Dynamics of Automobile Ownership under Rapid Growth

Little research has focused on how the factors that influence travel behavior change in rapidly developing and increasingly motorized cities. This paper examines household motor vehicle ownership and focuses on potential variations in the preferences revealed through vehicle choice models estimated for Santiago, Chile, in 1991 and 2001 and includes measures of relative location, subway proximity, residential density, and land use mix. The results indicate that preferences changed between 1991 and 2001 and suggest that as incomes rise and vehicle ownership becomes increasingly affordable, the apparent influence of demographic, land use, and other contextual variables changes. The results vary across land use and locational variables; most notably, the relationship between vehicle ownership and land use mix appears to weaken over time, whereas the effect of the distance to the central business district strengthens, and the effect of residential density varies in the apparent direction of change, depending on the vehicle ownership category. By 2001, proximity to the subway had an apparent effect on the household decision to own three or more vehicles. This research shows that although income and motorization rates rapidly increased in Santiago, certain elements of the built environment influenced household vehicle ownership, and these influences changed over time. Future research should focus on potential market segments, such as suburban versus urban; aim to control for self-selection regarding land use and locational characteristics; and better understand the implications for travel forecasting.


Dynamics of Automobile Ownership Under Rapid Growth
Case Study of Santiago, Chile P. Christopher Zegras and Veronica Adelle Hannan national comparative evidence shows a strong relationship between income and motorization (1). In much of the developing world, the vast majority of the population is still at income levels well below the minimum vehicle ownership threshold. This situation is changing, however, as incomes rise and vehicle prices fall, spurred in part by the entrance of companies from countries like China and India into the international motor vehicle production market, as well as the international trade of used vehicles (2).
Within a country or city, the same income-ownership relationship exists, but at this finer resolution, the influences of local policies and other more nuanced factors appear. In Chile, for example, household vehicle ownership data suggest that cities within freetrade zones (zones free of import tariffs)-namely, Punta Arenas, Iquique, and Arica-have the lowest share of zero-car households in the country (3). Other local policies, not originally aimed at vehicle ownership, also play a role. For example, Mexico City's Hoy No Circula program, a restriction on driving by certain vehicles (based on license plate numbers) during high-pollution days, created the perverse impact of promoting the purchase of an additional secondhand vehicle (imported from other parts of the country) by many families (4). The government more recently adapted the ban to create an incentive to purchase cleaner vehicles, an approach also adopted in Santiago, which uses a similar restriction policy.
Evidence also suggests a relationship between household automobile ownership and transportation levels of service, urban form, and urban design. In fact, increasing motorization and its attendant impacts may further induce motorization (5). For example, motorization fuels spatial decentralization, which in turn drives motorization. Furthermore, motorization exacerbates congestion, but congestion may create the perverse incentive of increased automobile ownership and use. Increased congestion further encumbers main arteries and makes buses and other surface transit slower than private cars because private cars can choose less congested routes and destinations (5). Zegras found, for Santiago in 2001, that household vehicle ownership could be partly explained by the household's location in the city, the distance to subway stations, the accessibility levels between automobiles and buses, and various neighborhood design characteristics that included density and land use mix (6).
Concerns about motorization relate not only to the overall magnitude-that is, the total motor vehicle fleet size-but also to the rate of the increase; the increase may outpace relevant physical and institutional capabilities. Ultimately, motorization is a driving force behind increases in transportation greenhouse gas, increases in local pollutant emissions, pressures for land conversion to urban uses, dependency on petroleum, and demands for infrastructure expansion. In the face of these challenges, this paper takes on a relatively modest topic: an attempt to discern apparent changes in household vehicle Little research has focused on how the factors that influence travel behavior change in rapidly developing and increasingly motorized cities. This paper examines household motor vehicle ownership and focuses on potential variations in the preferences revealed through vehicle choice models estimated for Santiago, Chile, in 1991 and 2001 and includes measures of relative location, subway proximity, residential density, and land use mix. The results indicate that preferences changed between 1991 and 2001 and suggest that as incomes rise and vehicle ownership becomes increasingly affordable, the apparent influence of demographic, land use, and other contextual variables changes. The results vary across land use and locational variables; most notably, the relationship between vehicle ownership and land use mix appears to weaken over time, whereas the effect of the distance to the central business district strengthens, and the effect of residential density varies in the apparent direction of change, depending on the vehicle ownership category. By 2001, proximity to the subway had an apparent effect on the household decision to own three or more vehicles. This research shows that although income and motorization rates rapidly increased in Santiago, certain elements of the built environment influenced household vehicle ownership, and these influences changed over time. Future research should focus on potential market segments, such as suburban versus urban; aim to control for selfselection regarding land use and locational characteristics; and better understand the implications for travel forecasting.
