Review of traffic data estimations extracted from cellular networks

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Published in IET Intelligent Transport SystemsReceived on 4th January 2008doi: 10.1049/iet-its:20080003

ISSN 1751-956X

Review of traffic data estimations extractedfrom cellular networksN. Caceres J.P. Wideberg F.G. BenitezTransportation Engineering, School of Engineering, University of Seville, Seville, SpainE-mail: [email protected]

Abstract: One of the main characteristics of modern society is the never-ending increase in mobility. This leads to aseries of problems such as congestion and increased pollution. To resolve these problems, it is imperative to have agood road network management and planning. To efficiently identify the characteristics of traffic in the roadnetwork, it would be necessary to perform a permanent monitorisation of all roadway links. This would involvean excessive cost of installation and maintenance of road infrastructure. Hence, new alternatives are requiredwhich can characterise traffic in a real time with good accuracy at an acceptable price. Mobile telephonesystems are considered as a promising technology for the traffic data collection system. Its extensive use inconverting its subscribers in a broad sample to draw information from phones becomes anonymous probes tomonitor traffic. It is reviewed how to obtain parameters related to traffic from cellular-network-based data,describing methods used in existing simulation works as well as field tests in the academic and industrial field.

1 IntroductionThe increased mobility in society requires more sophisticatedmechanisms and techniques to properly plan and manage theroad network. One way to achieve this is to characterise theroads in terms of a series of parameters such as speed,travel times, traffic flows etc. Obtaining these traffic detailsin an accurate manner and in ‘quasi-real time’ is of vitalimportance for traffic planning and management. Newtechnologies play a key role to introduce improvements inthe estimation of these parameters and solve the maintraffic problems: mobility, saturation and safety. One wayto achieve this is using mobile systems. Recent marketsurveys show that cellular phone penetration levels arereaching to 100% of the population in a large number ofcountries [1]. So it is safe to assume that each vehicle willcarry at least one phone. The mobility management incellular networks employs certain location data for itsproper operation. The processing of this data will allowdetermining certain details regarding vehicle mobility basedon the phones found on board without the need to revealsubscriber confidential information.

This paper will introduce traffic parameters, such as speed,travel times, flows etc., which are possible to obtain based on

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information from a cellular phone service provider. Existinginvestigation such as simulation together with field tests areused for describing the parameters collection. Finally, it isnecessary to emphasise that this paper does not try tosummarise the state of the art and the practice in traffic-monitoring systems using cell phones as probe, butalternatives used to generate traffic data from cell phones.Numerous research centres, universities and trafficdepartments worldwide (e.g. Department of CivilEngineering, University of Waterloo [2]) focus on this. Itis worth highlighting the review of the ‘State of the Art’presented by Yim [3] as well as the prospects and issuesanalysis by Rose [4]. The emerging interest in it hasproduced, in recent years, numerous works and field testsas well as some patents [5]. A ‘State of the Practice’ reviewis made by Smith and Fontaine in an appendix of theNCHRP 70-01 report [6], where they summarise projectsthat employ traffic-monitoring systems based on wirelesslocation technology.

2 Mobility management inmobile phone networksThe mobility required today is modifying the life style both atan individual and collective level. The result is the need for

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solutions that support communication between persons whilethey travel. This is the reason why mobile systems areexperiencing rapid growth, especially boosted by theubiquitous availability of services to facilitate exchange ofinformation (voice, data, video, image etc.) between usersindependent of time, location or access arrangements. Toguarantee such user mobility while maintaining servicequality, a cellular network includes a series of processes,such as location update (LU) or handover. The followingsubsections provide a revision of such processes to betterunderstand how mobile telephony can help to track traffic-associated parameters [7, 8].

2.1 Location update

There is no fixed relationship between a cell phone and itslocation, so a series of processes are required to supportuser mobility. The cellular network must have an estimateon the position of these phones to define the station theymust connect to (e.g. in the case of an incoming call). Sothere is an automatic process that maintains the networkinformed of the phone location depending on the phonestatus. Location management is responsible for thisfunctionality. Hence, the zone is divided into cells andthese are grouped in location areas (LA), that is, largerservice areas covered by grouped cells (Fig. 1).

This location management commonly uses a two-levelhierarchical strategy, which maintains a system of databases[Home Location Register (HLR) and Visitor LocationRegister (VLR)] to keep track of the phone locations. TheLU process is a mechanism to achieve this, which involvesan exchange of signalling messages between the phone andthe network, and the subsequent record in the database toupdate them in accordance with these messages. Therecord content consists of phone ID, LA identifier (LAI),Cell Identity (CI), timestamp and the reason for update.The frequency of the LU process execution depends on thephone status. In the case of a phone on-call, the networkalways knows the base station (cell) which the phone isconnected by means of handover. However, whenever thephone is turned on and not on-call (idle status), thenetwork does not require to know the precise cell where aphone is located but a set of candidate cells for becomingthe serving cell. This set is defined by the LA. Then, thenetwork always knows the current LA when a phone is inidle status by means of the LU process.

The LU process can be triggered by different events. Oneof them is when the cell phone is just powered on andconnects to the network. Furthermore, cell phone mobility

Figure 1 Definition of a typical cell and location area

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can provoke a change in position that involves the entry ina new LA. The LA, where the terminal is located, isidentified through an analysis of the signal sent through‘broadcast’ channels, which contains one LAI. It istherefore possible to identify the entrance in a new areacomparing the new value received with the previous LAIstored. Then, whenever a phone moves from one LA toanother, the ‘LA update’ procedure is executed. Also, if thephone does not have any kind of activity, a periodicmechanism launches automatic reports to the network withthe last position stored by the phone (the time period isdetermined by the cellular carriers). Therefore when a timerends, a record is made to notify its presence in the networkeven if it is located in the same LA, using a ‘periodiclocation update’ procedure.

The arrival of next generations of mobile systems (2.5G/3G) has produced new and more complete services forsubscribers. GSM, GPRS and UMTS share a networkstructure for packet switching that is substantially the same,as UMTS inherits the architecture created for GSM andGPRS. However, the new services offered imply theinclusion of new elements to optimise the various existingservices in the network at the location of the terminals.These networks must also keep information on the positionof each phone updated to know where to route data packetsas they arrive. In these cases, greater cell accuracy isnecessary to transmit a warning message to a phone to senddata packets. To obtain a compromise solution between thesignalling traffic for notices and that originated by positionupdates, other levels appear such as RA (routing area) inaddition to the cell and LA hierarchy. An RA is defined asa subset of cells of an LA and offers similar functionality tothe GSM LAs. The size of an RA is always less or equalto the LA it belongs to. There is an RA update process tomanage mobility in these situations, among otherprocedures. In this way, when the phone detects it hasentered a new RA, comparing the routing area identifier(RAI) it had stored with the RAI received by radio, itautomatically initiates a process for the corresponding update.

