Research Article Comparative Analysis of Travel Patterns from Cellular Network DataandanUrbanTravelDemandModel Nils Breyer ,ClasRydergren ,andDavidGundleg˚ ard Department of Science and Technology, Link¨ oping University, Link¨ oping, Sweden CorrespondenceshouldbeaddressedtoNilsBreyer;nils.breyer@liu.se Received 6 June 2019; Accepted 16 January 2020; Published 13 February 2020 AcademicEditor:RakeshMishra Copyright©2020NilsBreyeretal.isisanopenaccessarticledistributedundertheCreativeCommonsAttributionLicense, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Dataontravelpatternsandtraveldemandareanimportantinputtotoday’strafficmodelsusedfortrafficplanning.Traditionally, traveldemandismodelledusingcensusdata,travelsurveys,andtrafficcounts.Problemsarisefromthefactthatthesamplesizes areratherlimitedandthattheyareexpensivetocollectandupdatethedata.Cellularnetworkdataareapromisinglarge-scaledata sourcetoobtainabetterunderstandingofhumanmobility.Toinfertraveldemand,weproposeamethodthatstartsbyextracting tripsfromcellularnetworkdata.Tofindoutwhichtypesoftripscanbeextracted,weuseasmall-scalecellularnetworkdataset collectedfrom20mobilephonestogetherwithGPStrackscollectedonthesamedevice.Usingalarge-scaledatasetofcellular networkdatafromaSwedishoperatorforthemunicipalityofNorrk¨ oping,wecomparethetraveldemandinferredfromcellular networkdatatothemunicipality’sexistingurbantraveldemandmodelaswellaspublictransittap-ins.eresultsforthesmall- scaledatasetshowthat,withtheproposedtripextractionmethods,therecall(tripdetectionrate)isabout50%forshorttripsof1- 2km,whileitis75–80%fortripsofmorethan5km.Similarly,therecallalsodiffersbyatravelmodewithmorethan80%for publictransit,74%forcar,butonly53%forbicycleandwalking.Afteraggregatingtripsintoanorigin-destinationmatrix,the correlationisweak(R 2 < 0.2)usingtheoriginalzoningusedinthetraveldemandmodelwith189zones,whileitissignificantwith R 2 � 0.82whenaggregatingto24zones.Wefindthatthechoiceofthetripextractionmethodiscrucialforthetraveldemand estimation as we find systematic differences in the resulting travel demand matrices using two different methods. 1.Introduction Inordertomeetanincreasingtraveldemandandtheneedto reduce environmental impacts, today’s traffic system needs tobecomemoreefficient.Tomakewell-informeddecisions whenimprovingthetrafficsystem,adetailedunderstanding of human mobility is needed. is calls for comprehensive informationontravelpatternsandtheactualtraveldemand, which today is difficult to obtain [1, 2]. Cellular network data are seen as a promising data source which can be used to augment both traffic man- agement[3]andtrafficplanning[4,5].Asalarge-scaledata source, it can give new insights on mobility with all travel modes.Itisalsoeasiertokeepuptodatethantravelsurveys. Several studies have investigated the possibilities to infer traveldemandfromcellularnetworkdata.Estimatingtravel demand from cellular network data involves a number of processing steps. Few studies have used real-world cellular network data and compared all outputs generated in these processingstepstootherexistingdata.erefore,thereisno comprehensive understanding of the quality and potential problems that can arise in the data processing steps. ispaperaimstoanalysethepotentialandlimitations oftraveldemandinferencefromlarge-scalecellularnetwork data. We propose a process to obtain travel patterns from cellular network data, consisting of two alternative algo- rithms to extract trips and a method to infer time-sliced travel demand. In order to evaluate the trip extraction performance in terms of recall and precision, we compare tripsextractedfromcellularnetworkdatatotripsobtained fromGPStrackscollectedonthesamemobiledevice.Using this method, we can analyse which types of trips can be detectedfromcellularnetworkdatabyapplyingthetwotrip extractionmethods.Weinferthetime-slicedtraveldemand Hindawi Journal of Advanced Transportation Volume 2020, Article ID 3267474, 17 pages https://doi.org/10.1155/2020/3267474