing world, such as China, remain largely rural but are

rapidly urbanising and industrialising. In these areas a motorised infrastructure is being

developed alongside motorisation of the population. Great disparity of wealth means

that only a fraction of the population can motorise, and therefore the highly dense

multimodal transportation system for the poor is cross-cut by the highly motorised

transportation system for the rich. Intelligent transport systems vary in technologies

applied, from basic management systems such as car navigation, traffic signal control

systems, container management systems, variable message signs, automatic number

plate recognition or speed cameras to monitor applications, such as security CCTV

systems and to more advanced applications that integrate live data and feedback from

a number of other sources, such as parking guidance and information systems, weather

information, bridge de-icing (US deicing) systems, and the like.

2.1.1 Wireless communications

Various forms of wireless communications technologies have been proposed for intelli-

gent transportation systems. Radio modem communication on UHF and VHF frequen-

cies are widely used for short and long range communication within ITS. Short-range

communications of 350 m can be accomplished using IEEE 802.11 protocols, specifi-

cally WAVE or the Dedicated Short Range Communications standard being promoted

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by the Intelligent Transportation Society of America and the United States Department

of Transportation. Theoretically, the range of these protocols can be extended using

Mobile ad hoc networks or Mesh networking.

Longer range communications have been proposed using infrastructure networks such

as WiMAX (IEEE 802.16), Global System for Mobile Communications (GSM), or 3G.

Long-range communications using these methods are well established, but, unlike the

short-range protocols, these methods require extensive and very expensive infrastructure

deployment. There is lack of consensus as to what business model should support this

infrastructure. Auto Insurance companies have utilised ad hoc solutions to support

eCall and behavioural tracking functionalities in the form of Telematics 2.0.

2.1.2 Computational technologies

Recent advances in vehicle electronics have led to a move towards fewer, more capable

computer processors on a vehicle. A typical vehicle in the early 2000s would have be-

tween 20 and 100 individual networked microcontroller/Programmable logic controller

modules with non-real-time operating systems. The current trend is toward fewer, more

costly microprocessor modules with hardware memory management and Real-Time Op-

erating Systems. The new embedded system platforms allow for more sophisticated

software applications to be implemented, including model-based process control, artifi-

cial intelligence, and ubiquitous computing. Perhaps the most important of these for

Intelligent Transportation Systems is artificial intelligence.

2.1.3 Floating car data/floating cellular data

Floating car or probe data collection is a set of relatively low-cost methods for obtain-

ing travel time and speed data for vehicles travelling along streets, highways, motorways

(freeways), and other transport routes. Broadly speaking, three methods have been used

to obtain the raw data:

• Triangulation method

In developed countries a high proportion of cars contain one or more mobile

phones. The phones periodically transmit their presence information to the mobile

phone network, even when no voice connection is established. In the mid-2000s,

attempts were made to use mobile phones as anonymous traffic probes. As a car

moves, so does the signal of any mobile phones that are inside the vehicle. By

measuring and analysing network data using triangulation, pattern matching or

cell-sector statistics (in an anonymous format), the data was converted into traf-

fic flow information. With more congestion, there are more cars, more phones,

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and thus, more probes. In metropolitan areas, the distance between antennas is

shorter and in theory accuracy increases. An advantage of this method is that no

infrastructure needs to be built along the road, only the mobile phone network

is leveraged. But in practice the triangulation method can be complicated, es-

pecially in areas where the same mobile phone towers serve two or more parallel

routes (such as a motorway (freeway) with a frontage road, a motorway (freeway)

and a commuter rail line, two or more parallel streets, or a street that is also a

bus line). By the early 2010s, the popularity of the triangulation method was

declining

• Vehicle re-identification

Vehicle re-identification methods require sets of detectors mounted along the road.

In this technique, a unique serial number for a device in the vehicle is detected at

one location and then detected again (re-identified) further down the road. Travel

times and speed are calculated by comparing the time at which a specific device

is detected by pairs of sensors. This can be done using the MAC (Machine Access

Control) addresses from Bluetooth devices, or using the RFID serial numbers from

Electronic Toll Collection (ETC) transponders (also called ”toll tags”).