2.2 Handover

To establish a communication (voice/data call) to a cellphone, the network needs to know the access point (basestation) to channel the communication. This impliesknowing the exact cell where the phone is located. For this,the network maintains system databases (HLR and VLR)updated with information of its subscribers, and especiallylocation data. During a call, certain information is storedfor billing purposes, such as cell from which the call ismade, starting time, duration etc.

Because a phone may move between service areas ofdifferent base stations during a call, a network must includesuitable mechanisms so that this phone is permanentlyconnected during this time. The HandOver is a solution tothe problem of managing a connection when the signal

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quality becomes insufficient, mainly when the phone movesthrough two cells of the network. With suitablemechanisms, the phone call changes from one base station(cell) to another transparently to the user without qualityloss (Fig. 2). This process updates the system databaseswith the current cell in which the phone is located duringa call.

Handover involves three phases: measurement, decisionmaking and execution. When handover is decided theinformation is exchanged with the network, derivingcertain parameters associated with each one of thesetransferred calls, such as cells involved or instant andduration of handover. In the case of GSM, the handoverconsists in a transfer of resources that implies theinterruption of the existing link and the creation of a newone. This is basically because the neighbouring basestations use different carriers and/or time bases and theterminals are not capable of connecting simultaneously toboth. In these cases, called hard-handover, the terminaldisconnects from its base station of origin and is notconnected to any other base station for a given period oftime (milliseconds). This generates an interruption of voicesignal that is hardly noticeable to the subscriber. However,this situation is not applicable to data communications(2.5G/3G), where an interruption of this type may resultin data losses. That is why there is another handover mode,called soft-handover, during which the phone is connectedto the base station of origin through a channel and thedestination base station through another. Hence, thetransmission is performed in parallel over two channels andthe link is not interrupted. This is the scheme used inmore advanced networks.

2.3 Erlang

The cellular network planning demands a great deal ofinformation, such as market demographics, area to beserved or traffic offered. This traffic varies on base station,month, day and even instant of the day. Traditionally, theunit of measurement of the telephone traffic is Erlang. Thetelephone traffic indicates the amount of voice/data traffictransmitted over a communication channel. This traffic ismeasured in terms of usage time and depends on thenumber of communications and their duration (e.g. if thetotal use per hour of a phone in an area is 180 min, thisrepresents 3 Erlangs). As an example, assume that a cell

Figure 2 Phone has moved out of range of serving cell (A)Phone call is transferred from the cell A to B

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operates with 40 channels and that the mean blockingprobability is required to be 5%. Using the Erlang-Bformula, the traffic offered is calculated as 34.6 Erlang. Ifthe traffic per subscriber is assumed to be 0.02 Erlang, atotal of 34.6/0.02 ¼ 1730 subscribers in the cell arefound. Then, this traffic measurement provides anestimation of the number of subscribers per cell, which isrelated to vehicles because drivers or passengers handle cellphones.

2.4 Global navigation satellite system

The global navigation satellite system (GNSS) solves theproblem of identifying the exact location on the earth’ssurface. The most used positioning system today is theNAVSTAR/GPS, developed during the 1960s and 1970sby the US Department of Defence. The GPS is capable ofproviding accuracy down to centimetre level for static users,while in vehicles, because they move, it is possible to obtainaccuracy down to 3 m using other more complexmethodologies. These systems are necessary to effectivelymanage transportation as well as sustain user mobility.These systems are vital for intelligent transportationmanagement systems, to guarantee the safety, to improvetraffic management and controlling congestion togetherwith environmental impact. In addition, having highlocation accuracy offers a broad range of traffic data estimates.

Although the use of GNSS can provide better locationaccuracy, the revision presented in this paper focusesexclusively on the possibilities offered by cellular systemswithout any modification as well as mobile phone itself.Data from phones equipped with GPS are typically limitedin its sample size, so that they are not necessarilyrepresentative of the entire population. Cellular-network-based solution has potential larger sample size than thephones equipped with assisted-GPS (A-GPS) because cellphones reached extensive market penetration. So thepotential of using location data of A-GPS phones forestimating general traffic data is more reduced, although itmay be used for completing other Advanced trafficinformation systems. Besides, phones equipped with GPSare expensive because they are specially designed. However,there are studies that use these navigation systems to obtainbetter accuracy of the estimated parameters. In 2005, theTransportation Development Centre [9] in Canadadeveloped a project that use cell phones equipped withA-GPS to demonstrate their efficiency in measuring trafficspeeds with a series of these terminals.

3 Review of traffic data based onmobile networkThe section above described procedures executed in mobilesystems to retain location data about users in the networkto route telephone traffic to the correct destination. Theseprocedures provide phone data that, after the suitabletreatment, can be useful to characterise roadway traffic with

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parameters such as speed, traffic volume or origin–destination (O–D) mobility. The following subsectionsprovide a more detailed review of the system used inexisting simulation works and field tests in the academicand the industrial field for the parameters collection.

3.1 O–D matrix

The O–D matrices are used to quantify and synthesisemobility associated with person or goods. These matricesprovide information on the number of trips performedbetween an origin and a destination area during a givenperiod of time. Hence, it provides an information structurethat represents the transportation demand in an area.Traditionally, the areas that comprise the centre origins anddestinations of a matrix, that is, the areas where trips startand end are defined by social and economic criteria orpopulation aggregates that correspond with areas that mayhave a cause relation with the transportation movementsthat occur between them. These matrices can be producedwith different levels of aggregation, depending on the levelof detail desired (size of areas) or the type of informationrequired: by transportation mode by type of good beingtransported (persons, goods etc.). The units employed todefine an O–D matrix can be the number of vehicles orpersons, the weight or even mobile phones.

