From e-bike to car: A study on factors influencing motorization of e-bike users across China

Household car ownership has risen dramatically in China over the past decade. At the same time a disruptive transportation technology emerged, the electric bike (e-bike). Most studies investigating motorization in China focus on macro-level economic indicators like GDP, with few focusing on household, city-level, environmental, or geographic indicators, and none in the context of high e-bike ownership. This study examines household vehicle purchase decisions across 59 cities in China with broad geographic, environmental, and socio-economic characteristics. We focus on a subset of households who own e-bikes and rely on a telephone survey from an industry customer database. From these responses, we estimate two three-level hierarchical choice models to assess attributes that contribute to (1) recent car purchases and (2) the intention to buy a car in the near future. The results show that the models are dominated by household characteristics including household income, household size, household vehicle ownership, number of licensed drivers and duration of car ownership. Some geographic, environmental and socio-economic factors have significant influences on car purchase decisions. Only two city-level transportation variable have an effect – higher taxi density and higher bus density reducing car purchase. Cold weather, population density gross domestic product per capita positively influence car purchase, while urbanization rate reduces car purchase. Because of supply heterogeneity in the data set, described by publicly available urban transportation data, this is the first study that can include geographic and urban infrastructure differences that influence purchase choice and suggests potential region-specific policy approaches to managing car purchase may be necessary.     

A dynamic model system of household car ownership,trip generation,and modal split: model development and simulation experiment

A dynamic model of household car ownership and mode use is developed and applied to demand forecasting. The model system consists of three interrelated components: car ownership, mechanized trip generation, and modal split. The level of household car ownership is represented as a function of household attributes and mobility measures from the preceding observation time point using an ordered-response probit model. The trip generation model predicts the weekly number of trips made by household members using car or public transit, and the modal split model predicts the fraction of trips that are made by public transit. Household car ownership is a major determinant in the latter two model components. A simulation experiment is conducted using sample households from the Dutch National Mobility Panel data set and applying the model system to predict household car ownership and mode use under different scenarios on future household income, employment, and drivers’ license holding. Policy implications of the simulation results are discussed.     

A repeated cross-sectional evaluation of car ownership

This paper studies changes in the relationship between household car ownership and income by household type. Ordered response probit models of car ownership are estimated for a sample of households repeatedly at six time points to track the evolution of income elasticities of car ownership over time. Elasticities of car ownership are found to change over time, questioning the existence of a unique equilibrium point between demand and supply that is implicitly assumed in traditional cross-sectional discrete choice car ownership models. Moreover, different household types and households that underwent household type transitions showed differing patterns of change in elasticities. Observed trends in car ownership and income clearly show behavioral asymmetry where the elasticity of procuring an additional car is greater than that of disposing a car. This too shows the inadequacy of traditional cross-sectional models of car ownership which tend to predict symmetry in behavior. The study suggests the importance of incorporating dynamic trends into the forecasting process, which can be accomplished through the use of longitudinal data.     

Household demand and willingness to pay for clean vehicles

This paper examines the factors and incentives that are most likely to influence households’ choice for cleaner vehicles in the metropolitan area of Hamilton, Canada. Data collection is based on experimental design and stated choice methods through an Internet survey. Choice alternatives included a conventional gasoline, a hybrid and an alternative fuelled vehicle. Each option is described by a varying set of vehicle attributes and economic incentives, customized per respondent. Controlling for individual, household and dwelling-location characteristics, parameters of a nested logit model indicates that reduced monetary costs, purchase tax relieves and low emissions rates would encourage households to adopt a cleaner vehicle. On the other hand, incentives such as free parking and permission to drive on high occupancy vehicle lanes with one person in the car were not significant. Furthermore, limited fuel availability is a concern when households considered the adoption of an alternative fuelled vehicle. Finally, willingness-to-pay extra for a cleaner vehicle is computed based on the estimated parameters.     

A panel data switching regression model of mobility and car ownership

The objective of this paper is to present a panel data model of car ownership and mobility. Unobserved heterogeneity is controlled for by including correlated random effects in the equations describing car ownership and mobility. A mass-points approach is adopted to control for unobserved heterogeneity. The results show that decisions concerning the first car in the household are difficult to affect; a large number of households are inclined to keep one car. Second car ownership may be more sensitive to changes in the observed contributing factors. This suggests that in The Netherlands policies aimed at changing second car ownership will be more successful than those aimed at influencing decisions concerning the first car in households. A major part of the correlation between the unobservables in the car ownership and the mobility equations is attributable to random effects. The time-variant errors of the mobility equations are not significantly correlated to car ownership decisions. This implies that mobility can only be influenced to a small extent by policy makers without measures aimed at reducing (second) car ownership.     

