road cell report

8/8/2019 Road Cell Report

1/51

ftw. Technical Report

FTW-TR-2009-003Printed March 17th, 2009

Road Traffic information fromCellular Network Signaling

Danilo Valerio

ftw. Forschungszentrum Telekommunikation WienDonaucitystrasse 1, 1220 Vienna, AUSTRIA


2/51

Abstract

Abstract

In this report we address the problem of using cellular network signaling for inferringreal-time road traffic information. We survey and categorize the approaches that havebeen proposed in the literature for a cellular-based road monitoring system and identifyadvantages and limitations. We outline a unified framework that encompasses UMTS andGPRS data collection in addition to GSM, and prospectively combines passive and active

monitoring techniques. We identify the main research challenges that must be faced indesigning and implementing such an intelligent road traffic estimation system via third-generation cellular networks. Finally, by analyzing real UMTS signaling traces, we showhow collecting and processing mobility signaling from cellular networks can improve aroad traffic detection system and pave the way to an Intelligent Traffic Management.

This work has been supported by the Telecommunications Research Center Vienna (ftw.).

Ftw is supported by the Austrian Government and by the City of Vienna within thecompetence center program COMET.

2 ftw. - Forschungszentrum Telekommunikation Wien


3/51

CONTENTS

Contents

1 Introduction 3

2 UMTS basics 6

2.1 Architecture ................................................................................... 6

2.2 Protocol stack ................................................................................ 8

2.3 Location concepts............................................................................ 9

2.4 CN Mobility Management ....... ........ .......... ........ ......... ......... ......... ..... 11

2.4.1 Circuit Switched Mobility Management (MM) .... .... .... .... ... .... .... .. 12

2.4.2 Packet Switched Mobility Management (PMM).. .... .... .... ... .... .... ... 13

2.4.3 GPRS Mobility Management (GMM) ............ .......... ........ ......... . 13

3 Road monitoring via cellular systems 15

3.1 Active techniques ....... .......... ........ ......... ......... ......... ......... ........ ....... 16

3.2 Passive techniques ........ ......... ........ .......... ........ ......... ......... ......... ..... 17

4 Extended monitoring framework 19

4.1 Motivation ..................................................................................... 19

4.2 Architecture ................................................................................... 20

4.3 Event types.................................................................................... 21

4.3.1 Events from the Gb interface ............ ........ .......... ........ ......... .... 21

4.3.2 Events from the IuCS interface ....... ........ .......... ......... ........ ....... 224.3.3 Events from the IuPS interface ......... ........ ......... .......... ......... .... 23

ftw. - Forschungszentrum Telekommunikation Wien 1


4/51

CONTENTS

4.4 Geographical information ......... ........ ......... .......... ....... .......... ........ ..... 23

4.4.1 C# application ........ ......... ......... ......... ........ .......... ......... ....... 23

4.4.2 TEMS files ........................................................................... 25

5 Preliminary results 26

5.1 Generic statistics........ ........ .......... ........ ......... ........ .......... ......... ....... 26

5.2 Opposite traffic flows ......... ......... ........ .......... ......... .......... ......... ....... 28

5.3 Railways........................................................................................ 29

5.4 Why UMTS helps ........................................................................... 30

5.5 Accidents....................................................................................... 31

6 Research challenges and future works 33

6.1 Single out road users. ......... ......... ........ .......... ......... .......... ......... ....... 33

6.2 Road condition estimation............ .......... ......... ........ .......... ......... ....... 34

6.3 Smart integration of active and passive techniques ....... ... .... .... .... .... ... .... 34

7 Privacy issues 36

8 Conclusions 38

A Related projects and commercial products 39

A.1 CAPITAL...................................................................................... 40

A.2 STRIP .......................................................................................... 41

A.3 OPTIS .......................................................................................... 42

A.4 Do-iT ........................................................................................... 43

A.5 Traffic.online ......... ........ ......... ........ .......... ......... ........ ......... ........ .... 44

A.6 Cellint Trafficsense ........ ......... ......... ........ .......... ....... .......... ........ ..... 45

A.7 Estimotion CFVDTM........................................................................ 46

A.8 TomTom RoDIN24 ....... .......... ........ ......... .......... ....... .......... ........ ..... 47

References 48



5/51

CHAPTER 1. INTRODUCTION

Chapter 1

Introduction

Providing real-time road traffic information to road users has become the natural nextstep in traffic telematic after the proliferation of navigation system equipments. Theknowledge of the user location only does not allow navigation equipments to estimatejourney durations and calculate the best routes by considering the current road trafficconditions. In order to provide such a service to the road users, GPS technology needs tobe combined with other systems for the collection, the processing, and the distributionof road status data to the end users. Currently, data collection is done by road operatorsby using road sensors, cctvs, and emergency calls from road users (see Figure 1.1). Datais then processed in traffic control centers and forwarded to third party entities for thefinal dissemination to the road users via FM radio or other communication means. Thisapproach presents a cost hurdle: a complete coverage of the road network would not bepossible without the employment of new infrastructure.

CCTVs

Road Operator

Traffic Control Center

Broadcaster

Authorities

Variable signs

Sensors

Figure 1.1: Typical system for road traffic data collection

Driven by the fact that each road user on a car is also a potential user of a cellularnetwork, it is natural to consider mobile operators as an alternative source of road trafficinformation. The use of cellular networks for estimating patterns of human mobility

within a country has been subject of several studies in the past decade (e.g. [1]). Onlyrecently, the increased network coverage, the refinement of positioning methods, and a



6/51

CHAPTER 1. INTRODUCTION

full market penetration of cellular technologies led to the idea of using mobile networks tomonitor the road traffic. A feasible scheme would let cellular operators grab a potentialshare of market by either selling this service to navigation system equipment vendorsand/or road operators, or providing this added value service to their customers.

Sensors,

CCTV

Nodes B

Road OperatorTraffic Control Center

Cellular users

Road Operator

GPS vendors

Cellular

Road user = Potential cellular users

Figure 1.2: Road traffic data collection from cellular systems

Informing the users about the real-time road traffic condition would inherently optimizethe traffic flows and reduce the congestion events. The ultimate advantage is twofold.First, the environmental impact of a fluent traffic, in terms of emitted CO2, is lower thanin a stop-start traffic pattern, i.e. congestion [2]. Second, the fuel consumption and thetime spent on cars would decrease, paving the way for a better quality of life.

In this work we make the following contribution. First, we present a survey of existingapproaches for inferring road traffic condition by using cellular network signaling. Wesummarize and categorize research works presented in literature and cite the main activ-

ities that have been conducted by industries and public institutions. Second, we reviewthe existing approaches and identify the main problems and limitations in the light ofthe ongoing evolution toward third and fourth generation cellular networks. Finally, wepropose a novel framework that extends the existing monitoring systems to UMTS andGPRS data and combines passive and active monitoring techniques, highlighting the mainresearch challenges. We start to explore the collected data and show the high potentialityof such a system in the context of road traffic management.

The remainder of this report is organized as follows: in chapter 2 we provide a shortintroduction to the Universal Mobile Telecommunication System (UMTS). This is farfrom being a comprehensive overview of the technology. We rather focus on some aspects

relevant for our work. In chapter 3 we categorize the existing approaches for road trafficmonitoring via cellular networks and describe their main advantages and limitations.



7/51

Several test-fields, simulations, and projects are cited and described in the context ofboth data collection and data processing. Chapter 4 describes our framework and thetechnical aspects in the development of a road monitoring system via cellular network.In chapter 5 we present some statistics derived by the collected traces. In chapter 6 a

research agenda is presented and the main challenges are identified. Chapter 7 deals withthe privacy issues that could be arisen against such a system and shows how users privacyis effectively protected. Finally, in chapter 8 the main conclusions are drawn.



8/51


9/51

2.1. ARCHITECTURE

domains, i.e. the Circuit Switched (CS) and the Packet Switched (PS) domains. Differententities are connected with each other by means of interfaces (also called referencepoints). This allows the upgrade of specific components without affecting the rest ofthe network. From the protocol point of view, the network is divided into two strata.

The Access Stratum (AS) comprises all the protocols for handling the communicationbetween UE and RAN domain, while Non-Access Stratum (NAS) protocols are used forcommunications UE-CN (both PS and CS).