In mobile systems, the service coverage area is classified incells and LAs, although one can also find RAs for 2.5G and3G. As seen in Section 2, cellular system containsinformation about the locations of its users over time atdifferent accuracy levels depending on phone status. If thisconcept is applied to phones on board moving vehicles, aphone with a voice/data call in progress provides frequentdata on its trip: in terms of cells it travels through. Even ifa phone is in idle status (turned on and not on-call), it canalso offer valid information on its trip, as the system alwaysknows what LA the phone is in. In this case, although lessaccurate and frequent, it is also possible to obtaininformation about its trip. So to build an O–D matrixfrom the phone location details, it is necessary to define theaccuracy/aggregation level to be achieved, that is, if thecentroids shall be cells or the LAs found in the servicecoverage area of the studied region.

It is necessary to indicate that the matrix aggregation levelis intimately related to the phone location data that generatesit. So to obtain a trip matrix between origin and destinationcells, it is necessary to know all cells that the phones travelthrough at each time. This is possible in the case oflocation data associated with calls (voice or data) performedby the phones. However, it is not probably that the call islong enough to infer the complete trip. Besides, the samplesize used is more reduced if using all the phones turned on.

Numerous works has been presented on this O–D cellsmatrix idea during recent years. In 2002, after years ofstudy, White and Wells [10] published the results of a

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project developed jointly by Transport Research Laboratory(TRL) and Highways Agency in the UK. This projectconsisted in the extraction of information on mobilitybetween O–D cells with mobile phone location detailsassociated with calls. We know that, when a phone initiatesa call, a serving base station is known at all times andtherefore the cell it is located in. For billing purposes,multiple details associated with each call are stored, such asphone position at the start and end of the call or itsduration, among others. TRL developed algorithms toanalyse anonymous phone data originating from billingdata, which were supplied by the operator BTCellnet(currently O2). So the study makes use of this informationof initial and ending position (O–D cells) of a call toobtain mobility data of the region under study. Thisproduces a trip matrix where the centroids correspond tothe cells that define the coverage of the area being analysed.

The previous study only manages a subset of all phones. Inone case, they only use data of those that make calls and in theother data from a group of phones capable of transmittingtheir position to a server. It would be of vital importance toincrease the sample size to produce the OD matrices. Thismeans using data from idle phones. Mobility managementin cellular network requires that a cell phone updates itslocation once it enters another new LA from the old LAby means of the LU process. This process can handle allthe cell phones in idle status (turned on and not on-call).

This was used by Caceres et al. [11]. They proposed amethodology to infer OD matrices using the scheme ofcellular system contains information about the locations ofits users over time in terms of LA. In this case, thecentroids of the matrix were defined by the set of LAsexisting in a cellular network of a certain region. By asimulation tool, which combined a traffic simulator forvehicle management with a specific module to modelmobility management used in a GSM network over a set ofphones associated to vehicles, synthetic databases withlocation records were generated. The analysis of recordsallowed to identify the origin LA and destination LA ofthe trip making by each phone. This analysis of recordsalso allowed to distinguish between valid trips, which wereassociated with origin and destination LAs of the matrixunder consideration from the rest of trips passing through,which are trips initiated or ended in origins or destinationsother than that included in the matrix. Furthermore, sincethe estimate values were expressed in terms of number ofphones, this work implemented a correction algorithm toconvert data from switched-on phones handled by a singlecarrier into equivalent vehicles data. The use of asimulation study has the advantage of that informationabout the real location of each vehicle every second can beextracted from the simulation to assess the estimates. Thus,after comparing the real and estimated trip matrix, thereduced errors proved that information of turned-onphones about the LA may be useful to obtain an O–Dmatrix correlated with the trips carried out over an area.


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However, LA has large coverage area, so intra-LA trips thatoccur in such a large area would not be able to capture usingLA changes (LU process).

In any case, both handover and LU process, the advantageis that the data is collected directly from the traffic stream andnot from survey data (such as a home-based, roadside orlicense plate journey survey), which are time-consumingand often produce biased results. However, both processeshave the disadvantage of poor location accuracy. Thecollected data provides information about the cell or theLA, respectively, where phones are located. These twozones have coverage areas whose size varies according tourban or suburban/rural areas and many intra-area tripscould occur in larger areas. So both methodologies arefeasible to obtain a matrix to be adjusted by any traditionaladjustment process, but not to build a reliable trip matrix.

3.2 Volume (traffic flow)

Traffic volume is the number of vehicles that passing througha point or section of a lane or roadway during a specified timeperiod. This time period of evaluation defines different typesof volume, for example, hourly volume is the number ofvehicles to be passing a point during an hour. Flowrepresents the number of vehicles passing a point during atime interval ,1 h, but expressed as an equivalent hourlyrate. A flow rate is found by taking the number of vehiclesobserved in a sub-hourly period and dividing it by the time(in hours) over which they were observed. The temporaldistributions of traffic volumes are originated by differentlifestyles, which determine traffic patterns based on thetime (seasons, weekends, peak-hours etc.). Traffic volumeand flow are the variables used to quantify demand(number of vehicle that desire to travel past a point duringa specified period). Thus, these parameters can beunderstood as the use of the roadway by transportationdemand, providing an intuitive description of trafficbehaviour during a given period of time.

Volume in general is measured using different ways such asmanual counting, detector/sensor counting or moving-carobserver method, which requires the installation ofadditional elements on roads (loop detector, cameras, . . .).Traffic counters provide information about the amount ofvehicles travelling through a given point or section of aroadway, also known as traffic count data. Traditionally,traffic counts are obtained automatically at a series of pointsassociated with a subset of roadway links, thus providinglimited coverage of the entire transportation network. So itdepends on existing detectors infrastructure, which tends tobe expensive to extend or modify because of the cost ofinstallation and maintenance. Traffic volume is notconstant but varies depending on the mobility ofpopulation, road category, season etc. Therefore newalternatives are required that can characterise traffic in afast, accurate and continuous manner, at an acceptable price.


One way to obtain this information is through cellularsystem. In comparison with fixed sensors, cell phonesprovide traffic behaviour information in any area withphone coverage, without requiring additional infrastructure.In these cases, the volume would be associated with phonetransit through a point or area. Hence there are certainevents and processes in GSM related to mobilitymanagement that allow detecting change of areas, either atcell level (handover) or at LA (LU). There is even amonitoring of changes at RA level for 2.5G and 3Gnetworks.

Analysing the network topology and distribution of cellsor LAs, it is possible to associate borders between such areaswith road sections where in a ‘virtual’ traffic counter islocated. This way, each phone changes cell during a call(handover) or changes LA while powered on (LUprocess), we can consider that it has passed through the‘virtual’ traffic counter located at that border. This requiresanalysing the records stored in the system databases anddetect the instant in which the event occurs as well ascells involved in a handover or LAs associated with anLU process, to identify the traffic count produced byphones transit.

