The motorcycle to passenger car ownership ratio and economic growth: A cross-country analysis

Cross-country statistics have revealed steady growth in the number of motorcycles in many less advanced economic countries (LAEC) with emerging economies due to increased urbanisation and personal wealth. In contrast, an opposite trend is occurring in advanced economic countries (AEC), with cars replacing motorcycles as income grows. Motor vehicle crashes and injuries are an inevitable consequence of a high motorcycle population. This study focused on understanding how economic growth affects the motorcycle to passenger car (MPC) ownership ratio and what factors underlie this relationship. The data used in this analysis contained a sample of 80 countries at various levels of economic developmental growth over the 48-year period between 1963 and 2010. The results pointed to an inverted U-shaped relationship between the MPC ownership ratio and the per capita Gross Domestic Product (GDP). Generally, the MPC ownership ratio increased with income at a lower level and decreased with income at a higher level. The evidence indicated that urbanisation, the total road length per thousand population, and a proxy for purchasing power with regard to vehicle purchases were the underlying factors that contributed to this relationship.     

A multilevel analysis on the determinants of household tourism expenditure

Previous studies on household tourism expenditure determinants focused mainly on the effects of family characteristics on tourism spending behaviour. However, possible influence of regional characteristics on tourism expenditure and hierarchical data structure in their analyses were overlooked. This study is the first to apply a multilevel analysis to investigate the determinants of household tourism expenditure. The analytical results indicate that household head age, household income, car ownership and Internet usage positively influence tourism expenditures. Home loan and health and insurance expenditures negatively impact tourism expenditures. Regional factors, such as average household income and industry structure, have direct and moderating effects on tourism expenditure.     

Estimating local car ownership models

Many studies in the transport demand literature have shown that income is an important factor in determining how many cars a household owns. When the models used to measure the strength of this relationship are estimated on cross-sectional data, they typically yield one overall value as the estimate. Local circumstances will, however, vary. This paper illustrates the use of the Geographically Weighted Regression technique to estimate the individual strength of this relationship for each of the United Kingdom electoral wards. Use of this type of model enables a wards’ income elasticity to be based on both the local estimate of the strength of this relationship and the current local level of car ownership. How the use of this local elasticity changes future forecasts of the size of the vehicle fleet is illustrated.     

Car ownership and urban development in Chinese cities: A panel data analysis

Growth in car ownership has significant impacts on the use of urban space and management of urban environments, which makes it a topic of increasing interest especially for developing countries such as China. The dynamics of and factors influencing ownership in Chinese cities need careful investigation. Using fixed effects models applied to annual panel data (1994–2012; 293 cities) this study aimed to achieve the following: 1) assess the relationships between car ownership and average annual income per capita, population, built-up area, road area per capita, urban population density, number of taxis and bus passenger volume; 2) examine the variation of these relationships across geographical regions (East, middle, and West China) and city sizes (cities with small, medium, large, and super-large populations). The results showed that car ownership was positively associated with average annual income per capita, built-up area, road area per capita, urban population density, and number of taxis at the national level. All associations, except with the number of taxis, varied significantly across geographical regions. Built-up area, road area per capita, and number of taxis had different associations with car ownership depending on city sizes. The findings improve the understanding of relationships between car ownership and urban environments vis-a-vis variations in income and infrastructure per capita, population density, and transportation alternatives. These results have important policy implications for managing cars and health problems related to cars in China.     

Investigating household vehicle ownership,mode choice and trip sharing decisions using a combined revealed preference/stated preference Nested Logit model: case study in Bangkok Metropolitan Region

This study proposes a Nested Logit model to investigate household travel behaviour in respect to vehicle ownership, mode choice and trip sharing decisions. The model is analysed using revealed preference (RP) and stated preference (SP) data since a combined estimation of RP/SP data is an effective method of expressing complex travel behaviour and forecasting travel demand for new transport services. In the proposed model, the nesting structure has two levels. The upper level shows car ownership, motorcycle ownership, and no vehicle-ownership choices, and the lower level shows the mode choice combinations for two-traveller households. Trip sharing is considered as one of the mode-choice options in the model. The proposed model is analysed using data from the Bangkok Metropolitan Region. The analysis conducted informs that Central Business District (CBD) travel, long distance travel, household income, job status, age of travellers and presence of school children in households are key aspects in household travel decisions. Based on these aspects, households make important decisions on vehicle ownership, mode choice and trip sharing. In addition, this study reveals commuters’ hidden preferences for modes that are not in existence, in particular the Mass Rapid Transit System in the Bangkok Metropolitan Region due to be fully implemented in 2010.     

