Household vehicle ownership in vietnam a comparative analysis of hochiminh and hanoi metropolitan areas

2009 Master’s Thesis Household Vehicle Ownership in Vietnam: A Comparative Analysis of Hochiminh and Hanoi Metropolitan Areas February 5, 2010 22 Department of Civil Engineering, Nagoya University Quoc Chinh HO ABSTRACT Simultaneously owning different types of vehicle including car, motorcycle and bicycle is addressed in this study by using random utility models The empirical analysis adopts generalized extreme value (GEV) models as approaches to describe vehicle ownership at household level The analysis then extended to incorporate multivehicle ownership explicitly in the model and its results are compared with those of binary vehicle ownership The estimation results indicate that income is a dominant factor for vehicle ownership of any type and its effects are larger on car and motorcycle ownership than on bicycle ownership The developed multi-vehicle ownership models are also applied to analyze effects of collecting vehicle ownership fee in an effort of alleviating traffic congestion in Vietnam (GEV) Table of Contents Introduction Data sources 3 Random utility models 3.1 Multinomial logit (MNL) model 3.2 Nested logit (NL) model 3.3 Generalized nested logit (GNL) model 10 Results .11 4.1 Binary vehicle ownership .11 4.2 Multi-vehicle ownership .23 Model application .28 Conclusions .30 Acknowledgements References Appendices Introduction The world has witnessed an impressive economic growth of Asian countries from the second half of the 20th century up to now, especially in the last two decades From 1993 to 2003, GDP per capita has increased the most in Asian region, which is about 1.44 times, exceeding all other regions worldwide (Senbil et al., 2007) While it is without doubt that economic growth increases incomes and contributes to the improvement in quality of life of Asian countries, rapid increase in populations and urbanization as well as motorization worsen traffic congestion, safety levels and environment Like other Asian countries, Vietnam now faces with negative impacts of urbanization, industrialization as well as motorization such as traffic congestion, noise and air pollution in big cities Since public transport network in Vietnam is not sufficient enough for large transport demand, private transportation mode like motorcycle is chosen as a major means of transport, leading to the high dependence on private vehicle The two biggest cities in Vietnam, namely Hochiminh metropolitan (HCM) and Hanoi are characterized by a high trip rate and extremely high ownership rate of motorcycles Transport demand in Hochiminh city only in 2002 was estimated at about 13.5m trips a day (excluding walking) (JICA, 2004) Of these, about 78% trips are by motorcycle while that of car is still low as 1.6% The share of private transport including that of bicycle (about 14%) is therefore higher than 90% which is unique in Asian countries According to a report by Vietnam’s Ministry of Natural Resources and Environment in 2007, transportation sector is responsible for up to 70% of polluted gas in urban areas The report estimates that road transportation is the main source of air pollution in urban areas which emitted 85% CO, 95% HC and approximately 61% NOx Based on the present and near future situation that urban transport is still highly dependent on motorized vehicles, especially motorcycle, the report also further forecasts that polluted emission by private motorized vehicles will increase by 2-5 times by 2010 in HCMC, directly proportional to the increase in number of vehicles1 while that of Hanoi is about 1.5 times, compared to FY 2005 Policies aimed at reducing private motorized transport mode dependence such as road pricing and fuel tax have been employed in order to alleviate burdens that road transport places on environment It is believed that urban cities where the negative impacts of urbanization and motorization become more serious must be leaders in shifting people to environmentally friendly transport modes It is also the reason why in the literature many studies have investigated the relationship between demographics, built environment in urban cities and household vehicle ownership, vehicle type choice (e.g., Bhat and Sen, 2006; Bhat et al., 2009; Dissanayake and Morikawa, 2002; Yamamoto, 2009) Lower limit, i.e., times is obtained by the assumption of an annual 10% increase in private vehicles while the assumption for upper bound is 20% Recent annual rates of private vehicle increase in HCMC are 10.6% for automobile and 14% for motorcycle (HCMC Department of Transport, 2007) 1 Contributing to the vast literature in this field, this research examines the effects of household demographics and urban structure represented by built environment characteristics on vehicle ownership behavior In particular, the interactions of different types of vehicle including bicycle, motorcycle and car are investigated in this study by developing simultaneous vehicle ownership models proposed by Yamamoto (2009) The research then widens the model by Yamamoto (2009), which only takes into account binary vehicle ownership, to incorporate multi-vehicle ownership explicitly to further investigate the interaction effects between bicycle and motorcycle at household level This is important because at present most households in developing countries are at low or middle income level which confines them to the vehicle choice of affordable modes such as bike and motorcycle but car In the near future, however, the rapid increase in income in developing countries will make currently expensive mode of transport become more affordable; and a trend of shifting mode of transport from bike to motorcycle and then car occurs as a result Recent research carried out by Van et al (2009) has shown by scenario simulation that if 30 - 40% of motorcycle users in HCMC upgrade their mode of transport to car, average travel speed in a typical corridor will dramatically decrease from currently 17.8 km/h to 10 km/h or even less, and the percentage of total congested length increases to 30 - 50% respectively.2 In addition, scenario analysis by JICA (2004) indicated that if until 2020 bus share in HCMC could be increased to 