Different studies have been published related to this idea inrecent years. One of them was the one developed byThiessenhusen et al. on the DLR using the data suppliedby Vodafone [12]. The research uses the handover as eventto detect phone transit of a given operator through ‘virtual’traffic counters located on the borders between two cells.During a handover, a series of associated parameters arestored, such as cells involved or the instant in which theyoccur. The analysis of these parameters shall allow to detecttransits through the corresponding border. The resultspublished show how this phone flow (calls) is closelyrelated to the flow of vehicles measured by detectorsinstalled at those points in the network. Fig. 3 comparesboth values during 1 day. There are typical flow peaksassociated with peak times both in the morning andafternoon, showing the influence of phone habits (calltimes) in the changes of flow of typical vehicles.

However, the handover only monitors the passage throughtwo cells for phones that have a call in progress. Hence, thesize sample of terminals that we obtain details for is smalland it would be interesting to extend the sample size toobtain a more representative sample of all vehicles.Therefore there is an alternative to monitoring trafficthrough two areas but, in this case, at LA level.

The work presented by Caceres et al. in 2006 uses thiscriterion [11]. A phone, which is merely turned on,updates its position once it enters a new LA from the oldone by the LU process, reflecting the transit through thepair of LAs. This research considered the existence of‘virtual’ traffic counters located in road sections thatcoincided with border zones between LAs. Even though

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Figure 3 Flow (black line) and phone calls number (grey dotted line) as function of time of day

Left: northbound (from city); right: southbound (to city)All data are sliding averages over a 15 min sample interval [12]

this scheme uses LA borders, it works at cell accuracy. Thepassage from an LA to another is performed by cells of theLAs involved. So the location of ‘virtual’ traffic countersreally identifies the precise cells of these LAs where aphone is travelling through. Fig. 4 depicts various ‘virtual’traffic counters in the border between the LA1 and theLA2; one of them (from LA1 to LA2 towards the cell C)detects the passage of phones that move from road section1 to road section 2 through this border zone.

In order to validate this idea, Caceres et al. developed asimulation study that used fictitious HLR and VLRdatabases with location records of phones in the simulatedvehicles. An LU record contains information on both thenew LA and the cell within that LA. The location datawere analysed seeking the records associated with LAchanges for each phone. This analysis makes possible to seeif the LA change belongs to a set of borders of interest,and detect the instant of passage to compute it for theappropriate ‘virtual’ traffic counter. This flow measurementis based on a sample of cell phones switched-on handled bya single carrier. So it was extended to an equivalent vehicleflow data using suitable correction algorithms. Fig. 5compares the flow estimated from phone countstransformed into vehicle data for a ‘virtual’ traffic counterlocated in a border between two LA with the flow measureby a loop detector in the same position.

The results show the high correlation between flowsmeasured by a loop detector and the estimated by phonescounts using the LA update process over an inter-urbanroad. This is because of the fact that the majority ofvehicles have phones on board and they are regarded as a

Figure 4 Example of location of two ‘virtual’ trafficcounters


probe vehicle. All cell phones switched on execute ‘LAupdate’ when entering a new LA and therefore its passagewill be detected by the ‘virtual’ traffic counters. This is oneof the advantages that the LU process alternative offerswith regard to the handover, which considers the sample asall switched-on phones and not exclusively those in a callwhile crossing a pair of cells. Furthermore, the use of anaggregation level in terms of LA does not involve loosingprecision with regard to the handover (cell level) becausethe entrance in a new LA (group of cells) is performed byone of its cells. So the accuracy is the same as with thehandover. However, the borders between LA are spatiallyless frequent than those between cells and, then, there willbe less ‘virtual’ traffic counters.

3.3 Speed

Speed is one of the parameters most studied to assess thequality of service of a road depending on the demand itsupports, especially in urban environments. Speed isnormally measured at a point or short section of a road todetermine the rate of motion of vehicles passing through it.Speed may be represented by different definitions such astime-mean speed, space-mean speed, spot speed or travelspeed. Its measure depends on many factors, such as roadcategory (urban, inter-urban, motorway, . . .), road width ornumber of lanes. Furthermore, there are other variablefactors that also affect it, such as climate, seasonality (peaktime, holidays, . . .). Hence, different alternatives are usedto measure it such as floating cars (equipped vehicles mergein the traffic stream and collect traffic data) or sensorsembedded in the roadway which use vehicle identificationtechniques. Both alternatives represent an additionalexpense in terms of installation and maintenance ofequipment.

Ygnace et al. presented an alternative for measuring trafficspeed based on cell phone data in the report of the STRIPproject [13]. This work used the installation of elements onthe studied freeway segments to monitor Abis/A radiointerfaces. Combining with neural networks, it allowed tomonitor signalling messages exchanged between a phoneand the network to obtain a periodic location estimates


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Figure 5 Comparison between real and estimate flows through a ‘virtual’ traffic counter [11]

(accuracy: 100–150 m). This estimate, together with certainalgorithms and historical information in databases, served toproperly identify the position of the vehicle on the road andpredict the speed and travel times. Fig. 6 compares cell-phone-based speed data with loop detector speed data overa 24 h period on freeways in the surrounding area of Lyon(France). The average speeds in an intercity freeway fromcell phone probe data were about 10% lower than thatobtained from loop detector data. These average speedsfrom phone probes were even lower (24–32%) in urban

Figure 6 Mean speed: phones probes against loops inter-city (up) and intra-city (down) Motorway Northbound [13]


freeway sections because the incorrect positioning ofphones that in reality was near the road but not on it.Despite this, the results showed that the Abis/Atechnology can be considered feasible for roadway segmentsbetween cities, although it required an improvement to thepositioning algorithms, especially for applications focusedon real-time usage.

The previous work required the installation of additionalelements over the network and/or terminals modification.However, it is possible to obtain speed measures usingmobile system without modifications. The work presentedby Thiessenhusen et al. on the DLR with data fromVodafone evidences this fact [12]. It employed data fromthe handovers to measure traffic parameters such as speed.If the call that produced the handover is long enough tocross the new cell entirely, a second handover is executed.The idea of double handover (Fig. 7) to speed estimate isalso studied in a similar test field presented in 2006 byBirle ans Wermuth [14] with regard to the TrafficOnLineproject.