Destination choice and the identity of places: A disaggregated analysis for different types of visitor population environment in the Netherlands

Individual behaviour relates to the ecological structure or identity of places. In this study, we investigated the relationship between the space–time ecologies of different types of visitor population environment in the Netherlands and destination choice. Multinomial logistic regression modelling for car users was applied to determine the relative importance of various personal and household attributes on choosing for a particular purpose a particular type of visitor population environment during a particular time period of the day. The attributes with the strongest link to the space–time ecologies of destination environments proved to be age, educational level, car ownership, and household income and type.     

The geography of car ownership in Mexico City: a joint model of households’ residential location and car ownership decisions

Suburbanization and car-ownership have increased rapidly in developing-world cities over the past half-century. This study examines the relationship between income, car ownership, and suburbanization across geographies and households in metropolitan Mexico City. Neither car-ownership trends by geography nor a mixed logit model of 43,000 households’ joint car-ownership and residential-location decisions suggest that car ownership and suburbanization are moving hand-in-hand. Instead, wealthier households tend to live in central locations and own and drive cars, while poorer households tend to live further from the urban center and rely more heavily on transit. If a random household’s income doubles, that household is around 44% more likely to get an additional car and 29% more likely to live in the urban center. Given the sticky nature of housing supply and the current model of peripheral housing production, aggregate responses to higher income are more difficult to predict, as they will almost certainly be offset by higher prices. Nevertheless, the findings suggest that higher income and car ownership do not tend to encourage a move to larger houses on larger suburban lots in Mexico City.     

Journey quality as the focus of future transport policy

While personal travel seems to be growing inexorably, along with car ownership, other transport parameters are invariant: average travel time, trip rate, and the proportion of household income spent on travel. Past growth in personal travel may, therefore, be attributed to longer trips carried out at higher speeds and made possible by higher expenditure. For the future, the need to limit environmental detriments associated with the transport system requires the diversion of growing personal expenditure, from further increasing the quantity of travel to enhancing the quality of the journey. Policies and technologies to achieve this objective are discussed, including discriminatory road pricing.     

‘60-20 emission’—The unequal distribution of greenhouse gas emissions from personal,non-business travel in the UK

Relatively little is known about the composition of greenhouse gas emissions from personal, non-business travel at the disaggregate levels. This paper aims to give insights into the distribution of emissions amongst the UK population. When including non-carbon climate effects air travel dominates overall greenhouse gas emissions. There is a huge range in emissions, with the highest 20% of emitters producing 61% of emissions. This ‘60-20 emission’ rule is surprisingly similar across units and scale of the analysis. Disaggregated data tell a different story than aggregated data. While income, working status, age and car ownership are significantly related to overall emissions, factors related to accessibility, household location and gender are not.     

Exploring the effects of the rural built environment on household car ownership after controlling for preference and attitude: Evidence from Sichuan,China

With the rapid infrastructure development and economic growth in China, household car ownership in the country's rural areas has changed dramatically in the past 16 years. The total number of cars owned by households in rural areas is currently 12 times higher than it was 16 years ago. The exploration of the effects of the built environment on household car ownership in China's rural areas is worthwhile. However, few studies have investigated this topic. To fill in the research gap, this work collected 374 household data from rural areas in China to examine the effects of the built environment in Sichuan's rural areas on the number of cars in a household. It considered family structure, socioeconomic characteristics, and individual's perceptions of the built environment, preferences towards the built environment and attitudes towards car ownership (shortened to perceptions, preferences and attitudes from now on). Geographic information system (GIS) technology, combined with on-site measurement, was used for data collection. The multinomial logit model was applied for estimation. Household structure and the built environment (including the perceived built environment and the objective built environment) significantly influence the number of cars in a household. By contrast, preference and attitude attributes have less influence on car ownership. Most of the findings are in line with the literature in the context of Chinese cities. Nevertheless, new results are also found. For example, rural hukou, and building density have significant positive impacts on household car ownership in China's rural areas, which is in contrast with their effects on cities. As the first study on rural areas in China, this research provides some insights for rural planners and policymakers to understand better the relationship between built environment and household car ownership.     