30% while motorcycle and car usage could be limited to 50% and 20% respectively, average travel speed would nevertheless reduce from 23.8 km/h in 2002 to 13.3 km/h and average volume – capacity (V/C) ratio increase to 1.8, compared to 0.7 in 2002 Similar study in Hanoi shown that in 2020 average travel speed will be 9.4 km/h and V/C ratio is 1.13 while those network performance indicators in 2005 were 26.0 km/h and 0.40, respectively (JICA, 2007) The aforementioned studies has shown that transportation in HCMC as well as in Hanoi metropolitan areas will experience serious consequences of upgrading transportation mode from bike to motorcycle and then car if no specific measures are taken In order to prevent people from this shifting trend, which in turn reduces the level of motorization, substitution and supplementation effects between different types of private transport modes are necessary to be looked into Simultaneous vehicle ownership models, however, may provide biased parameter estimates if the correlations among alternatives are not appropriately considered Thus, nested logit (NL) models as well as multinomial logit (MNL) models are developed and compared with each other in order to investigate such correlations in this study However, as a part of the bundle is identical amongst some alternatives, and a part of unobserved terms are shared by some bundles, the random components of bundles may have correlations with each other (Yamamoto, 2009) Furthermore, different types of private vehicle may have their own correlations, so generalized nested logit (GNL) models proposed by Wen and Koppelman (2001) are Percentage of total congested length is defined as the total average queue length on the corridor at all intersections divided by the length of that corridor employed to capture such correlations amongst unobserved components as well as different alternatives Large scale person trip survey data for HCM and Hanoi are used for empirical analysis in this study The remainder of this paper is organized as follows: section describes data sources used in this study Section explains three types of alternative GEV models used in this study and constrained conditions to be consistent with maximum utility theory Section presents and compares the estimation results between the two locales Model application is then shown in section while conclusions are drawn in section Data sources The empirical analysis presented here basically used the datasets of the Urban Transport Master Plan and Feasibility Study in Hochiminh Metropolitan Area and the Comprehensive Urban Development Program in Hanoi Capital City, Vietnam The former is usually known as HOUTRANS while the latter is referred to as HAIDEP HOUTRANS survey was conducted in August 2002 by Japan International Cooperation Agency (JICA) in cooperation with Hochiminh City People’s Committee The study area comprises HCM city, districts of adjacent provinces which are currently forming or will form part of the metropolitan area and the other regions related to the two aforementioned areas from the viewpoint of regional development HOUTRANS study area had a population of million, and million of which resided in HCM city On the other hand, HAIDEP household interview survey was carried out in December 2004 by JICA and Hanoi People’s Committee The study area of Hanoi Metropolitan covers Hanoi capital city and the seven surrounding provinces Of which, Hanoi city and parts of the two neighborhood provinces, namely Vinhphuc and Hatay were chosen to conduct household interview survey These areas had a population of 3.15 million, some million of which located in Hanoi capital city Basic characteristics of the survey for the two study areas are shown in Table 2.1 As can be seen in Table 2.1, sampling rate is higher at Hanoi area in comparison with that of HCM (2.63% at Hanoi relative to 1.45% at HCM) However, sample size is about 40 per cent larger at HCM area for household and approximately 30% for individual More importantly, the sample sizes for both of the areas are sufficient enough to carry out disaggregate analysis, which is the approach used in this study Sample distribution of mode share is shown in the lower part of Table 2.1 The majority of the passengers choose motorcycle as a representative mode and this rate is higher at HCM than at Hanoi area Conversely, modal share of bus and environmentally friendly transport modes, namely walking and bicycling is higher at Hanoi than at HCM Car share is small at both areas and the market share of car is higher at Hanoi than at HCM The difference is possibly because of the difference in time of data collections, that household interview survey at Hanoi is carried out two years later than at HCM As the unique urban public transport mode in Vietnam up to now, bus accounts for only 5% share in Hanoi while less than 2% in HCM, leading to the high dependence on private transport modes shown in the same table The distribution of trip purposes is similar for both areas The slight difference in percentage of trips to school between the two areas may be caused by the difference in average number of children per household between the two regions, which is found to be 0.34 for Hanoi area while 0.26 in HCM dataset However, reasons for the difference in the share of trips to work between both areas are unidentified since the number of workers in each household is unknown for Hanoi dataset Table 2.1 Study areas and person trip survey data Hochiminh metropolitan Hanoi metropolitan Survey area in km2 5,190 1,049 Population (x1000) 7,653 3,150 1,928,000 760,000 2002 2004 Number of households Year of survey Sample size Cases Percentage Cases Percentage Household 28,001 1.45 20,020 2.63 Individuals 102,407 1.34 78,994 2.51 Modal share (%) Walking 17.1 25.5 Bicycle 14.4 18.7 Motorcycle 62.5 47.5 Car 1.2 2.7 Bus 1.4 5.0 Others 3.4 0.6 To work 16.9 20.0 To school 8.7 10.6 To home 46.6 46.1 Business 1.7 5.1 Private/Other 26.1 18.1 Trip purpose (%) Source: Japan International Cooperation Agency (2004, 2007) Table 2.2 presents sample distribution of vehicle ownership and household ownership breakdown in HCM and Hanoi datasets As can be seen, bicycle ownership rate is significantly higher at Hanoi than at HCM while the reverse holds