An overview of a UMTS network structure is presented in Figure 2.1 containing themost important nodes and interfaces. The picture highlights again the clear separationbetween RAN and CN. Starting from the bottom, the first entity is the UE. This can be acombination of (U)SIM and 2G or 3G phone, handheld, PDA, laptop, etc. In case of a 2Gequipment, the radio connection is established towards the GSM/EDGE Radio AccessNetwork (GERAN). The user terminal communicates with a Base Transceiver Station

(BTS), which in turn is connected through the Abis interface to a Base Station Controller(BSC). One BSC and all the BTS connected to it form a Base Station Subsystem (BSS). Incase of 3G UE, the radio connection can also be established toward the UMTS TerrestrialRadio Access Network (UTRAN). The UE communicates with one or more Nodes B,which in turn are connected to a Radio Network Controller (RNC) via the Iub interface.A group of Nodes B and the RNC to which they are connected form a Radio NetworkSubsystem (RNS). Different RNSs can be connected with each other by means of the Iurinterface.

Both GERAN and UTRAN are connected to the CN, via A/Gb and IuCS/IuPS interfacesrespectively. On the left, the Mobile serving Switching Center and Visitor Location

RNC

SGSN

GGSN

BSC

MSC/VLR

GMSC

IubAbis

Gb/IuPSIuCSA/IuCS

RNSBSS

Gn

IuPS

IurRNC

Core Network

Radio Access Network

Gs

BTS BTS Node B Node B

Figure 2.1: UMTS network overview



10/51

CHAPTER 2. UMTS BASICS

Register (MSC/VLR) is the switch (MSC) and the database (VLR) serving the terminal inits current location for CS services. The Gateway MSC (GMSC) is the MSC connectingthe network to external CS networks. On the right, the Serving GPRS Support Node(SGSN) is the counterpart of the MSC/VLR for the PS domain, while the Gateway

GPRS Support Node (GGSN) connects the network to external IP networks.

2.2 Protocol stack

Figure 2.2 depicts a simplified overview of the UMTS protocol stack. While the previoussection outlined the clear distinction between RAN and CN, from the protocols pointof view a distinction can be made between Access Stratum and Non-Access Stratum

protocols. The AS includes radio access protocols between the UE and the UTRAN. TheNAS includes CN protocols between the UE and the CN itself.

There are three protocol layers in the AS: physical layer (L1), data-link layer (L2), andnetwork layer (L3). The data-link layer can be further divided into several sublayers:Medium Access Control (MAC), Radio Link Control (RLC), Broadcast/Multicast Control(BMC), and Packet Data Convergence Protocol (PDCP). The network layer also includesseveral sublayers, but only the Radio Resource Control (RRC) protocol belongs to the AS.The other sublayers within the network layer, i.e. Mobility Management (MM), GPRSMM (GMM), Call Management (CM), and Session Management (SM) are part of theNAS protocols.

MM GMM

RRC

MAC

RLC

3G PHYSICAL LAYER

BMC PDCP

CM SM

Control plane User plane

Non-access

stratum

Access

stratum

L1

L2

L3

User plane

data protocol

Figure 2.2: UMTS protocol stack



11/51

2.3. LOCATION CONCEPTS

UE RNC SGSN

MSC

RRC

MM/GMM

GGSN

GMSC

CM/SM

MAC

RLC

Node B

PHY

Figure 2.3: Third layer protocols

Besides AS and NAS distinction, one can also distinguish between two vertical planes:the Control (C) and User (U) planes, for the signaling and the data respectively. TheC-plane carries control data information that is needed by the protocol tasks to run thesystem. The U-plane, on the other hand, carries data that is generated by the user, orby a user application. The U-plane data is typically digitally coded voice, but also otherforms of data. The MAC and RLC layers exist in both the C and U-planes. The RRC ispresent only in the C-plane and the BMC and PDCP are only in the U-plane. RRC, MM,

and GMM get the most thorough treatment in this report, since they are strictly relatedwith the amount of mobility information present across the various network nodes. Eachprotocol layer performs strictly defined functions, possibly exchanging information withother layers via protocol interfaces.

Figure 2.3 shows the termination point of each protocol in the control plane from the UEstack perspective. The RRC protocol is responsible for the establishment, maintenance,and release of the radio connection between the UE and the RNC. The MM protocol trans-ports authentication and mobility information between the UE and the SGSN. Finally,the CM and the SM manage the call and session establishment procedures respectively forthe end-to-end connection. All these protocols keep a finite state machine in the involvedentities. Therefore, at any time a UE will have a RRC state, a MM state, and a SM state.

A thorough description of the physical, MAC, and RLC procedures is out of the scope ofthis report. In the following sections we go into the details of the RRC and MM protocols.

2.3 Location concepts

Before going into the details of the mobility management in the cellular network, we depict

in Figure 2.4 the logical entities used for the localization in GSM and UMTS networks.Though the terms location and position are often used as synonyms, throughout this



12/51


LA

RA

URA URA

RA

URA URA

Cell

Cell

Cell

Cell

Cell

Cell

Cell

Cell

Figure 2.4: Logical network structure

report we refer to:

location as the location of the user terminal in the logical structure of the network,and to

position as the geographical position of the user terminal in terms of geographicalcoordinates.

Therefore, the location of a user refers to its presence in one of the four logical entitiesdepicted in Figure 2.4.

The smallest location entity is the cell, which is the area covered by a sector of a nodeB (or BTS in GSM). Cells are grouped in Routing Areas (RA) for the PS domain, andLocation Areas (LA) for the CS domain. A Location Area is uniquely identified by a

Location Area Identity (LAI). The LAI is composed of the following elements:

Mobile Country Code (MCC) identifies the country in which the network is located,

Mobile Network Code (MNC) is a code identifying the network in that country,

Location Area Code (LAC) is a fixed length code (of 2 octets) identifying a locationarea within a network.

RAs are usually smaller than LAs, i.e. a LA can include one or more RAs. A RA

is uniquely identified by a Routing Area Identity (RAI). The RAI is composed of thefollowing elements:



13/51

2.4. CN MOBILITY MANAGEMENT

MCC LACMNC RAC CellID

LAI

RAI

CGI

Figure 2.5: Location identifiers

LAI is defined above,

Routing Area Code (RAC) is a fixed length code (of 1 octet) identifying a routingarea within a location area.

A RAI is therefore composed of MCC+MNC+LAC+RAC. A cell is uniquely identifiedby a Cell Global Identity (CGI). The CGI is composed by:

LAI is defined above,

CellID is a fixed length code (of 2 octet) identifying a cell within a location area.

Thus, the CGI is composed of MCC+MNC+LAC+CellID.

The terminal informs the network about location changes by means of Location AreaUpdates (LAU), Routing Area Updates (LAU), and Cell Updates. Such procedures arepart of the mobility management protocols, described in section 2.4. Finally, a RA isfurther divided in UTRAN Routing Areas (URA). The URA is a new concept introducedby UMTS. While in GSM the mobility is completely handled between UE and CN, inUMTS the UTRAN is partially involved into the mobility management. For this reason,the UTRAN has its own mobility entities.

2.4 CN Mobility Management

While the RRC manages the mobility at the UTRAN level (between UE and RNC), theUE and the CN exchange mobility information by using the Mobility Management (MM)protocol. We can distinguish between three different MM state machines, depending onthe type of domain to which the terminal is attached:

CS MM (often simply abbreviated as MM) is the state machine used by UEand MSC when using CS services.



14/51


Packet MM (PMM) is the state machine used by UE and SGSN when using PSservices in GPRS Iu mode.

GPRS MM (GMM) is the state machine used by UE and SGSN when using PS

services in GPRS A/Gb mode.

2.4.1 Circuit Switched Mobility Management (MM)

Figure 2.6 depicts the CS MM state machine. UE and MSC switch between the followingthree states: MM detached, MM idle, and MM connected. When the UE is switched off,

MM

_Connected

MM_Detached

MM

_Idle

IMSIattachorLAupdate(terminalswitchedon)

IMSIdetachCallsetuporLAupdate

Callcompletion

Figure 2.6: Circuit-Switched MM states

it is said to be in MM detached state. Clearly, the UE does not perform any action andthe network has no information on its location. When the terminal is switched on, itcamps onto a suitable cell and monitors its control channel. If the monitored LocationArea Identifier (LAI) is equal to the one stored in the USIM, the mobile performs an IMSIattach. If not, it performs a Location Area Update (LAU) with IMSI attach. In both caseit transits from the MM detached state to the MM idle state through the MM connectedstate. In MM idle state the UE needs to be reachable by the network. To allow thenetwork to route incoming phone calls, every time the UE moves from one cell to another,it checks the LAI of the new cell. If the latter is different from the LAI of the old cell,the UE informs the MSC of this change by sending a LAU message. The CN knowsthe position of the UE with the accuracy of a LA 1. When the user makes a call, theUE transits from MM idle state to MM connected state. In this state the UE sends CellUpdates to the MSC whenever the monitored CellID changes, i.e. the CN has informationof the UE location on a cell level.

