Household vehicle type holdings and usage: an application of the multiple discrete-continuous extreme value (MDCEV) model

Bhat, Chandra R.; Sen, Sudeshna

doi:10.1016/j.trb.2005.01.003

Cited by 181 publications

(93 citation statements)

References 23 publications

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“…After examining data from the San Francisco Bay Area, Bhat and Sen (2006) found that households living in denser areas are less inclined to drive SUVs and pickup trucks. However, these studies seldom reveal which specific characteristics of the built environment matter to vehicle type choice.…”

Section: Introductionmentioning

confidence: 99%

Neighborhood design and vehicle type choice: Evidence from Northern California

Cao

Mokhtarian

Handy

2006

Transportation Research Part D: Transport and Environment

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Previous studies have found that suburban development is associated with the unbalanced choice of light duty trucks (LDTs). However, the specific aspects of the built environment that influence vehicle choice have not been well-established. Further, these studies have not shed much light on the underlying direction of causality: whether neighborhood designs themselves, as opposed to preferences for neighborhood characteristics or attitudes towards travel, more strongly influence individuals' decisions regarding vehicle type. Using a sample from Northern California, this study investigated the relationship between neighborhood design and vehicle type choice, controlling for residential self-selection. Correlational analyses showed that neighborhood design has a strong association with vehicle type choice. Specifically, traditional neighborhood designs are correlated with the choice of passenger cars, while suburban designs are associated with the choice of LDTs. The nested logit model suggests that sociodemographic and attitudinal factors play an important role, and that an outdoor spaciousness measure (based on perceptions of yard sizes and off-street parking availability) and commute distance also impact vehicle type choice after controlling for those other influences. Therefore, this study supports the premise that land use policies have at least some potential to reduce the choice of LDTs, thereby reducing emissions.

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Section: Introductionmentioning

confidence: 99%

Neighborhood design and vehicle type choice: Evidence from Northern California

Cao

Mokhtarian

Handy

2006

Transportation Research Part D: Transport and Environment

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“…The output of this model is used as input to the Multiple Discrete Continuous Extreme Value (MDCEV) model of vehicle fleet composition (13). This model uses the total mileage as a travel budget which is allocated across the fleet of vehicles in the household.…”

Section: Individual Level Modelsmentioning

confidence: 99%

Application of Socioeconomic Model System for Activity-Based Modeling: Experience from Southern California

Pendyala

Bhat

Goulias

et al. 2012

Transportation Research Record

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This paper presents results from the application of a comprehensive socio-economic and demographic model system performed in conjunction with the development of a continuous time activity-based microsimulation model of travel demand for the Southern California Association of Governments. The socio-economic model system includes two major components. The first is a synthetic population generator that is capable of synthesizing a representative population for the entire region while controlling for both household and person level marginal distributions. The second is an econometric microsimulator that models various socio-economic and demographic attributes for each person in the synthetic population with a view to develop a rich set of input data for the activity-based microsimulation model system. The results show that the socio-economic model system is capable of replicating known distributions of demographic attributes in the population and can be easily scaled for implementation in large regions such as the Southern California area that includes a population of more than 18 million people in its model boundaries.Keywords: planning applications, model applications, socio-economic model system, synthetic population generation, activity model development, model validation and demonstration Pendyala, Bhat, Goulias, Paleti, Konduri, Sidharthan, Hu, Huang, and Christian 3 3 INTRODUCTIONPlanning agencies are increasingly moving towards the development and deployment of tourbased and activity-based microsimulation models of travel demand as the complexity of transportation planning questions they must address becomes greater (1). Activity-based microsimulation model systems are capable of simulating the activity-travel patterns of each individual in a region's population, essentially replicating a day in the life of a human. The model systems include a series of submodels or components that are sensitive to a host of socioeconomic, land use, accessibility, and cost variables, thus providing the ability to assess the impacts of a wide range of travel demand management strategies and land use policies (2). The Southern California Association of Governments (SCAG) embarked on a multi-year effort to develop a comprehensive continuous-time activity-based microsimulation model system so that impacts of alternative policy and land use scenarios could be accurately assessed in response to the mandates of California Senate Bill 375 (3).The Comprehensive Econometric Microsimulator of Daily Activity Patterns (CEMDAP) serves as the core engine of the activity-based model system being implemented in SCAG (4). The overall model system, dubbed SimAGENT (Simulator of Activities, Greenhouse Emissions, Networks, and Travel), includes CEMDAP tied together with a series of additional model components needed to generate inputs for CEMDAP as well as process outputs from CEMDAP (5). The key model components that provide inputs to CEMDAP constitute the focus of this paper.Virtually all activity-based travel microsimulation model sy...

