Estimating spatial interdependence in automobile type choice with survey data

Adjemian, Michael K.; Lin, C.-Y. Cynthia; Williams, Jeffrey C.

doi:10.1016/j.tra.2010.06.001

Cited by 34 publications

(39 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Several studies (4,5) have side-stepped the high-dimensional problem inherent in global and general spatial dependency structures by assuming that the dependency originates only from observed exogenous covariates of proximate decision agents. However, this is rather untenable in the context of several choice situations where the spatial dependence naturally arises from didactic interactions between decision agents.…”

Section: Spatial Dependence In Choice Modelingmentioning

confidence: 99%

Modeling of Household Vehicle Type Choice Accommodating Spatial Dependence Effects

Paleti

Bhat

Pendyala

et al. 2013

Transportation Research Record

View full text Add to dashboard Cite

Household vehicle ownership and fleet composition are choice dimensions that have important implications for policy making, particularly in the energy and environmental sustainability arena. In the context of household vehicle ownership and type choice, it is conceivable that there are substantial spatial interaction effects due to both observed and unobserved factors. This paper presents a multinomial probit model formulation that incorporates spatial spillover effects arising from both observed and unobserved factors. The model is estimated on the California add-on data set of the 2009 National Household Travel Survey. Model estimation results show that spatial dependency effects are statistically significant. The findings have important implications for model development and application in the policy forecasting arena.Paleti, Bhat, Pendyala, and Goulias 1 INTRODUCTIONThe contribution of transportation to energy consumption and greenhouse gas emissions is undoubtedly dependent on the nature of vehicular travel undertaken by households. The number of vehicles owned, the types of vehicles owned (in terms of size, weight, fuel type, and age), and the extent to which different vehicles are used (miles of travel) are all key determinants of energy consumption and greenhouse gas emissions. Over the past 25 years, the split between cars and light duty trucks in the nation's vehicle fleet has changed dramatically; whereas light duty trucks (including pick-up trucks, minivans, and sport utility vehicles) accounted for just about 20 percent of the fleet 25 years ago, they now account for about one-half of all vehicles on the nation's roadways (1). This dramatic shift in the vehicular fleet composition and utilization has had far reaching energy and environmental consequences.The impact of the composition and utilization of the household vehicular fleet on energy consumption and greenhouse gas emissions calls for the incorporation of behavioral models of vehicle type choice and utilization in transportation demand forecasting models. Such models would provide the ability to forecast energy and environmental impacts of shifting vehicle ownership and utilization patterns arising from alternative policy decisions, the advent of new alternative fuel vehicle technologies, and changes in household and personal vehicular preferences. In this context, while there have been several earlier efforts in the literature on vehicle ownership analysis, much remains to be done in developing behavioral models of household vehicle fleet composition and utilization choices -and connecting such choices to energy and emissions estimates.In particular, an important issue that has not been adequately addressed in the vehicle ownership and utilization literature is that there may be spatial interaction effects in household vehicle ownership and type choice that are both observed and unobserved. Vehicle choices that households make are likely to be influenced by their interactions with neighboring households and the choices that neighbori...

show abstract

Section: Spatial Dependence In Choice Modelingmentioning

confidence: 99%

Modeling of Household Vehicle Type Choice Accommodating Spatial Dependence Effects

Paleti

Bhat

Pendyala

et al. 2013

Transportation Research Record

View full text Add to dashboard Cite

show abstract

“…The attribute selection is also in line with the broad literature on vehicle type choice, which has long emphasized the importance of observable vehicle characteristics, related to purchasing and operating costs, technical and performance characteristic, safety, comfort and luxury features, as determinants of vehicle type choice (e.g., Adjemian et al, 2010;Bhat et al, 2009;Bhat & Sen, 2006;Fang, 2008;Hess et al, 2006;Whelan, 2007;Yamamoto & Kitamura, 2000).…”

Section: Data and Variablesmentioning

confidence: 73%

Optimizing differentiation and commonality levels among models in car line-ups: An empirical application of a nature-inspired heuristic mechanism

Saridakis

Tsafarakis

Delias

et al. 2015

Expert Systems with Applications

View full text Add to dashboard Cite

The product life cycle of cars is becoming shorter and carmakers constantly introduce new or revised models in their lines, tailored to their customer needs. At the same time, new car model design decisions may have a substantial effect on the cost and revenue drivers. For example, although a new car model configuration with component commonality may lower manufacturing cost, it also hinders increased revenues that could have been achieved through product differentiation. This paper develops and illustrates a state of the art, nature-inspired approach, to design car lines that optimize the degree of differentiation vs commonality among models in the line. More specifically, we apply a swarm intelligence mechanism to stated preference data derived from a large-scale conjoint experiment that measures consumer preferences for passenger cars in a sample of 1,164 individuals. The proposed two-step methodology is also incorporated into a prototype system, which has been developed in an attempt to facilitate managerial decision making. Our approach provides interesting insights into how new and existing car models can be combined in a product line and identifies the desired balance between differentiation and commonality levels among models within a product line, which elevates customer satisfaction.

show abstract

“…The existing e-bike mode choice models were generally developed by the discrete choice model without incorporating spatial autoregressive structure. The conventional modelling technique may lead to bias and invalid parameter estimates, if, in fact, the spatial interdependence affects the mode choice [14][15]. The policies and strategies based on the biased model may not effectively promote the e-bike use.…”

Section: Methodsmentioning

confidence: 99%

Investigating Spatial Interdependence in E-Bike Choice Using Spatially Autoregressive Model

Xu¹,

Wang²,

Wei³

et al. 2017

PROMET

View full text Add to dashboard Cite

show abstract

Estimating spatial interdependence in automobile type choice with survey data

Abstract: Manu4cript '. Click h~re to view lin ked Ref eren ces

Cited by 34 publications

References 24 publications

Modeling of Household Vehicle Type Choice Accommodating Spatial Dependence Effects

Modeling of Household Vehicle Type Choice Accommodating Spatial Dependence Effects

Optimizing differentiation and commonality levels among models in car line-ups: An empirical application of a nature-inspired heuristic mechanism

Investigating Spatial Interdependence in E-Bike Choice Using Spatially Autoregressive Model

Contact Info

Product

Resources

About