Interdependences between household residential and car ownership behavior: a life history analysis

Once a transportation system is built or a land-use policy is carried out, it influences people’s travel behavior and their lives for a long time period. It is therefore important for policy makers to understand people’s decisions on travel behavior and lives over a longer time period. However, little has been known about the interdependences between life domains, especially over the life course (i.e., biographical interdependences) in the context of residential and car ownership behavior. To fill this gap, this study aims to clarify households’ biographical interdependences relating to residential and car ownership biographies by explicitly incorporating the influence of household structure and employment/education biographies. Biography is defined based on a general concept of mobility that indicates a change occurring in a life domain. For this purpose, a Web-based life history survey was conducted in November 2010 and 1000 households living in major Japanese cities provided valid data. Aggregate analysis and exhaustive CHAID analysis were carried out, focusing on the occurrence times of mobilities in each biography. Results confirm obvious two-way cause–effect relationships over the life course between residential and car ownership biographies that are further influenced by household structure and employment/education biographies. Especially, not only short-term but also long-term state dependence and future expectations within and across life domains are clarified. Household structure and employment/education biographies are found to be more influential on residential biography than car ownership biography. Though residential biography is seen to be more influential on car ownership biography, the other two biographies also play an important role in explaining the car ownership mobility decision. All these findings suggest the necessity of developing intra-domain and inter-domain biographical interdependence models with flexible structures that capture the influences of state dependence and future expectations over different time scales in the life course in a unified framework.     

Hong Kong’s low car dependence: lessons and prospects

Reducing car dependency, and hence traffic, have become key transport objectives of many cities and countries. Despite relatively high levels of wealth, Hong Kong has never become a car-dependent location. Using results of a survey of 340 young Hong Kong people and five focus group discussions, this paper argues that there are lessons to be learned from Hong Kong, and that transport policies, rather than population density, are mainly responsible for the low levels of car ownership and use. It also shows, however, that without even stricter policies, car ownership and use could increase substantially in the future.     

Shared autonomous vehicles and their potential impacts on household vehicle ownership: An exploratory empirical assessment

Emerging transportation technologies have the potential to significantly reshape the transportation systems and household vehicle ownership. Key among these transportation technologies are the autonomous vehicles, particularly when introduced in shared vehicle fleets. In this paper, we focus on the potential impact that fleets of shared autonomous vehicles might have on household vehicle ownership. To obtain initial insights into this issue, we asked a sample of university personnel and members of the American Automobile Association as to how likely they would consider relinquishing one of their household's personal vehicles if shared autonomous vehicles were available (thus reducing their household vehicle ownership level by one). For single-vehicle households, this would be relinquishing their only vehicle, and for multivehicle households (households owning two or more vehicles) this would be relinquishing just one of their vehicles. Possible responses to the question about relinquishing a household vehicle if shared autonomous vehicles are present are: extremely unlikely, unlikely, unsure, likely, and extremely likely. To determine the factors that influence this response, random parameters ordered probit models are estimated to account for the likelihood that considerable unobserved heterogeneity is likely to be present in the data. The findings show that a wide range of socioeconomic factors affects people's likelihood of vehicle relinquishment in the presence of shared autonomous vehicles. Key among these are gender effects, generational elements, commuting patterns, and respondents' vehicle crash history and experiences. While people's opinions of shared autonomous vehicles are evolving with the continual introduction of new autonomous vehicle technologies and shifting travel behavior, the results of this study provide important initial insights into the likely effects of shared autonomous vehicles on household vehicle ownership.     