Estimating the long-term effects of different passenger car technologies on energy/fuel consumption and emissions of greenhouse gases in Europe

Abstract

A stated preference (SP) experiment of car ownership was conducted in Mumbai Metropolitan Region (MMR) of Maharashtra in India. A full factorial experiment was designed to considering various attributes such as travel time, travel cost, projected household income, car loan payment and servicing cost. Data on 357 individuals were collected which resulted in 3213 observations for the calibration of the work trip and recreational trip car ownership models. The car ownership alternatives considered 0, 1 and 2 cars. A multinomial logit framework was used to develop the car ownership model taking the household as a decision unit. The specification and results of the SP car ownership model are discussed. The observed and predicted values matched reasonably when the validity of the SP car ownership model was tested against revealed preference (RP) data. The car ownership models developed in this study exhibit a satisfactory goodness of fit. It is concluded that the SP modelling approach can be successfully used for modelling car ownership decisions of households in developing countries.     

The effect of the oil crisis on the growth in the ownership and use of cars

Since the oil crisis of 1973, a number of studies have been made in various countries of the effects of the rise in petrol prices on the level of traffic flow, but rather fewer have attempted to delineate the complex chain of reactions within the car market set off by this impulse. We attempt to do this, using data from the UK.Since 1966 during the prediction stage of the first London Transportation Study it became obvious that low income and high income households had different rates of growth of car ownership, mainly because low income households bought cheap, old cars which vary in quantity and price differently from expensive, new cars. The Greater London Council therefore sponsored a study of car prices by age and size, starting from 1957 annually, and since the oil crisis, evaluated monthly. This has enabled us to examine the strong change in trend that had occurred, with large cars depreciating 15% per annum more than the smallest. The quantities of cars of each size registered each month are available from national statistics and this enables us to say that the previous 1% per annum increase in car size was arrested, with new cars becoming substantially smaller.A model of the car market has been developed which relates on the one hand the price distribution of cars by age, and on the other hand the price. distribution of the stock of cars owned at each household income level. Via the expenditure on car purchase at each household income level and the distribution of the length of time between purchase and resale of cars, a fully dynamic model has been developed to relate expenditure flow and stock. This enables us to test the effect of different trends on the dynamic equilibrium in the car market.The implications of the two trends noted above on the prediction of future car ownership growth are discussed, with the standstill since the oil crisis attributed to petrol prices via the split in household expenditure between purchase and use.     

Modelling car ownership in Great Britain

Over the last 50 years there has been a tenfold increase in the number of cars in Great Britain, rising from 2.6 million vehicles in 1951 to 27 million vehicles in 2001. Over the same period there has been a steady reduction in the proportion of households without access to a car and a steady increase in the proportion of households with two or more cars. If such trends continue, it is likely that there will be increased energy consumption, increased problems with traffic congestion and atmospheric pollution, and reductions to the financial viability of public transport. Given the importance of car ownership to transport and land-use planning and its relationship with energy consumption, the environment and health, it is the objective of this research to develop econometric models of household car ownership and apply the models to generate forecasts across Britain to the year 2031. To achieve this objective, the research develops discrete choice models of the household’s decision to own zero, one, two or three or more vehicles as a function of market saturation, licence holding, household income and structure, household employment, company car provision, and purchase and use costs. The models are validated to data from the 2001 Census and are used to develop a range of forecasts taking into account changes to the socio-demographic characteristics of Britain.     

Evaluating policies to reduce greenhouse gas emissions from private transportation

This paper proposes a model system to forecast household greenhouse gas emissions (GHGEs) from private transportation. The proposed model combines an integrated discrete-continuous car ownership model with MOVES 2014. Four modeling components are calibrated and applied to the calculation of GHGEs: vehicle quantity, vehicle type and vintage, miles traveled, and rates of GHGEs. The model is applied to the Washington D.C. Metropolitan Area. Three tax schemes are evaluated: vehicle ownership tax, purchase tax and fuel tax. We calculate that the average GHGEs per vehicle is 5.15 tons of carbon dioxide-equivalent (CO₂E) gases. Our results show that: (a) a fuel tax is the most effective way to reduce vehicle GHGEs, especially for households with fewer vehicles; (b) a purchase tax reduces vehicle GHGEs mainly by decreasing vehicle quantity for households with more vehicles; and (c) an ownership tax reduces vehicle GHGEs by decreasing both vehicle quantity and miles traveled.     

