Preferred citation style Axhausen, K.W. (2016) How many cars are too many? A second attempt, distinguished transport lecture at the University of Hong Kong, Hong Kong, October 2016..
How many cars are too many? A second attempt KW Axhausen IVT ETH Zürich October 2016
Acknowledgments A Loder for the mobility tool ownership work G Sarlas and R Fuhrer for the work on Swiss wages/productivity L Sun for the big data analysis FCL M8 for the SG MATSim model
Singapore everywhere?
Some numbers first
Some SG numbers: Mode shares by income 2008 Income#[kSG]# 8k+# 7k# 6k# 5k# 4k# 3k# 2k# 1k# MRT,#LRT# Bus# Company,#school,# shukle#bus# Car,#motorcycle,#light# truck#driver# Car,#motorcycle,#light# truck#passenger# Taxi# no#income# 0%# 20%# 40%# 60%# 80%# 100%# Mode#share#of#trips#
Current problems in Singapore
Bus speeds in Singapore by time of day (2012) Sun, 2013
Headways along a bus line in Singapore (2012) Sun, 2013
A model of Singapore s travel demand and traffic
What type of model would be enough?
Would this be enough? tax income Income Productivity Number Pop, Firm tax car tax GA n car n GA Acc car Acc bus Acc rail fee car fee PT q car q bus q rail mmfd v car v bus v rail budget transport %cap car %cap bus%cap rail
What do we know?
Access and productivity: Switzerland Income Productivity Acc car Acc bus Acc rail
Population accessibility by public transport: 2010 Axhausen et al., 2015 15
Income levels: 2010 Axhausen et al., 2015 Grey: less then 20 observations Pink to purple: Low to high wages 16
Spatial error model (some variables not shown) 2000 2005 2010 Y: Ln mean salary Estimate Sig. Estimate Sig. Estimate Sig. Axhausen et al., 2015 Intercept 6.43*** 7.07 *** 6.89*** Ln car accessibility 0.01** 0.02 *** 0.01** Ln public transport accessibility 0.01** 0.01*** 0.01* Ln number of local employed 0.02 *** 0.01*** 0.01*** From outside Switzerland -0.11 *** -0.09 *** -0.09 *** Average duration in-post 0.00 * 0.01 *** 0.01 *** Ln average age 0.36 *** 0.24 *** 0.32 *** Men 0.17 *** 0.07 *** 0.13 *** lamda parameter 0.33*** 0.41*** 0.40 *** Nagelkerke pseudo-r-squared 0.693 0.665 0.623 # observations 1448 2298 2229 17
Accessibility and mobility tools: Swiss case n car n GA Acc car Acc bus Acc rail
Accessibility and car ownership in Switzerland
Switzerland: general accessibility
Switzerland: Probabilities by general accessibility
Switzerland: Probabilities by log of income
Switzerland: Conditional probabilities by log of income
Mobility tools and use: Swiss case n car n GA q car q bus q rail
Travel, car and season-ticket ownership (CH, 1984-2010) Trips,with,motorized,vehicles/day 3.0 2.5 2.0 1.5 1.0 0.5 Mikrozensus,Schweiz,1984 Mikrozensus,Schweiz,1989 Mikrozensus,Schweiz,1994 Mikrozensus,Schweiz,2000 Mikrozensus,Schweiz,2005 Mikrozensus,Schweiz,2010 Vehicle,and,season,ticket,, No,vehicle,,but,season,ticket Vehicle,,but,no,season,ticket Neither 0.0 0.0 0.5 1.0 1.5 Public,transport,trips/day
Fee and car ownership fee car q car
Singapore: COE Category B prices 2001-2013 1.4000 Growth rate.50 1.50 3.00 1.2000 COE/Mean income 1.0000.8000.6000.4000.2000.0000 250 500 750 1000 1250 Quota
CH: Quality- and inflation adjusted car prices 1400 1200 Frei, 2004 Raff und Trajtenberg, 1990 Source: nach Frei (2005) Qualitätsbereinigter Preisindex (2004 = 100) [%] 1000 800 600 400 200 0 1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010 28
CH: Car always available by sex 100 1970-79! 1960-69! 1950-59! 1940-49! 1930-39! Men Women! 80 Dirving License Ownership [%] 60 40 1980-89! 1990-99 1910-29! Before 1910! 20 0 0 10 20 30 40 50 60 70 80 90 100 Average Cohort Age [Years] 29
Fleet size and speeds q car MFD v car
Macroscopic fundamental diagram MFD (Yokohama) Geroliminis and Daganzo, 2008
Fleet size and speeds q car q bus q rail mmfd v car v bus v rail
3d MFD (Zürich, FCD & loops) City centre Loder et al., 2016
3d MFD (Zürich, FCD & loops) city centre Loder et al., 2016
3d MFD (Zürich, FCD & loops) city centre - max speed Loder et al., 2016
What we kind of know and what we don t know
What we kind of know fee car fee PT q car q bus q rail
What we kind of know tax income tax car tax GA fee car fee PT budget transport
What we don t know budget transport %cap car %cap bus%cap rail
What we don t know mmfd %cap car %cap bus %cap rail
What we don t know Number Pop, Firm Acc car Acc bus Acc rail v car v bus v rail
Further research questions Close knowledge gaps for Switzerland Replicate results beyond Switzerland Add estimates of externalities Closed form optimisation model For desired speed (accessibility) level For welfare maximisation
Questions?