1

It is worthy to mention that in order to send a LAU, the UE needs to establish a RRC connection,i.e. change temporarily RRC state. For this reason, at each LAU also the CellID (of the cell where theLAU takes place) will be available



15/51

2.4. CN MOBILITY MANAGEMENT

2.4.2 Packet Switched Mobility Management (PMM)

The finite state diagram for PS services is depicted in Figure 2.7. Although the namesare similar to the CS counterpart, there are some important differences that need tobe pointed out. Clearly, transitions from one state to another are triggered by differentprocedures. But also the location information available in each state is rather different. In

PMM

_Connected

PMM

_Detached

PMM

_Idle

PSdetach

PSsignallingconnectionestablish.

PSsignalingconnectionrelease

PSattachPSdetach

PSattachrejectRAUreject

Figure 2.7: Packet-Switched MM states

PMM detached, as expected, there is no communication between the UE and the SGSN.Therefore the SGSN does not hold valid routing information for the UE. In order toestablish a MM context, the UE shall perform a PS attach procedure, also called PacketIMSI attach. In PMM idle state the CN knows the location of a UE down to the accuracyof a RA: every time the UE moves from one cell to another and the RAI of the new celldiffers from the RAI of the old cell, the UE sends a RAU message to the SGSN. Recall thata RA is a subgroup of cells in a LA. This makes PS information in idle state more accuratethan the CS counterpart. On the other side, the location of a UE in PMM connectedstate is known by the SGSN with an accuracy of the Serving RNC (SRNC). It is then theSRNC in the RAN that tracks the UE location. This state machine is used whenever thePS domain is used, including GPRS operating in Iu mode.

2.4.3 GPRS Mobility Management (GMM)

For backward compatibility MM should also support GPRS in A/Gb mode. In this casethe state diagram reported in Figure 2.8 is used. Not that the terminology changes incomparison to the diagrams we have seen so far. There are three states: Idle, Ready, andStandby. In GMM Idle state the SGSN does not hold any information on the UE location.In order to establish a MM context the UE shall perform a GPRS attach procedure andtransit to the GMM Ready state. In GMM Ready state the SGSN knows the location of

the UE on a cell level. The UE remains in GMM Ready state until the expiration of theso-called Ready timer, even if no data is being transferred. When such a timer expires



16/51


ReadyIdle

GPRSattach

GPRSdetach

Standby

PDUtransmission

Readytimerexpiryor

Forcetostandby

Figure 2.8: MM states for GPRS (A/Gb mode)

the UE moves from GMM Ready state to GMM Standby state. In the latter, the SGSNcontains information on the UE location down to the RA level.

Terminals that are both capable of CS and PS services do not always need to performboth LA updates and RA updates. If the network works in operation mode I, the UEperforms only RA update and SGSN and MSC/VLR exchange location information bymeans of the new interface Gs (see section 4.1).



17/51


18/51

CHAPTER 3. ROAD MONITORING VIA CELLULAR SYSTEMS

3.1 Active techniques

In active monitoring, the procedure used by the network to gather information about users

location and/or position generates additional signaling traffic. Recall that we defined theterm location as the location of the user terminal in the logical structure of the network(cell, routing area, location area, etc.) and position as the geographical position of theuser terminal in terms of geographical coordinates.

A typical procedure used for actively refine a user location information is the pagingprocedure, which is normally used by the network for localizing a User Equipment (UE)in case of incoming calls/connections. Paging might be used by the network to localizegeneric UE, even when no incoming calls are present. On the other hand, the positioningmechanisms, defined in the context of location services (LCS), are more complex proce-dures used for retrieving information from a UE and calculating its geographical position.

At the moment of writing 3GPP standardized three positioning methods for UMTS:

CellID-based positioning,

Observed Time Difference Of Arrival (OTDOA),

Assisted-GPS (A-GPS).

Positions derived by active monitoring can be highly accurate, ranging from the celldimension (Cell-ID-based positioning) down to a few meters (A-GPS).

Solutions based on active monitoring are quite common on the market. A good surveyon various field-tests in Europe and USA can be found in [3] and [4]. Simulation-basedanalysis of active monitoring systems have also been presented in literature. In [5] theimpact of several system parameters is studied, such as sampling frequency, accuracy ofthe locations, number of locations available in a given area, etc. In [6] a segment-basedmethod for active monitoring is analyzed by considering several variables, such as datacollection interval, location update interval, and mobile penetration rate.

Application-based active monitoring is a special subcategory of active monitoring systems.It is based on a client-server model. The mobiles run a dedicated software that reports

its location or its movements to a server outside the cellular network (Figure 3.2. Suchreports are transparent from the network perspective.

A typical example consists of a car-mounted GPS receiver equipped with a GPRS transceiver,which reports the position to a server via the cellular network. Application-based activemonitoring has gained popularity in the last years. In 2000-2002 the Optimized Traffic inSweden (OPTIS) project [7] focused on the estimation of road traffic conditions by GPSreport via the GSM network. Results showed that travel time information of good qualitycan be produced with the OPTIS concept. However computer simulations showed thatpenetration of probes needs to be around 3-5% in a mid sized city (1 million inhabitants)to give good quality travel times with updates each minute. In the Netherlands, GPS user

positioning data are used together with other GSM information to generate road trafficdata [8]. At the time of writing the product is available on the market. In [9] a software



19/51

3.2. PASSIVE TECHNIQUES

Mobile Client

(Java,

Symbian, orWM)

Cellular

Network

External

Server

Figure 3.2: Application-based active monitoring

installed on volunteers mobile phones is used for collecting Cellular Dwell Time (CDT).The latter is then used to characterize walking users and sky trains.

3.2 Passive techniquesIn contrast to active monitoring, with passive techniques, signaling is silently collectedfrom one or more points in the network with no impact in the offered network load.Real-time information, such as users behavior, terminal movement history, and terminalpositions are then retrieved by processing the collected signaling. The amount/type ofthe retrieved information depends on the placement of the monitoring points and on thestate of the user terminal. Details on such a dependency will be given in section 4 forboth GSM/GPRS and UMTS technologies. In general, the closer the monitoring point isto the base station (node B in UMTS), the larger the amount of relevant data that can

be collected. Similarly, the higher the activity of the user on its terminal, the larger theinformation that can be collected.

The first project aimed at using mobile phone as traffic probes was the Cellular Appliedto ITS Tracking and Location (CAPITAL) project [10]. The goal was to generate trafficcondition estimates, such as speed and travel time, by using monitoring equipments ineight cellular towers to geolocalize all the mobile phones being actively used. The systemwas not able to accurately estimate the car speeds and detect incidents. In Europe,the STRIP project [11] focused on the calculation of travel times from GSM signalingcaptured on A/Abis interfaces. A field test in the Rhone Corridor of Lyons (France)[12] showed little variation between the cellular phone data and the loop detector data

in the motorway segments. However a speed underestimation of ca. 30% was observedin correspondence of urban ring segments with many commercial stops. In [13] cellular



20/51

CHAPTER 3. ROAD MONITORING VIA CELLULAR SYSTEMS

phones are tracked using measurements report records generated by the terminal to thenetwork. Results showed that the number of measurements was sufficient to generateaverage speeds with an accuracy of 8-16 Km/h. In the same period, several other projectshave focused on GSM passive monitoring, e.g. [3], [14], [15]. In mid 2003, a product of

Applied Generics was tested in the Netherlands in partnership with Logica plc (RoDIN24).The software evolved and it is now used by Vodafone Netherlands in cooperation withTomtom Mobility Solutions [8]. It provides Road status information by using GSM userspositioning combined with GPS positions reports. However the technical approach andthe used business model are not clear, because of lack of publicly available information.In Germany, the final project report for the research initiative Trafficonline is expected forthe end of 2008 [16] and a following commercial exploitation by the Vodafone traffic.onlineservice is expected. First results of a field test are given in [17]. The objective of theproject is to use the GSM network data to generate traffic data information and todetermine whether call volume is related to traffic volume. Other commercial projects

are recently active in the area of Floating Phone Data (FPD), or equivalently CellularFloating Vehicle Data (CFVD) (e.g. [18] and [19]), but none of them present a detaileddescription of methods, algorithms, and results to the research community.