true for motorcycle ownership The statistical results shown in Table 2.2, which have only taken into consideration vehicle ownership but multi-vehicle ownership, possibly indicate that part of second motorcycles in HCM area is substituted by bicycles in Hanoi The substitution is further confirmed by the results of multi-motorcycle ownership percentage of the two areas which are found to be 40% at Hanoi area and 59% at HCM (JICA, 2004, 2007) The difference in multi-motorcycle ownership rate between Hanoi and HCM is therefore 19% which is about the same as difference in simultaneously owning bicycle and motorcycle between the areas as shown in the lower part of Table 2.2 Car ownership rate is the same between the two areas and is less than 2%, leading to the small share of trips made by car mode as shown in Table 2.1 Table 2.2 Sample distribution of vehicle ownership and household ownership breakdown HCM Metropolitan Hanoi Metropolitan Cases Percentage Cases Percentage Bicycle 13781 50.5 16090 80.9 Motorcycle 25844 94.6 16657 83.7 458 1.7 373 1.9 None 154 0.6 399 2.0 Bike only 1283 4.7 2790 14.0 Motorcycle only 13083 47.9 3287 16.5 Bicycle and motorcycle 12327 45.1 13045 65.6 Car only 10 0.0 14 0.1 Bicycle and car 14 0.1 34 0.2 Motorcycle and car 277 1.0 104 0.5 All three vehicle types 157 0.6 221 1.1 Vehicle ownership a Car Household ownership breakdown Sample size a 27305 19894 Sum of the percentage is not equal to 100% since the choice set is neither exclusive nor exhaustive There are majority of households simultaneously owning more than one type of vehicle as shown in the lower part of the table Source: Japan International Cooperation Agency (2004, 2007) Looking at the household ownership breakdown of car at the bottom of Table 2.2, one more interesting thing is found That is, the sum of percentage of owning “motorcycle and car” and that of owning “all three types” is the same for both areas The result, after taking into account the higher rate of owning motorcycle and car in HCM, again confirms the possibility that bicycle and motorcycle are able to substitute for each other and the latter is chosen in HCM while the former is selected in Hanoi Reasons for such a difference in households’ vehicle ownership behavior between the two areas as well as interactions among different kinds of vehicle will be investigated by modeling households’ vehicle ownership with random utility models in the next sections Descriptive statistics of explanatory variables used in households’ vehicle ownership models are shown in Table 2.3 In order to make comparison possible, the number of explanatory variables is limited to those available for both areas Public transportation-related explanatory variables were not available for Hanoi area while household interview survey in HCM locale was limited to the availability of bus route but other attributes such as fare, travel time, frequency, etc The number of non-workers is computed as the non-working household members over 6-year old including students, unemployed persons and retirees The number of workers and that of non-workers are not available for Hanoi area because of the difference in survey formats between the two locales To keep comparable between the two areas, number of household members larger than years old (non-children hereafter) is calculated for Hanoi area and plays a role as counterpart of number of workers and non-workers in HCM dataset Though the number of non-children is not included in utility functions of any households’ vehicle ownership alternative for HCM, its descriptive statistics are also shown in Table 2.3 for the sake of convenient comparison between the two areas As can be seen in the table, the number of non-children is about the same in the both areas, both in terms of mean value and standard deviation In contrast, average number of children per household is slightly larger at Hanoi than at HMC, leading to the higher percentage of trip to school in Hanoi than in HCM as shown in Table 2.1 Unlike the previous study (Ho and Yamamoto, 2009) which merely used housing ownership rate as an explanatory variable, this study takes into account the effect of house area on vehicle ownership This variable is introduced into the model to capture the difficulty of inside house parking, which is very common in Vietnam Furthermore, research carried out by Osara et al (2009) has shown that the style of house in terms of house area and housing structure has some effects on the intention of car ownership in Hanoi locale In this study, different values of house area were tried with the models and house area of 50 square meters was chosen as benchmark in defining dummy variable for large housing ownership After taking into account the same deviations, the results suggest that owning large house is more likely to occur at Hanoi area than at HCM locale Average monthly gross income of households living in HCM is higher than that of households located in Hanoi regardless of the fact that household interview survey was conducted years later in Hanoi compared to HCM Furthermore, the average monthly usage cost of all vehicles per household is found to be higher at HCM than at Hanoi (0.212 and 0.187 million VND for HCM and Hanoi, respectively; US$ = VND 15,500 as of average in 2003) irrespective of the difference in price of gasoline at different time of data collections (Gasoline price in Vietnam increased about 43 percent during two years from 2002 to 2004) The results indicate that people in HCM area are more dependent on private motorized transport modes than those living in Hanoi area Built environment and household location characteristics are also allowed to enter the explanatory variable list for the two areas Average population density of HCM is higher than that of Hanoi though survey area is significantly larger at HCM than at Hanoi as shown in Table 2.1 The results reflect that population is mainly distributed in densely inhabited areas in HCM while population distribution is more scattered in Hanoi area Nevertheless, deviation of population density for HCM area is also larger than that of Hanoi metropolitan, ensuring a wide variety of population density at HCM area in this survey Table 2.3 Descriptive statistics of explanatory variables