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1 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
2 How many cars are too many? A second attempt KW Axhausen IVT ETH Zürich October 2016
3 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
4 Singapore everywhere?
5 Some numbers first
6 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#
7 Current problems in Singapore
8 Bus speeds in Singapore by time of day (2012) Sun, 2013
9 Headways along a bus line in Singapore (2012) Sun, 2013
10 A model of Singapore s travel demand and traffic
11 What type of model would be enough?
12 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
13 What do we know?
14 Access and productivity: Switzerland Income Productivity Acc car Acc bus Acc rail
15 Population accessibility by public transport: 2010 Axhausen et al.,
16 Income levels: 2010 Axhausen et al., 2015 Grey: less then 20 observations Pink to purple: Low to high wages 16
17 Spatial error model (some variables not shown) 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 *** *** *** 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 # observations
18 Accessibility and mobility tools: Swiss case n car n GA Acc car Acc bus Acc rail
19 Accessibility and car ownership in Switzerland
20 Switzerland: general accessibility
21 Switzerland: Probabilities by general accessibility
22 Switzerland: Probabilities by log of income
23 Switzerland: Conditional probabilities by log of income
24 Mobility tools and use: Swiss case n car n GA q car q bus q rail
25 Travel, car and season-ticket ownership (CH, ) Trips,with,motorized,vehicles/day 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 Public,transport,trips/day
26 Fee and car ownership fee car q car
27 Singapore: COE Category B prices Growth rate COE/Mean income Quota
28 CH: Quality- and inflation adjusted car prices Frei, 2004 Raff und Trajtenberg, 1990 Source: nach Frei (2005) Qualitätsbereinigter Preisindex (2004 = 100) [%]
29 CH: Car always available by sex ! ! ! ! ! Men Women! 80 Dirving License Ownership [%] ! ! Before 1910! Average Cohort Age [Years] 29
30 Fleet size and speeds q car MFD v car
31 Macroscopic fundamental diagram MFD (Yokohama) Geroliminis and Daganzo, 2008
32 Fleet size and speeds q car q bus q rail mmfd v car v bus v rail
33 3d MFD (Zürich, FCD & loops) City centre Loder et al., 2016
34 3d MFD (Zürich, FCD & loops) city centre Loder et al., 2016
35 3d MFD (Zürich, FCD & loops) city centre - max speed Loder et al., 2016
36 What we kind of know and what we don t know
37 What we kind of know fee car fee PT q car q bus q rail
38 What we kind of know tax income tax car tax GA fee car fee PT budget transport
39 What we don t know budget transport %cap car %cap bus%cap rail
40 What we don t know mmfd %cap car %cap bus %cap rail
41 What we don t know Number Pop, Firm Acc car Acc bus Acc rail v car v bus v rail
42 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
43 Questions?
44 Appendix
45 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 *** *** *** Ln car accessibility ** *** ** Lnpublic transport accessibility ** *** * Ln number of local employed *** *** *** Commuter from outside Switzerland *** *** *** Short residence permit *** *** *** Average duration in-post * *** *** Ln average age *** *** *** Men *** *** *** Tertiary education *** *** *** Professional training *** *** *** Further vocational training *** *** *** Teaching degree ** *** *** Highschool diploma *** * ** Vocational training *** Positions with highest demands *** *** *** Positions with qualified indep. work *** *** *** Positions with professional skills *** *** *** Working (3rd sector) *** *** Working (other private sector) *** *** *** Working (manufacturing) *** *** *** Working (FIRE) *** *** Working (hotel, restaurants) *** *** *** lamda parameter *** *** *** AIC AIC ols Nagelkerke pseudo-r-squared Residuals' spatial autocorrelation OLS residuals' spatial autocorrelation *** *** *** # observations Signif. codes: 0 *** ** 0.01 *
46 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
47 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
48 Switzerland: Ownership models (1/2) Seasonticket owner Car available Age *** *** Age squared *** *** Male *** *** Working *** *** University level education *** ** Log of monthly household income *** *** Center of agglomeration *** *** Constant ***
49 Switzerland: Ownership models (2/2) Seasonticket owner Car available Local access to public transport: E *** *** Local access to public transport: D *** *** Local access to public transport: C *** *** Local access to public transport: B *** *** General accessibility *** *** Surplus public transport acc *** *** Surplus workplace accessibility *** ***
50 Switzerland: GA given season ticket (2/2) General abonnement Secondary residence *** Log of monthly household income *** Self-reported distance [1000km] *** Constant *** Error correlations Car available GA Season ticket Car available -0.24
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