An examination of the factors that impact upon multiple vehicle ownership: The case of Dublin, Ireland

This paper examines the characteristics of households with multiple car ownership in Dublin, Ireland. Data from the 2006 Census of Ireland are analysed to ascertain the characteristics of these households. The analysis of multiple car ownership presented herein examines individual specific, transport availability, and household characteristics to provide an indication of the individuals most likely to have access to more than one vehicle. Understanding the characteristics of households with more than one car is important for many reasons, such as how policies for emissions reductions or pricing regimes might affect households. Ireland, like many countries, has recently launched a number of electric vehicle and car sharing schemes. Traditionally these schemes have been aimed at reducing multiple car ownership, therefore it is important to develop an understanding of the households that would most likely give up an extra car and use a car sharing scheme or an electric vehicle. Also from a sustainability point of view, greater levels of car ownership can result in unsustainable transport patterns.This paper examines the Census data using a multinomial logit regression model to determine the relationships between multiple car ownership levels and several household characteristics. The findings of the paper demonstrate that occupation, public transport availability and residential density all have an impact upon the decision to own more than one vehicle.     

Examining spatial variations in minimum residential parking requirements in Melbourne

Minimum off-street residential parking requirements are used in many cities as a way to accommodate parking demand associated with new residential development. In some cases, variations to these requirements are used in the form of reduced (or eliminated) minimums and/or maximum parking requirements to more actively manage parking demand. This paper assesses how such variations affecting new residential apartment development in Melbourne, known locally as parking overlays, compare against residential parking demand. Using household car ownership data as a proxy for off-street residential parking demand, a case-control analysis was undertaken to compare car ownership within and immediately outside areas affected by the parking overlays, while controlling for a range of built environment, public transport, demand management and socio-demographic variables. Key findings indicate that car ownership is generally lower in areas affected by parking overlays, yet this was either roughly the same or well below the actual parking requirement. Through regression modelling, the results highlighted the importance of public transport service quality, car parking requirements and demographics in influencing car ownership within and immediately outside the parking overlay areas. These results were used to develop a parking overlay index to identify other areas that could benefit from more flexible residential parking requirements. Despite parking overlays considered as a form of parking management, the results imply that, in Melbourne, they represent little more than a conventional supply-side approach to parking policy. The results indicate that residential off-street parking requirements could be reduced further in Melbourne, both within and outside of areas affected by parking overlays, to more actively manage parking demand.     

Landscape pattern and car use: Linking household data with satellite imagery

Landscape pattern has long been hypothesized to influence automobile dependency. Because choices about land development tend to have long-lasting impacts that span over decades, understanding the magnitude of this influence is critical to the design of policies to reduce emissions and other negative externalities associated with car use. Combining household survey data from Germany with satellite imagery and other geo-referenced data sources, we undertake an econometric analysis of the relation between landscape pattern and automobile dependency. Specifically, we employ a two-part model to investigate two dimensions of car use, the discrete decision to own a car and, conditional upon ownership, the continuous decision of how far to drive. Results indicate that landscape pattern, as captured by measures of both land cover (e.g. the extent of open space and landscape diversity) and land use (e.g. the density of regional businesses) are important predictors of car ownership and use. Other policy-relevant variables, such as fuel prices and public transit infrastructure, are also identified as correlates. Based on the magnitude of our estimates, we conclude that carefully considered land development and zoning measures – ones that encourage dense development, diverse land cover and mixed land use – can have beneficial impacts in reducing car dependency that extend far into the future.     

The factors in residents' mobility in rural towns of China: Car ownership,road infrastructure and public transport services