Variable description Variable Dummy for professional household head HCM Hanoi Mean SD Mean SD PRO 0.11 0.31 0.13 0.34 Monthly usage cost divided by household income UC 0.10 0.18 0.17 0.39 Number of workers WK 2.13 1.03 n.a* n.a* Number of non-workers NWK 1.65 1.16 n.a* n.a* Number of non-children a NKID 3.77 1.38 3.61 1.37 KID 0.26 0.58 0.34 0.61 HH50 0.40 0.49 0.59 0.49 INC 2.78 1.65 2.55 1.90 Population density at residential zone in 10,000/km PPD 2.02 1.83 1.47 1.54 Mixed land use index MIX 1.05 0.10 0.99 0.11 Distance from city center in 10km DIST 0.91 0.87 0.95 0.76 0.03 0.16 0.03 0.16 AIN 0.08 0.27 0.06 0.23 AHD 0.04 0.20 0.08 0.28 Number of children b Dummy for housing ownership with area ≥50m2 Household monthly gross income in million VND Interaction term between household head and income c HDINC Interaction term between household area and income Interaction term between household area and head d e Sample size 27305 19894 * Number of workers and that of non-workers are not available for Hanoi area a Number of non-children is the number of household members larger than years old b Number of children is defined as the number of household members less than or equal to years old c Dummy variable, equals one if household head is professional and household monthly gross income is larger than 5m VND, zero otherwise d Dummy variable, equals one if household area is larger than 50 m2 and household monthly gross income is larger than 5m VND, zero otherwise e Dummy variable, equals one if household area is larger than 50 m2 and household head is professional, zero otherwise Source: Japan International Cooperation Agency (2004, 2007) Mixed land use index used in this study is calculated by the entropy of trips of four types including work, to school, return home and shopping Mixed land use index of zone i, Ei, is given as (Bodea et al., 2008; Senbil et al., 2009): Ei = −∑ [ pit ln( pit )] (2.1) t where pit is the relative frequency of the trips with purpose t in the total number of trips attracted to each destination zone i Average mixed land use index for the two areas is shown in Table 2.3 which indicates that the level of mixed land use is slightly higher at HCM than at Hanoi Household location attribute is represented by distance from city center variable which is developed by measuring the distance between the resident zone and the corresponding city center as the crow flies In HCM area, the city center is assumed to be Ben Thanh Market while it is Dong Xuan Market in Hanoi capital city Though the majority of variance (between 70 and 90 percent) may be explained by main effects, there is still room for improving the linear models’ predictability by incorporating interaction effects, especially two-way interactions which account for between to 15 percent of variance (Hensher et al., 2005) In the effort to maximize the models’ explaining capacity, three two-way interactions are introduced into utility functions where appropriate Their statistics are also shown in Table 2.3 Random utility models Vehicle ownership has received substantial attention in the literature because it plays a major role in influencing transportation-related policy-makers, land use planning process, energy consumption cutting route, etc Consequently, varieties of models have been developed to describe vehicle ownership at different levels such as household level, community level and regional level Random utility maximization (RUM) discrete choice models are dominantly used for disaggregate level analysis to examine the causal relationship between vehicle ownership determinants and household vehicle holdings and use At the household level, ordered-response choice mechanism and unordered-response choice mechanism are used in the literature Bhat and Pulugurta (1998) has shown that the unordered response class of models such as MNL or the likes which are based on RUM is more appropriate for household vehicle modeling Adopting the result, MNL model, NL model and GNL model are used for empirical analysis in this study 3.1 Multinomial logit (MNL) model Consider household n who is faced with a finite set of alternative discrete choices j = 1, 2, … , J For the problem of simultaneously owning different types of vehicle including car, motorcycle and bicycle and keeping away from the level of vehicle ownership, the choice set contains eight mutually exclusive alternatives of none, bicycle only, motorcycle only, car only, bicycle and motorcycle, bicycle and car, motorcycle and car and all of three types However, since the number of household samples who choose the bundles containing car is too small compared to others, it is decided that the four carcontaining alternatives are collapsed into one bundle, say car As a result, the universal choice set contains five alternatives only The estimation results of NL model for Hanoi dataset are consistent with those obtained by MNL model The largest pairwise differences in corresponding coefficients between the two models are those of “monthly usage cost divided by household income” (variable UC) where the estimation of NL model have higher values of t-statistics Moreover, relative differences in magnitude of UC parameters estimated by NL model are more consistent with common sense than by MNL model For example, coefficient corresponding to UC variable of B2+MC2+ alternative is larger in magnitude than that of B1MC2+ bundle in MNL while the reverse occurs with NL model In fact, households owning B2+MC2+ have more chance to use bike and motorcycle interchangeably, compared to those owning B1MC2+ It is therefore the estimate of UC variable for the former alternative should be smaller than that of the latter one So the results of NL model are more acceptable than those of MNL More importantly, hypothesis test has shown that NL model rejects MNL one at very high level of significant, in excess 0.001 The resultant tree structure of NL model for Hanoi area is rather intuitively understandable It is the similarity in monetary value of the alternatives, which is unobserved by the study, that causes the correlation among affordable modes and expensive modes, leading to the allocation of B1MC1, B2+MC1, B2+MC2+ alternatives in one nest and MC2+, B1MC2+, car in the other (see Figure 