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“…Over the past several years, there has been considerable progress in the modeling of household vehicle fleet composition and utilization behavior (Bhat and Sen, 2006;Bhat, et al, 2009). These models make it possible to forecast the mix of vehicles that households will own and the extent to which each vehicle in a household fleet will be driven (utilized) under a wide variety of scenarios and system conditions.…”

Section: Introductionmentioning

confidence: 99%

“…Although alternative fuel vehicles are entering the market at a torrid pace and gaining market share, the vast majority of vehicles (93.2 percent) continue to be fossil-fuel powered entities that depend on petroleum and emit greenhouse gas emissions (USDOE, 2012). Policy actions aimed at enhancing the share and use of alternative and clean fuel vehicles, and reducing the carbon footprint of personal travel, can be identified and their potential costs and benefits evaluated only if planning professionals have the ability to forecast the vehicle fleet mix and associated energy and emissions impacts under alternative scenarios.Over the past several years, there has been considerable progress in the modeling of household vehicle fleet composition and utilization behavior (Bhat and Sen, 2006;Bhat, et al, 2009). These models make it possible to forecast the mix of vehicles that households will own and the extent to which each vehicle in a household fleet will be driven (utilized) under a wide variety of scenarios and system conditions.…”

mentioning

confidence: 99%

Assessing the Impact of Transportation Policies on Fuel Consumption and Greenhouse Gas Emissions Using a Household Vehicle Fleet Simulator

Paleti

Bhat

Pendyala

et al. 2014

Transportation Research Record

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The carbon footprint of personal travel is dependent on the composition of the vehicle fleet and the extent to which vehicles of different types are utilized. Transportation model systems have previously not explicitly incorporated the ability to forecast vehicle fleet composition and utilization patterns of households in a region. In the absence of such modeling capability, it is difficult to predict the energy and environmental impacts of alternative policy, market, and technology scenarios in the future. This paper describes the application of a comprehensive vehicle fleet composition and evolution model system that is capable of taking a base year vehicle fleet and evolving it over time in annual time steps through the events of vehicle disposal, replacement, and acquisition. Results of the scenario application exercise documented in this paper demonstrate the efficacy of the model system. Keywords: vehicle fleet composition modeling, vehicle miles prediction, microsimulation modeling, vehicle and population evolution model application, estimation of energy and greenhouse gas emissions. 1 INTRODUCTIONTransportation plays a major role in contributing to energy consumption and greenhouse gas emissions in the United States and around the world. In the United States, on-road vehicular travel accounts for nearly 70 percent of all petroleum consumption in the country (USDOE, 2012). Also, travel by light duty vehicles (passenger cars, sport utility vehicles, pickup trucks, and vans and minivans) accounts for nearly two-thirds of the emissions attributable to vehicular travel in the United States (EPA, 2006). Although alternative fuel vehicles are entering the market at a torrid pace and gaining market share, the vast majority of vehicles (93.2 percent) continue to be fossil-fuel powered entities that depend on petroleum and emit greenhouse gas emissions (USDOE, 2012). Policy actions aimed at enhancing the share and use of alternative and clean fuel vehicles, and reducing the carbon footprint of personal travel, can be identified and their potential costs and benefits evaluated only if planning professionals have the ability to forecast the vehicle fleet mix and associated energy and emissions impacts under alternative scenarios.Over the past several years, there has been considerable progress in the modeling of household vehicle fleet composition and utilization behavior (Bhat and Sen, 2006;Bhat, et al, 2009). These models make it possible to forecast the mix of vehicles that households will own and the extent to which each vehicle in a household fleet will be driven (utilized) under a wide variety of scenarios and system conditions. More recently, a comprehensive vehicle fleet simulation and evolution model system was developed as part of a larger activity-based travel demand model development effort for the Southern California Association of Governments (SCAG). The activity-based travel demand model system, called SimAGENT (Simulator of Activities, Greenhouse emissions, Energy, Networks, and Travel), is ...

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Household vehicle type holdings and usage: an application of the multiple discrete-continuous extreme value (MDCEV) model

Abstract: The increasing diversity of vehicle type holdings and the growing usage of vehicles by households have serious policy implications for traffic congestion and air pollution.

Cited by 181 publications

References 23 publications

Neighborhood design and vehicle type choice: Evidence from Northern California

Neighborhood design and vehicle type choice: Evidence from Northern California

Application of Socioeconomic Model System for Activity-Based Modeling: Experience from Southern California

Assessing the Impact of Transportation Policies on Fuel Consumption and Greenhouse Gas Emissions Using a Household Vehicle Fleet Simulator

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