Requirements for Modelling Bicycle Traffic A project funded by Ministry of Transportation and Telecommunications (Finland)

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1 Requirements for Modelling Bicycle Traffic A project funded by Ministry of Transportation and Telecommunications (Finland) Jukka Räsänen, Tuuli Järvi-Nykänen (Technical Research Centre of Finland) Jukka-Matti Laakso, Nina Karasmaa (Helsinki University of Technology) Matti Keränen (SCC Viatek Ltd) Pohjantie 3, P.O.Box 4, 02101Espoo,Finland matti.keranen@viatek.fi Goal - study factors why people choose bike for their trips - clarify possibilities to use mathematical models for bicycle trips Method To analyse data from six travel behaviour surveys - Oulu region, Area, 1995 (all modes) - Area, 1997 (only bikers) - Vaasa region, Turku region, The area of Mid-Uusimaa and cities of Hyvinkää and Riihimäki, 1998 In most cases the surveys were carried out in September or early spring The main purpose of the surveys was to collect base data for transportation model estimation (demand, mode choice, destination and route choice models) Year Time Method Age groups Sample Return % Area Postal >7 (trip diary), % (Tue-Thur) >18 (backgroud info) Helsinki Metropolita Phone interview/ > % (Mon-Thur) Postal Postal % (Mon-Thur) Helsinki Metropolita ja Phone interview/ % (Mon-Fri) Postal ja Phone interview/ % (Mon-Thur) Postal Mid-Uusimaa, , ( ) Phone interview/ % Hyvinkää-Riihimäki (Tue-Thur) Postal

2 Area Area Year Towns included Population (squear km) Population / km2 Oulu, Haukipudas, Kempele, Kiiminki, Muhos, a Oulunsalo, Ii, Liminka, Ylikiiminki, Hailuoto, Temmes Helsinki Metropolita 1995 Helsinki, Vantaa, Espoo, Kauniainen b Vaasa, Mustasaari, Laihia, Maalahti, Isokyrö, Jurva, c Vähäkyrö, Vöyri, Oravainen, Korsnäs, Maksamaa Helsinki Metropolita 1997 Helsinki, Vantaa, Espoo, Kauniainen c Turku, Raisio, Naantali, Lieto, Piikkiö d Mid-Uusimaa 1998 Hyvinkää, Järvenpää, Kerava, Nurmijärvi, Tuusula, e Loppi, Hausjärvi, Riihimäki, Inkoo, Karkkila, Kirkkonummi, Lohja, Mäntsälä, Nummi-Pusula, Pornainen, Porvoo, Sipoo, Siuntio, Vihti Area Information of data Year persons age group share 25,1 % 34,0 % 28,2 % 12,7 % women men Helsinki Metropolita share 16,8 % 13,4 % 26,4 % 30,9 % 12,5 % women men share 11,2 % 14,6 % 19,3 % 31,2 % 23,7 % women men Helsinki Metropolita share 15,3 % 18,7 % 26,5 % 31,9 % 7,6 % women men share 20,0 % 19,3 % 21,0 % 22,7 % 17,0 % women men Mid-Uusimaa, share 18,4 % 11,5 % 25,8 % 31,7 % 12,7 % Hyvinkää-Riihimäki women men