The growth in motor vehicle fleets is arguably the most important factor that influences developing countries' mobility and accessibility. Most people want the convenience, status, and comfort of private motorized travel, but, in aggregate, these desires translate into well-documented problems. The potent force of global motorization raises numerous interesting questions. Does a foreseeable ceiling exist for motorization (vehicles per capita) in cities of the developing world? Will resource constraints or the accumulation of externalities attenuate motorization? Do the dynamics of vehicle ownership structurally change cities? Do the dynamics of urban growth change vehicle ownership preferences and trends?
This study set out to shed some light on the last of these questions by examining the case of Santiago, Chile, during the 1990s. Inter-ownership. Specifically, the paper looks at households in Santiago in 1991 and 2001 and estimates and compares vehicle ownership models. The following section describes the modeling approach. The third section briefly introduces the empirical setting. The fourth section presents the model estimations and discusses the results. The final section concludes the paper.

VehiCle OwnerShip MOdeling
Two basic approaches to household vehicle ownership exist: aggregate and disaggregate, as reviewed in detail by de Jong et al. (7). Aggregate analyses model vehicle ownership at zonal, urban, or national levels and can be used as inputs for travel forecasting models and for intracity, intercity, or international comparative efforts to derive, say, the income elasticity of demand for motor vehicles or to develop forecasts of future vehicle fleets. Such models generally use ordinary least squares-type regression models.
Disaggregate models, however, typically use household-level data to examine more detailed behavioral relationships at the decisionmaker level. Disaggregate models comprise a range of types, depending on the purpose and data availability (7). The most basic static vehicle-holding models aim to predict the number of cars owned by a household at a given time (i.e., a cross-sectional prediction) and can reveal the effects of relevant household characteristics, such as the number of employed adults, as well as household locational attributes, such as the distance to the central business district (CBD) and the local land use mix (6). More refined static models focus on vehicle type (e.g., alternative fuel vehicles) and can contain a number of vehicle-specific attributes, such as operating cost and top speed (8). These disaggregate approaches catch a single glimpse in time and can be used to develop long-run forecasts with fairly moderate data demands (7); however, these approaches do not capture more finegrained behavioral dynamics, such as the timing of decisions about new vehicle purchases, vehicle disposal, additional acquisitions, and so forth (9). Dynamic disaggregate models aim to model such effects explicitly over time through the use of household panel (or pseudopanel) data (10). Such approaches can reveal changes that are attributable to lifestyles, cohort effects, vehicle prices, fuel prices, and so on and enable short-and long-term forecasting; the challenges lie in the data requirements of these approaches (7).
Published research on disaggregate vehicle ownership modeling in the developing world remains somewhat limited. Wu et al. modeled automobile ownership in Xi'an, China, on the basis of a 1997 survey and developed the concept of "symbolic utility," or psychological satisfaction, in the ownership model (11). The results supported the role of symbolic utility in influencing vehicle ownership, although income dominated; the authors also indicated the significant roles of bus stop accessibility and neighborhood parking availability. Dissanayake and Morikawa modeled automobile and motorcycle ownership in a nested logit model of vehicle ownership, mode choice, and trip chaining in Bangkok, Thailand, by using data from 1995 to 1996 (12). Srinivasan (14). The authors found that popula-tion density was negatively associated with automobile ownership in both cities and, in contrast to studies in the West, that the likelihood of vehicle ownership declined with an increased distance from the CBD. As discussed in the introduction, Zegras, through the use of a multinomial logit model and data for Santiago in 2001, found a number of transport level of service, relative location, and built environment variables to be related to vehicle ownership (6). In this paper, that same data set for the 2001 model is applied.

