Changing Influence of Factors Explaining Household Car Ownership Levels in the Netherlands

Maltha, Yannick; Kroesen, Maarten; Wee, Bert van; Daalen, Els van

doi:10.3141/2666-12

Cited by 22 publications

(13 citation statements)

References 21 publications

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“…The decision to own a private vehicle and the type of vehicle depends on different HH characteristics, such as its income, size, the number of license holders, composition in full-time workers and children, education level, gender and age [1], [2]. Ha, et al (2019), using important variable ranking methods as Multi-nominal Logit model, Neural Networks and Random Forests, found that income is the most potent variable influence on motorisation among other HH characteristics [5].…”

Section: Literature Reviewmentioning

confidence: 99%

“…Maltha, Y. (2016) found that the HH income was the most influential factor in vehicle ownership together with HH size, gender, age, education, suburbanisation, and working status in the Netherlands between 1987 and 2014 [2].…”

Section: Literature Reviewmentioning

confidence: 99%

“…Vehicle ownership affects the ability of road transport infrastructure capacity in a country to cope with traffic congestion and delays; particularly when demand exceeds the road space supply. Vehicle ownership depends on several factors, including HH income, HH size, HH composition, gender, and social status [1], [2]. Increased income makes vehicles more affordable to own and operate, and the rate of multiple vehicle ownership also increases.…”

Section: Introductionmentioning

confidence: 99%

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Analysis of Vehicle Ownership Attributes in Western Province, Sri Lanka

Dilini

Amalan

Kumarage

2021

J. S. Asian Logistics Transport

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This paper develops a mathematical model to predict motor vehicle ownership based on household (HH) characteristics. The model is tested using household visit surveys in the Western Province of Sri Lanka (CoMTrans, 2014). The province which has the country's highest population density (1,600/km 2 ) and road density (0.9 km/km 2 ) as well as a motor vehicle ownership of 206 vehicles per 1,000 people. The modelling is disaggregated into motorcycles, three-wheelers, vans, and cars (including jeeps and pick-ups). The motor vehicle fleet comprises 51% motorcycles, 20.2% threewheelers, 6.7% vans, and 17.7% cars apart from commercial vehicles. The purchasing cost of motor vehicles in Sri Lanka varies widely due to different taxes imposed at importation. A binary logistic regression with cross-validation statistical theories was used to predict the HH ownership of different vehicles, based on an income-based testing scenario for determining a HH's likelihood of owning a particular type of vehicle.Motorcycles, three-wheelers, vans, and cars listed in ascending order of cost of ownership and operation were tested against the characteristics of 35,850 HHs using R, a software analytical tool. The analysis found that private vehicle ownership depends on attributes of a HH, such as its size, average monthly income, and the percentage of workers, school and kindergarten children, and males in that HH.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Analysis of Vehicle Ownership Attributes in Western Province, Sri Lanka

Dilini

Amalan

Kumarage

2021

J. S. Asian Logistics Transport

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“…Determinants of uncertainty for the transport sector are multifaceted and include the: changing demand for travel in society, its causes and likely continuation (Marsden et al 2018;Maltha et al 2017); impacts of climate change on infrastructure resilience and investment needs (Buurman and Babovic 2016;Dawson et al 2016); speed and nature of any transition away from fossil fuel powered transport (Contestabile et al 2017;Brand et al 2019); future way in which road travel will be priced in a shift to electric vehicles (NIC 2017;Volterra Partners and Jacobs 2017); digital age maturing with a myriad of developments in information and communications technology (ICT) that in turn influence behaviours (Lyons et al 2018a); advent, deployment and impacts of increasingly intelligent, automated and connected vehicle technology (Shaheen et al 2018;Rohr et al 2015); and the traditional factors of population growth, fuel price, disposable income and land-use distribution (NIC 2016;OBR 2018).…”

Section: A Wicked Problemmentioning

confidence: 99%

Opening out and closing down: the treatment of uncertainty in transport planning’s forecasting paradigm

Lyons

Marsden

2019

Transportation

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Since the 1960s, development of the transport system has been framed by the notion of forecasting future demand. Yet the past decade or more appears to signal some significant changes to the role of travel in society which are having a material impact on how much people travel (and may travel in the future). Coupled with the potential for major technological changes and a range of climate adaptation scenarios, the future of mobility presents today's decision making on transport strategy and investment with a broader set of uncertainties than has previously been considered. This paper examines current mainstream practice for incorporating uncertainty into decision-making, through an illustrative case study of the highly codified approaches of the Department for Transport in England. It deconstructs the issue by first focussing on different ways in which there is an opening out or acceptance of new uncertainties and how this creates a (wider) set of potential futures. It then turns to consider how this set of futures is used, or not, in decision-making, i.e. the process of closing down uncertainty to arrive at or at least inform a decision. We demonstrate that, because the range of uncertainties has broadened in scope and scale, the traditional technocratic approach of closing down decisions through sensitivity testing is at odds with the greater breadth now being called for at the opening out stage. We conclude that transport decision-making would benefit from a rebalancing of technical depth with analytical breadth. The paper outlines a plausible new approach to opening out and closing down that is starting to be applied in practice. This approach must be accompanied by an opening up of the processes by which technical advice for decisions are reached and how uncertainties are understood and negotiated.

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“…The Dynamic Automobile market (DYNAMO) model in the Netherlands has been developed for the Dutch Transport Research Centre and the Netherlands Environmental Assessment Agency (see MuConsult, 2006;Maltha, 2016). The most important car ownership prediction model in the Netherlands, its latest version (3.0) assesses the effects of general developments and government policy on the size, composition and use of the Dutch car fleet for the period up to 2050.…”

Section: Who Is Doing What?mentioning

confidence: 99%

Understanding Consumer Vehicle Choice

2019

International Transport Forum Policy Papers

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Changing Influence of Factors Explaining Household Car Ownership Levels in the Netherlands

Cited by 22 publications

References 21 publications

Analysis of Vehicle Ownership Attributes in Western Province, Sri Lanka

Analysis of Vehicle Ownership Attributes in Western Province, Sri Lanka

Opening out and closing down: the treatment of uncertainty in transport planning’s forecasting paradigm

Understanding Consumer Vehicle Choice

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