For all the mentioned projects, appendix A provides a detailed description of projectcontributors, time-frames, and results.



21/51

CHAPTER 4. EXTENDED MONITORING FRAMEWORK

Chapter 4

Extended monitoring framework

In this chapter we first address problems and limitations of the approaches that have beenproposed in literature. We then propose a novel framework, which aims at overcomingsuch limitations by combining different techniques in an integrated framework. The ideais to deploy a system that goes beyond GSM monitoring by also covering UMTS andGPRS, i.e. the packet switched domain, whose market penetration is increasing quickly.

4.1 Motivation

A thorough review of the various mechanisms presented in literature reveals a numberof limitations. Active techniques might present scalability problems when used in thecontext of road monitoring, where a massive number of UE should be localized. Usingpaging or LCS positioning mechanisms for a large number of UEs requires the transferof a considerable amount of data. The risk is to exhaust precious network resources,particularly on control channels of the radio interface, and impact the service availabilityfor conventional cellular users. Another drawback relates to the UE battery: each timea terminal reports information to the network it consumes power, reducing the stand-bytime.

In case of application-based active techniques battery life is not an issue (the typicalscenario is a car-mounted GPS receiver equipped with a GPRS transceiver). However,such a technique is ineffective until the devices running the dedicated software reach aconsiderable market penetration. Also the billing issue needs to be taken into account.

Passive techniques suffer from different types of problems, primarily the quality and gran-ularity of the available information. All previous studies on passive techniques focus onthe CS GSM network and ignore the ongoing evolution towards PS networks, which is themain feature of 3G and 4G systems [20]. Though most of the 3G devices can be attachedto both CS and PS domains, the trend is to migrate some common mobility functions to

the PS domain.A clear example of this trend is the combined Location and Routing Area Updates. When



22/51


a UE is attached to both domains, a change of LA implies also a simultaneous change ofRA (RA are subsets of LA). The UE should therefore send a LAU and a RAU. However,in order to avoid this duplicated signaling, networks might be set to operation mode I:the UEs perform only RA update terminating at the SGSN, and the latter communicates

the location change to the MSC/VLR through the newly introduced Gs interface (Figure4.1). Therefore, monitoring exclusively the CS domain might lead to a serious shortage

Node B

SGSN

GGSN

BSC

BTS

MSC/VLR

GMSC

IubAbis

GbIuCSA/IuCS

Gn

IuPS

Gs

RNC

Node B

SGSN

GGSN

BSC

BTS

MSC/VLR

GMSC

IubAbis

GbIuCSA/IuCS

Gn

IuPS

Gs

Operation mode II

(1) RA update to the SGSN

(2) LA update to the MSC

Operation mode I

(1) Combined RA/LA update to the SGSN

(2) LA update from SGSN to MSC

RNC

(2)

(1)(2)

(1)

(1)

(1)

(2)

Figure 4.1: UMTS operation modes

of data in future cellular network.

To the best of our knowledge, at the moment of writing only one publication providesa theoretical description of the new possibilities offered by 3G networks in the contextof road traffic monitoring[21]. The latter however focuses on the radio access network,leaving the new CN mechanisms unexplored in this field. In all cases, the lack of pub-licly available results prevents the scientific community from validating and refining thesemechanisms.

4.2 Architecture

Figure 4.2 depicts the architecture of our monitoring system. Probes are installed acrossthe CN infrastructure of an Austrian cellular operator. In particular, we collect signalingmessages from Gb, IuPS, and IuCS interfaces. We plan to extend our system with Iubinterface by 2009/Q2. In this study we focus on the CN data. Signaling messages arefirst anonymized to preserve the privacy of the users. Tracing units collect and aggregatesignaling data, extract events, and deliver them to a processing unit via UDP in formof event-based tickets. In the processing unit, the anonymized signaling events from the

operational network are explored for the design of road status inference algorithms.We use mainly GnuAWK and Perl for analyzing the data and producing aggregated



23/51

4.3. EVENT TYPES

RNC

NodeB NodeB

SGSN

GGSN

BSC

BTS BTS

MSC/VLR

GMSC

IubAbis

Gb/IuPS

IuCSA/IuCS

BSS

Gn

IuPS

CN

RAN

Gs

Tracing

Unit

Road

events

Tracing

Unit

Network

events

ProcessingUnit

Figure 4.2: Monitoring framework

statistics. Plots are then generated by using GnuPlot, a command-line driven interactivedata and function plotting utility.

The accuracy of mobility events collected from the network probes is bound to the 3GPP

MM protocol described in section 2.4. We have seen that MM lays its foundation intothree logical entities: Location Area, Routing Area, and cell. Without the use of activetechniques, these are the only location information that we can gather from our probes.

By monitoring both CS and PS domain interfaces, the system is able to capture thelocation at cell granularity for terminals in GMM ready state and MM connected state,at RA granularity for terminals in GMM Stand-by state and PMM idle state, and at LAgranularity for terminal in MM idle state (see section 2.4).

4.3 Event types

The processing unit processes signaling information that might relate to particular roadconditions. For each event the system stores a timestamp, the anonymized IMSI, TMSI/TLLI,and the location information (MCC+MNC+LAC+RAC+CellID). We describe in this sec-tion the type of monitored events that might be important to our purpose.

4.3.1 Events from the Gb interface

The Gb interface is used for communication between the SGSN and the 2G BSC. There-fore, information on the Gb concerns GPRS-capable terminals attached to the 2G RAN.



24/51


On this interface, the signaling events that could relate to the road traffic conditions arethe following:

Cell Update: A GPRS terminal in GMM ready state sends a Cell Update, i.e. itchanges cell;

CS Paging: The SGSN pages a terminal in the CS domain (used in UMTS operationmode I).;

Routing Area Update : A GPRS terminal attached to the PS domain sends a RAU,i.e. it changes RA;

Combined Routing Area and Location Area Update: A GPRS terminal attached to

both CS and PS domain changes LA and therefore also RA.

Combined Routing Area and Location Area Update with IMSI attach: A GPRS ter-minal attaches to the PS domain and the current RA differs from the one previouslystored in the SIM.

Periodic Routing Area Update : A GPRS terminal sends a periodic RAU. This hap-pens when terminals do not change RA for longer than a specific timer.

4.3.2 Events from the IuCS interface

The IuCS is used for communication between the RNC and the (3G) MSC. Informationon this interface concerns 3G customers that are IMSI attached to the network. Signalingevents relevant for our work include:

Setup: A UMTS terminal request the establishment of a call;

Connect ACK: A call is answered;

Disconnect: A call is disconnected;

SMS originated: A UMTS terminal sends an SMS;

SMS terminated: A UMTS terminal receives an SMS;

Location Area Update : A UMTS terminal attached to the CS domain sends a LAU,i.e. it changes LA;

Periodic Location Area Update: A UMTS terminal sends a periodic LAU. Thishappens when terminals do not change LA for longer than a specific timer.



25/51

4.4. GEOGRAPHICAL INFORMATION

4.3.3 Events from the IuPS interface

This interface is used for communication between the RNC and the SGSN. Data concernscustomers that are PS attached to the network. From this interface relevant signalingevents include:

Routing Area Update: A UMTS terminal attached to the PS domain changes RA;

Combined Routing Area and Location Area Update : A UMTS terminal attached toboth CS and PS domain changes LA and therefore also RA.

Combined Routing Area and Location Area Update with IMSI attach: A UMTS ter-minal attaches to the PS domain and the current RA differs from the one previouslystored in the SIM.

Periodic Routing Area Update: A UMTS terminal sends a periodic RAU. Thishappens when terminals do not change LA for longer than a specific timer.

4.4 Geographical information

In order to estimate road conditions from network events, algorithms must be fed withdetailed geographical information of the operator radio access network. Without thisdata, network events are just related to a number, rather than to an area.

In a first stage, we made a rough estimation of cellular coverage on the Austrian highwayby using a software written in C# that collects and logs radio events on a generic GPS-capable Windows Mobile Pocket PC. In a second stage, a refinement of the geographicalinformation was required. Therefore we used data provided by the mobile operator.

4.4.1 C# application

In Windows Mobile OS, the communication between the system software and the radio

hardware is handled by the Radio Interface Layer (RIL). The latter is composed of twomodules: the RIL proxy and the RIL driver (see Figure 4.3). The RIL proxy is a dynamic-link library (DLL) that manages the calls to the RIL driver. It serves as an abstractionlayer and provides common functions to the users for accessing the hardware-dependentRIL driver.