MOdeling ApprOACh
Through an analysis of household vehicle ownership in four data sets, Bhat and Pulugurta found that the unordered (i.e., multinomial logit) model outperformed an ordered response model (according to several measures of fit); this finding led them to conclude that the unordered response choice mechanism was a better representation of the household automobile ownership decision (consistent with global utility maximization) (15). In the traditional multinomial logit model, a decision maker, faced with multiple alternatives, chooses that which provides the greatest utility; utility itself is viewed as a random variable. If a logistically distributed error term (ε n ) is assumed, the probability (Pr) of choosing alternative i can be estimated as (16) where P n (i) = probability of decision maker n choosing option i, U in = random utility of option i to decision maker n, U jn = random utility of option j to decision maker n, e = exponential function, V in = systematic utility of option i to decision maker n, V jn = systematic utility of option j to decision maker n, and µ = positive scale parameter, arbitrarily assumed to be one.
In this formulation, U in = V in + ε in and U jn = V jn + ε jn , and ε in and ε jn represent the random disturbances.
In this paper, the focus is an examination of the stability of vehicle ownership decision making over time through a comparison of models from two time periods: 1991 and 2001. For this examination, it must be ensured that any observed differences in the model estimations do not actually derive from variances in the underlying data; potential differences in the magnitudes of the variability of the random error component must be accounted for. Such an approach allows differences in the model parameter estimates that arise from different error variances to be distinguished from those differences that arise from real differences in the model parameters (17). Specifically, as discussed for Equation 1, the scale parameter (µ) cannot be identified and is arbitrarily assumed to be one, an assumption with no effect on the utility order within a particular data set. Between data sets, however, the values of the scale parameters may differ significantly, thereby implying variability across the data sets. Although µ cannot be identified in a single data source, the ratio of the µ's from different data sets can be estimated, relative to an arbitrary reference.
Thus, when comparing parameters from models in different times, the possible differences in error component variances must be accounted for. By following the method used in Severin et al., consider β 91 and β 01 to represent, respectively, vectors of parameters for choices in data sets from 1991 and 2001, and µ 91 and µ 01 to represent the respective scale parameters (17). If the differences in the parameter estimates arise from variance scale ratios not equal to one, as opposed to actual differences in the β's, then β 91 = β 01 and µ 91 /µ 01 ≠1. In other words, if the estimation produces different model parameters over two periods, it could be inferred that the effects of common observed factors (e.g., income) have changed over time, when the difference actually comes from the variance scale ratio (µ 01 /µ 91 ). These differences can be tested through different model estimations, specifically (a) the estimation of separate models for each data set (allowing different parameters for each) and (b) the estimation of a single model with the combined data set, restricting the parameter values (β's) to be identical but allowing for different variance scale ratio parameters (µ's) for each data set. The preferred model, either (a) the unrestricted model or (b) the restricted model, can be determined by applying a likelihood ratio test. This approach is known as the stability of preferences test.

EMPIRICAL CASE: SANTIAGO
Santiago is the capital of one of Latin America's most consistently growing economies. The country has sustained strong economic growth since its return to democracy in 1990 and entered into the Organisation for Economic Co-operation and Development in 2010. Santiago has a large share (greater than 40%) of the nation's population, jobs, and wealth. Because this analysis focuses on the vehicle ownership changes between 1991 and 2001, the following description primarily concerns basic city dynamics in that period.