4.6) In order to further investigate the difference between binary vehicle and multi-vehicle ownership models, four scenarios of household income increases by 20%, 50%, 100% and 200% are analyzed and compare with the results predicted by binary vehicle ownership models Based on the above comparison between MNL and NL models for multi-vehicle ownership in Hanoi area, NL model is chose to conduct scenario analysis whereas there is no option other than MNL for HCM data The results are shown in Table 4.9 for HCM area while in Table 4.10 for Hanoi locale Scenarios’ bundle shares estimated by multi-vehicle ownership models are added up to be comparable with corresponding share of alternative predicted by binary vehicle ownership models The difference in future vehicle ownership shares in HMC area predicted by binary and multi-vehicle ownership models is not much for the first three scenarios For the last scenario, i.e., income increases by 200%, the largest difference is -3.67% in motorcycle only bundle (56.22% in binary vs 52.54% in multi-vehicle ownership model), followed by motorcycle and bike bundle (2.04%), and car alternative (1.66%) Scenario analysis of Hanoi model, on the other hand, shows that future vehicle ownership shares predicted by the two models are almost similar to each other as income increases up to 200%; with the highest value of 0.64% in motorcycle and bike bundle, -0.46% in car, followed by MC (-0.32%) Though the aggregate comparison between the two models does not show significant difference in terms of owning or not owning one type of vehicle, especially when income increases less than 100%, the multi-vehicle ownership models are really essential for this study because of the high levels of multiple bike and motorcycle ownership at both locales It is believed that high degree of multi-vehicle ownership at household level does cause more serious damage to the network as a whole such as lower travel speed, more congestion in peak hours and to the environment as well 27 Table 4.9 Projected vehicle ownership shares in HCM metropolitan, predicted by MNL model Alt Actual case Base model NONE 0.56 B1 Household income increases by 20% 50% 100% 200% 0.56 0.43 0.30 0.18 0.08 2.23 2.23 1.72 1.21 0.73 0.32 B2+ 2.47 2.47 1.96 1.43 0.89 0.41 MC1 13.78 13.78 12.83 11.46 9.41 6.34 B1MC1 14.20 14.20 13.02 11.37 9.04 5.76 B2+MC1 7.12 7.12 6.32 5.29 3.96 2.31 MC2+ 34.14 34.14 36.92 40.36 44.19 46.20 17.86 17.86 18.32 18.61 18.27 16.16 5.97 5.97 6.08 6.06 5.73 4.81 1.68 1.68 2.40 3.91 7.62 17.62 B1MC2 + + B2 MC2 + CAR Table 4.10 Projected vehicle ownership shares in Hanoi metropolitan, predicted by NL model Alt Actual case Base model NONE 2.01 B1 20% 50% 100% 200% 1.97 1.69 1.39 1.04 0.65 5.52 5.53 4.64 3.65 2.56 1.44 8.50 8.52 6.94 5.23 3.45 1.75 MC1 5.92 5.92 5.74 5.41 4.82 3.73 B1MC1 16.97 16.99 16.17 14.88 12.81 9.42 B2+MC1 20.24 20.19 18.83 16.87 13.99 9.68 10.60 10.60 12.24 14.50 17.78 22.75 17.37 17.36 19.28 21.61 24.36 27.00 B2+MC2+ 10.99 11.02 12.06 13.19 14.25 14.54 CAR 1.87 1.88 2.40 3.27 4.96 9.04 B2 + MC2 + B1MC2 Household income increases by + Model application With the rapid increase in GDP in Asian developing countries recently, the rate of private motorized use and ownership has also increased at that fast rate or even more, causing many significant negative consequences on the network and environment, especially in big cities For the case of HCM city, the recent report by HCMC Department of Transport (2007) revealed that GPD of the city annually increased at the rate of 12% while motorcycle and car ownership increased by 10.6% and 14% per year, respectively Those figures for Hanoi capital were 12.6% growth in GDP (2003) but average annual 28 growth rate of motorcycle and car from 2000 to 2005 were 14.8% and 11.1%, respectively (JICA, 2007) It is therefore crucial to use transport policies as means of reducing motorcycle and restraining car ownership; and developed models are effectively applied for this purpose The latest proposal of HCMC People’s Committee which proposed an annual fee of VND500,000 (approx.US$28) per motorcycle and VND10 million (approx US$560) per car is applied in this study Ignoring the effectiveness in terms of social equity, the study focuses on the extent to which the proposed policy reduces the levels of private motorized vehicle ownership and modal shifting trend in Vietnam Multi-vehicle ownership model type MNL is used for HCM area while it is NL model for Hanoi locale Table 5.1 presents the change in household behavior regarding to vehicle ownership shares in response to the abovementioned vehicle ownership fees Table 5.1 Projected vehicle ownership shares (VND500,000/MC per year and VND10m/car per year) HCM (predicted by MNL model) Choice Hanoi (predicted by NL model) Actual case Base case Predicted case NONE 0.56 0.56 0.59 2.01 1.97 2.00 B1 2.23 2.23 2.32 5.52 5.53 5.60 B2+ 2.47 2.47 2.59 8.50 8.52 8.64 MC1 13.78 13.78 14.08 5.92 5.92 5.99 B1MC1 14.20 14.20 14.56 16.97 16.99 17.25 B2+MC1 7.12 7.12 7.34 20.24 20.19 20.54 MC2+ 34.14 34.14 33.95 10.60 10.60 10.68 17.86 17.86 17.83 17.37 17.36 17.54 5.97 5.97 5.99 10.99 11.02 10.91 1.68 1.68 0.77 1.87 1.88 0.86 B1MC2 + + B2 MC2 + CAR Actual case Base case Predicted case Note: MC = Motorcycle; US$1 = VND17,850 (average in 2008) 10.00 AR C C B1 M C2 + 2+ B2 +M C 2+ -20.00 M B2 +M C C B1 M C1 M B1 B2 + O N -10.00 NE 0.00 HCM Hanoi -30.00 -40.00 -50.00 -60.00 Figure 5.1 Change in vehicle ownership in response to vehicle ownership fee (in per cent) 29 Figure 5.2 Change in vehicle ownership in response to vehicle ownership fee (in absolute number) With the proposed policy, it is found that car and motorcycle ownership can be reduced to some extent while bike and supplementary bundles of bike and motorcycle are encouraged to be widely used Figure 5.1 illustrates the percentage change of the alternatives in response to the policy application whereas Figure 5.2 shows the policy effects in absolute number With the same level of charge, changes in vehicle ownership between corresponding alternatives at the two locales are somewhat different Conclusions Simultaneously owning different types of vehicle including car, motorcycle and bicycle were investigated in this study Large scale person trip survey