Figure 1: RFID E-ZPass reader by using vehicle re-identification method

• GPS based methods

An increasing number of vehicles are equipped with in-vehicle satnav/GPS (satel-

lite navigation) systems that have two-way communication with a traffic data

provider. Position readings from these vehicles are used to compute vehicle speeds.

Modern methods may not use dedicated hardware but instead Smartphone based

solutions using so called Telematics 2.0 approaches.

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2.1.4 Sensing technologies

Technological advances in telecommunications and information technology, coupled

with ultramodern/state-of-the-art microchip, RFID (Radio Frequency Identification),

and inexpensive intelligent beacon sensing technologies, have enhanced the technical

capabilities that will facilitate motorist safety benefits for intelligent transportation

systems globally. Sensing systems for ITS are vehicle- and infrastructure-based net-

worked systems, i.e., Intelligent vehicle technologies. Infrastructure sensors are inde-

structible (such as in-road reflectors) devices that are installed or embedded in the road

or surrounding the road (e.g., on buildings, posts, and signs), as required, and may be

manually disseminated during preventive road construction maintenance or by sensor

injection machinery for rapid deployment. Vehicle-sensing systems include deployment

of infrastructure-to-vehicle and vehicle-to-infrastructure electronic beacons for identifi-

cation communications and may also employ video automatic number plate recognition

or vehicle magnetic signature detection technologies at desired intervals to increase sus-

tained monitoring of vehicles operating in critical zones.

2.1.5 Inductive loop detection

Inductive loops can be placed in a roadbed to detect vehicles as they pass through

the loop’s magnetic field. The simplest detectors simply count the number of vehicles

during a unit of time (typically 60 seconds in the United States) that pass over the loop,

while more sophisticated sensors estimate the speed, length, and weight of vehicles and

the distance between them. Loops can be placed in a single lane or across multiple

lanes, and they work with very slow or stopped vehicles as well as vehicles moving at

high-speed.

2.1.6 Video vehicle detection

Traffic-flow measurement and automatic incident detection using video cameras is

another form of vehicle detection. Since video detection systems such as those used in

automatic number plate recognition do not involve installing any components directly

into the road surface or roadbed, this type of system is known as a ”non-intrusive”

method of traffic detection. Video from cameras is fed into processors that analyse the

changing characteristics of the video image as vehicles pass. The cameras are typically

mounted on poles or structures above or adjacent to the roadway. Most video detec-

tion systems require some initial configuration to ”teach” the processor the baseline

background image. This usually involves inputting known measurements such as the

distance between lane lines or the height of the camera above the roadway. A single

video detection processor can detect traffic simultaneously from one to eight cameras,

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depending on the brand and model. The typical output from a video detection system is

lane-by-lane vehicle speeds, counts, and lane occupancy readings. Some systems provide

additional outputs including gap, headway, stopped-vehicle detection, and wrong-way

vehicle alarms.

Figure 2: Saw cut loop detectors for vehicle detection

2.1.7 Bluetooth detection

Bluetooth is an accurate and inexpensive way to measure travel time and make ori-

gin/destination analysis. Bluetooth is a wireless standard used to communicate between

electronic devices like mobile/smart phones, headsets, navigation systems, computers

etc. Bluetooth road sensors are able to detect Bluetooth MAC addresses from Bluetooth

devices in passing vehicles. If these sensors are interconnected they are able to calculate

travel time and provide data for origin/destination matrices. Compared to other traffic

measurement technologies.

2.2 Intelligent transport applications

2.2.1 Emergency vehicle notification systems

The in-vehicle eCall is an emergency call generated either manually by the vehicle

occupants or automatically via activation of in-vehicle sensors after an accident. When

activated, the in-vehicle eCall device will establish an emergency call carrying both

voice and data directly to the nearest emergency point (normally the nearest E1-1-2

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Public-safety answering point, PSAP). The voice call enables the vehicle occupant to

communicate with the trained eCall operator. At the same time, a minimum set of data

will be sent to the eCall operator receiving the voice call.