A dynamic analysis of household car ownership

This paper examines the determinants of household car ownership, using Irish longitudinal data for the period 1995–2001. This was a period of rapid economic and social change in Ireland, with the proportion of households with one or more cars growing from 74.6% to 80.8%. Understanding the determinants of household car ownership, a key determinant of household travel behaviour more generally, is particularly important in the context of current policy developments which seek to encourage more sustainable means of travel. In this paper, we use longitudinal data to estimate dynamic models of household car ownership, controlling for unobserved heterogeneity and state dependence. We find income and previous car ownership to be the strongest determinants of differences in household car ownership, with the effect of permanent income having a stronger and more significant effect on the probability of household car ownership than current income. In addition, income elasticities differ by previous car ownership status, with income elasticities higher for those households with no car in the initial period. Other important influences include household composition (in particular, the presence of young children) and lifecycle effects, which create challenges for policymakers in seeking to change travel behaviour.     

The influences of past and present residential locations on vehicle ownership decisions

This study explores the relationship between historical exposure to the built environment and current vehicle ownership patterns. The influence of past exposure to the built environment on current vehicle ownership decisions may be causal, but there are alternative explanations. Households may primarily select to live in neighborhoods that facilitate their vehicle ownership preferences, or they may retain preferences that they have developed in the past, irrespective of their current situations. This study seeks to control for these alternative explanations by including the built environment attributes of households’ past residences as an influence on vehicle ownership choices. We use a dataset from a credit reporting firm that contains up to nine previous residential ZIP codes for households currently living in the 13-county Atlanta, Georgia, metropolitan area. Results show that past location is significant, but of marginal influence relative to the attributes of the current location. From a practical perspective, our results suggest that models that include current but not past neighborhood attributes (also controlling for standard socioeconomic variables) can forecast vehicle ownership decisions reasonably well. However, models that include both current and past neighborhood attributes can provide a more nuanced understanding of the built environment’s potentially causal influences on vehicle ownership decisions. This better understanding may provide more realistic forecasts of responses to densification or other travel demand management strategies.     

Household-level commuting mode choices,car allocation and car ownership level choices of two-worker households: the case of the city of Toronto

The paper presents a comprehensive investigation on household level commuting mode, car allocation and car ownership level choices of two-worker households in the City of Toronto. A joint econometric model and a household travel survey dataset are used for empirical investigations. Empirical models reveal that significant substitution patterns exist between auto driving and all other mode choices in two-worker households. It is revealed that, female commuters do not prefer auto driving, but in case of a one car (and two commuters with driving licenses) household, a female commuter gets more preference for auto driving option than the male commuter. Reverse commuting (commuting in opposite direction of home to central business district) plays a critical role on household level car allocation choices and in defining the stability of commuting behaviour of two-worker households. Two worker households in higher income zones and with longer commuting distances tend to have higher car ownership levels than others. However, higher transit accessibility to jobs reduces household car ownership levels. The study reveals that both increasing two worker households and reverse commuting would increase dependency on private car for commuting.     

Volatility of car ownership, commuting mode and time in the UK

This paper has two objectives: to examine the volatility of travel behaviour over time and consider the factors explaining this volatility; and to estimate the factors determining car ownership and commuting by car. The analysis is based on observations of individuals and households over a period of up to 11 years obtained from the British Household Panel Survey (BHPS). Changes in car ownership, commuting mode and commuting time over a period of years for the same individuals/households are examined to determine the extent to which these change from year-to-year. This volatility of individual behaviour is a measure of the ease of change or adaptation. If behaviour changes easily, policy measures are likely to have a stronger and more rapid effect than if there is more resistance to change. The changes are “explained” in terms of factors such as moving house, changing job and employment status. The factors determining car ownership and commuting by car are analysed using a dynamic panel-data models.     

Influences of car type class and carbon dioxide emission levels on purchases of new cars: A retrospective analysis of car purchases in Norway

The impact of socio-demographic and psychological factors on purchases of new cars is examined. Data were gathered in an online retrospective survey using a sample of 198 Norwegian households who purchased a new car in December 2010. A latent class analysis was performed to identify car type classes followed by a path analysis to investigate the determinants of the purchased car type class and the influence on the purchased car’s level of carbon dioxide emissions. The results revealed that car type class is the strongest determinant of the car’s level of CO₂ emissions. Socio-demographic factors have little impact on choice of car type class when psychological factors are controlled for. Intention to purchase an environmentally friendly car has a direct effect on the car’s CO₂ emissions.     

Auto travel fuel elasticity in a rapidly developing urban area

This paper presents results of a study conducted to quantify the effect of fuel cost increases on household auto travel in Riyadh, the rapidly developing capital of Saudi Arabia. Responses of a stratified random sample of 1648 individual households provided the data base for the analysis. The auto trip measures of shrinkage ratio, arc and log-arc elasticities were calculated for households categorized by income and family size. The elasticity measures suggested the existence of significant relationship among the factors of fuel cost, the number of daily auto trips, and family size. It was found that as fuel prices increased, the number of daily trips decreased, and that this decrease in daily trips was greater with larger family size. A step-wise multiple regression analysis with three independent variables of car ownership, family size, and daily fuel expenditures was developed. The model was fairly accurate in predicting variations in daily household travel. The regression parameter of the variable fuel cost was also used to derive demand elasticity to fuel expenditures. Elasticity measures ranged between -0.30 and -0.37.     