From 1991 to 2001, the greater metropolitan area experienced a mean annual growth in average household income of 6.5%, from US$4,700 to US$9,000 (2001 U.S. dollars). By 2001, a burgeoning middle class was evident. This burgeoning middle class can be seen in Figure 1. The figure was derived from household travel surveys provided to the authors by Chile's national transportation authority (Secretaría de Planificación de Transporte, or SECTRA); the 1991 origin-destination (O-D) survey reported household income by range; the 2001 survey values were deflated to 1991 pesos through the Chilean consumer price index and were converted to U.S. dollars at the 1991 average observed exchange rate (350 pesos = US $1). Although by 2001 more than 50% of the households earned, on average, between US$6,000 and US$13,000 per year, a large share still earned less than US$4,000 per year. In fact, 15% of the households earned, on average, less than the minimum monthly wage (equivalent to approximately US$2,400 per year), just enough income for subsistence. (The average household size in this income stratum is 2.95 persons; the per capita subsistence cost is US$36 per month.) At the other extreme are the wealthy: a small share of Santiago's households in 2001 already enjoyed average incomes on a par with the industrialized world. In 2003, 5% of the households earned roughly the same income as did the median U.S. household. Santiago's residential geography has historically been characterized by socioeconomic spatial segregation, a pattern that has moderated somewhat in recent years (18).
In terms of physical structure, Santiago began experiencing accelerated urban expansion in the early 1980s, when the citywide population density began declining more rapidly than in any other period of the previous 50 years, influenced by factors such as road building, low-density suburban subdivisions, distant public housing projects, and industrial relocations (19). By the mid-1990s a new CBD emerged, 4.5 km east of the traditional CBD, thus giving the city an emerging duocentric business core, linked by a major commercial thoroughfare. Office parks and shopping malls were already apparent on the urban fringe by 2001.
The city's residential dwelling characteristics largely match the spatial income distribution patterns: low-income areas are associated with high dwelling unit densities. The primary exception comes from the high densities of large apartment buildings in the traditional and eastern CBDs and apartments that continue to sprout up in the wealthier east. Land use mixes, for the most part, radially follow the primary commercial arterials, and there are large swaths of areas across the city with a low mix of land uses. The historical street grid pattern still predominates, until a fairly large distance from the center of the city is reached, most notably, the far eastern foothill suburbs. Zegras provides a more detailed analysis of Santiago's urban structure, form, and design (18). Urban growth in Greater Santiago is regulated by an intermunicipality land use plan. An update to the 1960 plan was approved in 1994; the 1994 plan was subsequently modified in 1997 to add 19,000 ha of land capable of being urbanized (approximately 35% of the metropolitan area's urban area at the time) in the north of the metropolitan area (19).  As would be expected, Santiago's changing socioeconomic characteristics and physical form have affected household travel behavior. The motorization rate (vehicles per 1,000 persons) increased by over 4% per year between 1991 and 2001 (although in 2001, the rate still stood at only 20% of the U.S. level and just 30% of Western European levels); the private motorized modal share increased even more rapidly (almost 7% per year), and the public transit modal share declined by 3% per year (6). In 2001, Santiago still enjoyed remarkably ubiquitous bus service, but the subway coverage was more limited; this limited coverage is not an insignificant fact, because for most periods of the day, walking provided the primary means of subway access and egress (20).
