Development of Indicators of Opportunity-Based Accessibility

Chen, Yali; Ravulaparthy, Srinath; Deutsch, Kathleen; Dalal, Pamela; Yoon, Seo Youn; Lei, Ting; Goulias, Konstadinos G.; Pendyala, Ram M.; Bhat, Chandra R.; Hu, Hsi Hwa

doi:10.3141/2255-07

Cited by 66 publications

(55 citation statements)

References 11 publications

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“…The desire to limit the sample size (and thus avoid inflated t-statistics that might arise from the use of large samples) was another consideration in the selection of a subsample from a limited geographic region. Finally, the selection of this specific subsample made it possible to merge census tract level accessibility measures and land use data that have been compiled in connection with an ongoing parallel effort to develop a comprehensive activity-based microsimulation model system for the Southern California Association of Governments (7). The accessibility measures are opportunity-based indicators which measure the number of activity opportunities by 12 different industry types as well as total roadway length of different roadway types that can be reached within 10 minutes using the auto mode from the home census tract during the morning peak period (6 AM to 9 AM).…”

Section: Datamentioning

confidence: 99%

Modeling of Household Vehicle Type Choice Accommodating Spatial Dependence Effects

Paleti

Bhat

Pendyala

et al. 2013

Transportation Research Record

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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...

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Section: Datamentioning

confidence: 99%

Modeling of Household Vehicle Type Choice Accommodating Spatial Dependence Effects

Paleti

Bhat

Pendyala

et al. 2013

Transportation Research Record

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“…Walk accessibility quantifies the ability to access different services/destinations from a property at a micro-scale level. Based on the generalized definition of accessibility (Chen et al 2011), a residential property's walk accessibility is defined as the ease with which opportunities for activity can be reached by walking from that property. Previous studies (e.g., Manaugh and Kreider 2013) proposed different methods to quantify walkable environment or attempted to address the potential impact of walkable environment on property values, but these methods often have either methodological or applicability limitations.…”

Section: Introductionmentioning

confidence: 99%

The impact of walkable environment on single-family residential property values

Guo

Peeta

Somenahalli

2015

JTLU

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Abstract:Due to the impact of urban sprawl, the need for responsible property investing, and the emerging evidence supporting the linkage between walkable environment (in terms of built environment and walk accessibility) and residential property value, there is a critical need to develop systematic methodologies to quantify the impact of walkable environment on residential property value. This study provides a new generalized dissimilarity index for quantifying landuse mix, a key component of built environment, and a new method for measuring a property's walk accessibility and then links them to residential property values. Ordinary least squares (OLS) regression models are used to validate these methods by examining the empirical property data in Eastern Adelaide, Australia. The results of the OLS models show that the proposed dissimilarity index and property walkaccessibility method outperform other commonly used land-use mix quantification and walk-accessibility methods in estimating singlefamily residential property values in terms of the goodness-of-fit and explanatory power. This study provides insights for investors to understand the impact of walkable environment on single-family residential property values to enable them to make more informed decisions on property investment, and for planners to design neighborhoods featuring better walkable environments.

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“…Much of this data is based on census information and is therefore representative of the trends in the population. Accessibility indicators which measure the number of employees that can be reached from any zone within various travel time windows were constructed using detailed micro-level land use data provided by SCAG (16). Models of work location, work flexibility, and labor force participation at the person level, and household income at the household level, were estimated using travel survey data for the region.…”

Section: Results From the Application Of Cemseltsmentioning

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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Development of Indicators of Opportunity-Based Accessibility

Cited by 66 publications

References 11 publications

Modeling of Household Vehicle Type Choice Accommodating Spatial Dependence Effects

Modeling of Household Vehicle Type Choice Accommodating Spatial Dependence Effects

The impact of walkable environment on single-family residential property values

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

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