Joint Vehicle Holdings, by Type and Vintage, and Primary Driver Assignment Model with Application for California

Vyas, Gaurav; Paleti, Rajesh; Bhat, Chandra R.; Goulias, Konstadinos G.; Pendyala, Ram M.; Hu, Hsi-Hwa; Adler, Thomas; Bahreinian, Aniss

doi:10.3141/2302-08

Cited by 21 publications

(14 citation statements)

References 20 publications

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“…They translated the estimated VMT into GHGEs by using EPA's conversion factors and fuel economy assumptions (EPA, 2007). Vyas et al (2012) proposed a joint MDCEV -multinomial logit (MNL) model to estimate households' vehicle quantity, type, annual mileage, and the primary driver for each vehicle. The estimated vehicle type and quantity served as an engine for a household vehicle composition and evolution simulator which is embedded in a larger activity-based travel and emissions forecasting system -the Simulator of Activities, Greenhouse Emissions, Energy, Networks, and Travel (SimAGENT) .…”

Section: Literature Reviewmentioning

confidence: 99%

“…Therefore, the combination of the number of vehicles owned by a household, vehicle type, and the usage (i.e. VMT) of vehicles is an important determinant of households' vehicle GHGEs and fuel consumption (Vyas et al, 2012). The state-of-the-art in calculating GHGEs from vehicle usage employs either the standard values of conversion that consider lifecycle emissions from the Environmental Protection Agency (EPA) or the emission rates per miles from the California Air Resources Board (CARB) (Feng et al, 2005;Fullerton, 2005;Fullerton and Gan, 2005;Musti and Kockelman, 2011).…”

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confidence: 99%

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Evaluating policies to reduce greenhouse gas emissions from private transportation

Liu

Cirillo

2016

Transportation Research Part D: Transport and Environment

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Article history: Keywords: Discrete-continuous car ownership models Vehicle type choice Vehicle miles traveled (VMT) Motor Vehicle Emission Simulator (MOVES) Greenhouse gas emissions Taxation policy a b s t r a c tThis 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 (CO 2 E) 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.

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Section: Literature Reviewmentioning

confidence: 99%

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confidence: 99%

Evaluating policies to reduce greenhouse gas emissions from private transportation

Liu

Cirillo

2016

Transportation Research Part D: Transport and Environment

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“…Therefore, the earliest studies on this issue commenced in the 1970s, with the activities of Chamberlain [6], Lerner et al [19] and Ayres et al [20]. These studies, which aimed to identify the factors affecting the type of car based on size and function, have been continued by different researchers until now (e.g., in the 1980s, 4 Manski and Sherman [21], Hocherman et al [22], and Mannering and Winston [23]; in the 1990s, Golob et al [24], McCarthy and Tay [1], and Wu et al [25]; in the 2000s, Mohammadian and Miller [26], Bhat and Sen [27], and Spissu et al [28]; and in the 2010s, Paleti et al [29], Vyas et al [30], and Liu et al [3]). This historical process shows that over many years, the type of vehicle has always been an interesting subject for researchers.…”

Section: Literature Reviewmentioning

confidence: 99%

The effect of transportation and safety attitudes, personality, and lifestyle on vehicle type choice: a case study of Tehran

Mohammadi

Kermanshah

2019

Scientia Iranica

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In recent years, researchers have paid specific attention to safety, air pollution, and energy consumption, which are all affected by the vehicle type. The primary objective of this study is to achieve a thorough understanding of the relationships between transportation attitudes, safety attitudes, personality, as well as lifestyle and vehicle type for the first time in Iran, as a developing country. For this purpose, an interview questionnaire was designed, and 740 participants were interviewed in Tehran. Vehicle types are categorized into seven groups: mini, compact, medium, heavy, SUV, pickup, and motorcycle. In the first stage, factor analysis was used to identify the latent variables regarding transportation attitudes, safety attitudes, personality, and lifestyle. In the second step, by using these latent variables, individuals are classified, and a two-level clustering is formed. The correlation analysis performed in the third step shows a significant relationship between each category of people and the type of car. Likewise, the analyses carried out in this study reveal the significant relations between the vehicle type (classified based on fuel economy level, production country, vehicle emission, and vintage) and the attitudes of its owner. This information is beneficial to both vehicle manufacturers and transportation planners.

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“…In order to allocate cars to drivers, different strategies may be applied within a household (Mannering, 1983). First, the allocation may be based on individual members consistently using a specific vehicle, perhaps out of some notion of vehicle ownership within the household (Golob et al, 1996;Vyas, Paleti, Bhat, Goulias, Pendyala, Hu, Adler and Bahreinian, 2012). Second, car assignment may be determined by a process in which household members bargain to obtain access to available vehicles for activities (Anggraini, Arentze and Timmermans, 2007;Anggraini, Arentze and Timmermans, 2008;Petersen and Vovsha, 2005).…”

Section: Car Allocation Within the Household Contextmentioning

confidence: 99%

Who gets the key first? Car allocation in activity-based modelling

Beige

Heinrichs

Krajzewicz

et al. 2017

International Journal of Urban Sciences

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Activity-Based Modelling. International Journal of Urban Sciences, 1-15. Official version can be downloaded at http://dx.Abstract: 296 words Number of words: 4929 words Number of tables: 5 tables Abstract Decisions concerning household car ownership and the corresponding usage by the household members have significant implications on vehicle usage, fuel consumption and vehicle emissions. In this context, long-term and short-term choices which are strongly interrelated with one another play an important role. The long-term aspects involve the number of vehicles and their different types owned by a household as well as the assignment of a main driver, acting as the primary user, to each vehicle. The short-term dimension is represented by the vehicle allocation within a household at a daily level.In order to better understand the vehicle allocation process in the household context, the paper at hand investigates the importance of the short-term and long-term aspects in this process and explores several approaches to model them. For this purpose, four different methods for car allocation within a household, which strongly differ in their complexity, are implemented into a microscopic agent-based travel demand model and subsequently evaluated. The respective approaches are the following: (1) random car allocation, (2) car allocation by age, (3) car allocation by main driver assignment, and (4) car allocation by household optimisation. Given a population of a bigger region that is described by a set of attributes, these various models determine which person of a household uses one of the available cars within the household for his/her daily trips.The simulations show that all four implementations of car allocation result in good representations (with deviations of less than 10%) of observed travel behaviour, their results being closer to each other than initially expected. Model (4), which optimises car allocation for the entire household, shows the best results when compared to realworld data, while model (3) allows for the adaptation of changes in car ownership and/or socio-demographic and socio-economic attributes of the population.

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Joint Vehicle Holdings, by Type and Vintage, and Primary Driver Assignment Model with Application for California

Cited by 21 publications

References 20 publications

Evaluating policies to reduce greenhouse gas emissions from private transportation

Evaluating policies to reduce greenhouse gas emissions from private transportation

The effect of transportation and safety attitudes, personality, and lifestyle on vehicle type choice: a case study of Tehran

Who gets the key first? Car allocation in activity-based modelling

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