In the past 40 years, the Santiago bus system has changed dramatically. In the 1970s and 1980s, service provision and fares were completely deregulated. The early 1990s brought the gradual reintroduction of regulation for road-based public transit, thereby producing incremental improvements in the quality of the bus system through the route concessions tendered in 1992, 1994, and 1998. The results included a reduction in the overall number of vehicles (from a high of approximately 14,000 in 1992 to 9,000 in 2000) (Figure 2), stabilized fares (see Figure 3), and improved service quality (21). Nonetheless, the system remained loosely regulated and was marked by an atomized ownership structure, competition in the streets for passengers, fairly unsafe operations, congestion, air pollution, and an   important degree of collusion among the companies (18). Moreover, fares on the system were not integrated, neither between buses nor between buses and the subway; transfers were, therefore, expensive. By 1999, the bus system carried 4.7 million passengers per day and operated without any direct government subsidy (22). Most recently, and perhaps most famously (or notoriously), in 2007 Santiago launched a massive, single-stroke reform of its public transportation system, known as Transantiago. Because this reform occurred after the time period of interest in this paper, the reform is not detailed here. Interested readers should consult Muñoz and Gschwender (23). Santiago's two original urban heavy rail lines were completed at the end of the 1970s and totaled approximately 27 km and 37 stations. A third line (known as Line 5) was brought into service in 1998 (see Figure 4), thereby adding 13 km and 15 stations. By 2001, the system carried an average of 725,000 passengers per work day (20). In 1991, Lines 1 and 2 had two separate fares, and a transfer from Line 2 to Line 1 required an additional fee ( Figure 3). In 1994, an integrated, differential fare for both lines replaced the separate fee system, and customer experience was likely improved.
Despite a history of relative demand management-focused transportation planning, by the early 1990s, large-scale roadway upgrades began, and by the late 1990s, plans were in place for Chile's highway concessions program to bring large investments to highway upgrades and expansions in the city. Nonetheless, by 2001 none of the concessioned highways had yet entered operation. By one estimate, Santiago in 2001 had a slightly smaller share of its urban area dedicated to highways than Chicago, Illinois, did in 1956 (2.1% versus 2.8%), the time when Chicago was just at the beginning of the massive highway investments that would come by way of the U.S. federal highway investment program (18).

Data Sources
Data for this analysis came from the 1991 and 2001 household O-D surveys carried out for SECTRA. The surveys were consistent in their implementation approach, team, and authority (24,25); the surveys also benefit from the long tradition of transportation engineering excellence in Chile. The 1991 survey was based on a randomly generated sample of approximately 32,000 homes that were surveyed in April, May, and June. In total, 3% of Greater Santiago's households were surveyed. The urban area included 34 comunas (municipalities), distributed over 420 km 2 (see Figure 5). The region was divided into 409 O-D survey zones that ranged in size from 18 to 27,000 ha, with an average of 478 ha. The 2001 survey was based on a smaller sample of 15,000 households, of which 12,000 were surveyed during the normal work and school season, and 3,000 were surveyed during summer time. In total, 1% of households in the Santiago metropolitan area were surveyed. The urban area had also expanded to include 38 comunas that were distributed over 2,000 km 2 and further divided into 778 OD zones that ranged in size from 17 to 19,000 ha, with an average of 250 ha.
Both surveys contained information on family size, reported income, and the number of vehicles per household, as well as each family member's job status. In 1991, income was reported by income categories; the 2001 survey recorded reported monthly income. To minimize the differences between each data set, all 2001 incomes were deflated to 1991 values (through the use of the official consumer price index for Chile) and assigned a corresponding income bracket from the 1991 survey. The household survey information was geocoded to the O-D zone level for the 1991 data and to the center of each census block for the 2001 data. For consistency in this analysis, only O-D zone-level characteristics were considered. Lastly, both surveys included expansion factors that related the characteristics of the sample population to Greater Santiago's actual population characteristics. These expansion factors were used to determine the number of dwelling units per O-D zone in the dwelling unit density calculation.

Land Use Variables and Accessibility Proxies
A consolidated form of the 1991 and 2001 national tax records data is available on SECTRA's website (26). The data aggregate all land use into the travel model (ESTRAUS) zones and into five general categories (e.g., residential, commercial, educational) and provide the size   This measure attempts to capture the relative mix of uses in a given zone: a value of zero implies that the zone is single use; a value of one indicates that the land is perfectly divided between all five uses. Theoretically, the mix of uses generates different transportation demands for the zone's residents; it would be expected, everything else being equal, that a greater land use mix would be associated with lower household vehicle ownership.
Without comparable measures of transportation levels of service for the 2 years studied, relatively crude measures of accessibility were tested: the distance to the traditional CBD (in meters) from the households O-D zone's centroid, and whether the household O-D zone's centroid was within 500 m of a subway station.