A model system for the assessment of the effects of car and fuel green taxes on CO2 emission

This study aims at developing a model system to examine the changes in the car market configuration, the life cycle CO₂ emission from automobile transport and the tax revenues due to different taxation policies. The model system specifically determines the effect of varying the weights of the tax components in the stages of a) car purchasing, b) car owning, and c) car using to the changes in the car class and age mix and the car users' driving pattern and behavior towards car class choice and decommissioning. Five sub-models comprise the model system, formulated using car ownership related data in Japan from 1980 to 1994. Performance tests conducted against the sub-models generally yielded encouraging results. The sensitivity analysis identified car usage tax as the most significant parameters in reducing CO₂. An increase in, ownership tax, on the other hand, significantly results to a shift to smaller cars, while the propensity to decommission and repurchase can be reduced by increasing the purchase tax and can be decreased by increasing the ownership tax. The model system was utilized to determine the impact of the 1989 tax reform and to forecast future scenarios using different taxation schemes. The model system is being further developed for possible future application in other countries.     

The effect of income on car ownership: evidence of asymmetry

This paper examines the effect of income on car ownership, and specifically the question of hysteresis or asymmetry. Although there is little doubt that rising income leads to higher car ownership, less is understood about the effect of falling income. Traditional demand modelling is based on the implicit assumption that demand responds symmetrically to rising and falling income. The object of this study is to test this assumption statistically. Using a dynamic econometric model relating household car ownership to income, the number of adults and children in the household, car prices and lagged car ownership, income decomposition techniques are employed to separately estimate elasticities with respect to rising and falling income. The equality of these elasticities – no hysteresis – is tested statistically against the inequality – hysteresis – hypothesis. Various functional specifications are tested in order to assure the robustness of the results to assumptions concerning functional form. The estimation is based on cohort data constructed from 1970 to 1995 UK Family Expenditure Surveys, and a pseudo-panel methodology is employed. The results indicate that car ownership responds more strongly to rising than to falling income – there is a ‘stickiness’ in the downward direction. In addition, there is evidence that the income elasticity is not constant, but instead declines with increasing car ownership.     

Patterns of car usage and restraint modelling

Transport demand forecasting procedures have traditionally employed household based modal split models implicitly assuming a selection of mode for each trip based on relative generalised cost. A detailed examination of the trip patterns of a sample of household in West Yorkshire shows that in fact there is little discretionary choice of public transport; public transport trips in car owning households generally being explained in terms of the specific unavailability of the car for such trips. Two versions of a category analysis model for modal split are based on this observation and applied to household data for Glamorgan and Monmouthshire to show that such a procedure is workable and produces results comparing favourably with traditional approaches. The likely implications of three types of restraint policy are examined and it is concluded that the existing interdependence in trip patterns and modal choice within the household is of great significance in determining their effects. In particular it appears that positive attempts to increase vehicle occupancy at the peak are likely to be more favourable to public transport finances than the more negative policies to restrain use of the car for journey to work, or second car ownership.     

Joint analysis of the spatial impacts of built environment on car ownership and travel mode choice

Concerns over transportation energy consumption and emissions have prompted more studies into the impacts of built environment on driving-related behavior, especially on car ownership and travel mode choice. This study contributes to examine the impacts of the built environment on commuter’s driving behavior at both spatial zone and individual levels. The aim of this study is threefold. First, a multilevel integrated multinomial logit (MNL) and structural equation model (SEM) approach was employed to jointly explore the impacts of the built environment on car ownership and travel mode choice. Second, the spatial context in which individuals make the travel decisions was accommodated, and spatial heterogeneities of car ownership and travel mode choice across traffic analysis zones (TAZs) were recognized. Third, the indirect effects of the built environment on travel mode choice through the mediating variable car ownership were calculated, in other words, the intermediary nature of car ownership was considered. Using the Washington metropolitan area as the study case, the built environment measures were calculated for each TAZ, and the commuting trips were drawn from the household travel survey in this area. To estimate the model parameters, the robust maximum likelihood (MLR) method was used. Meanwhile, a comparison among different model structures was conducted. The model results suggest that application of the multilevel integrated MNL and SEM approach obtains significant improvements over other models. This study give transportation planners a better understanding on how the built environment influences car ownership and commuting mode choice, and consequently develop effective and targeted countermeasures.