Then, a double handover would have time information onthe entrance (initial handover) and exit (final handover) froma precise cell. These times, together with the location ofboundary between cells (known to the operator), will helpto produce an average speed estimate for the road sectionrunning through the cell between two border zones thatinvoked the handover (Fig. 7). Fig. 8 compares speedmeasurements taken with three different sources: doublehandover GSM data, loop detectors and Floating Car Data(FCD) taken by GPS installed in taxis.

One problem of this approach is that the sample onlyconsiders those phones that make sufficiently long calls tocross through a cell entirely, which is not frequent. Inaddition, this scheme shall be reliable in those cases inwhich a single roadway link exists into the border zonebetween two cells, so it can be uniquely identified. That is,generally, on main motorways or roadways where there areno other roads nearby to confuse the identification.However, the identification of the route followed by thephone probe can be problematic when multiple roadwaylinks exist within the border zone between cells, especiallyin urban areas where multiple street passing through a cell.So this identification is more difficult and insufficient tocollect speed data. Hence, in the case of urban areas, Birle

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Figure 7 Concept of speed estimation using double handover [14]

and Wermuth [14] propose to use areas where a handoveroccurs because of an overlapping of coverage caused by theexistence of buildings (Fig. 9).

This helps to make identification of unique locations inurban areas within a cell more feasible than with typical celloverlapping. However, this method requires additionalinformation on signal strength of adjacent cells, that is,measurement reports to identify the route taken by themobile terminal as it crosses the cell. The results obtainedwith it greatly improve the accuracy of speed estimates inurban environments.

In conclusion, the relatively low cost of implementing aspeed data collection systems based on cell phones probesallows considering it as a feasible alternative to determinespeed. Furthermore, they provide a more realisticmagnitude of the real mean speed of the traffic flow. Loopdetectors and other detection devices deliver spot-meanspeed (i.e. the time-mean speed) because they measure in apoint along a roadway. However, the spot speed at a singlepoint is not representative of measuring the overall speed ofa roadway. In almost all cases involving the calculation ofaverage speeds, the space-mean speed should be used,which is associated with a specified length of roadway.Cell-phone-based systems report space-mean speed becausedata are based on a certain spatial region. Speed iscalculated using the average travel time and length for theroadway segment.

Figure 8 Speed as function of time from GSM data (plussymbols), loop (minus symbols) and FCD (multiplicationsymbols) [12]


3.4 Travel time

Travel time prediction is also essential in road network-planning studies. Travel time is the time used by eachvehicle to travel between two fixed points. These times aremeasured in road sections of a given length to identify theservice quality provided as well as their variations. Thedetermination of these times varies depending on thesection being observed. Observing units are typically usedin sections of short and/or medium length that record theinstant in which each vehicle enters and leaves the roadsection, identifying it by its number plate, vehicle type etc.However, in the case of sections of greater length, it is alsopossible to cover the same section several times with onevehicle and measure the time. The inclusion of newtechnologies is providing the process with a certain amountof automation through the use of cameras and imagerecognition.

Logically, the time concept is closely related to speedtherefore methods described above are also used to estimatetravel times for a road section. In general, the concept ofhandover is the most used to estimate times and speeds inexisting research work and field tests. Linauer and Leihs[15] presented a work that identifies areas with anoverlapping of coverage between cells (handover areas) andmeasures the travel times between two adjacent ‘handoverareas’ by analysing the signals sent to execute handovers.However, this alternative has the disadvantage of matchingthese areas to locations on the road network is a difficultchallenge because of its relatively large size. Recently, Bar-Gera examined the performance of a new operational systemdeveloped by Estimotion Ltd. (ITIS Inc.) for measuring

Figure 9 Evolution of the handover approach [14]


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traffic speeds and travel times based on handover event [16].This system can be capable of computing the location ofeach handover area within a probability of 85%. In thisstudy, cellular-phone-based measurements are comparedwith those obtained by loop detectors as well as withfloating car data from 25 vehicles. The results showed thatthe overall correlation was very good. Only four outlierswere excluded because the floating car measurements weresubstantially longer than the loop and cellular data.

Other alternative is in the research work performed byFinnra (Finnish Road Administration) in 2002 [17, 18].This work is based on the idea that cell phones moving ona certain route always change base station (cell) almostexactly at the same place. As already mentioned, aswitched-on phone regularly exchanges information withthe base stations that comprise the network. Therefore thework developed by Finnra proposes to monitor thissignalling exchanged between the phone and the networkto estimate traffic data. Specifically, it analyses the timerequired by each phone to cross a road section from themoment it enters the service area of a base station (cell)until the next. This allows estimating the travel timeassociated with that section. According to this, it was onlynecessary to monitor 5% of the phones during the pilot testto update the time patterns every 30 min. The travel timemeasures from cameras [licence plate recognition (LPR)]were compared with those obtained from cell phone data inobservation points close to the monitoring points of theLPR system, showing a good correlation (Fig. 10). Themain differences in measured travel times were mostlyattributable to the differences in location of the parallelsystems. However, this study revealed possible technicalproblems derived from the cellular-network-based data incertain cases, such as parallel roads or pedestrians, as wellas the location of the base stations is not always optimal fortraffic-monitoring purposes.

3.4.1 Traffic congestion: Traffic congestion isoriginated by the existence of some event that causes a


change in typical average speed values and travel timesassociated with a section. The previous paragraphsdescribed some of the methods based on cellular networksto characterise road sections in terms of speed and times.An analysis of abrupt variations in any of these parameterswith regard to typical values can be used to detectcongestions. On other hand, a measure of the telephonetraffic can also be used. The design of a cellular networkdepends on many aspects such as telephone traffic. Inurban areas, where the traffic is more heavily concentrated,the volume of calls is high. In suburban and in rural areas,the traffic demand tends to be small. Generally, these areasare associated with main inter-urban roads (motorways,trunk roads, . . .), which are far from urban areas. Hence,the telephone traffic supported by base stations on theseroads mainly corresponds to that generated by theoccupants of vehicles travelling on it. To detect events suchas accidents, jams or any incident that may alter the normaltraffic stream, it is possible to use historical measures of thetelephone traffic (Erlangs) at these base stations duringdifferent typical time periods. In congestion situations,mobile users tend to make more calls because of reasonssuch as calling the office or talking to someone. Thisreason triggers traffic intensity by increasing the volume ofcalls processed by the mobile system. This way, a simplecomparison with historically measured values can detect theexistence of any event that has caused traffic congestion.