The RIL proxy furnishes several radio functions. Particularly, RIL GetCellTowerInfo()and the RIL GetSignalQuality() return pointers to the following structures:



26/51


Figure 4.3: Windows Mobile RIL architecture (from [22])

typedef struct {

DWORD cbSize;

DWORD dwParams;

DWORD dwMobileCountryCode;

DWORD dwMobileNetworkCode;

DWORD dwLocationAreaCode;

DWORD dwCellID;

DWORD dwBaseStationID;DWORD dwBroadcastControlChannel;

DWORD dwRxLevel;

DWORD dwRxLevelFull;

DWORD dwRxLevelSub;

DWORD dwRxQuality;

DWORD dwRxQualityFull;

DWORD dwRxQualitySub;

DWORD dwIdleTimeSlot;

DWORD dwTimingAdvance;

DWORD dwGPRSCellID;DWORD dwGPRSBaseStationID;

DWORD dwNumBCCH;

BYTE rgbBCCH[MAXLENGTH_BCCH];

BYTE rgbNMR[MAXLENGTH_NMR];

} RILCELLTOWERINFO;

typedef struct {

DWORD cbSize;

DWORD dwParams;

int nSignalStrength;

int nMinSignalStrength;



27/51

4.4. GEOGRAPHICAL INFORMATION

int nMaxSignalStrength;

DWORD dwBitErrorRate;

int nLowSignalStrength;

int nHighSignalStrength;

} RILSIGNALQUALITY;

We made a measurement campaign at the end of 2008. We traveled from Vienna tothe Austrian-Italian border (via A2 - Sud-Autobahn). A software was running duringthe whole experiment in our UE and saving every 5 seconds on an internal database thefollowing information:

timestamp;

GPS coordinates (Latitude, Longitude, Altitude, Angle, and Speed); Network information (MCC, MNC, LAC, CellID, and Received Signal Strength

Indicator).

4.4.2 TEMS files

Data collected in our measurement campaign was meant to provide a rough idea of thecellular network design in the Austrian highway. Clearly, the deployment of road trafficinference algorithms requires more detailed information on the operator RAN. To thispurpose we used information provided by the radio department of the mobile operator,regarding the cells along the Sud-Ost-tangente in Vienna, and part of the Sud-Autobahnin Lower-Austria. More specifically, for each cell covering the mentioned road sections weused the following information:

GPS coordinates of the Cell (Latitude, Longitude);

Technology (UMTS, GSM 900MHz, and GSM 1800MHz);

Cell Global Id (MCC, MNC, LAC, CellID);

Routing Area Code;

Cell Type (Macro, Micro, etc.);

Horizontal Beamwidth;

Azimuth.



28/51

CHAPTER 5. PRELIMINARY RESULTS

Chapter 5

Preliminary results

In this chapter we will explore some of the traces collected from our system. We first showsome generic statistics. We then point out some characteristics typical of cells coveringhighways and freeways. We will focus on the Sud-Ost-tangente and the Sud-Autobahn inVienna and Niederosterreich). A first statistical differentiation between cells in proximityof road sections and generic cells is discussed. In a second stage, we examine some specialcases, such as cells covering railways parallel to road sections. Finally, we will see theeffect of a road accident on the UMTS signaling.

All the plots in this chapter are rescaled in order not to disclose sensitive informationof the operator. For each plot we indicate the used tic (Time Interval Counter). The

latter has a low-pass filter effect on the statistics, therefore its value needs to be set bytrading off between large short-term fluctuation of the signal (in case of small tics) andthe inability to detect anomalies (in case of large tics). Note that the timestamps inthe plots always refer to the Coordinated Universal Time (UTC).

5.1 Generic statistics

Figure 5.1 depicts the total amount of signaling events with a tic of 10 minutes received

by the system along an entire week. Although the plot aggregates all the signaling events,some important considerations can be already drawn.

As expected, it is possible to note a strong daily variation of the collected signaling. Notethe marked difference between the working days and the weekends days. On Saturdayand Sunday the amount of signaling decreases of 30%-40% within respect to the workingdays. This implies that a system that detects network anomalies and maps them intoroad anomalies cannot be based on simply thresholding aggregate data. The peak hour,i.e. when the largest amount of signaling flows across the network, during the workingdays is in the afternoon at around 4 PM UTC (5 PM local-time). This may indicate thatpeaks are mostly generated by mobility events, i.e. at this time many customers travel

from work to home. On the other hand, the lowest amount of signaling (around 10%within respects to the peaks) is registered between 12 PM and 3 AM during the working



29/51

5.1. GENERIC STATISTICS

0.2

0.4

0.6

0.8

1

Mon,00:00

Mon,04:00

Mon,08:00

Mon,12:00

Mon,16:00

Mon,20:00

Tue,00:00

Tue,04:00

Tue,08:00

Tue,12:00

Tue,16:00

Tue,20:00

Wed,00:00

Wed,04:00

Wed,08:00

Wed,12:00

Wed,16:00

Wed,20:00

Thu,00:00

Thu,04:00

Thu,08:00

Thu,12:00

Thu,16:00

Thu,20:00

Fri,00:00

Fri,04:00

Fri,08:00

Fri,12:00

Fri,16:00

Fri,20:00

Sat,00:00

Sat,04:00

Sat,08:00

Sat,12:00

Sat,16:00

Sat,20:00

Sun,00:00

Sun,04:00

Sun,08:00

Sun,12:00

Sun,16:00

Sun,20:00

Mon,00:00

NumberofEvents(Rescaled)

Figure 5.1: Time chart of total number of events; TIC=600s.

0.2

0.4

0.6

0.8

1

Mon,00:00

Mon,04:00

Mon,08:00

Mon,12:00

Mon,16:00

Mon,20:00

Tue,00:00

Tue,04:00

Tue,08:00

Tue,12:00

Tue,16:00

Tue,20:00

Wed,00:00

Wed,04:00

Wed,08:00

Wed,12:00

Wed,16:00

Wed,20:00

Thu,00:00

Thu,04:00

Thu,08:00

Thu,12:00

Thu,16:00

Thu,20:00

Fri,00:00

Fri,04:00

Fri,08:00

Fri,12:00

Fri,16:00

Fri,20:00

Sat,00:00

Sat,04:00

Sat,08:00

Sat,12:00

Sat,16:00

Sat,20:00

Sun,00:00

Sun,04:00

Sun,08:00

Sun,12:00

Sun,16:00

Sun,20:00

Mon,00:00

NumberofEvents(Rescaled)

Figure 5.2: Time chart of total number of events in proximity of highways; TIC=600s.

days, and between 2 AM and 5 AM during the weekends. This is also consistent with

the fact that on weekends cellular users switch off their mobile phone later than in theworking days.



30/51


Figure 5.2 shows the same plot, this time restricted to all the cells covering the Sud-Ost-tangente and the Sud-Autobahn. Note that the peaks during the rush hours are morepronounced. This is explained by the fact that in these cells a big portion of the signalingis related to mobility, i.e. Cell Updates, LAU, RAU, and Combined Rau and LAU.

5.2 Opposite traffic flows

We have chosen a convenient location in the Sud-Autobahn between Vienna and WienerNeustadt (See Figure 5.3). The peculiarity of this location is that it is at the borderbetween two LA and that the cells at these borders are on the highway and far fromany residential area. We investigated the correlation between mobility in the cellular

network and road traffic flows. Clearly without a trusty ground truth it is not possibleto validate such a correlation. Figure 5.4 and 5.5 show the number of combined RAU and

Vienna

Wiener

Neustadt

(LAC_a)

(LAC_b)

Figure 5.3: Road section for Figure 5.4 (LAC a) and 5.5 (LAC b)

LAU with a tic of ten minutes for the two border cells. Figure 5.4 depicts the numberof location changes toward the LAC a, while Figure 5.5 depicts the number of changestoward LAC b. LAU toward LAC b increase in the morning, when most of the traffic islikely directed toward Vienna. On the other hand, in the evening LAU toward LAC bdecrease and the number of mobile phones entering LAC a increase. Moreover, a peak

every Friday evening is registered. The dependency of the LAU from the road traffic flowis clear.