The minimum set of data contains information about the incident, including time,

precise location, the direction the vehicle was traveling, and vehicle identification. The

pan-European eCall aims to be operative for all new type-approved vehicles as a stan-

dard option. Depending on the manufacturer of the eCall system, it could be mobile

phone based (Bluetooth connection to an in-vehicle interface), an integrated eCall de-

vice, or a functionality of a broader system like navigation, Telematics device, or tolling

device. eCall is expected to be offered, at earliest, by the end of 2010, pending stan-

dardization by the European Telecommunications Standards Institute and commitment

from large EU member states such as France and the United Kingdom.Congestion pric-

ing gantry at North Bridge Road, Singapore. The EC funded project SafeTRIP is

developing an open ITS system that will improve road safety and provide a resilient

communication through the use of S-band satellite communication. Such platform will

allow for greater coverage of the Emergency Call Service within the EU.

2.2.2 Automatic road enforcement

A traffic enforcement camera system, consisting of a camera and a vehicle-monitoring

device, is used to detect and identify vehicles disobeying a speed limit or some other

road legal requirement and automatically ticket offenders based on the license plate

number. Traffic tickets are sent by mail. Applications include:

• Speed cameras that identify vehicles traveling over the legal speed limit. Many

such devices use radar to detect a vehicle’s speed or electromagnetic loops buried

in each lane of the road.

• Red light cameras that detect vehicles that cross a stop line or designated stopping

place while a red traffic light is showing.

• Bus lane cameras that identify vehicles traveling in lanes reserved for buses. In

some jurisdictions, bus lanes can also be used by taxis or vehicles engaged in car

pooling.

• Level crossing cameras that identify vehicles crossing railways at grade illegally.

• Double white line cameras that identify vehicles crossing these lines.

• High-occupancy vehicle lane cameras that identify vehicles violating HOV require-

ments.

8


• Turn cameras at intersections where specific turns are prohibited on red. This

type of camera is mostly used in cities or heavy populated areas.

Figure 3: Automatic speed enforcement gantry

2.2.3 Variable speed limits

Recently some jurisdictions have begun experimenting with variable speed limits that

change with road congestion and other factors. Typically such speed limits only change

to decline during poor conditions, rather than being improved in good ones. One exam-

ple is on Britain’s M25 motorway, which circumnavigates London. On the most heavily

traveled 14-mile (23 km) section (junction 10 to 16) of the M25 variable speed limits

combined with automated enforcement have been in force since 1995. Initial results

indicated savings in journey times, smoother-flowing traffic, and a fall in the number of

accidents, so the implementation was made permanent in 1997. Further trials on the

M25 have been thus far proven inconclusive.

Figure 4: Variable speed limit sign in the United States

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2.3 Challenges

• Many of the proposed ITS systems focused on surveillance of the roadways. The

high cost and low mobility of monitoring equipment limit the deployment scale

and density. Data from auxiliary instruments such as traffic detectors also cannot

meet the requirements of more complex and accurate calculation and reduction.

• Pervasive data from transportation has not been collected. The vehicles, as the

main component of traffic flow, need to be monitored for their position, direction,

velocity and even destination. More comprehensive collection of information will

facilitate intelligent analytics.

• Large-scale of user leads to the requirements of enormous computing and storage

resources. Furthermore, It needs to apply the more intelligent prediction model

and approach to make full use of pervasive traffic data.

• User-driven Scheme: User-driven is a crowd sourcing process that involves out-

sourcing tasks to a distributed group of agent. In ITS, pervasive data can be

collected by user-driven architecture for traffic model construction and prediction.

