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Liikennemallin Ja Paikkatietoanalyysin Soveltuvuus Joukkoliikenteen Matkustajamäärien Ennustamiseen Tampereella

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Liikennemallin Ja Paikkatietoanalyysin Soveltuvuus Joukkoliikenteen Matkustajamäärien Ennustamiseen Tampereella TEEMU SILLANPÄÄ LIIKENNEMALLIN JA PAIKKATIETOANALYYSIN SOVELTUVUUS JOUKKOLIIKENTEEN MATKUSTAJAMÄÄRIEN ENNUSTAMISEEN TAMPEREELLA Diplomityö Tarkastaja: TkT Kalle Vaismaa Tarkastaja ja aihe hyväksytty 27. elokuuta 2018 i TIIVISTELMÄ TEEMU SILLANPÄÄ: Liikennemallin ja paikkatietoanalyysin soveltuvuus joukko- liikenteen matkustajamäärien ennustamiseen Tampereella Tampereen teknillinen yliopisto Diplomityö, 71 sivua, 4 liitesivua Joulukuu 2018 Rakennustekniikan diplomi-insinöörin tutkinto-ohjelma Pääaine: Liikenne- ja kuljetusjärjestelmät Tarkastaja: TkT Kalle Vaismaa Avainsanat: liikennemalli, paikkatieto, joukkoliikenne, liikenne-ennuste, regressio Tampereella joukkoliikenteen matkustajamääriä on perinteisesti mallinnettu nelipor- rasajatteluun perustuvalla seudullisella TALLI-mallilla. Neliporrasmallien verkkokuvaus ja osa-aluejako ovat usein niin karkeita, että ne kuvaavat paikallisia olosuhteita ja py- säkki- tai linjatason matkustajamääriä epätarkasti. Tässä työssä oli tavoitteena löytää vaihtoehtoisia, mahdollisesti paikkatietoon perustuvia, menetelmiä näiden ongelmien rat- kaisemisemiseksi ja arvioida niiden soveltuvuutta joukkoliikenteen matkustajamäärien ennustamiseen Tampereella. TALLI-mallin lisäksi työssä tutustuttiin Helsingin työssäkäyntialueen Helmet-malliin. Tutkimuksissa vahvistui käsitys siitä, että neliporrasmallit eivät ennusta joukkoliikenteen matkustajamääriä pysäkkitasolla tarkasti. Ratkaisuksi etsittiin vaihtoehtoisia malleja kir- jallisuudesta. Selvityksessä löytyneistä malleista parhaaksi vaihtoehdoksi ongelman rat- kaisemiseksi tunnistettiin maantieteellisesti painotettu regressio eli GWR. Se toimii hyvin yhteistyössä paikkatieto-ohjelmistojen kanssa, mallien estimointi on nopeaa ja mene- telmä vaikutti edistyksellisemmältä kuin muut vaihtoehdot. Aiemmissa tutkimuksissa sillä oli saatu hyviä tuloksia myös pysäkkitasolla. Työssä estimoitiin GWR-malli Tampereelle syksyn 2017 nousijamäärätilastojen perus- teella. Mallin selittävinä muuttujina käytettiin työpaikkojen, asukkaiden ja valintamyy- mälöiden lukumäärää pysäkin lähellä, matka-aikaa joukkoliikenteellä pysäkiltä lähim- pään alakeskukseen ja Tampereen yliopistolle sekä binäärimuuttujia Keskustorin ja Kos- kipuiston pysäkeille. Mallilla saatiin tuotettua parempi nykytilan nousijamääräkuvaus kuin TALLI-mallilla sekä yksittäisten että kaikkien pysäkkien tarkkuudella. Tampereen raitiotiehanke on viimeisin hanke, johon Tampereella on laadittu matkustaja- määräennusteita. Viralliset ennusteet on laadittu TALLI-mallilla. Lisäksi Eero Kauppi- nen laati kesän ja syksyn 2018 aikana paikkatietoanalyysin avulla pysäkkikohtaisen nou- sijamääräennusteen raitiotielle. Myös tässä työssä määritetyllä GWR-mallilla laadittiin ennuste raitiotien avaamisen jälkeisille joukkoliikenteen linjastovaihtoehdoille. Eri mal- leilla tuotettuja ennusteita verrattiin toisiinsa. Kaikille työssä tarkastelluille malleille löy- dettiin soveltuva käyttötarkoitus. TALLI-malli soveltuu suurten hankkeiden tarkasteluun seututasolla. Kauppisen menetelmällä saatiin paras pysäkkikohtaisten nousijamäärien ku- vaus raitiotielle. GWR-mallilla voidaan puolestaan tutkia erilaisten tekijöiden vaikutusta matkustajamääriin ja sitä voidaan käyttää mallijärjestelmän tuotosmallina. Malli vaatisi vielä jatkokehitystä toimiakseen joukkoliikennesuunnittelun käytännön työkaluna. Kai- killa menetelmillä on puutteensa ja todennäköisesti paras tulos saadaan, kun matkustaja- määrien ennustamisessa käytetään useita menetelmiä yhdessä. ii ABSTRACT TEEMU SILLANPÄÄ: Suitability study of transport models and spatial analysis for forecasting the public transport passenger demand in Tampere Tampere University of Technology Master of Science Thesis, 71 pages, 4 Appendix pages December 2018 Master’s Degree Program in Civil Engineering Major: Transport and Logistics Engineering Examiner: D.Sc. (Tech.) Kalle Vaismaa Keywords: modelling, public transport, transport forecast, GWR, GIS Traditionally public transportation passenger demand forecasts are made with a regional four step model TALLI in Tampere. In four step models the network is usually very rough and zones are large. Therefore, the stop or line level passenger volume forecasts are usu- ally inaccurate. To solve this problem this thesis aims to find alternative methods based on GIS analysis to estimate passenger volumes in Tampere. The suitability of the models is evaluated through literature review, interviews and quantitative analysis. In the study Helsinki region’s transportation model Helmet was investigated in addition to TALLI. It was confirmed that forecasting stop level passenger volumes with four step model