31/51

5.3. RAILWAYS

0.2

0.4

0.6

0.8

1

Tue,

12:00

Tue,

16:00

Tue,

20:00

Wed,

00:00

Wed,

04:00

Wed,

08:00

Wed,

12:00

Wed,

16:00

Wed,

20:00

Thu,

00:00

Thu,

04:00

Thu,

08:00

Thu,

12:00

Thu,

16:00

Thu,

20:00

Fri,

00:00

Fri,

04:00

Fri,

08:00

Fri,

12:00

Fri,

16:00

Fri,

20:00

Sat,00:00

Sat,04:00

Sat,08:00

Sat,12:00

Sat,16:00

Sat,20:00

Sun,

00:00

Sun,

04:00

Sun,

08:00

Sun,

12:00

Sun,

16:00

Sun,

20:00

Mon,

00:00

Mon,

04:00

Mon,

08:00

Mon,

12:00

Mon,

16:00

Mon,

20:00

Tue,

00:00

Tue,

04:00

Tue,

08:00

Tue,

12:00

NumberofCombinedRAUandLAU(R

escaled)

Figure 5.4: Time chart of Combined RAU and LAU (Direction: Outgoing Vienna);TIC=600s

0.2

0.4

0.6

0.8

1

Tue,

12:00

Tue,

16:00

Tue,

20:00

Wed,

00:00

Wed,

04:00

Wed,

08:00

Wed,

12:00

Wed,

16:00

Wed,

20:00

Thu,

00:00

Thu,

04:00

Thu,

08:00

Thu,

12:00

Thu,

16:00

Thu,

20:00

Fri,

00:00

Fri,

04:00

Fri,

08:00

Fri,

12:00

Fri,

16:00

Fri,

20:00

Sat,00:00

Sat,04:00

Sat,08:00

Sat,12:00

Sat,16:00

Sat,20:00

Sun,

00:00

Sun,

04:00

Sun,

08:00

Sun,

12:00

Sun,

16:00

Sun,

20:00

Mon,

00:00

Mon,

04:00

Mon,

08:00

Mon,

12:00

Mon,

16:00

Mon,

20:00

Tue,

00:00

Tue,

04:00

Tue,

08:00

Tue,

12:00

NumberofCombinedRAUandLAU(R

escaled)

Figure 5.5: Time chart of Combined RAU and LAU (Direction: Incoming Vienna);TIC=600s

5.3 Railways

In figure 5.6 we plot the number of Combined RAU and LAU per minute in a LA border

cell that covers both a highway and a railway heavily used by international trains. Theplot considers two working days. Besides the normal night-day trend, it is possible to see



32/51


33/51


34/51


different LA. In Figure 5.9 we plot the number of Combined RAU and LAU for two days,including the day of the accident. In other words, the plot depicts the number of usersmoving from the LA where the accident occurred to the following LA in the directionof travel of the lane of interest. In both days the typical morning-peak is visible, i.e.

0.2

0.4

0.6

0.8

1

Tue,00:00

Tue,01:00

Tue,02:00

Tue,03:00

Tue,04:00

Tue,05:00

Tue,06:00

Tue,07:00

Tue,08:00

Tue,09:00

Tue,10:00

Tue,11:00

Tue,12:00

Tue,13:00

Tue,14:00

Tue,15:00

Tue,16:00

Tue,17:00

Tue,18:00

Tue,19:00

Tue,20:00

Tue,21:00

Tue,22:00

Tue,23:00

Wed,00:00

Wed,01:00

Wed,02:00

Wed,03:00

Wed,04:00

Wed,05:00

Wed,06:00

Wed,07:00

Wed,08:00

Wed,09:00

Wed,10:00

Wed,11:00

Wed,12:00

Wed,13:00

Wed,14:00

Wed,15:00

Wed,16:00

Wed,17:00

Wed,18:00

Wed,19:00

Wed,20:00

Wed,21:00

Wed,22:00

Wed,23:00

Thu,00:00

NumberofComb

inedRAUandLAU(Rescaled)

Figure 5.9: Number of Combined RAU and LAU with tic=300s (Accident)

when the majority of cars is traveling toward Vienna. On Tuesday, at 4 PM UTC (5PM in Austria) the signal is characterized by a notch immediately followed by a peak.These abrupt changes are caused by the accident. The notch indicates that the numberof customers changing LA decreased suddenly: they were blocked or slowed down by theaccident. When the road traffic was restored, a large number of users changed LA in thesame tic, hence the plot presents a subsequent spike.

This event highlights the potentiality of using statistical analysis of the cellular network

signaling for detecting road anomalies.



35/51

CHAPTER 6. RESEARCH CHALLENGES AND FUTURE WORKS

Chapter 6

Research challenges and future works

In the previous chapter we have shown that specific statistics of the network signalingcan give a general indication of the current road condition. However, the final goal of theproposed system is to map events on the cellular network to events on the road network.To this end, we believe that two software agents should run in sequence on the processingunit. The first agent takes as input network events (i.e. mobility and activity patternsof anonymized users) and singles out data concerning road users on the road of interest.The output of the first module is then processed by the second agent in order to infer theroad traffic condition. The complexity of each of these steps depends on the consideredscenario. For the sake of simplicity, in a first phase our future work will be still restrictedto highways in rural areas. We will then extend our framework to highways near urbanareas, where several entry points or parallel roads/railways increase the complexity of thesystem. Finally, we will consider urban area roads where data collected from probes onthe radio access network are needed.

6.1 Single out road users

The first step is to obtain a data subset of road users only. To this purpose, we will

consider individual trajectory tracking instead of aggregated statistics. Static cellularusers (e.g. in-building users) present specific characteristics as they remain camped intoa single LA/RA/CELL for considerably longer periods in comparison to moving outdoorusers. Hence, active users can be easily filtered out by looking at the cell update rate,whereas idle users can be filtered out by looking at LA/RA update rate.

However, the remaining subset is not yet restricted to road users only. Walking usersand users on transportation systems different than cars (e.g. trains, trams, etc.) need tobe identified and excluded from the processing. In [9] a model for differentiating walkingusers from sky train passengers is proposed by statistically analyzing the user permanencein a cell. Experimental results showed promising performance with accuracy up to 93%.

In [17] a method for filtering train passengers is evaluated in a scenario where the railwayis parallel to the road. Finally, [24] proposes methods to classify subscribers in public



36/51

CHAPTER 6. RESEARCH CHALLENGES AND FUTURE WORKS

transport vehicles in spite of low position accuracy.

Once the subset of the road users has been obtained, users on different roads should bedistinguished. Note that this task can be difficult when considering users on parallel roads,

since they might present extremely similar LA/RA/CELL update time sequences. In thiscase we propose to track LA/RA/CELL updates history, for each user, for a suitableamount of time. Looking at the recent trajectory records for individual users would helpto disentangle walking users and slow moving car user on a congested segment.

6.2 Road condition estimation

The road estimation module will perform the mapping of the signaling information to

the road conditions and rise warnings in case of relevant deviations from the typical be-havior observed in the past, i.e. anomalies. The main challenge in developing anomalydetection algorithms is that the ground truth about the real traffic conditions is gen-erally unknown. The idea is that abrupt changes in some network signaling events mightbe the symptom of a road anomaly (accident/congestion), for example:

1. drop in the handover rate;

2. abrupt change in the LR update;

3. increase in the number of calls/SMS;

4. drastic change in the number of road users.

Though, properly detecting these events is challenging due to the non-stationarity of theprocesses, as shown in Section 5.1.

6.3 Smart integration of active and passive techniques

Although monitoring CS and PS domain can suffice in detecting road condition in simplescenarios, a pure passive monitoring might fail in more complex situations. For this reasonit can be necessary to complement a passive system with active techniques. In two casesactive techniques can be used to improve the accuracy of passive systems:

1. Low number of active users: Passive monitoring can deliver road status infor-mation only if a sufficiently high number of users is active (calling or connected).When the number of active users decreases in a certain area the system looses res-olution. On the other hand, a low number of active users means that the networkcapacity is underutilized, therefore active monitoring can be activated without fear

of impacting the network performance.



37/51

6.3. SMART INTEGRATION OF ACTIVE AND PASSIVE TECHNIQUES

2. Event uncertainty: When passive monitoring detects some abnormal conditionin the road that cannot be clearly mapped to a road event, active monitoring canbe temporarily enabled as a magnifying lens to gather more detailed information.



38/51


39/51

und sind vom Betreiber nach Beendigung der Verbindung unverzuglich zu loschen oder zuanonymisieren.