• Context-aware Publish/Subscribe Mechanism: Publish Subscribe user informa-

tion collection mechanism based on SOA (Service-Oriented Architecture) gener-

ally provides a way for user information collection by perfectly connect the mobile

platform, center cloud processing system and background management system

• Cloud Computing infrastructure: Cloud computing is the delivery of computing

and storage capacity as a service to a community of end-recipients. With the

help of cloud computing platform, big data processing and complicated intelligent

analyses can be supported elastically. ITU-T anticipates the Cloud Computing

a next revolutionary technology and has formed a focus group on various cloud

computing researches.

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3 USER-DRIVEN CLOUD TRANSPORTATION

SYSTEM

3.1 Cloud computing

Cloud computing is internet-based computing in which large groups of remote servers

are networked to allow the centralized data storage, and online access to computer ser-

vices or resources. Clouds can be classified as public, private or hybrid.Cloud computing

relies on sharing of resources to achieve coherence and economies of scale, similar to a

utility (like the electricity grid) over a network. At the foundation of cloud computing

is the broader concept of converged infrastructure and shared services.

Cloud computing, or in simpler shorthand just ”the cloud”, also focuses on maximiz-

ing the effectiveness of the shared resources. Cloud resources are usually not only shared

by multiple users but are also dynamically reallocated per demand. This can work for

allocating resources to users. For example, a cloud computer facility that serves Euro-

pean users during European business hours with a specific application (e.g., email) may

reallocate the same resources to serve North American users during North America’s

business hours with a different application (e.g., a web server). This approach should

maximize the use of computing power thus reducing environmental damage as well since

less power, air conditioning, rackspace, etc. are required for a variety of functions. With

cloud computing, multiple users can access a single server to retrieve and update their

data without purchasing licenses for different applications.

The term ”moving to cloud” also refers to an organization moving away from a tra-

ditional CAPEX model (buy the dedicated hardware and depreciate it over a period

of time) to the OPEX model (use a shared cloud infrastructure and pay as one uses

it).Proponents claim that cloud computing allows companies to avoid upfront infras-

tructure costs, and focus on projects that differentiate their businesses instead of on in-

frastructure. Proponents also claim that cloud computing allows enterprises to get their

applications up and running faster, with improved manageability and less maintenance,

and enables IT to more rapidly adjust resources to meet fluctuating and unpredictable

business demand. Cloud providers typically use a ”pay as you go” model. This can

lead to unexpectedly high charges if administrators do not adapt to the cloud pric-

ing model.The present availability of high-capacity networks, low-cost computers and

storage devices as well as the widespread adoption of hardware virtualization, service-

oriented architecture, and autonomic and utility computing have led to a growth in

cloud computing.

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3.2 Application Scenario

Workflow of ITS application generally involves three phases, namely Route Decision,

Vehicle Travelling and Finish Driving.

Figure 5: Application Scenario and Workflow

Three groups of participants are involved in this workflow, including user (P1), vehicle

(P2) and system (P3). In the stage of Route Decision, user can select the destination

(S1) and send it to the server. The system receives the message (S2) and the user starts

to drive (S3). The vehicle uploads its information (S4) in the Vehicle Travelling stage.

The system receives the user data (S5) and predicts the traffic condition (S6). Results

will be push to the user automatically. User can decide whether to change his route

(S7). In the last stage, Finishing Driving, the vehicle, user and system finish their work

successively (S8-S10).

3.3 Overview of User-Driven CTS

Traditional ITS which relies on sensing devices such as CCTV cameras, infrared radar

detector and optical detectors is restricted by their mobility for a widely deployment

to collect pervasive traffic information and transportation system optimization. A user-

driven pattern of ITS based on cloud computing technology named user-driven cloud

transportation system (CTS) to enhance data collection capability. The user-driven

CTS has unique features in the following aspects:

• CTS operates around users, in the least help of external data source, to calculate

the gathered data and provide real time guidance back to them. From the systems

point of view, the users are treated as cloud services and discovered and invoked

using a cloud computing methodology.

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• User data is processed to reconstruct the traffic model, based on which the pre-

diction algorithm can be performed to guide an optimized route for each user and

the hole transportation system.