3 agegroup share of mode Area Year persons trips all trips walking bicycle car public transport all ,4 % ,4 % ,4 % 270 4,6 % ,7 % ,4 % ,2 % ,3 % 78 4,3 % ,2 % ,7 % ,8 % ,1 % 99 4,1 % ,5 % ,2 % ,2 % ,9 % 72 5,2 % ,6 % 53 19,4 % 54 19,8 % ,1 % 21 7,7 % 1995 all ,0 % ,1 % ,7 % ,8 % ,1 % ,0 % ,1 % ,9 % ,1 % ,8 % ,2 % 105 7,9 % ,3 % ,9 % ,4 % ,7 % 195 7,7 % ,3 % ,8 % ,5 % ,9 % 193 7,3 % ,6 % ,4 % ,1 % ,0 % 34 4,7 % ,0 % ,1 % all ,5 % ,3 % ,9 % 556 5,4 % ,0 % ,4 % ,0 % ,9 % ,7 % ,8 % ,8 % ,0 % ,1 % 58 2,9 % ,0 % ,5 % 165 6,7 % ,7 % 59 2,4 % ,2 % ,2 % 297 9,6 % ,6 % 111 3,6 % ,1 % ,9 % 119 9,6 % ,3 % 88 7,1 % all ,5 9084,5 27,3 % ,4 % ,5 % ,6 % ,1 % ,8 % ,0 % ,3 % ,2 % ,2 % ,9 % ,9 % ,6 % 794 9,8 % ,2 % 1769,5 21,9 % 723 9,0 % ,5 % 717 8,9 % ,5 20,9 % ,7 % 559 8,0 % 3728,5 53,5 % ,4 % ,5 % ,0 % 154 4,9 % ,8 % ,0 % Mid-Uusimaa, 1998 all ,5 % ,9 % ,2 % ,5 % Hyvinkää-Riihimäki ,6 % ,5 % ,8 % ,8 % 306 9,5 % ,2 % ,1 % ,9 % ,3 % 163 8,5 % ,7 % ,1 % 301 6,5 % ,4 % 308 6,6 % ,5 % ,6 % ,0 % ,1 % 363 7,6 % ,1 % ,0 % ,8 % ,1 % 44 4,0 % The share of bicycle use by age group 0,45 0,4 (%) 0,35 0,3 0,25 0,2 0,15 0,1 0,05 Oulu Vaasa Turku Mid-Uusimaa Age group

4 The share of bicycle use by trip purpose 0,5 0,45 0,4 0,35 0,3 Oulu (%) 0,25 Vaasa 0,2 0,15 0,1 Turku 0,05 0 Home-based work Homebased,non work Not homebased School Study Missing Matkaryhmä The share of bicycle use by income groups 0,35 0,3 (%) 0,25 0,2 0,15 0,1 Vaasa Turku Mid-Uusimaa 0,05 0 puuttuu Household income

5 85 % 80 % 75 % 70 % 65 % 60 % 55 % 50 % 45 % 40 % 35 % 30 % 25 % 20 % 15 % 10 % 5 % 0 % Modal split during the survey day all trips modal split of persons not used bicycle modal split of persons used bicycle walking bicycle car P.T other missing 60 % 55 % 50 % 45 % 40 % 35 % 30 % 25 % 20 % 15 % 10 % 5 % 0 % Distribution by purpose of trip Home-based work home-based,non work not home-based school missing all trips modal split of persons not used bicycle modal split of persons used bicycle

6 Primary mode of the first trip of the day and primary mode fot the rest of the day's trips 100 % 90 % 80 % 70 % 60 % 50 % 40 % 30 % 20 % 10 % 0 % Ensimmäisen matkan pääkulkutapa ja päivän muiden matkojen pääkulkutavat walking kävely bicycle pp ha car P.T jkl walking kävely bicycle pp ha car P.T jkl walking kävely bicycle pp car ha P.T jkl walking kävely bicycle pp hacar P.T jkl Modal split of the other trips of the day indicated by the mode of first trip of the day päivän muiden matkojen pääkulkutapojen osuudet, kun päivän ensimmäisen matkan pääkulkutapa on osoitettu nuolella Summary - 30% to 50% of the data gathered consists of young people s (aged 7 to 17) trips - Decision over the primary mode used during the whole day is done before the first trip - Standard survey methods do not produce data that is usable for analysis or for estimation of models for bicycle traffic Conclusions - Geocoding has to be used in coding the data - Specially targeted surveys are recommended for the analysis of bicycle use - Surveys have to place emphasis on trip-chains and to the mode-choice-determining trip of the day Why bicycle traffic should be modelled: - Biking and walking should have equal position with other modes in Transportation Master Plans - (Bicycle) network planning and impact analysis become easier and more solid - Cost-benefit and other economic analyses need quantitative methods - Promotion of bicycle use is more convincing by using scientific methods