101. Inhaltsdaten(1) Inhaltsdaten durfen - sofern die Speicherung nicht einen wesentlichen Bestandteil des

Kommunikationsdienstes darstellt - grundsatzlich nicht gespeichert werden. Sofern austechnischen Grunden eine kurzfristige Speicherung erforderlich ist, hat der Anbieter nachWegfall dieser Grunde die gespeicherten Daten unverzuglich zu loschen.

102. Andere Standortdaten als Verkehrsdaten

(1) Andere Standortdaten als Verkehrsdaten durfen unbeschadet des 98 nur verarbeitet

werden, wenn sie

1. anonymisiert werden oder

2. die Benutzer oder Teilnehmer eine jederzeit widerrufbare Einwilligung gegeben haben.

Finally, some considerations should be drawn on data security. A potential software built

upon the presented approach would run within the operator infrastructure (see Figure7.1). Data collection, filtering, and processing would be completely done in the trusteddomain of the mobile operator. A data distributor agent then acts as interface betweenthe trusted and untrusted domain and dispatches road information to external entities,such as road operators or other interested parties.

Tracing

Unit

Road

events

Tracing

Unit

Network

events

Processing

Unit

IuPS

IuCS

A

Gb

Iub

others

Cellular Users

Road Operator

GPS Vendors

Trusted Domain (mobile operator) Untrusted domain

Content

Distributor

Figure 7.1: Information flow: trusted to untrusted domain



40/51

CHAPTER 8. CONCLUSIONS

Chapter 8

Conclusions

In this report we have explored the possibility of using cellular networks for inferring roadtraffic condition. We proposed an extended framework for the collection and the analysisof signaling traces both from the CS and the PS domains of a mobile operator. We haveexplored the collected data and preliminary results show the potentiality of such a systemfor mapping cellular network signaling patterns into road anomalies. In particular, wehave shown that the road traffic flow does have an impact into the mobility signaling inthe cellular network. Events, such as accidents or congestions, produce abrupt changesin the network signaling. A characterization of such changes could allow to build anintelligent traffic management system with the help of the mobile operators.

Moreover a thorough review of the existing approaches for road traffic estimation viacellular networks has been presented. In order to follow the ongoing evolution of thecellular infrastructure, passive monitoring of PS and CS signaling must be combinedin a single framework. Furthermore, we have outlined the vision of a hybrid bi-modalsystem that complements passive monitoring with active techniques whenever a passive-only monitoring is not sufficient to achieve the required accuracy.

As for our future work, we are set to progress the development of an integrated roadestimation system, with particular focus on the research challenges highlighted in thisreport.



41/51

APPENDIX A. RELATED PROJECTS AND COMMERCIAL PRODUCTS

Appendix A

Related projects and commercialproducts



42/51


43/51

A.2. STRIP

A.2 STRIP

Partners Societe Francaise de Radiotelephone (SFR) and French Government.

Project period 1999-2001

Description STRIP (System for TRaffic Information and Positioning) was a subprojectof SERTI (Southern European Road Telematic Implementations), which is in charge ofmanaging the heavy traffic flow from Germany, Switzerland, France, and Spain to Italyduring summer and winter holidays. The objective of the project was to estimate highwaytravel times by collection and processing the GSM signaling on the A/Abis interfaces. Afield test has been conducted on two urban freeways near Lyon. An A/Abis probing

system was installed by the French cellular operator SFR. Mobile Originated calls arelocated by sniffing measurement reports on the SACCH (each 480ms), and by collectingTiming Advance (TA), Rxlev, and Rxqual values. A dedicated computer processes thenthe data, discarding users outside a strip of 1100 meters centered on the motorway axisand by using algorithms of map matching.

Results At the time of writing the final project report is not available on the internet,tough it is cited by several scientific publications. In [3] a summary of the project resultsis presented. The accuracy of the system is calculated by comparing data from loops

detectors and from Cellular probes. A little variation between the cellular phone dataand the loop detector data in the motorway segments was observed. However a speedunderestimation of ca. 32% was observed in correspondance of urban ring segments withmany commercial stops. Moreover, the mean speed variations from the cellular probeswere much larger than the mean speed variation from the loops detector. Finally, thestatistical analysis showed a strong relationship between the number of outgoing cellularcalls and the level of incidents.



44/51


A.3 OPTIS

Partners Volvo Trucks, Volvo Cars, Scania Trucks, Saab Automobile, and Swedish

National Road Administration (SNRA).

Project period 2000-2002

Description Project goal was to develop a cost effective method of collecting traffic datain order to create good traveler information. Vehicles sends GPS position to an OPTISserver via GSM/SMS or GPRS. The server processes the data, applies map matchingalgorithms (so that in-vehicle equipments do not need digital maps), and estimates roadconditions. A simulator has been created within the project and its accuracy verified with

a field test.

Results System performance has been evaluated by a field trial in Gothenburg. Asummary of the results can be found in [25]. 50% of the probes were installed in taxis.The field trial comparisons show that OPTIS travel times are equal to camera and queuewarning measurements. Computer simulation shows that penetration of probes needs tobe around 3-5% in a mid sized city (1 million inhabitants) to give good quality traveltimes with updates each minute.



45/51

A.4. DO-IT

A.4 Do-iT

Partners DDG Gesellschaft fur Verkehrsdaten mbH, Universitat Stuttgart, Innenminis-

terium Baden-Wurttemberg, Stadt Karlsruhe - Tiefbauamt, Landeshauptstadt Stuttgart- Technisches Referat.

Project period April 2005 - April 2008

Description The Do-iT (Data Optimization for Integrated Telematics) project aimsat providing optimal routes by extracting real-time road traffic information by passivemonitoring the GSM infrastructure. Data is collected from T-Mobile (T-D1) in Germany.A and Abis interfaces are tapped for acquisition and both active and stand-by mobile

phones are tracked. Measurements are collected and combined with network information,e.g. network geometry, to calculate users speed. Individual users and public transporta-tions are separated and a map-matching algorithm is applied. Source-Destination matrixare also generated for facilitating road infrastructure improvements.

Results A final project report is not available at the moment. Preliminary results in [24]showed that the system is able to differentiate between individual and public transportparticipants increasing the reliability of the traffic estimates. In [26] positioning accuracyis evaluated in a test run. When compared to GPS data, a simple matching to operator

signal strength maps presents a standard deviation of 300 meters. This accuracy is notsufficient for applying normal map-matching algorithms. For this reason, a so-calledmap-aiding algorithm is developed in Do-iT, based on trajectories instead of positions.



46/51


A.5 Traffic.online

Partners Technische Universitat Braunschweig, VMZ Berlin Betreibergesellschaft mbH,

OECON Ingenieurgesellschaft fur Industrieberatung und Projektmanagement mbH, Voda-fone Pilotentwicklung GmbH, Deutsches Zentrum fur Luft- und Raumfahrt e. V.(DLR),Volkswagen AG.

Project period January 2005 - February 2007

Description Similarly to the Do-iT project, traffic.online aims at using a GSM networkmonitoring system to provide road status information. Data is collected on the VodafoneGermany GSM network by monitoring the A and the Abis interfaces. Handover infor-

mation, location area updates and signaling data are used combined with digital mapfor calculating travel times and velocities on different road segments. The main projectcharacteristics are listed in [16]:

1. The system should be based on data from cellular network only,

2. The system should be able to calculate average travel velocity and number of vehiclesin both interurban and urban roads,

3. A live demonstration should validate the system,

4. Limitations, such as required number of passive/active users and QoS, should beidentified and quantified.

Data is collected via probes at the BSC. A filtering and a first data processing is performedand, finally, anonimized data is sent to the traffic.online platform. A lack of publicavailable information prevented us to deepen our knowledge on traffic.online.

Results A field test has been developed in Berlin and other tests has been performedvia simulation with the opensource SUMO simulator. The final project report is notpublicly available. However some results have been presented in Intelligent TransportationSystems conferences. In [16], authors claim that the system is able to provide high qualityinformation on motorways and A-Roads, while it presents shortcomings in urban roads.Reasons are related to the slow car speeds, short call-durations, and high volume of UMTSusers, all characteristics typical to the urban environment. In [17], the filtering of trainpassengers is evaluated in a scenario where the railway is parallel to the road. The test islocated in a Location Area border, thus results are partially significant.