• The central processing system applies the cloud computing architecture to support

mass data storage and distributed computing from user cloud. Traffic data can

be naturally divided by area which perfectly fits the MapReduce pattern in cloud

computing applications.

Figure 6: User-Driven CTS Review

Figure 6 depicts a CTS application scenario when a driver uses the system to guide

his driving route. The user data including location and velocity are calculated using

the GPS receiver and positioning techniques. After that the mobile application sends

message to the cloud server for storage and computing. The communication between

server and user is implemented through the cellular network or other technology in-

cluding wireless network and WAVE. Each client needs to register in the system and

provides username and password for authentication. The server collects data, processes

them and reconstructs a traffic model. According to this traffic model, the server should

be able to send to each client personalized and dynamic information about traffic. The

information is consisted of average travel time to the desired destination and real-time

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route guidance about the optimal route to avoid the congestion. Additionally, other

information such as customized points of interest (POIs) are also sent and displayed on

the user’s device according to users habits.

The CTS is composed of three main components, namely cloud server, user cloud and

background management system. Figure 7 illustrates the user-driven CTS structure.

Figure 7: User-Driven CTS Structure

The cloud server is responsible for the receiving, processing, querying, pushing and

other functions on traffic information. Cloud server is a cluster of virtualized resources

providing services. The server listens and receives traffic data submitted by the mobile

agents, and audit the legality of contents. Data processing algorithm was adapted

to aggregate the data into distributed cloud database with specific standard, such as

position, time-stamp and event type. The cloud server calculates the results and pushes

them to the user with the information of POI, activities and user information. The

cloud server structure and service model will be discussed in detail in section IV. The

mobile agents in user cloud include smart phone and sensing device. The basic functions

of the mobile device is sending and receiving traffic data. Other related functions such

as map and information demonstration can be provided by mobile devices. Background

management system includes the management interface for central cloud server, which

implements the function of system performance monitoring, information querying, user

management, etc.

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3.4 Core CTS Services

The proposed CTS operates by means of interactions between its components in

providing services, as shown in Figure 8.

Figure 8: User-Driven CTS Service Model

Services implemented to support intelligent transportation include the following core

functions:

1. Service Discovery

Along with the popularity of the web services, how to discover the service quickly,

automatically and accurately is a key research issue. The Service Discovery (SD)

keeps an up-to-date database of services. This work is usually done in Cloud and

Grid systems by using data replication techniques. Although a lot of approaches

have been proposed to solve this problem like Enterprise Service Bus (ESB) and

Universal Description Discovery and Integration (UDDI), these approaches are

designed for restricted area or scale. Things are different and need further discuss

when the web services outbreaks in scale.

2. Routing Service

Routing Service (RS) is responsible for the computing of best route from current

position to the destination inputted by mobile agent. The RS receives messages

and replies the best route and also the time expected for driving with the Timing

Service (TS).

3. Timing Service

Timing Service (TS) is responsible for the computing of time expected to spend on

the giving route from the Routing Service (RS). The RS and TS will be recalled

when the user change route according to the suggestion replied by Prediction

Service (PS).

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4. Prediction Service

Prediction Service (PS) pushes message of real-time traffic situation and location-

specific alerts. The function is implemented with algorithm to fuse and analyze

the pervasive traffic data collected anonymously from user. PS is aware of the

road network topology and aims to optimize traffic flow on users perspective and

also on a macro scale.

5. Communication Service

A Communication Service (CS) is a service wrapper for message conveys be-

tween cloud server and vehicles mobile devices. CS uses the structure of pub-

lish/subscribe to push real-time message between server and user bi-directionally

according to the subscriptions of user-interested topic.

6. Other Services

Besides the core CTS services, system allows other types of services implemented

easily. Examples of additional services could include:

• POI Service: A service finding POI closest to the selected area or the current

position.

• Emergency Service: Vehicles could be registered to submit emergency signal

for a higher priority to pass through.