The improvement of rural people's mobility in developing countries has informed many policies. Still, debates remain on which policies are efficient, for instance, building more roads, providing public transport or promoting car ownership. The empirical evidence for these debates at the national level remains scarce. As a result, this paper aims to provide fresh evidence for discussions by examining residents' mobility in China using nationwide survey data with 12,524 respondents from 119 rural towns. The results of the analysis show car ownership is the most significant factor influencing rural people's mobility than other factors. Higher car ownership relates to a higher travel frequency to counties or cities. Other kinds of transport vehicles (i.e. electric cars, motorcycles and electric bikes) also have positive but relatively weaker impacts on rural mobility. For public transport, it is more accessible to access bus stops, which encourages travel to higher-order centres rather than increasing the frequency of county bus services. The accessibility of high-quality road systems tends to have a negative influence and has combined effects with levels of local services. People from towns with insufficient local services and poor access to highways travel the most frequently to higher-order centres. This study highlights the critical role of road investments and car ownership enhancement policies in improving mobility. Moreover, this study also underscores the supplemental role of public transport services given the current low car ownership rates in rural towns of China and the global consensus on sustainable green transport development. It highlights the importance of engaging eco-friendly and locally adaptive transport alternatives, such as electric cars and electric bikes. It also calls for a rational distribution of bus stops and more flexible, convenient, and physically accessible public transport and carshare modes in rural China.     

Understanding the process that gives rise to household car ownership level changes

Quantitative studies have revealed that changes to the number of cars owned by households are more likely to occur at the time of life events. However, causal explanations of such relationships are either absent or lacking evidence. To address this knowledge gap, this paper presents a qualitative study which enabled the development of a new conceptual framework to explain the process through which the number of cars owned by households changes over time. The framework emerged through an inductive analysis of 15 in-depth biographical interviews and was validated through a mixed methods survey of 184 households located in Bristol (UK). The following mechanisms of the process are identified: Life events alter roles, relationships, spatial contexts and lifestyle preferences. This can lead to a condition of stress which relates to a discrepancy between satisfaction with the current car ownership level and a more desirable alternative. Attempts to adjust to the new situation are made through processes of travel behaviour adaptation and consideration of whether the car ownership level ought to be altered. A propensity to change car ownership level can emerge from this. However, given the effort involved in taking action, households tend to resist making changes to their car ownership level in the short term. Action to change car ownership level is found to often be prompted by another external stimulus such as the receipt of a maintenance bill. A key message from the analysis is that changes in household car ownership level should be considered as the outcome of a continuous process of development over the life course, rather than as discrete decisions.     

The built environment and vehicle ownership modeling: Evidence from 32 diverse regions in the U.S.

Vehicle ownership is an important determinant of the travel demand forecasting process. Vehicle ownership models are used by policy makers to identify factors that affect vehicle miles traveled, and therefore address problems related to energy consumption, air pollution, and traffic congestion. For the conventional travel demand forecasting, it logically follows land use forecasting, before trip generation, which is commonly treated as step one. The most critical limitation of the vehicle ownership models, especially in the conventional process, is that they are often related mainly to sociodemographic variables, not so much to built environmental variables. In this study, by pooling regional household travel survey data from 32 diverse regions (almost 92,000 households) of the U.S., and by controlling for socio-demographic and the built environmental variables, we estimated a vehicle ownership model that contributes to the understanding of vehicle ownership and improves the accuracy of travel demand forecasts. Two main findings of this research are: 1) The number of vehicles owned by a household increases with socio-demographic variables and decreases with almost all of the built environmental variables. For the urban planning and design practices, this finding suggests that car shedding occurs as built environments become more dense, mixed, connected, and transit-served. 2) We used both count regression and discrete choice models, and the results suggest that count regression models have better predictive accuracy. The model developed in this study can be directly used for travel demand modeling and forecasting by metropolitan planning organizations.     

Nonlinear effects of residential and workplace built environment on car dependence

The relationships between the built environment (BE) and car dependence have been thoroughly evaluated, with a primary focus on the residential BE; however, the effects of the BE at workplaces have remained largely unexplored. Little is known about the potential nonlinear effects of the BE at both locations. Using data from a household travel survey in Changchun, China, we aimed to reveal the nonlinear effects of the residential and workplace BE on car dependence by building a gradient boosting decision trees model. The results show that the BE at both locations has strong explanatory power for car ownership and car purchasing intention. With relative contributions values of 17.90% and 18.13%, respectively, the BE at workplaces contributes less to explaining the two dependent variables than the BE at residences. All BE attributes show nonlinear effects on car ownership, and car purchasing intention and the effects differ between residential and workplace locations.