47/51

A.6. CELLINT TRAFFICSENSE

A.6 Cellint Trafficsense

Description Cellint, an Israel based company, developed TrafficSense, based on the

patented VirtualSensor technology. According to the product brochure TrafficSense mea-sures traffic data by analyzing the movement of anonymous cellular phones in vehicles.The system connects passively to cellular networks and continuously monitors the controlchannel to extract traffic information from anonymous cellular signaling data. Proprietarypattern matching and analysis algorithms are used to correlate each vehicle to the road itis traveling on, while differentiating even between roads that are less than 50 yards apart.TrafficSense measures the exact travel-time of each vehicle in small intervals, every fewhundred yards and provides complete real-time traffic data. The output data can thenbe interfaced with the customers control centers or to traffic data delivery systems togenerate traffic predictions according to historical patterns. TrafficSense is compatiblewith all types of cellular networks and does not require any network or handset changes.It is completely passive and does not load the cellular network.

Performance TrafficSense performance has been evaluated by independent institutions.Evaluation reports are available on request on the company website [27]. The followinggeneral results are reported:

Slowdown Detection delay of a few minutes (1 minutes in dense cellular environ-ments).

Less than 5 mph difference between TrafficSenses local speeds and sensors speed.

Less than 10% average error in travel times measurements during speed fluctuation.

Customers

Kansas City for the Kansas Department of Transportation (USA) 2006 1

Highway 1 in Israel for the Israeli National Road Company 2005

Atlanta for Georgia Department of Transportation (USA) 2006

Ayalon highway for Ayalon HW Co. (an Israeli DOT agency) 2004

1Free live traffic information are accessible at http://www.kcscout.com/



48/51


A.7 Estimotion CFVDTM

Description CFVD (Cellular Floating Vehicle Data) was developed by Israel based

Estimotion Inc.[28] which was founded in July 2000 and was acquired by ITIS HoldingPlc [19] in December 2003. CFVD is a patented technology, which provides a systemfor measuring and forecasting real time traffic flow based on anonymously sampling thepositions of mobile phones. In comparison to the other products, CFVD is claimed toproduce accurate forecasts of traffic situations, including the identification of hintsto congestion buildup and the prediction of the propagation of traffic problems alongthe network. Details on the underlying technology are not publicly available. It seemshowever that multiple sources are used together with cellular data for increasing thesystem accuracy.

Performance An independent evaluation has been performed in Antwerp (Belgium) byTransport and Mobility Leuven and Be-Mobile N.V. and in Tel-Aviv (Israel) by Universityof Negev. The evaluation reports are not available on the internet. Some information onthe Antwerp site is quoted in [29]: On motorways the relative error between CFVD andGPS travel times is below 15% for over 70% to even 90% of the time. In relation to theinstantaneous travel times from the single loop detectors they agree for some 81% of thetime within 15% of each other.



49/51

A.8. TOMTOM RODIN24

A.8 TomTom RoDIN24

Description RoDIN24 is a product developed in 2003 by Applied Generics, a small

Scottish company. In RoDIN24, signaling from the Abis interface is collected. TA valuereports from a particular handset over a short period of time are analyzed for determiningits route and speed. Details on the technology are not publicly available. The solutionhas been implemented in 2003 in the province of Noord Brabant. The company wasacquired in 2006 by Tomtom, which signed an agreement with Vodafone Netherlands forthe collection of GSM data. Lately, agreements have been signed with other Europeanmobile operators and the service is now provided (or planned) in the United Kingdom(Vodafone UK), Germany (Vodafone Germany), France (SFR), Switzerland (Swisscom),and Belgium (BASE).

Performance The RoDIN24 solution has been validated in 2003 by Goudappel Coffeng,a Dutch consultancy company in the field of traffic and transport. The evaluation reportis only available in dutch [30]. In [4], general results are reported. RoDIN24 producesresults comparable to GPS-assisted system and loop detector data. Additionally, theproduct is also reliable at speeds less than 20km/h and calculates journey time accuratelyacross junctions.



50/51

BIBLIOGRAPHY

Bibliography

[1] M. C. Gonzalez, et al. Understanding individual human mobility patterns. Nature,453(7196):779782, June 2008.

[2] Robin Smit. An examination of congestion in road traffic emission models and theirapplication to urban road networks. PhD thesis, Griffith University, Faculty of En-vironmental Science, March 2006.

[3] Y. Youngbin. The State of Cellular Probes. Technical report, California PATHResearch Project, University of California, CA, USA, July 2003.

[4] NHCRP Project 70-01. Probe-based Traffic Monitoring - State-of-the-Practice Re-port. Technical report, University of Virginia - Center for Transportation Studies,Nov. 2005.

[5] R. Cayford and T. Johnson. Operational Parameters Affecting the Use of AnonymousCell Phone Tracking for Generating Traffic Information. Technical report, Trans-portation Research Board Annual Meeting, TRB 2003, Washington, D.C., USA,

Jan. 2003.

[6] W. C. Mark Hsiao and S. K. Jason Chang. Segment based Traffic InformationEstimation Method Using Cellular Network Data. In IEEE Intelligent TransportationSystems Conference, Austria, Sept. 2005.

[7] Karlsson and Niclas. Floating Car Data Deployment and Traffic Advisory Services,The OPTIS project 2000-2002. Presentation, Volvo Technology Corporation, 2003.

[8] B. Rutten and R. Schafer. The Connected Car. Whitepaper, TOMTOM MobilitySolutions, June 2007.

[9] K. Puntumapon and W. Pattara-atikom. Classification of Cellular Phone Mobilityusing Naive Bayes Model. In IEEE Vehicular Technology Conference, VTC 2008Spring, Singapore, May 2008.

[10] University of Maryland Transportation Studies Center. Final Evaluation Report forthe CAPITAL-ITS Operational Test and Demonstration Program. Technical report,University of Maryland, College Park, 1997.

[11] J. Remy. Computing travel time-estimates from GSM signalling messages: theSTRIP project. In IEEE Intelligent Transportation Systems Conference, Oackland,CA, USA, Aug. 2001.

[12] J. Ygnace. Travel timespeed estimates on the French Rhone Comdor Network usingcellular phones as probes. Technical report, INRETS, Lyon, France, Dec. 2001.



51/51

BIBLIOGRAPHY

[13] R. Cayford and Y. Youngbin. Field Operational Test using Anonymous Cell PhoneTracking for Generating Traffic Information. Technical report, Transportation Re-search Board Annual Meeting, TRB 2006, Washington, D.C., USA, Jan. 2006.

[14] J. Virtanen. Mobile phones as probes in travel time monitoring. Technical report,Finnish Road Administration, Helsinki, Finland, 2002.

[15] A. Hillson and M. De Santis. Traffic Monitoring Application of Cellular PositioningTechnology: Proof of Concept. Technical report, Cell-Loc Inc., Calgary, Canada,June 2002.

[16] C. Birle. Via GSM network data to traffic information in real time. Presentation,Intelligent Transport Systems Munich, Dec. 2007.

[17] M. Hopfner and et. al. Cellular Data for Traffic Management. In 6th EuropeanCongress and Exhibition on Intelligent Transport System and Services, June 2007.

[18] Cellint TrafficSense. Performance and Limitations of Cellular Based Traffic Moni-toring Systems. Presentation, 6th European Congress and Exhibition on IntelligentTransport System and Services, June 2007.

[19] ITIS Holdings plc. http://www.itisholdings.com/cfvd.asp.

[20] P. Beming, et al. Lte-sae architecture and performance. Ericsson Review, 20(3),2007.

[21] D. Gundlegard and J. Karlsson. Generating Road Traffic Information from CellularNetworks - New Possibilities in UMTS. In 6th International Conference on ITS

Telecommunications, June 2006.[22] Microsoft Developer Network - RIL Architecture. http://msdn.microsoft.com/en-

us/library/aa919462.aspx.

[23] Osterreichische Bundesbahnen, Austrian Federal Railways. http://www.oebb.at.

[24] R. Schollmeyer and T. Wiltschko. Classification of public transport vehicles usingcellular mobile radio data. In 6th European Congress and Exhibition on IntelligentTransport System and Services, June 2007.

[25] G. Lind and A. Lindkvist, OPTIS Consortium. OPTIS - Optimised traffic in Sweden.Technical report, Movea Traffic Consultancy Ltd, Feb. 2006.

[26] T. Wiltschko, at al. Acquisition of traffic state information by mobile phone posi-tioning. In 6th European Congress and Exhibition on Intelligent Transport Systemand Services, June 2007.

[27] Cellint. http://www.cellint.com.

[28] Estimotion Inc. http://www.estimotion.com.

[29] ITIS holding plc. Cellular Traffic Data Collection. Presentation, 6th European

road cell report

Documents