data of HCM and Hanoi were used for empirical analysis which found out similarities and differences in households’ vehicle ownership between the two areas To overcome the problem of small sample of alternatives containing car, the study decided to add up four separate bundles having car inside to one alternative only named car bundle From the results of empirical analysis, income is confirmed as a determinant for households’ vehicle ownership in both areas and the effects are larger at HCM than at Hanoi Moreover, income has different impacts on owning different types of vehicle, that its effects on car and motorcycle ownership are stronger than on bicycle ownership The empirical analysis results also indicated that households locating in densely inhabited areas are more likely to own motorcycle than bike, all else being equal because the fact that the attractiveness of motorcycle is overweight troubles (e.g., finding parking lot) incurred by the holders Regarding to the difference in effects of occupation status on vehicle ownership, the results indicated that the number of non-workers has smaller effect on motorcycle ownership than that of workers, at least for HCM area 30 In the effort of increasing the models’ explaining capacity, two-way interactions were incorporated along with the main effects in the models The statistically significant estimates of these terms suggested that professional household head, household income and house areas act in concert in the development in preferences towards the alternatives As a result, these variables should also be investigated in interaction with each other Though these interaction terms were not experimentally designed to be orthogonal to the main effects, binary correlation coefficients and VIF values indicated that multicollinearity among explanatory variables in the model is not to the extent that causes problems for estimation results Regarding to the question of what kind of random utility model should be used to describe household behavior in terms of owning different types of vehicle simultaneously, the estimation results showed that NL model performs statistically better than MNL model in terms of overall model fit, though the results yielded by the two models are consistent with each other and both are rejected by GNL models The difference between the tree structures of both NL and GNL models for the two datasets also allows further investigation into the similarities and differences in this comparative analysis, i.e., the differences in usage pattern as well as the supplementation and substitution effects of bike and motorcycle between the two locales Furthermore, GNL model may be more appropriate given that some bundled alternatives share a common alternative within It is therefore GNL that should be developed and used in policy analysis for multiple vehicle ownership problems but it remains as a future task Further, based on the cross-tables of travel distance and mode choice, and estimation results, it is found that the level of motorization in HCM metropolitan area is higher than that in Hanoi locale regardless of the fact that data collection in Hanoi was carried out two years later than in HCM area Finally, the empirical analysis results have shown that there is a strong trend in modal upgrading from bike to motorcycle and then car in Vietnam as a result of economic growth Specific measures are therefore necessarily taken to alleviate the consequences of upgrading transport mode However, implementing electronic road pricing (ERP) in developing countries like Vietnam is particularly difficulty given budget and time constraints on setting up such a system An alternative to ERP, vehicle ownership fee, has been applied using the developed models The results have revealed that this transport policy can be used as a means of reducing level of motorization in Vietnam Also, the policy analysis results have indicated that there is a little difference in response to the application of vehicle ownership fee between the two locales Acknowledgements The author would like to express his sincere gratitude and appreciation to his academic supervisor, Associate Professor Toshiyuki Yamamoto for his critical supervision, continuous guidance, encouragement and support throughout the years of this study The author also gratefully acknowledges his helpful suggestions and invaluable encouragement in the initial period of Master’s Course at Nagoya 31 University Appreciation is also extended to Professor Takayuki Morikawa and Associate Professor Tomio Miwa for their helpful comments and beneficial suggestions through midterm reports The datasets used in this study were provided by Japan International Cooperation Agency Special thanks to Japanese Government for its financial support that the author was privileged to receive through Monbusho Scholarship scheme The author also would like to give many thanks to the other members of NUTREND laboratory as well as his classmates for their support, kindness and cooperation during his study at Nagoya University Above all, the author wishes to extend his deepest gratitude to his parents, brothers and sisters for their love, support, understanding and encouragement throughout the years of education Constant support and encouragement from the family makes the author confident, optimistic and consistent in doing research References Aptech System (1995): Gauss Applications: Constrained Maximum Likelihood Aptech Systems Inc., Maple 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Bicycle only Motorcycle only Bicycle and motorcycle Car Constant Dummy for professional household head Monthly usage cost divided by household income Number of workers 0.515(9.12) 1.592(14.34) - 1.179(6.67) -0.266(-2.47) 0.523(11.33) -3.614(-19.25) - Number of non-workers Number of children 0.253(8.63) 0.644(2.92) 0.800(3.71) 0.498(38.51) 0.723(3.35) 0.354(8.83) 0.878(3.89) Dummy for housing ownership with area ≥50m2 Household monthly gross income in million VND 0.485(5.08) 0.508(7.61) 1.405(15.09) 0.545(8.24) 1.267(13.48) 0.997(7.21) 1.792(18.98) -0.135(-3.23) -0.095(-2.44) -0.272(-12.95) 0.194(3.05) - -0.170(-4.42) -0.828(-6.27) - -0.227(-4.70) 0.503(2.95) - Population density at residential area in 10,000/km2 Mixed land use