• Social Service: Social service maintains a social network in CTS allowing

user to share information between friends.

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4 SYSTEM DESIGN AND IMPLEMENTATION

4.1 System Architecture

Based on CTS model , a three layers structure of cloud application system is designed

and a prototype system has been implemented.

1. Infrastructure as a Service (IaaS)

In IaaS model, cloud providers offer storage resources, network resources and com-

puting resources. A unified source layer is constructed above the fabric layer with

the help of virtual machines and their monitoring system. IaaS clouds providers

supply these unified resources on demand for cloud storage and computing. Net-

work resources link the user cloud, central cloud server, background management

system and digital map to a whole system. Storage resources is provided for data

storage and management. The cloud server centralizes all kinds of traffic informa-

tion from the terminal and other system information. It uses the traditional SQL

database to store the system information such as registration, configuration and

social networks data. NoSQL database, a class of database management system

identified by its non-adherence to the widely used relational database management

system model, is applied for traffic data cloud storage . NoSQL is often highly

optimized for retrieve and append operations and often offer little functionality

beyond record storage (e.g. key-value stores) which is useful when working with

a huge quantity of data and the data’s nature does not require a relational model

for the data structure. Traffic data can be naturally divided by area which fits

the NoSQL and MapReduce pattern in cloud storage and computing. The system

components of IaaS model, as shown in Figure 9, include: Hadoop, a framework

for running applications on large clusters of commodity hardware, MapReduce,

a programming model for distributed large data sets processing on clusters of

computers, HBase, a non-relational, distributed database, Hive, a data warehouse

infrastructure built on top of Hadoop providing data summarization, query, and

analysis, Chukwa, a data collection system for monitoring large distributed sys-

tems, Pig, a platform for analyzing large data sets, ZooKeeper, a centralized

service for maintaining configuration information, naming, providing distributed

synchronization, and providing group services.Commodity hardware is a term for

affordable devices that are generally compatible with other such devices. In a pro-

cess called commodity computing or commodity cluster computing, these devices

are often networked to provide more processing power when those who own them

cannot afford to purchase more elaborate supercomputers, or want to maximize

savings in IT design.

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Vehicle velocity data is stored in key-value pair < V ehicleID, V elocity > after

pre-processing. A key-value pair represents a vehicles speed in a certain times-

tamp. Average velocity of each vehicle can be mapped into the corresponding

road velocity. Obviously, each task of the procedure is independent, only need

to ensure that data of a vehicle allocated into same processing job. Therefore,

MapReduce computing model is suitable for traffic data processing.

Figure 9: CTS Server Architecture

Figure 11 depicts the MapReduce computing model proposed. The model is

consist of two parts namely Map and Reduce. MapReduce master uses hash

function (Hash(V ehicleID)modM) to split data into M Map jobs. When map

worker received the job data, user-defined Map function will be called to calculate

the average velocity for each vehicle < V ehicleID, avg > and the mapping from

vehicle to corresponding RoadID into intermediate result in the collection of <

RoadID, avg >. Intermediate result will be copy and merge into R Reduce jobs

in < RoadID, [avg1, avg2, ...] > by the same hash function. Reduce function

will return the final result < RoadID, avg >.The algorithm of average velocity

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computing are described in Algorithm 1. Leveraging the MapReduce computing

framework, user only need to define a Map function and a Reduce function. Job

allocation and result collection will be implemented automatically. This high-

level abstraction allows user to develop parallel and distributed algorithm without

professional experience.

Figure 10: Components of IaaS Model

Figure 11: MapReduce Computing Model

2. Platform as a Service (PaaS)

In the PaaS model, system deliver a storage and computing service from IaaS

platform for higher level functions including web service framework, data access

object framework, Internet service and publish/subscribe mechanism. With PaaS

offers, the underlying computer and storage resources scale automatically to match

platform services demand.

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• Web Service Framework: Different functions are linked through well-defined

interface by SOA-based lightweight web service framework. The definition

of interface is independent of the hardware platforms, operating systems and

programming languages. This makes the interaction in system performs in a

unified and common way.

