Can we forecast the demand for high speed rail? MARIA BÖRJESSON 27 november 2009, 1.

En presentation över ämnet: "Can we forecast the demand for high speed rail? MARIA BÖRJESSON 27 november 2009, 1."— Presentationens avskrift:

1 Can we forecast the demand for high speed rail? MARIA BÖRJESSON 27 november 2009, 1

2 Presentationen Jämföra Sampers’ elasticiteter och korselasticiteter med andra modeller och aggregerade svenska data. Jämföra den prognostiserade efterfrågan av Götalandsbanan med aggregerad svenska data. Nya och gamla modellen Långväga resande viktigt: 23% av allt privat resande. 27 november 2009, 2

3 Varför är långväga resande svårare/mer ifrågasatt? Majority deals with regional travel. Ben-Akiva (2010), de Bok et al. (2010), Outwater et al (2010) and Rohr & Fox (2010) Non-linearity in the sensitivity to travel time (Gaudry, 2008) Daly (2010) Algers. Long-distance travel is more heterogonous (Axhausen et al., 1997) Large shift in technology? 27 november 2009, 3

4 Paris-Lyon (2h) 9 % air; 91 % Madrid-Seville (2h 15m) air 20 %; rail 80% Madrid-Barcelona corridor (2h 38m): 47%; 53% air. London–Paris route, (2h 15m) air 20 %; rail 80% In Germany, where the HSR uses existing networks, only 12 % has shifted. HSR experiences in Europe

5 Elasticities 27 november 2009, 5 StudyElasticityComment Rohr et al. (2010) -0.9 (bns) -0.4 (priv) RP data. Average travel distance elasticity Román et al. (2010) -0.4 (Madrid-Barcelona) -0.6 (Madrid-Zaragoza) RP/SP data Atkins (2002) -0.9/-1.3 (bsn); -0.8/-0.9 (priv) RP/SP data UK 2 corridors. Bok et al. (2010) -0.6 (bsn) -0.5 (commute) -0.3 (other) Cross-sectional travel survey. Portugal Cabanne (2003) 0.3-0.5 (access) < 0.16 (Air cross-elasticity) Time series data – France Dargay (2010)-2 Aggregate time series UK Paris-Lyon -1.6 (Phase 1) -1.1 (Phase 1) HSR line 1981 - 1983 Madrid-Barcelona-1.3HSR line 2008 Madrid-Sevilla-1.2HSR line 1992,

6 Can we forecast using aggregate data? Source: Nelldal and Jansson, 2010

7 SAMPERS Nested logit model: frequency, destination and mode Car, bus, train and air Estimated on large RP data set (National travel survey 1994- 2000) Car as driver, 28985; Car as passenger; 19530; Train, 7013; Bus, 4809; Air, 4406; other modes, 1072 VTT €/h In-vehicle time car One-day trips 17.9 In-vehicle time car Overnight stays 11.0 In-vehicle time other modes; One- day trips 7.7 In-vehicle time other modes; Overnight stays 5.5

8 Kalibrering Trafikräkningar för flyg och tåg 61 % ökning av tågresor; 16 % for air. Observera för lite flyg (enl. mina siffror) Bilresor har kalibrerats mot RVU’n 27 november 2009, 8

9 Data Korta långväga bilresor är underrepresenterade i data resvaneundersökning Viktigt hur man ställer frågan 27 november 2009, 9 Traffic production [billion km] 0,0 1,0 2,0 3,0 4,0 5,0 6,0 7,0 8,0 9,0 100-149 km150-199 km200-399 km400-799 km800- km One-day survey Long distance survey

10 Elasticities 27 november 2009, 10 PurposeSAMPERS Rohr et al. (2010) Elasticity, car in- vehicle time Business-0.87-1.40 Private-0.53-0.46 Elasticity, Fuel Price Business-0.14-0.13 Private-0.15-0.10 Elasticity, rail in vehicle time Business-1.50-0.88 Private-1.01-0.39 Elasticity, rail fare Business-0.72-0.47 Private -0.59-0.62

11 Elasticities 27 november 2009, 11 StudyElasticityComment Sampers -1.5 (bsn) -1.0 (priv) RP data. Average travel distance elasticity Rohr et al. (2010) -0.9 (bns) -0.4 (priv) RP data. Average travel distance elasticity Román et al. (2010) -0.4 (Madrid-Barcelona) -0.6 (Madrid-Zaragoza) RP/SP data Atkins (2002) -0.9/-1.3 (bsn); -0.8/-0.9 (priv) RP/SP data UK 2 corridors. Bok et al. (2010) -0.6 (bsn) -0.5 (commute) -0.3 (other) Cross-sectional travel survey. Portugal Cabanne (2003) 0.3-0.5 (access) < 0.16 (Air cross-elasticity) Time series data – France Dargay (2010)-2 Aggregate time series UK Paris-Lyon -1.6 (Phase 1) -1.1 (Phase 1) HSR line 1981 - 1983 Madrid-Barcelona-1.3HSR line 2008 Madrid-Sevilla-1.2HSR line 1992,

12 Gamla modellen Base case 3:05 h HSR 2:14 h (-28%) Rail trips +40% +29% private +67% business Direct elasticity: business : -1.6; private: -0.8; all: -1.0 Cross elasticity (air): business: 0.54; private: 0.14; all: 0.38 Rail/air split increases from 65 percent to 75 percent (Data ger 55 procent i nuläge)

13 Nya modellen Base case 3:05 h HSR 2:15 h (-28%) Rail trips +45% +28% private +95% business Direct elasticity: business : -2.1 (-1.6); private: -0.77 (0.8); all: - 1.15 (-1.0) Cross elasticity (air): business: 0.71 (0.54); private: 0.15 (0.14); all: 0.34 (0.38) Rail/air split increases from 65 percent to 75 percent

14 SJ-data 2006 ger 1.4 milj/år resor Sthlm län - gbg LA Sampers 2006: 1.2 milj/år Sampers 2020 JA: 1.6 milj/år UA: 2.2 milj/år Sampers 2030 UA-bas: 2.5 milj/år US 1: 3.3 US 2: 3.4 Resandenivåer i prognoserna

15 Validation against aggregate data We validate the air-rail split using aggregate Swedish data. We can control for Accessibility to airports and train stations Frequency and travel times taken from time tables Share of business travel. 27 november 2009, 15

16 Business Trips 27 november 2009, 16

17 Private trips 27 november 2009, 17

18 Underskattning av den privata modellen Sampers 55 till 67 procent Exponentiella modellen ger 55 till 71 procent Skillnaden indikerar att reduktion av flygresor underskattas med 62 000 resor per år. Marginell påverkan på CBA 27 november 2009, 18

19 Conclusion Own elasticities in the SAMPERS model is comparable with other models and experiences Elasticity - 1.0 for the HSR. Close to second phase Paris-Lyon Validation indicate too low elasticity for private trips, under estimate reduction of air trips. Market share predicted by Sampers lower than London-Paris; Madrid-Seville: Bromma? Congestion air? Error? Prices? 27 november 2009, 19