Transition of Vehicle Ownership and its impact on willingness to use public transport in Bogor, Indonesia

Transition of Vehicle Ownership and its impact on willingness to use public transport in Bogor, Indonesia

Bogor is a rapidly urbanizing (Wimbadi, 2017). Urbanization is contributing to climate change and other environmental stresses. Low-carbon development is urgently needed to mitigate climate change and reduce these stresses. Moving down a low-carbon path requires evidence-based policies. Starting Point Evidence-based policies necessitate improving quality and quantity of GHG emissions and related data. We aim to gather and analyze the needed data. 2

Personal Attributes Household Attributes Contextual Variables Built Environment Variables Analytical Framework Level of Vehicle Ownership: 0: Non-owner 1: Single Motorcycle (1 MC) 2: Multiple Motorcycle (>2 MC) 3: Single Car (1 Car) 4: Multiple Vehicles (MC & Car) Study/Survey in 2015 Results of study in 2015 (Please see in Appendices): 1. Vehicle Ownership model for Bogor 2. Evaluation on the willingness to use public transport in Bogor Trip Characteristics: Trip chain/joint trip; Multiple destination; etc Private Modal Choice Public Transport Study/Survey in 2017 Vehicle Kilometre Travelled Emission from Road Transport Expected Results : 1. Mode Choice 2. Estimation on GHG Emission from Road Transport 3

Type of Vehicle Ownership in Asia Source: Vu Anh Tuan, 2011 4

Vehicle Ownership Model Ordered Logit Model Y*= β x + ε, 0 if y* µ 1, 1 if µ 1 < y* µ 2, Y =.. N if µ N < y* Y* : Level of Vehicle Ownership by Household b ' : Constant Y 0 : Non-Owner Y 1 : Single Motorcycle Owner Y 2 : Multiple Motorcycles Owner Y 3 : Single Car Owner Y 4 : Multiple Vehicles (Car& MC) Owner x: indicates a set of explanatory variables including both individual/household attributes and situational/contextual factors e : error terms

The Survey: An Overview Sub-District Population Percentages Survey was distributed to 600 respondents in total; further broken down by the percentage of population living in subdistricts. No of Respondents South Bogor 191,468 19% 116 East Bogor 100,517 10% 59 North Bogor 182,615 18% 110 Central Bogor 103,719 10% 66 Bogor Barat 224,963 22% 131 Tanah Sereal 209,737 21% 118 Total 1,013,019 100% 600

Key variables (Full Survey Attached) Group of Variables Individual and Household Attributes Household Socio-economic 1. Household Income 2. HH Member 3. Family Type Detail of Variables 4. No of workers in the HH 5. No of Driving License in HH Personnel Attributes 1. Age 3. Environmental Knowledge 2. Education level 4. Awareness on Environmental issues Built Environment Variables Built Environment 1. Population Density * 2. Distance from City Center; Shopping Mall; Hospital, etc 3. Distance from Transport Facilities (station, bus shelter, etc) Trip Characteristic Variables Fuel Consumption 1. Motorcycle fuel per month 2. Car fuel per month * : Secondary Data from statistical office at sub-district level of Bogor 7

Results from 2015 Study/Survey: 1. Vehicle Ownership in Bogor In the context of private vehicle ownership in Asia, Motorcycle is a phenomenon because: (a) affordable (cheap price); (b) weather/climate is suitable; and (c) flexibility to move in high density urban form and inadequate road space (Senbil, 2007) Group Vehicle Ownership Ownership Level Percentage (%) 1 Non-vehicle owner Non-owner 19.3 2 Single motorcycle Entry Level 45.8 3 Multiple motorcycles Transition 1 21.5 4 Single car Transition 2 2 5 Multiple vehicles (Car & Motorcycle) Highest Level 11.2 140 120 100 80 60 40 20 0 Distribution of Vehicle Ownership in Bogor by Subdistrict South East North Central West T.Sereal Non-vehicle owner Single car Multiple Cars Single Motorcycle Multiple Motorcycles Multiple Vehicles (Car & MC)

Results from 2015 Study/Survey: 1. Vehicle Ownership in Bogor 250 Distribution of Vehicle Ownership by Income in Bogor 200 150 100 50 0 < 1 Mill 1-2.5 Mill 2.6-5 Mil > 5.1 Mill Don't want to answer Non-vehicle owner Single car Multiple Cars Single Motorcycle Multiple Motorcycles Multiple Vehicles (Car & MC) Results of cross tabulations suggest the following: the type and number of vehicles (vehicle transition) owned is influenced by sub district (built environment) and income levels (personal attributes) It is possible that other variables also affect the type and number of vehicles

Estimation Results of Vehicle Ownership Model & Policy Recommendation No Variable N=600 T-Statistic Estimated Parameter 1 Constant 1.422 4.879 *** Observed Variables 2 Age -0.081-2.093 ** 3 Education Level -0.296-6.040 *** 4 Income Level 0.128 2.836 *** 5 Social Activity 0.063 1.171 6 Income change in 10 Years -0.015-0.283 7 Income change in 5 years 0.058 0.985 8 Income change in 1 year -0.044-0.820 9 Owned house/home 0.489 3.676 *** 10 Have two kids or less -0.137-1.048 11 Small Family -0.227-2.043 ** 12 Living in Central Bogor (CBD) -1.281-1.611 * Threshold Parameter for Index (μ) μ (1) : 1.363 24.720 *** μ (2) : 2.169 31.265 *** μ (3) : 2.281 30.894 *** Model s attributes Degree of Freedom=11 AIC = 2.555 BIC = 2.664 Mc Fadden Pseudo R-squared: 0.059 Results of statistical analysis suggest the following: Wealthier people and people owning a home tend to own multiple vehicles and/or a car Older people, people with small families, and people living in central Bogor tend to have fewer vehicles Note: ***: significant at 99% (1 %) **: significant 95% (5 %) *: significant 90% (10%) 10