• Data Access Object Framework: A data access object (DAO) is an object that

provides an abstract interface to some type of database or other persistence

mechanism. By mapping application calls to the persistence layer, DAO pro-

vide some specific data operations without exposing details of the database.

This isolation supports the Single responsibility principle. It separates what

data accesses the application needs, in terms of domain-specific objects and

data types (the public interface of the DAO), from how these needs can be

satisfied with a specific DBMS, database schema, etc. (the implementation

of the DAO).ADO .NET entity framework is integrated to provide solution

of high-speed access on large amounts data while reducing the dependence

of the service layer to facilitate data migration and maintenance.

• Publish/Subscribe Mechanism:In software architecture, publishsubscribe is a

messaging pattern where senders of messages, called publishers, do not pro-

gram the messages to be sent directly to specific receivers, called subscribers.

Instead, published messages are characterized into classes, without knowl-

edge of what, if any, subscribers there may be. Similarly, subscribers express

interest in one or more classes, and only receive messages that are of interest,

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without knowledge of what, if any, publishers there are.This pattern provides

greater network scalability and a more dynamic network topology, with a re-

sulting decreased flexibility to modify the Publisher and its structure of the

data published.In the pub/sub model, subscribers typically receive only a

subset of the total messages published. The process of selecting messages for

reception and processing is called filtering. There are two common forms of

filtering: topic-based and content-based.

In a topic-based system, messages are published to ”topics” or named logical

channels. Subscribers in a topic-based system will receive all messages pub-

lished to the topics to which they subscribe, and all subscribers to a topic

will receive the same messages. The publisher is responsible for defining the

classes of messages to which subscribers can subscribe. In a content-based

system, messages are only delivered to a subscriber if the attributes or con-

tent of those messages match constraints defined by the subscriber. The

subscriber is responsible for classifying the messages.Some systems support

a hybrid of the two publishers post messages to a topic while subscribers

register content-based subscriptions to one or more topics.In many pub/sub

systems, publishers post messages to an intermediary message broker or event

bus, and subscribers register subscriptions with that broker, letting the broker

perform the filtering. The broker normally performs a store and forward func-

tion to route messages from publishers to subscribers. In addition, the broker

may prioritize messages in a queue before routing.Subscribers may register

for specific messages at build time, initialization time or runtime. In GUI

systems, subscribers can be coded to handle user commands (e.g., click of a

button), which corresponds to build time registration. Some frameworks and

software products use xml configuration files to register subscribers. These

configuration files are read at initialization time. The most sophisticated

alternative is when subscribers can be added or removed at runtime. This

latter approach is used, for example, in database triggers, mailing lists, and

RSS.Most Data Distribution Service (DDS) do not use a broker in the mid-

dle. Instead, each publisher and subscriber in the pub/sub system shares

meta-data about each other. The publisher and the subscribers cache this

information locally and route messages based on the discovery of each other

in the shared cognizance.

CTS provides solution to push message between central server and mobile

agent using MQTT protocol. MQTT is a publish/subscribe, extremely simple

and lightweight messaging protocol, designed for constrained devices and low-

bandwidth or unreliable networks. These principles make the protocol ideal

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of the emerging Internet of Things world of connected devices, and for mobile

applications where bandwidth and battery power are at a premium.

3. Software as a Service (SaaS)

In SaaS model, application software in the cloud and cloud users access the soft-

ware from cloud clients. The cloud users do not manage the cloud infrastructure

and platform on which the application is running. That makes a cloud application

different from other applications in its elasticity. SaaS model in CTS provides the

core business logic of applications including traffic, living, social and user infor-

mation services. All event information is packed in the form of JSON (JavaScript

Object Notation) string. Central server accepts the JSON message to response

according to the different business logic. It use a scene of information subscrip-

tion and push to describe the interaction flow of the system (Figure 12). Mobile

user calls the service of message broker Subscribe() to subscribe the topic inter-

ested. Message broker declares the interface and implements it with WebInvoke()

in cloud server when it receives the subscription. Cloud server uses readDB() and

writeDB() to interact with database for MapReduce calculation. CTS uses an

open source message broker Mosquitto to push message to mobile user. Manage-

ment personnel acquires background information through interface of database for

system maintenance.