As discussed, the city expanded outward considerably over the 1991 to 2001 period, and the 2001 household survey covers this broader expansion area, especially to the rapidly suburbanizing north. Nonetheless, this analysis was restricted to a common geo graphy for the 2 years, to control for the effects of motor vehicle ownership that might be unique to the newly formed suburbs in 2001; the implications of this restriction are discussed below.
data Summary Table 1 presents the variables used in the models and their descriptive statistics. The data revealed an increase in household incomes and a larger concentration of households in the middle income groups (seen also in Figure 1). Consistent with this finding, the share of zero-car households decreased by 5%; the largest increase was in one-car households. Labor participation increased modestly as zero-worker and one-worker households declined (consistent with an increased number of women in the workforce), and twoand three-worker households increased. The number of children per household decreased slightly; more households had one child in 2001, and fewer had three children.
In terms of land use characteristics and relative location, the average zonal distance to the CBD increased by about 800 m; this finding indicates that households are moving outward, even within the same geographical area. Despite the expansion of the subway network, the share of zones within 500 m of a subway station increased by less than 1%. Finally, the average zonal density increased by five dwelling units per hectare, likely reflecting the growth in apartment building construction, particularly in the inner eastern parts of the city (see Figure 5); the average zonal land use mix slightly declined.

MOdel reSultS
The household decision to own zero, one, two, or three or more vehicles was modeled through a multinomial logit model, and the stability of preferences test was applied, as described above. First, two unrestricted models were estimated that allowed different parameters for each data set (Model A and Model B, Table 2). The two data sets were then combined, and the coefficients were restricted to be identical, but different scale parameters were allowed for in each data set (Model C, Table 2). The log likelihood ratio test suggested that, although the variance differed modestly between the two data sets (the significance of the scale parameter in Model C), the unrestricted (separate) models represented a better model fit than the restricted models. This result suggests that preferences for household vehicle ownership changed over the 10-year period. A comparison of the differences between the model coefficients from 1991 and 2001 offered some indication of which variables had changed the most vis-à-vis their relationship with the household vehicle ownership decision. Technically, to be fully comparable, the 1991 parameter estimates should be divided by the scale parameter estimate (1.13).
In most cases, the parameters carried the predicted signs and were statistically significant. Moreover, similar patterns existed between the parameters estimated in 1991 and those for 2001, thereby indicating relative consistency in the data and analytical approach. In terms of basic socioeconomic characteristics, it can be seen that higher income brackets had a positive and significant effect, as was hypothesized. Higher income brackets also progressively increased the likelihood of owning multiple cars, which, again, follows intuition. Generally, income had a stronger effect in 1991 than in 2001, most notably in the lower income brackets; although income was always positive and significant for 1991, in 2001 the lower income brackets had less significance. This may be attributable in part to unobservable differences in the lowest income categories associated with 2001, which represented a much lower share of the population than in 1991 (a result of having used the 1991 income categories for consistency between the 2 years). Household demographics also played largely expected roles, albeit apparently changing, in vehicle ownership. In both 1991 and 2001, for each increase in the number of children, the likelihood of owning more cars increased (i.e., the parameters tended to increase across vehicle ownership categories for a given number of children); for each vehicle ownership category, the number of children increased the ownership likelihood, although this effect plateaued. The general tendency was that the strength of the effect of the number of children declined from 1991 to 2001. The relationship between the number of workers per household and vehicle ownership is more difficult to generalize. In 1991, a one-worker household had a positive association with owning one vehicle, but a negative one with owning two or three vehicles. In 2001, a one-worker household had a positive relationship with owning one vehicle, but an insignificant one with owning two or more vehicles. In both 1991 and 2001, the variable of three or more workers had a negative effect on owning one vehicle, but varying relationships with owning two or three or more vehicles. For the two-vehicle variable, the presence of three or more workers had a positive and significant effect in 1991, but a negative though equally significant effect in 2001. For the choice to own three or more vehicles, the positive relationship in 1991 became insignificant by 2001. A more nuanced understanding of these relationships is necessary, possibly bolstered by qualitative analysis of vehicle choice processes within different household types.