index Distance from city center in 10km Interaction term between household head and income a Interaction term between household area and income b Interaction term between household area and head c Sample size Log-likelihood function at zero Log-likelihood function at sample share Log-likelihood function at convergence 0.180(5.02) -1.348(-3.73) 27305 -43945 -26022 -23739 * 0.173(3.77) - t-ratio in parenthesis - means that corresponding variable is not statistically different from zero at 0.1 level of significant a Dummy variable, equals one if household head is professional and household monthly gross income is larger than 5m VND, zero otherwise b Dummy variable, equals one if household area is larger than 50 m2 and household monthly gross income is larger than 5m VND, zero otherwise c Dummy variable, equals one if household area is larger than 50 m2 and household head is professional, zero otherwise Source: Japan International Cooperation Agency (2004, 2007) 35 Appendix A2 Multinomial logit model of households' vehicle ownership - case of Hanoi Metropolitan* Variables Bicycle only Motorcycle only Bicycle and motorcycle Car Constant - -1.086(-7.38) -1.346(-11.43) -4.649(-14.71) Dummy for professional household head Monthly usage cost divided by household income Number of non-children Number of children -10.796(-8.40) 0.684(17.53) 0.706(5.66) 1.499(10.53) -1.520(-6.18) 0.527(11.66) 1.087(8.57) 1.312(9.68) -1.771(-9.6) 1.042(24.45) 1.312(10.55) 1.527(8.30) -0.921(-2.72) 0.865(14.26) 1.256(8.37) Dummy for housing ownership with area ≥50m2 Household monthly gross income in million VND -0.287(-8.66) 0.143(2.46) 1.044(26.88) 0.369(8.04) 0.891(24.04) 0.556(3.86) 1.172(25.91) -0.135(-4.02) -0.337(-10.10) -0.423(-8.57) Population density at residential area in 10,000/km2 Mixed land use index Distance from city center in 10km 0.863(15.32) 0.659(12.97) Interaction term between household head and income a -0.266(-2.14) 1.614(3.97) 0.504(2.76) Interaction term between household area and income b c Interaction term between household area and head Sample size 19894 Log-likelihood function at zero -32018 Log-likelihood function at sample share -19947 Log-likelihood function at convergence -16191 * t-ratio in parenthesis; - means that corresponding variable is not statistically different from zero at 0.1 level of significant a Dummy variable, equals one if household head is professional and household monthly gross income is larger than 5m VND, zero otherwise b Dummy variable, equals one if household area is larger than 50 m2 and household monthly gross income is larger than 5m VND, zero otherwise c Dummy variable, equals one if household area is larger than 50 m2 and household head is professional, zero otherwise Source: Japan International Cooperation Agency (2004, 2007) 36 Appendix A3 Nested logit model of households' vehicle ownership - case of HCM Metropolitan* Variables Bicycle only Motorcycle only Bicycle and motorcycle Car Constant Dummy for professional household head Monthly usage cost divided by household income Number of workers Number of non-workers Number of children 0.429(9.68) 0.310(16.29) 0.343(2.89) Dummy for housing ownership with area ≥50m2 Household monthly gross income in million VND Population density at residential area in 10,000/km2 Mixed land use index Distance from city center in 10km Interaction term between household head and income a Interaction term between household area and income b Interaction term between household area and head c IV parameter for (None, Bike, MC and Bike) nest Sample size Log-likelihood function at sample share Log-likelihood function of corresponding MNL model Log-likelihood function at convergence 2*[L(NL)-L(MNL)] 0.110(1.82) -0.114(-4.62) 0.075(3.14) -0.803(-3.58) 0.590(14.28) 27305 -26022 -23739 -23727 24.08 1.184(12.53) 0.116(2.68) 0.480(3.54) 0.311(5.87) 0.748(5.27) -0.142(-1.95) 0.459(11.47) 0.484(40.10) 0.399(3.40) 0.320(7.06) -4.113(-22.69) 0.356(9.01) 0.546(3.60) 0.795(6.02) 0.734(7.25) -0.051(-2.00) -0.284(-13.72) 0.151(2.37) - 0.582(7.25) -0.123(-5.00) -0.712(-6.10) - 1.106(10.48) -0.178(-4.64) 0.458(2.69) - 6.63 χ2(0.01,1) * t-ratio in parenthesis; - means that corresponding variable is not statistically different from zero at 0.1 level of significant a Dummy variable, equals one if household head is professional and household monthly gross income is larger than 5m VND, zero otherwise b Dummy variable, equals one if household area is larger than 50 m2 and household monthly gross income is larger than 5m VND, zero otherwise c Dummy variable, equals one if household area is larger than 50 m2 and household head is professional, zero otherwise Source: Japan International Cooperation Agency (2004, 2007) 37 Appendix A4 Nested logit model of households' vehicle ownership - case of Hanoi Metropolitan* Variables Constant Dummy for professional household head Monthly usage cost divided by household income Number of non-children Number of children Dummy for housing ownership with area ≥50m2 Household monthly gross income in million VND Population density at residential area in 10,000/km2 Bicycle only Motorcycle only Bicycle and motorcycle Car -13.041(-10.40) 0.986(20.82) 1.208(9.29) - -0.507(-5.66) 0.834(8.10) -1.379(-11.37) 0.889(16.65) 1.398(11.17) 0.082(2.51) -0.728(-8.43) 0.732(7.75) -1.614(-11.92) 1.168(29.29) 1.520(12.44) 0.199(6.56) -4.426(-14.47) 0.980(6.01) -1.245(-3.48) 1.051(16.35) 1.492(9.8) 0.408(2.89) -0.254(-8.16) 0.592(11.73) -0.193(-6.19) 0.511(11.66) -0.288(-9.02) 0.761(12.07) -0.361(-7.35) Mixed land use index Distance from city center in 10km 0.532(10.64) 0.416(10.24) -0.134(-2.01) Interaction term between household head and income a 1.015(4.44) 0.540(2.96) Interaction term between household area and income b Interaction term between household area and head c IV parameter for (Bike, MC and Bike, MC) nest 0.534(11.82) Sample size 19894 Log-likelihood function at sample share -19947 Log-likelihood function of corresponding MNL model -16191 Log-likelihood function at convergence -16165 2*[L(NL)-L(MNL)] 52.99 6.63 χ2(0.01,1) * t-ratio in parenthesis - means that corresponding variable is not statistically different from zero at 0.1 level of significant a Dummy variable, equals one if household head is professional and household