Vehicle Ownership and its impact on willingness to use public transport in Bogor 600 Respondents were asked to answer these questions: A. Would you choose to use the BRT instead of your own vehicle? (Q2.12-4 ) No (Y brt =0) 280 HH or 47% Yes (Y brt =1) 320 HH or 53 % B. Would you choose to use an angkot instead of your own vehicle? (Q2.12-5) No (Y Angkot =0) 272 HH or 45.3% Yes (Y Angkot =1) 328 HH or 54.7% Cross Tabulation of Willingness to use Angkot and BRT Willingness to use Angkot Willingness to use BRT Y Angkot =0 Y Angkot =1 Total Y brt =0 237 43 280 Y brt =1 35 285 320 Total 272 328 600 285 (47.5%) Respondents are willing to use both Angkot (Y Angkot =1) and BRT (Y brt =1) 237 (39.5%) Respondents don t want to use neither Angkot (Y Angkot =0) nor BRT (Y brt =0)

Next Questions: a. Why are respondents willing to use Angkot and BRT? b. What factors influenced the decision to use public transport? c. Is there any correlation between the willingness to use an angkot (Y Angkot =1) and BRT (Y brt =1)? To answer those questions: We propose a Bivariate Binary Probit regression model which depends on simultaneous observation of two discrete binary observed-dependent variables, i.e., YiAngkot : the willingness to use Angkot YiBRT : the willingness to use BRT Covariance (Angkot, BRT) = ρ

Bivariate Binary Probit Willingness to use Angkot and BRT Y* iangkot = β Angkot X iangkot + ε iangkot ; y iangkot = 1 if y* iangkot > 0, = 0 otherwise (1) Y* ibrt = β BRT X ibrt + ε ibrt ; y ibrt = 1 if y* ibrt > 0, = 0 otherwise (2) E(ε iangkot ) = E(ε ibrt ) = 0 ; Var(ε iangkot )=Var(ε ibrt ) = 1 ; Cov(ε iangkot,ε ibrt ) = ρ - i denotes an observation (i=1,2,3, n ) - β and X stand for the vectors of parameters and the independent variables - εiangkot and εibrt are random variates distributed jointly as standard Bivariate Normal and a free correlation parameter, ρ, i.e., BNV [0,0,1,1, ρ]

2. Estimation Result of Bivariate Probit in Bogor No Variable Y Angkot =1 Y BRT =1 Estimated Parameter T-Statistic Estimated Parameter T-Statistic 1 Constant 0.973 2.066 ** 0.322 0.719 2 Age 0.158 2.930 *** 0.163 3.168** 3 Female 0.631 5.124*** 0.401 3.376*** 4 Education Level -0.075-1.044-0.069-0.990 5 Income Level -0.074-1.335-0.114-1.984** 6 Social Activity 0.112 1.292 0.176 2.042** 7 Own House -0.375-2.040** -0.242-1.346 8 Have two kids or less -0.029-0.165-0.045-0.253 9 Small family -0.025-0.170 0.078 0.529 10 Knowledge on Energy Saving 0.006 0.066 0.006 0.074 11 Knowledge on National Climate Policy -0.137-1.048 0.159 2.799*** 12 Knowledge on Local Climate Policy -0.032-0.573-0.019-0.366 13 Vehicle ownership: One Motorcycle -1.054-4.190*** -0.874-3.840*** 14 Vehicle ownership: Multiple Motorcycle -1.158-3.760*** -0.819-2.835** 15 Vehicle ownership: One Car -1.024-1.958* -0.058-0.114 16 Vehicle ownership: Multiple Vehicle -1.691-4.983*** -0.980-3.106*** 17 Usage: Motorcycle no 1 for commuting 0.104 0.684 0.285 1.868* 18 Usage: Motorcycle no 2 for commuting -0.128-0.575 0.004 0.021 19 Usage: Car no 1 for commuting -0.134-0.395-0.362-1.181 20 User: Motorcycle no 1 used by father -0.510-3.864-0.453-3.368*** 21 User: Car no 1 used by father -0.522-1.927* -0.674-2.473** Covariance/Rho (Y Angkot =1, Y BRT =1) 0.923 (T-Statistic =37.619) ***

2. Estimation Result of Bivariate Probit in Bogor Results of statistical analysis suggest the following: Elderly people and women are willing to use Angkot and BRT in Bogor Households that own private vehicles do not want to use Angkot or BRT Social activity and knowledge of national climate policy will increase the willingness to use BRT Access to the first car by household head (father) will reduce the willingness to use of Angkot and BRT Covariance (Angkot, BRT) is positive and significant means that the households that are willing to use Angkot are also willing to use the BRT in Bogor

Preliminary Conclusions from 2015 Study/Survey: Results suggested that: Vehicle ownership in Bogor is still undergoing a transition The number of vehicles will increase in the future due to income growth Vehicle type owned by household may change in the future They also suggested that: Having private vehicle in the household will decrease the willingness to use public transport (Angkot and BRT Trans Pakuan) Preliminary Conclusion: Without well-designed strategies and actions, the traffic condition may worsen and externalities may increase in the future Future Research (2017): - Need to understand daily trip patterns by household in Bogor - Quantify GHG emissions as one of the negative impacts - Improve the parameters for calculating and developing scenarios for local action plan in Bogor (based on ExSS model)

Thank you! 17