Appendix
Estimation of models Spatial error model full model Axhausen et al., 2015 Year 2000 Year 2005 Year 2010 Independent Variable: Ln mean salary Estimate Pr(> t ) Estimate Pr(> t ) Estimate Pr(> t ) Intercept 6.432 *** 7.068 *** 6.887 *** Ln car accessibility 0.010 ** 0.019 *** 0.011 ** Lnpublic transport accessibility 0.014 ** 0.011 *** 0.012 * Ln number of local employed 0.016 *** 0.010 *** 0.013 *** Commuter from outside Switzerland -0.114 *** -0.094 *** -0.093 *** Short residence permit -0.236 *** -0.134 *** -0.226 *** Average duration in-post 0.003 * 0.008 *** 0.005 *** Ln average age 0.364 *** 0.237 *** 0.322 *** Men 0.169 *** 0.067 *** 0.132 *** Tertiary education 0.834 *** 0.663 *** 0.541 *** Professional training 0.553 *** 0.216 *** 0.324 *** Further vocational training 0.228 *** 0.171 *** 0.231 *** Teaching degree 0.197 ** 0.205 *** 0.321 *** Highschool diploma 0.601 *** 0.179 * 0.258 ** Vocational training 0.074 *** 0.030. 0.021 Positions with highest demands 0.420 *** 0.385 *** 0.409 *** Positions with qualified indep. work 0.199 *** 0.246 *** 0.247 *** Positions with professional skills 0.135 *** 0.195 *** 0.140 *** Working (3rd sector) 0.214 *** 0.152 *** 0.056. Working (other private sector) -0.096 *** -0.099 *** -0.059 *** Working (manufacturing) -0.226 *** -0.252 *** -0.107 *** Working (FIRE) 0.146 *** 0.006 0.085 *** Working (hotel, restaurants) -0.127 *** -0.132 *** -0.111 *** lamda parameter 0.331 *** 0.411 *** 0.402 *** AIC -2731-4754 -4234 AIC ols -2676-4651 -4143 Nagelkerke pseudo-r-squared 0.693 0.665 0.623 Residuals' spatial autocorrelation -0.009-0.009-0.007 OLS residuals' spatial autocorrelation 0.113 *** 0.103 *** 0.097 *** # observations 1448 2298 2229 Signif. codes: 0 *** 0.001 ** 0.01 * 0.05. 0.1 1 45
Model formulation 1/2 Choice environment Case Choice Probability 1 None P + = Φ / ( x + β + ; x / β / ;Ρ / ) 2 Car & no ticket P / = Φ / ( x + β + ;x / β / ;Ρ / ) 3 Car & local ticket P ) = Φ ) (x + β + ;x / β / x ) β ) ;Ρ ) ) 4 Car & GA P F = Φ ) (x + β + ;x / β / ;x ) β ) ;Ρ ) ) 5 No car & local ticket P F = Φ ) (x + β + ; x / β / ; x ) β ) ;Ρ ) ) 6 No car & GA P G = Φ ) (x + β + ; x / β / ;x ) β ) ;Ρ ) ) Likelihood function x AB L(α) = δ ' φ ) β + x- +, β / x- /, β ) x- ) ;P 3 dx5 + 1 δ x low x up : φ / β + x- +,β / x- / ;P 2 dx5 x low Estimation method: Maximum simulated likelihood in Stata using Newton Raphson technique Using draws to compute the integral
Model formulation 2/2 δ Φ H φ H β Sample selection dummy, equal to 1 if observation holds season ticket N-dimensional cumulative distribution function of the normal distribution N-dimensional probability density function of the normal distribution Parameters of the model Σ Symmetric correlation matrix with typical elements ρ KL and ρ KK = 1. The same correlations appear in both Σ / and Σ ) by using their Cholesky decomposition and estimating the Cholesky factors in the model α x NO,PQR Parameter vector to be estimated that contains all β and Cholesky factors of Σ Upper and lower limits of integration domain, determined by values of each observation
Switzerland: Ownership models (1/2) Seasonticket owner Car available Age -0.059 *** 0.099 *** Age squared 0.052 *** -0.088 *** Male -0.132 *** 0.439 *** Working 0.066 *** 0.258 *** University level education 0.146 *** -0.054 ** Log of monthly household income 0.075 *** 0.391 *** Center of agglomeration 0.132 *** -0.22 *** Constant 0.052-6.039 ***
Switzerland: Ownership models (2/2) Seasonticket owner Car available Local access to public transport: E -0.474 *** 0.505 *** Local access to public transport: D -0.348 *** 0.384 *** Local access to public transport: C -0.253 *** 0.286 *** Local access to public transport: B -0.097 *** 0.154 *** General accessibility 0.089 *** -0.028 *** Surplus public transport acc. -0.005 *** -0.066 *** Surplus workplace accessibility 0.729 *** -0.527 ***
Switzerland: GA given season ticket (2/2) General abonnement Secondary residence 0.302 *** Log of monthly household income 0.128 *** Self-reported distance [1000km] 0.005 *** Constant -2.188 *** Error correlations Car available GA Season ticket -0.44 0.62 Car available -0.24