monthly gross income is larger than 5m VND, zero otherwise b Dummy variable, equals one if household area is larger than 50 m2 and household monthly gross income is larger than 5m VND, zero otherwise c Dummy variable, equals one if household area is larger than 50 m2 and household head is professional, zero otherwise Source: Japan International Cooperation Agency (2004, 2007) 38 Appendix A5 (a) Bivariate correlations between independent variables in HMC dataset WK NWK KID HH50 INC MIX PPD DIST PRO UC HDIN AIN AHD WK 1.00 NWK -0.21 1.00 KID 0.03 -0.06 1.00 HH50 0.09 0.08 -0.03 1.00 INC 0.53 -0.02 0.04 0.10 1.00 MIX 0.02 0.01 -0.01 0.07 0.01 1.00 PPD 0.05 0.08 0.00 -0.10 0.20 -0.05 1.00 DIST -0.03 -0.07 0.00 0.06 -0.26 0.14 -0.66 1.00 PRO -0.02 -0.05 -0.02 0.01 0.11 0.01 0.03 -0.04 1.00 UC -0.16 0.00 0.00 -0.03 -0.33 0.00 -0.07 0.09 -0.04 1.00 HDIN 0.09 -0.04 0.00 0.03 0.31 0.01 0.07 -0.09 0.47 -0.06 1.00 AIN 0.31 0.00 0.03 0.36 0.55 0.03 0.07 -0.10 0.05 -0.11 0.25 1.00 AHD 0.01 -0.01 -0.02 0.26 0.10 0.03 0.00 -0.01 0.62 -0.02 0.35 0.17 1.00 Appendix A5 (b) Bivariate correlations between independent variables in Hanoi data set NKID KID HH50 INC MIX PPD DIST PRO UC HDINC AIN AHD NKID 1.00 KID -0.35 1.00 HH50 0.14 0.03 1.00 INC 0.16 0.11 0.10 1.00 MIX -0.04 0.01 0.00 0.01 1.00 PPD -0.10 -0.06 -0.23 0.28 -0.06 1.00 DIST 0.09 0.04 0.15 -0.30 0.08 -0.67 1.00 PRO 0.03 0.00 0.04 0.27 0.03 0.14 -0.14 1.00 UC -0.15 -0.04 -0.08 -0.45 -0.02 -0.17 0.20 -0.13 1.00 HDINC 0.05 0.03 0.04 0.43 0.02 0.11 -0.11 0.43 -0.10 1.00 AIN 0.09 0.09 0.21 0.62 0.00 0.10 -0.12 0.16 -0.15 0.47 1.00 AHD 0.05 0.01 0.25 0.24 0.02 0.05 -0.08 0.78 -0.10 0.38 0.23 1.00 Appendix A5 (c) Variance Inflation Factor value Variable description Number of workers Number of non-workers Number of non-children Number of children Dummy for housing ownership with area ≥50m2 Household monthly gross income in million VND Mixed land use index Population density at residential area in 10,000/km2 Distance from city center in 10km Dummy for professional household head Monthly usage cost divided by household income Interaction term between household head and income Interaction term between household area and income Interaction term between household area and head 39 Variable WK NWK NKID KID HH50 INC MIX PPD DIST PRO UC HDIN AIN AHD HCM 1.52 1.09 na 1.01 1.31 2.53 1.03 1.78 1.87 1.95 1.30 1.46 1.73 1.84 Hanoi na na 1.28 1.21 1.27 2.42 1.02 1.97 1.90 2.99 1.33 1.60 1.98 2.94 Appendix A6 Multinomial logit model of households' multi-vehicle ownership - case of HCM Metropolitan* Variable B1 B2+ MC1 MC2+ B1MC1 B2+MC1 B1MC2+ ASC 1.402(7.81) -2.371(-9.69) 2.746(15.68) -0.606(-3.41) 1.454(5.40) -1.305(-3.88) -1.673(-6.54) WK 1.186(14.66) -0.430(-5.98) 0.810(11.87) 0.289(4.10) 1.124(15.43) 1.088(15.71) NONWK 0.142(2.88) 1.009(23.01) 0.664(27.77) 0.578(22) 1.244(40.83) 1.085(41.86) CHILD 0.354(4.32) 0.452(8.43) 0.344(6.92) 0.479(9.17) 0.234(4.39) INC 0.416(5.80) 0.901(15.38) 1.346(23.51) 0.893(15.32) 0.868(14.27) 1.263(21.87) HH50 -0.245(-2.58) 0.476(10.63) 0.157(3.16) 0.228(3.64) 0.574(11.54) PPD -0.136(-2.64) -0.322(-6.09) -0.144(-3.32) -0.190(-4.45) -0.253(-5.98) -0.321(-7.00) -0.240(-5.53) MIX -0.651(-3.33) -1.324(-4.86) -0.67(-3.83) DIST 0.125(2.20) 0.132(2.62) -0.169(-5.06) -0.394(-14.17) 0.140(3.98) -0.277(-8.79) PRO -1.897(-5.80) -1.383(-5.29) -0.703(-7.42) 0.204(2.96) -0.469(-5.27) 0.355(4.82) UC HDINC AIN 0.636(4.18) AHD -0.798(-4.19) Sample size 27305 Log-likelihood function at zero -62872 Log-likelihood function at sample share -50655 Log-likelihood function at convergence -44138 * t-ratio in parenthesis; - means that corresponding variable is not statistically different from zero at 0.1 level of significant Source: Japan International Cooperation Agency (2004, 2007) 40 B2+MC2+ CAR -3.502(-9.67) 1.425(19.69) 1.449(47.53) 0.125(1.91) 1.221(19.77) 0.587(8.84) -0.259(-5.86) -1.418(-5.01) -1.133(-2.61) - -5.202(-20.73) 0.552(6.48) 0.969(20.55) 0.473(5.75) 1.681(26.41) 0.826(6.38) -0.291(-5.74) 0.488(2.95) - Appendix A7 Multinomial logit model of households' multi-vehicle ownership - case of Hanoi Metropolitan* Variable B1 B2+ MC1 MC2+ B1MC1 B2+MC1 B1MC2+ ASC -0.645(-3.17) -2.84(-11.45) -0.82(-4.29) -2.377(-9.15) -3.55(-15.56) ADULT 0.416(6.61) 1.332(21.71) 0.496(7.57) 1.07(16.91) 0.943(15.5) 1.618(26.44) 1.475(23.87) CHILD 0.529(3.64) 1.448(10.16) 1.122(7.81) 1.786(12.68) 1.479(10.77) 1.777(12.80) 1.905(13.69) PPD -0.092(-2.70) -0.307(-8.71) -0.117(-3.72) -0.187(-6.92) -0.405(-13.54) -0.256(-8.96) DIST 0.641(8.15) 0.787(10.94) -0.6(-5.91) 0.428(6.25) 0.782(11.64) -0.267(-3.41) MIX 0.53(2.79) -1.605(-8.51) -0.831(-4.20) HH50 0.141(2.04) 0.434(6.23) 0.243(4.30) 0.422(7.06) 0.733(11.60) INC -0.445(-5.41) -0.665(-8.24) 0.348(4.84) 0.771(10.66) 0.24(3.44) 0.13(1.83) 0.696(9.68) UC -10.429(-7.6) -23.953(-10.5) -2.442(-6.12) -2.484(-5.31) -3.071(-9.76) -3.781(-10.33) -3.19(-7.61) PRO -0.929(-4.02) -0.858(-4.51) 1.094(13.65) 1.016(13.79) HDIN 2.156(2.27) -0.438(-2.46) -0.282(-1.68) AIN 1.884(3) 1.562(2.29) -0.534(-3.86) -0.639(-5.09) AHD 0.443(4.49) Sample size 19894 Log-likelihood function at zero -45808 Log-likelihood function at sample share -41755 Log-likelihood function at convergence -34113 * t-ratio in parenthesis - means that corresponding variable is not statistically different from zero at 0.1 level of significant Source: Japan International Cooperation Agency (2004, 2007) 41 B2+MC2+ -4.98(-15.35) 1.857(29.61) 2.174(15.43) -0.406(-12.5) 0.312(4.12) -0.81(-3.61) 0.874(11.90) 0.638(8.67) -3.559(-7.46) 0.972(11.13) -0.359(-1.77) -0.7(-4.70) - CAR -6.461(-20.43) 1.33(17.65) 1.855(11.57) -0.293(-7.04) 0.741(5.42) 0.893(12.36) -0.479(-2.07) 0.894(6.06) - ... Vietnam (in Vietnamese) Yamamoto, T (2009): Comparative Analysis of Household Car, Motorcycle and Bicycle Ownership between Osaka Metropolitan Area, Japan and Kuala Lumpur, Malaysia Transportation,... higher than that in Hanoi locale regardless of the fact that data collection in Hanoi was carried out two years later than in HCM area Finally, the empirical analysis results have shown that there... at HCM than at Hanoi area Conversely, modal share of bus and environmentally friendly transport modes, namely walking and bicycling is higher at Hanoi than at HCM Car share is small at both areas