Modelling Long-Distance Travel in the UK. Why is long-distance (LD) travel important for transport policy?

Transcription

1 Modelling Long-Distance Travel in the UK Charlene Rohr, James Fox, Andrew Daly, Bhanu Patruni, Sunil Patil, Flavia Tsang RAND Europe 15 January 2013 Why is long-distance (LD) travel important for transport policy? LD trips (> 50 miles) account for about 30% of miles travelled in GB but not well represented in national data sources LD trips have a substantial impact on motorway and rail congestion Long-distance travel is different from more habitual travel Expect long-distance travellers to have higher values of time Some policy is focused on long-distance travel Therefore the UK DfT decided to commission a model to predict long-distance travel demand

2 A model of long-distance travel is required to address important transport policy issues High Speed Rail Charging Motorway Expansion Airport Development Structure of Presentation 1. Characteristics of Long-Distance Travellers 2. The Long-Distance Model 3. Model Findings 4. Conclusions

3 Structure of Presentation 1. Characteristics of Long-Distance Travellers 2. The Long-Distance Model 3. Model Findings 4. Conclusions Observations of long-distance trips in household surveys are sparse In GB LD trips account for 30% of miles travelled, but just 2.3% of all trips Household diary data on these trips is sparse In the 2005 National Travel Survey Individuals made an average of: 17.2 trips per week 0.4 long-distance trips per week Half (50.4%) of the individuals made no long-distance trips in the four-week interview period The scarcity of data means that long-distance travel is not well represented in national data sources

4 The distribution of purposes differs between long-distance and all trips Purpose All trips LD Trips Commute/education 23.4% 13.7% Business 4.8% 16.4% Personal business / shopping 42.7% 12.8% Visiting friends and relatives 14.6% 24.0% Holiday / Day Trip / Other leisure 14.5% 33.2% Total 100.0% 100.0% Mode shares also differ between long-distance and all travel Mode All trips LD Trips Car Driver 48.3% 49.5% Car Passenger 26.2% 33.7% Bus/Coach 10.8% 4.9% Rail 1.6% 10.0% Air 0.0% 0.9% Other 13.1% 0.9% Total 100.0% 100.0%

5 LD mode shares vary substantially with distance We also observe a clear relationship between longdistance trip making and household income Trips per person per week Income Quintile And the highest income travellers are more likely to travel by air and train (at the expense of bus/coach)

6 Structure of Presentation 1. Characteristics of Long-Distance Travellers 2. The Long-Distance Model 3. Model Findings 4. Conclusions Choice Data National Travel Survey Data (2002 to 2006) For , LD data collected for a 4-week period 3 weeks recall 1 week travel diary For 2006, recall period reduced to 1 week Provides over 48,000 tour records (for all purposes) Limitations Data reflects domestic travel only So, doesn t include travel by foreigners Can t model trade-offs between domestic and foreign travel Geographical specificity Origin information at District level (406 districts in GB) Destination information at County/UA level (146 counties/uas) NTS LD survey does not collect information on group size Because of limitations new HI data was collected for study

7 Model specification Disaggregate choices of long-distance tours Model long-distance tours (return journeys) per day Purposes Commute/education Business Visiting friends and relatives Other travel These had to be merged in estimation Simultaneous choice of frequency, mode, and destination Sought to estimate values of in-vehicle time parameters (by mode); but resulting values of time judged to be unreliable Therefore had to import in-vehicle time values LD choice model structure Person No LD Tour LD Tour Car PT Dest1 Dest2 DestN Rail Air Coach Dest1 Dest2 DestN

8 Structure of Presentation 1. Characteristics of Long-Distance Travellers 2. The Long-Distance Model 3. Model Findings 4. Conclusions Significant socioeconomic variation was identified in the frequency component Commute (workers) Business (workers) VFR/other (people) Income (by band) Males Age under 30 Age 30 to 59 years Retired Unemployed Households with children Households with no car

9 Significant socioeconomic variation was also identified in the mode component of the models Rail Air Bus Car High income All purps Business VFR/other Low income VFR/other Males VFR/other VFR/other Commute Business Students Commute Part-time Commute Business workers Car ownership Business All purps Structural Coefficients Commute Business VFR/Other Frequency λ freq / λ mode 0.72 (5.7) 1.00 (*) 0.51 (12.3) For all purposes, the frequency logsum term shows an important effect from mode-destination accessibility

10 Structural Coefficients Commute Business VFR/Other Frequency λ freq / λ mode 0.72 (5.7) 1.00 (*) 0.51 (12.3) PT λ mode / λ PT 1.00 (*) 0.57 (9.9) 0.50 (11.9) Evidence for correlation between public transport modes for Business and VFR/Other Structural Coefficients Commute Business VFR/Other Frequency λ freq / λ mode 0.58 (9.8) 1.00 (*) 0.27 (24.5) PT λ mode / λ PT 1.00 (*) 0.57 (9.9) 0.50 (11.9) Destination λ PT / λ destination 0.28 (26.6) 0.80 (3.6) 0.73 (7.1) We also observe significant correlation between the destination alternatives for all purposes

11 Fuel cost elasticities sensible for commute and business, low for VFR/other Tours Kilometres Car All modes Car All modes Commute Business VFR/other Average fuel cost elasticity in the UK is approx -0.3, but based on all trips (LD is 1/3 of total kms) Rail fare elasticities reasonable Tours Kilometres Rail All modes Rail All modes Commute Business VFR/other PDFH values to on rail competitive corridors; but model incorporates areas where rail is not competitive. Consistent with values published for the Swedish longdistance model.

12 Income elasticities, total changes in kms Total Model Elasticity NTS Analysis Commute Business VFR/other Total elasticity response higher for commute and business, values in line with separate analysis of NTS Income elasticities, km changes by mode Air Rail Coach Car Total Commute n/a Business n/a VFR/other Income increases result in shifts to air and rail, coach declines for VFR/other due to lower income term

13 Structure of Presentation 1. Characteristics of Long-Distance Travellers 2. The Long-Distance Model 3. Model Findings 4. Conclusions Long-distance travel merits special treatment LD trips account for 30% of miles, but just 2.3% of all trips household diary data on these trips is sparse Long-distance travel has distinct characteristics different purpose shares, more business, VFR and holiday different mode shares, with more rail, less bus In the UK, income has an important role strong relationship to frequency also impacts on mode choice, esp. air and rail

14 Model results Frequency above mode above destination structure for all three purposes, though relative sensitivities vary Socio-economic effects important for both frequency and mode, in particular income Model elasticities reasonable, changes in total travel play an important role and are influenced by structure Model implemented in a forecasting system with networks and equilibration