A study developed by INRETS analysed the relationbetween cellular call volumes (in and out) and the existenceof incidents [13], by considering the hypothesis of usersthat tends to make more calls when in a traffic jam. Fig. 11shows the relation obtained where call volume is theaverage number of cellular calls (in and out) for the 12 minperiod corresponding to a certain level of incident. Thelevel of incident is an indicator aggregating the state ofincidents which have been recorded along the 21 loops.Fig. 11 depicts that incoming calls did not provide relevantinformation since the incoming call volume remains moreor less constant (around 10) for different levels of incident.

Figure 10 Computed travel time from cell phone data and from LPR on Pukinmaki–Otaniemi (14 833 m) [17]

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Figure 11 Relationship between cellular call volume and the level of incidents on the A7 freeway in August and September2000 [13]

Note: Level of incidents: 0, no incident shown in any inductive loops; 14, 14 loops (out of 21) indicate an incident in 12 min intervals

Whereas there is a significant relation between outbound callsfrom phones on that road and the level of incidents since theoutbound call volume increases for higher levels of incident.So higher call volume represents a higher aggregated levelof occupancy of a cell because vehicles may be stopped on aroad because of an incident or congestion situation.

3.5 Traffic density

Traffic density is closely related to congestion, so the estimatemethodologies are similar. Traffic density measures theproximity of vehicles which reflects the freedom tomanoeuvre within the traffic stream. So it is another keyparameter to measure quality of service on a road section.Traffic density is defined as the number of vehiclesoccupying a given length of a lane or roadway at a certaininstant, excluding stationary vehicles. It is generallyexpressed as vehicles per kilometre (veh/km). Direct fieldmeasurement of density is difficult because it requiresvarious fixed measuring stations together with significantlengths of highway to be observed. However, if certain dataare available at points in a section, traffic density (veh/km)can be calculated by the quotient between flow (veh/h) andspeed (km/h), but this is only valid under certain conditions.

Mobile system suggests new ways of obtaining this trafficparameter. The Erlang unit of measurement is related withusage demand of base station resources intelecommunications systems, that is, telephone traffic thatoccurs during a period of time in each cell. This telephonetraffic also has a concept linked to occupation. As alreadymentioned in Section 2.3, this traffic measure provides anestimation of the number of subscribers (cell phones) per


cell it is servicing. This is what was used by Ratti et al. [19]in their work of the SENSEable City Laboratory (MIT).The project manages to represent certain traffic parameterson a city map, visualising the dynamics of their inhabitantsin real time. One of these parameters was phone trafficintensity, that is, density of calls (measured in Erlang) ofsubscribers for a given operator, for which they had thecooperation of the Austrian operator A1/Mobilkom thatprovided certain anonymous data. This research alsorepresents on maps information related to user mobilityusing handover-related information. Furthermore, througha prior record in a tracking-specific service, they monitoredthe positions of users, added previously and measured atregular intervals. Fig. 12 shows Erlang values of trafficintensity for each cell antenna in the metropolitan area ofMilan, interpolated using a colour-field intensity mapwhere white and light grey colours stand for higher

Figure 12 Cell phone traffic intensity at 10:00 a.m. in themetropolitan region of Milan [20]


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intensity, and dark grey and black for lower or zero intensity,respectively.

Then, the phone call volume is also closely linked tovehicular density, especially in areas that are not associatedwith residential areas but roads such as motorways or ringroads. Hence applying correction algorithms, it is possibleto convert that traffic intensity measurement for cell(CellPhonesCALL/cell) to measurements related to densityin terms of (vehicles/cell).

4 EvaluationThe best criterion for evaluating these methodologies is todetermine their impact in the use of data obtained fromthem in economic and technical terms. Therefore thissection reviews the main advantages and inconveniencesthat have been found in the research works and field testsdeveloped so far.

4.1 Advantages

The most significant advantage is sample size. The treatmentof these cell-phone-based data involves handling a very broadsample of population; nowadays, the penetration levels exceed90% of total population in most countries. In addition,cellular networks have coverage over a wide area thatimplies the ubiquitous usage. So in contrast with fixedsensor systems, cellular-network-based systems provideinformation over any area with service coverage, withoutany implementation of further sensors and without anymodification within the cellular network. Other advantageis the capability to produce traffic data regardless of thepredominant traffic circumstances or the environmentalconditions, as would occur in situations of limited visibilitywith other types of systems such as LPR devices.

On the other hand, some solutions based on cellularnetwork allow deriving results from information processedin a faster way than with traditional techniques. Thisoccurs in the case of traditional O–D matrix estimatesthrough surveys that, from initial data gathering until theexploitation of the first results, involve a long process thatmay cover a period of several years. The methodologiespresented in this revision show the speed obtained when itcomes to producing information of traffic characteristics(travel times, speed etc.), reaching some of them to provideinformation in real time. This capacity considerablyimproves information systems of passengers, as a WEB/WAP/SMS consultation or through variable message signs,since it helps to make decisions on the route to beemployed, departure time and even transportation methodto be used. A ‘feedback’ effect is generated as aconsequence that favours the flow of information byreducing travel times employed by travellers, as they choosefaster routes without congestions.

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On other hand, a cellular network is constantly ‘scanning’to determine where its subscribers are, collecting locationdata so that it can route telephone calls to the appropriatecell location. It is not difficult to imagine the economicusefulness of traffic-monitoring systems based on this dataas well. It is worth highlighting their relatively low cost ofimplementation. On one hand, it is the availability of thecell phone location data without any modification withinthe cellular network and, on the other hand, any terminalused today by most users can be valid as a traffic probe,with the mere condition that they are turned on.

Finally, the main advantage is the improvement of trafficmanagement and planning because of the shorter data-processing times. This improvement cannot be quantifiedin economic terms, but in service quality from theperspective of daily use of roads by users. Therefore thesecellular network solutions would revolutionise vehicletraffic-monitoring systems from a perspective of time, cost(using the existing cellular network infrastructure), coverageand statistical representation of the same (high rate ofmobile phone penetration).

4.2 Limitations

Cellular network-based systems have certain technicallimitations that reduce the success of the traffic dataestimation. These limitations have different effectsdepending on the measured parameters, which have beenrevised in detail in the respective subsection. This sectiondescribes the major issues associated with the application ofcell phones to estimate traffic data.