With respect to the land use variables, land use mix had a negative relationship with all vehicle ownership categories in 1991, but this relationship largely became insignificant by 2001, a result worth further examination. The dwelling unit density had a negative and significant effect on vehicle ownership in 1991 and 2001, as was hypothesized. The effect of density increased in magnitude for the first vehicle choice, decreased for the second, and remained virtually unchanged for the third. Again, further examination is warranted.
Regarding the accessibility proxies that were used in this analysis, the distance to the CBD was consistently associated with a higher likelihood of vehicle ownership, a relationship that strengthened over time and with the number of vehicles chosen. In other words, households farther away from the city center have a higher likelihood of owning more cars, and this effect has increased over time. When residential density, land use mix, and household demographic characteristics were crudely controlled for, the suburbs apparently became more automobile conducive. The quadratic of distance to the CBD was also significant, however, thus suggesting a subcentering effect: households very far from the CBD have a lower likelihood of vehicle ownership. Again, this relationship strengthened over time, perhaps indicating a perceived consolidation of subcenters or other unobserved aspects on the periphery that reduced vehicle ownership propensity. Various measures were tested in an attempt to segment the effect of the distance to the CBD by income category, but the more straightforward model implemented here proved the best model fit. Finally, the effect of proximity to the subway, or lack thereof, was somewhat surprising. In 1991, no significant relationship between subway proximity and household vehicle ownership emerged. In 2001, a marginally significant negative effect was detected for the choice to own two vehicles, and a modestly significant and negative effect (in the order of having one child) was detected for the choice to own three or more vehicles.

liMitAtiOnS And Further reSeArCh
This analysis faced a number of limitations. Most notably, the land use relationships identified may have been a product of residential self-selection; in other words, households may choose their locations on the basis of vehicle ownership preferences, at least in part. This self-selection may confound, for example, the strong detected effect of the distance to the CBD. Future research should control for self-selection in these models through, for example, joint residential and vehicle ownership models. In addition, the estimation process showed that the results were somewhat sensitive to the changing geographies over the two time periods, especially the emergence of distant suburbs in 2001 that were excluded from this analysis.
Note: Alternative specific constants are not included in the interest of space. Further research might also examine various market segmentation approaches, by income, demographic, and other household characteristics or by relative location; further research could also more effectively control for variations in transportation level of service, although doing so would require historical network performance data. Furthermore, effects that were detected in this study and attrib-uted to variation in preferences between the two model years may, in fact, be attributable to nonlinear effects that were not captured by the null hypothesis of changes in the model coefficients. Finally, more work is needed to understand the policy and planning implications of these model results, including accounting for temporal changes in behavioral parameter estimates in simulations.

COnCluSiOnS
This paper examined whether household vehicle ownership preferences had changed over a 10-year period in a rapidly developing city that had undergone important changes in its transportation system, urban development, and income. The results demonstrated that preferences that influence vehicle choice models have, in fact, changed. The results suggested some interesting variations in relative influences between socioeconomic and demographic factors and land use and locational variables. Among lower-income groups, the models showed a decreasing tendency for vehicle ownership, possibly as a result of improvements in public transit during the 1990s. The improved overall quality of service may have been notable enough to allow the lowest income brackets to prefer to depend on transit for their mobility needs.
Among the land use variables, countervailing changes can be seen: on the one hand, the influence of land use mix, although still negatively correlated with vehicle ownership, declined over time; on the other hand, the opposite trend appeared in the case of the distance to the CBD. The effect of residential density changed modestly, and a slight effect of the proximity to the subway appeared to emerge by 2001 for the choice to own three vehicles.
The results suggest that household preferences for vehicle ownership are dynamic over time and that they respond to rapidly developing and evolving environments. Nonetheless, the research raises a number of questions and areas for further investigation, including the role of self-selection, the need for market segmentation, and the implications for policy and planning.