Figure 12: System Interaction

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4.2 Prototype Implementation

An application prototype is implemented based the system design. The mobile ap-

plication is implemented on Android platform and the central server is built with .NET

framework on a private cloud structure. It presents a screenshot of mobile application

for prototype system in Figure 13. The interface of the Android application displays

current traffic conditions by different color mark. Green represents the traffic is smooth,

while yellow represents a slow traffic and red is congestion. The server will predict traffic

congested and feedback to the user in real-time also with the information about road

construction and accident.

Figure 13: Screenshot of Mobile Application Prototype

Figure 14 depicts a screenshot of background management system prototype. This

application is mainly focused on the traffic event showing, traffic data management and

auxiliary information management. The upper panel is able to mark events on the map

display. The lower panel is responsible for traffic data details display. Events before

audit is on the left side while audited data on the right panel.

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4.3 Preliminary User Feedback

This prototype is developed for smart driving. The Application implements the func-

tion of traffic date collection and real-time traffic information dissemination. Based on

the smart phone platform, it can provide user dynamic event display, parking slot in-

formation, social network and other functions which cannot be provided by traditional

GPS devices. The function of data collection, event audit, automatic aggregation, traffic

model construction and prediction are implemented in cloud central server. The appli-

cation still has many features to be improved, such as intelligent prediction algorithm,

automatic learning of user behaviour, traffic abnormality detection, etc. According

to the preliminary system prototype, user driven CTS is an effective model to inte-

grate crowd sourcing scheme, context-aware publish/subscribe mechanism and cloud

computing technology. CTS has theoretically solved the defects of sparse deployment

density, incomprehensive data collection and low data processing ability in traditional

ITS. Through underlying cloud platform interface package, CTS shields the complex be-

haviours of virtual resource management, distributed scheduling with easy-to-use web

service interface to upper layer user. The architecture takes advantage of the pervasive

user data to establish a precise traffic model, supporting the behavior of the traffic pre-

diction calculation. The application has a user-friendly and smooth GUI implemented

on Android platform. More future works of CTS remain to be extended.

Figure 14: Screenshot of Background Management System Prototype

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5 CONCLUSION AND FUTURE WORK

Intelligent transportation systems (ITS) are advanced applications which, without

embodying intelligence as such, aim to provide innovative services relating to different

modes of transport and traffic management and enable various users to be better in-

formed and make safer, more coordinated, and ’smarter’ use of transport networks. ITS

relies on sensing devices such as CCTV cameras, infrared radar detector and optical

detectors is restricted by their mobility for a widely deployment to collect pervasive

traffic information and transportation system optimization. A user- driven pattern of

ITS based on cloud computing technology named user-driven cloud transportation sys-

tem (CTS) to enhance data collection capability. It employs a scheme of user-driven

crowdsourcing to collect user data for traffic model construction and congestion pre-

diction which includes data collection, filtering, modeling,intelligent computation and

publish.In this paper its application scenario, system architecture, and core CTS services

model were described.

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References

[1] Meng Ma, Yu Huang, Chao-Hsien Chu, Ping Wang,User−Driven Cloud Trans-

portation System for Smart Driving,2012 IEEE 4th International Conference on

Cloud Computing Technology and Science.

[2] J.P. Zhang, F.Y. Wang, K.F. Wang, W.H. Lin, C. Chen , Data-driven Intelli-

gent Transportation Systems: A Survey,, IEEE Transactions on , vol.12, no.4,

pp.1624-1639, Dec. 2011.

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User-Driven Cloud Transportation System for Smart Driving

Engineering

Transcript of User-Driven Cloud Transportation System for Smart Driving