4.2.1 Size of sampled data: Obtaining a sufficientsample size of probe phones is important for reliableestimation of traffic data. In general, the larger the samplesize, the more likely it is that the sample mean reflects anaccurate representation of the mean of the whole datapopulation. As already mentioned, cellular carriers have theability to collect and store aggregate location data onhundreds of millions of subscribers. Thus, the size of cell-phone-based sample is high. However, this cell-phone-based data is also subject to potential biases because ofdifferences between the population of cell phone users andthe general population. Even so, cell-phone-based datahave a potential larger sample. The sample can be dividedinto two different categories according to the respectivestatus of phones that generate it. Sample data based onphones on-call depend on the number of calls made as wellas their duration. So its sample size, although it has morelocation accuracy, is lower than those ones from phonesthat are simply turned on. In the case of phones that areturned on and not on-call (most common status), thesample size is large, but they offer less frequent and lessaccurate location data, since the LUs are sporadic in thisstatus, with events such as expiry of a timer or change of LA.

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All these facts hinder obtaining a sufficiently significantdata sample for estimation of traffic data. However, thislimitation is not vital for a valid estimate because therequired size varies depending on the type of parameterbeing searched. In the case of speed or travel times, it ispreferred greater position accuracy to higher sample ofprobe phones. So data from calls may be sufficient. Insteadin other cases, the importance resides in the highpenetration rate of phones that act as ‘probes’ to infer trafficdata, as in the case of O–D matrices or traffic flow. Theestimate methods described in this revision reach a balancebetween the sample size and desired position accuracy,minimising the effects of these limitations on the quality ofthe estimate results.

4.2.2 Validity of traffic probes: If there is only onepassenger or driver with a cell phone in the vehicle, thevehicle can be regarded as a probe vehicle. However, thisassumption is not enough in densely populated areas wherepedestrians, cell phone users staying in buildings or anytype of motionless users can affect the traffic parametermeasurements. These subscribers do not disturb when thestudied roadway network is mainly made up of inter-urbanroads (motorways, trunk roads, . . .), which are far fromdensely populated areas. Whereas, their influence is severefor dense urban areas, so a differentiation of mobile andnon-mobile behaviour of the subscribers is indispensablefor traffic data estimates from cell phones. Those non-validsubscribers’ data should be identified and filtered out oftraffic probes. The existence of non-valid probes is relevantfor the majority of traffic parameters, although they do notaffect for speed/travel time measurements because they candirectly be filtered as unfeasible values are obtained. Thus,cellular-phone-based systems should be merged with othersystems that carried out the differentiation study based onspeed or other methods. In that respect, the Institute forApplications of Geodesy to Engineering in the project Do-iT has developed methods to ascertain and describe themoving behaviour using anonymous cell phone data and tofind out the kind of transport vehicle the phone is using[21]. They develop analysis functions to calculate anumerical membership for each traffic class, getting highermembership values using plausibility models. So thepositioning data of anonymous cell phone users obtainmore information on the traffic class memberships and theindividual traffic class ‘active’ or ‘non-active’. In addition,timetables and line routes of the public transport vehicles(line busses, local trains. . .) are included into thelocalisation network, and transformed with a fuzzytechnique because of imbalances in schedule times. Eventhe presence of various cell phones in a vehicle perturbs theestimate of traffic parameters, so cell-phone-based trafficdata should be calibrated against another monitoringmethod before giving the final estimate data.

4.2.3 Position precision: The location accuracyobtained from cell phone data depends on the phonestatus. It would be ideal to know the exact position in x, y,


z, t terms. However, this is not possible with unmodifiednetworks or terminals. The most accurate that can beachieved is the location of each phone in terms of cellduring a call. Thus, it locates a phone within the coveragearea of the base station that it is connected to. Thislocation is an approximation of the geographical area wherea phone is located, but not its exact position. There is amargin of error that depends on the cell radius. This radiusvaries between 100 and 1 km in cities. In rural areas, itdepends on antenna density, although it varies between 5and 20 km. This location accuracy worsens for phones thatdo not have an active connection to the network (not on-call), where the accuracy is at LA level. For example, ifworking at LA level (groupings of cells) instead of usingcell precision, the geographic area where the phone can befound is broader and therefore there is greater uncertainty.This error affects the matching of phones to locations onexisting roads over the service area of a cell or an LAbecause building the relation between cellular network androadway network is a difficult task. This matching can berelevant for certain parameters and erroneous matchingbrings some trouble.

One of these cases is multiple roadway links within a cellfor measures using handovers (Fig. 13), where it is likelythe handover sequences of two possible routes are the same.It is not possible to calculate the speed or travel timemeasurement for the respective road since each route hasdifferent length of cell section. However, this situation isnot so relevant in other cases; for example, the use ofaggregate information provided by cell phones on these tworoad sections jointly, is acceptable for traffic flow measures.Each method takes the appropriate accuracy of phonelocation data or aggregation level depending on therequirements for the measured parameter. It can even besolved with advanced map-matching algorithms orcomplementing the existing information with data fromother systems to determine a more accurate phone position.

In addition, there are differences in data qualityrequirements associated with different end uses of thedata. For example, data to be used for travellerinformation purposes likely have a lower-qualityrequirement than data to be used for traffic managementpurposes such as incident detection. In those cases ofhigh-quality requirements, cellular-network-based systems

Figure 13 Multiple roadway links within a cell


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could be used to complete another traffic-monitoringsystem. Modifications within the cellular network as wellas the cell phone itself could also be achieved, forexample, using AGPS-equipped phones to obtain betterlocation data accuracy.

4.3 Privacy issues

Logically, the use of data from cellular networks involves thecooperation of a carrier that provides them. Thus, privacy isan aspect to be mentioned in these systems. All these fallwithin the legal framework as they are governed byregulations in charge of protecting the privacy of phonesubscribers. The phone location data would be received andhandled in aggregate and anonymous manner, inaccordance with current regulations. The same would occurwith any kind of information taken from the cellularnetwork. Therefore the use of cell phone data does notbreak the law on private data protection, as anonymousdata do not associate information with specific users. Theobjective is only to obtain information from anonymousprobes that move through the network, regardless of thesubscriber’s characteristics or other kinds of informationassociated with it.