is inaccurate. Alternative models were searched from literature and the most prom- ising method found geographically weighted regression (GWR). According to the litera- ture review GWR performed well, GWR models are relatively easy to estimate and co- operation with GIS is possible. In the thesis a GWR model was tested in Tampere. The estimation was based on statistics of passengers boarding at each stop in November 2017. The explanatory variables were number of jobs, shops and population near the stop, travel time from the stop to University of Tampere and to the five biggest residential areas of Tampere and dummy variables for Keskustori and Koskipuisto stops. The predicted number of passengers boarding was more accurate with GWR than with TALLI both in stop level and in city level compared to the statistics’ values. Tampere light rail is the latest public transportation project in Tampere where passenger demand has been forecast. The official forecasts were made with TALLI. During summer and autumn of 2018 Eero Kauppinen used a GIS based method to predict the number of passengers boarding at every light rail stop. Also, the GWR model defined in this thesis was used to forecast stop level public transportation passenger volumes after the opening of the light rail. The forecasts of all methods were compared to each other and the suita- bility of the methods were evaluated. It was recognized that all the transport models ex- amined in the thesis are suitable for different applications. TALLI is recommended for strategic modelling and multimodal analyses. Kauppinen’s model is the most suitable at modelling stop level demand. GWR excels in land use analyses and it can be used as a demand model of a more complicated model system. All the methods had disadvantages as well. The best results are expected to be achieved when different models are used to- gether in analysis. iii ALKUSANAT Alussa oli huhu. Norjassa on tehty ihmeellisiä paikkatietoanalyysejä, joilla on ennustettu raitiotien matkustajamääriä neliporrasmallia tarkemmin. Huhusta syntyi ajatus. Voisiko tällaista menetelmää hyödyntää myös Tampereella? Siitä se ajatus sitten lähti. Työ on tehty Tampereen raitiotien kehitysohjelman tilauksesta. Kun Maija Musto ja Eero Kauppinen ensimmäisen kerran tulivat kertomaan, että ovat keksineet minulle diplomityöaiheen, olin kieltämättä melko pihalla, mitä tässä nyt oikein haetaan. Vielä työtä aloittaessakin oli hieman epäselvää, mihin suuntaan työtä lähdetään viemään. Keskustelut Jukka Räsäsen ja myöhemmin Kalle Vaismaan kanssa kuitenkin auttoivat löytämään oikean suunnan. Oleellinen osa työtä olivat asiantuntijahaastattelut. Oli ilo huomata, kuinka halukkaita asiantuntijat olivat auttamaan ja jakamaan tietotaitoaan. Kiitos kaikille haastatteluihin osallistuneille ja muilla tavoin osaamistaan ja ajatuksiaan jakaneille. Haluaisin kiittää Ville-Mikael Tuomista, Antti Haukkaa ja Juha-Pekka Häyrystä työn mahdollistamisesta ja ohjaamisesta. Kiitos Maija Mustolle työstä ja tuesta projektinhal- lintaan liittyvissä asioissa ja innostavasta otteesta työn ohjaukseen. Kiitos myös Riikka Sallille ja Kalle Vaismaalle asiantuntevasta ohjauksesta. Erityiskiitos Jukka Räsäselle, jonka apu ja tuki työn aikana oli korvaamatonta. Kiitos kollegoilleni Eero Salmiselle avusta paikkatietoanalyysin kanssa, Anne Jokiran- nalle abstractin oikolukemisesta ja Eero Kauppiselle monipuolisesta avusta työn kaikissa vaiheissa. Kiitos myös Tuomas Paloselle avusta TALLI-mallin kanssa. Lopuksi haluan kiittää Pilviä, joka huolehti jaksamisestani ja kannusti silloinkin, kun oma usko työn val- mistumisesta ajoissa horjui. Tampereella, 19.11.2018 Teemu Sillanpää iv SISÄLLYSLUETTELO 1. JOHDANTO ............................................................................................................. 1 1.1 Työn taustat .................................................................................................... 1 1.2 Tavoitteet ja rajaus ......................................................................................... 1 1.3 Tutkimuksen suoritus ..................................................................................... 2 1.4 Raportin rakenne ............................................................................................ 3 2. JOUKKOLIIKENNE JA LIIKENNEMALLIT ........................................................ 5 2.1 Joukkoliikenteen palvelutaso ......................................................................... 5 2.2 Liikennemallit ja -ennusteet ........................................................................... 6 2.2.1 Neliporrasmalli................................................................................. 7 2.2.2 Regressiomallit............................................................................... 10 2.2.3 Gravitaatiomallit ............................................................................ 11 2.2.4 Logittimallit
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