On other hand, it is illegal for someone to use a cell phonewhile driving according to driving legislation of manycountries [22]. Hence obtaining data from phones in use(i.e. not IDLE) in vehicles is not supported. However, ifone passenger with a cell phone exists in the vehicle, thevehicle can also be regarded as a probe vehicle, withoutinfringing legislations. In any case, it is necessary to bear inmind that not all of the proposed method require cellphone to be in use to act as a probe. Some of them onlyrequire to be switched-on cell phones, without havingeffect upon driver behaviour. When phones in use areneeded for traffic data estimates, phones in vehicles are alsoincluded in the sample data by means of passengers’phones, without the drivers’ contribution.


5 Concluding remarksThe increasing traffic mobility emerged in recent yearsrequires more complex mechanisms and techniques toproperly manage and plan the road network. Unfortunately,the traditional sensor systems are sometime expensive. Onlymain roads are equipped with sensors because economicallyit is almost impossible to install and maintain sensors forentire road network. So new alternatives are required tomonitor traffic in a fast, accurate, economic and continuousmanner. The emerging technologies should be deployed tointroduce improvements in transportation management. Anew alternative resides in mobile systems.

After reviewing various schemes to measure traffic databased on cellular networks presented in existing researchworks and field tests, it is possible to conclude that thecell-phone-based systems have potential large sample sizeand are more economic than traditional ones. They usetechnology available in cellular networks for their operation,without any modification, and the same terminalscommonly in use by the general population. It would seemto imply that traffic data estimates of any road would be aneasy task. However, factors such as technical problemsrelated to the location accuracy that may be relevant forexample, in the case of parallel roads, and other limitationssuch as pedestrians, cell phone users staying in buildings ormultiple phones per vehicle can affect the traffic parametermeasurements and reduce the viability using thistechnology for traffic data estimation. Table 1 summarisesthe main characteristics of traffic data based on cell phonesin comparison with traditional systems.

In general, the information derived from a cellular networkwithout any modification is useful to measure traffic data.However, depending on the type of application and theexisting limitations, for real-time applications or trafficmanagement purposes such as incident detection, it isrecommended that traffic-monitoring system based on cell

Table 1 Summary of main advantages and disadvantages

Traffic data Source event Advantages Disadvantages

O–D matrix handover orlocation update

sample size, data collected directly fromthe traffic stream, not time-consuming

large areas (not detected intra-area trips)

traffic flow handover orlocation update

work over a wide coverage (not limitedto main roads)

sample size, errors in densely populatedareas (pedestrians)

speed, traveltime

handover acceptable location accuracy, reportspace-mean speed, work over a wide

coverage

multiple roadway links within a cell(multiple routes)

trafficcongestion

call volume quick detection poor location accuracy

traffic density traffic intensityper cell (Erlang)

work over a wide coverage (not limitedto main roads)

sample size, errors in densely populatedareas (motionless users, pedestrians, . . .)

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phones should be tested and calibrated against anothermonitoring method. They could even be merged with othersystems to obtain a reliable and complete advanced trafficinformation system. Whereas, these systems can provideaccurate enough information for the traveller informationpurposes in comparison with information based on thecurrently implemented traditional technologies. Hencemobile systems are regarded as a promising technology forthe traffic data collection system.

6 References

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[2] HELLINGA B., FU L., TAKADA H.: ‘Traffic network conditionmonitoring via mobile phone location referencing – anemerging opportunity’. Proc. 6th Transportation SpecialtyConference held in conjunction with the AnnualConference of the Canadian Society of Civil Engineers,Toronto, Canada, 2005

[3] YIM Y.: ‘The state of cellular probes’. California PATHResearch Report, UCB-ITS-PRR-2003-25, 2003

[4] ROSE G.: ‘Mobile phones as traffic probes: practices,prospects and issues’, Transp. Rev., 26, (3), pp. 275–291

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[6] SMITH B.L., FONTAINE M.D.: ‘Private-sector provision ofcongestion data’. NCHRP 70-01, University of Virginia,November 2006

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[10] WHITE J., WELLS I.: ‘Extracting origin destinationinformation from mobile phone data’. 11th Int. Conf.


Road Transport Information and Control (Conf. Publ. No.486), London, March 2002, pp. 30–34

[11] CACERES N., WIDEBERG J.P., BENITEZ F.G.: ‘Deriving origin-destination data from a mobile phone network’, IET Proc.Intell. Transp. Syst., 2007, 1, (1), pp. 15–26

[12] THIESSENHUSEN K.-U., SCHAFER R.-P., LANG T.: ‘Traffic data fromcell phones: a comparison with loops and probe vehicledata’ (Institute of Transport Research German AerospaceCenter, Germany, 2003)

[13] YGNACE J.-L.: ‘Travel time/speed estimates on the FrenchRhone corridor network using cellular phones as probes’.Final report of the SERTI V program, INRETS, Lyon,France, 2001

[14] BIRLE C., WERMUTH M.: ‘The traffic online project’. SpecialSession: Cellular-based traffic data collection. SS45 (EU).13th ITS World Congress, London, UK, October 2006

[15] LINAUER M., LEIHS D.: ‘Generating floating car data byusing GSM-network’. Proc. 10th World Congress andExhibition on Intelligent Transport Systems and Services,Madrid, Spain, 2003

[16] BAR-GERA H.: ‘Evaluation of a cellular phone-basedsystem for measurements of traffic speeds and traveltimes: a case study from Israel’. 86th Annual Meeting ofthe Transportation Research Board, Washington, DC, 2007

[17] KUMMALA J.: ‘Travel time service utilising mobilephones’. Finnish Road Administration, Finnra Report 55/2002, Helsinki, 2002, p. 67

[18] VIRTANEN J.: ‘Mobile phones as probes in travel timemonitoring’ (Finnish Road Administration, 2002)

[19] RATTI C., PULSELLI R.M., WILLIAMS S., FRENCHMAN D.: ‘Mobilelandscapes: using location data from cell-phones forurban analysis’, Environ. Plann. B Plann. Des., 2006, 33,(5), pp. 727–748

[20] Mobile Landscape Graz in Real Time: ‘Download allimages in a zip file’, available at: http://senseable.mit.edu/graz/highres/highres.zip, accessed October 2007

[21] WILTSCHKO T., SCHWIEGER V., MOHLENBRINK W.: ‘Generatingfloating phone data for traffic flow optimization’. Proc.3rd Int. Symp. Networks for Mobility, Stuttgart, Germany,October 2006

[22] Cellular-news: ‘List of countries that ban cellphone usewhile driving’, available at: www.cellular-news.com/car_bans/, accessed November 2007


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Review of traffic data estimations extracted from cellular networks