Dynamic diurnal social taxonomy of urban environments using data from a geocoded time use activity-travel diary and point-based business establishment inventory

Yoon, Seo Youn; Ravulaparthy, Srinath; Goulias, Konstadinos G.

doi:10.1016/j.tra.2014.01.004

Cited by 13 publications

(14 citation statements)

References 62 publications

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“…Relatedly, an identification of the precise blend of land-use types needed for an efficient spatial-temporal distribution of activity locations informed by a spatialtemporal mix measure may help guide infill or new development projects and more precisely model the travel implications. An adoption of a spatial-temporal measure also holds potential toward informing the management of businesses considering an extension of opening and closing hours (Yoon, Ravulaparthy, and Goulias 2014). The liberalization of opening hours may enable individuals to conduct activities outside standard business hours; subsequently, providing individuals with an ability to combine out-of-home activities and improve their prospects for trip chaining (Neutens, Schwanen, and Witlox 2011).…”

Section: Benefits Of Representing Temporal Availabilitymentioning

confidence: 99%

“…By disregarding the temporal availability of certain activities or land uses, present models may underestimate the demand for conducting certain activities and in turn distort future travel demand patterns by poorly allocating where activities may occur in the future (Kwan 2013). For that reason and others, the representation of spatial-temporal travel decisions and the built environment factors influencing activity participation must be better understood within future activity-based travel research (Yoon, Ravulaparthy, and Goulias 2014). The creation of a comprehensive land-use mix measure accounting for the temporal availability of specific land-use types helps to fulfill this request.…”

Section: Benefits Of Representing Temporal Availabilitymentioning

confidence: 99%

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Toward a spatial-temporal measure of land-use mix

Gehrke¹,

Clifton²

2015

JTLU

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Urban planning and public-health research has long been interested in the connection between land-use mix and travel. Interest from urban planners stems from the potential of transportation efficiency gains achieved by an increased land-use mix and subsequent shortening of trip lengths; whereas, public-health research advocates an increased land-use mix as an effective policy for facilitating greater physical activity. Yet, despite the transportation, land-use, and health benefits related to improving land-use mix and the extent of topical attention given by researchers, no consensus has been reached regarding the magnitude of its effect on travel. This absence of agreement may largely be attributed to the theoretical and methodological failings persistent in present attempts to accurately reflect land-use interaction and operationalize its quantification within a defined spatial extent. To better evaluate the impact of land-use mix on travel behavior and assess more temporal policies, a robust mix measure accounting for these two elements of land-use interaction and geographic scale as well as a temporal element of land-use mixing-missing from present specifications-must be introduced. This paper establishes the research agenda for a spatial-temporal land-use mix measure by (1) identifying the conceptual and methodological faults inherent to current land-use interaction and geographic-scale representations and (2) describing strategies and practical benefits of representing the temporal availability of landuse mixing in guiding innovative transportation/land-use policies.

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Section: Benefits Of Representing Temporal Availabilitymentioning

confidence: 99%

Section: Benefits Of Representing Temporal Availabilitymentioning

confidence: 99%

Toward a spatial-temporal measure of land-use mix

Gehrke¹,

Clifton²

2015

JTLU

View full text Add to dashboard Cite

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“…It can be observed that most available papers refer to traffic engineering studies (Ciuffo et al 2011;Mazzella et al 2011;Zou et al 2012), vehicle emission gases (Pearce et al 2009;Kassteele and Velders 2006;Kassteele and Stein 2006), and traffic injuries (Gundogdu 2014;Manepalli and Bham 2011;Molla et al 2014). Although in the 1990s, there were some studies in the literature on travel demand forecasting with applications of estimation techniques related to kriging (Ickstadt et al 1998), this approach is still recent and has not yet been sufficiently investigated in the line of research of transportation planning (Yoon et al 2014;Chen et al 2015).…”

Section: Introductionmentioning

confidence: 99%

A Conjoint Approach of Spatial Statistics and a Traditional Method for Travel Mode Choice Issues

Lindner

Pitombo

2017

J geovis spat anal

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Conventional analysis of transportation demand is usually carried out using socioeconomic, travel, and land use attributes. Despite the effectiveness on travel demand forecasting, it is important to recognize that alternative approaches have been developed in recent years. Traditional methods, besides considering different explanatory variables, are appropriate to make estimates exclusively on previously surveyed households. On the other hand, recent studies have addressed spatial statistical concerns in the field of travel demand forecasting. The aim of this paper is to spatially estimate motorized travel mode choice probabilities in a continuous map using an Origin-Destination Survey database, conducted in the São Paulo Metropolitan Area in Brazil in 2007. Values were estimated in both sampled and non-sampled coordinates. This paper proposes a conjoint approach that combines the traditional procedure of travel demand forecasting (multiple logistic regression) with a spatial statistical method (ordinary kriging). A comparison is made with the one-step spatial method-indicator kriging (IK). Conjoint studies of spatial statistics and traditional methods are thriving in transportation analysis, giving rise to a travel mode choice surface in a confirmatory way. It is concluded that the proposed method can be used for future predictions of travel mode choices, unlike IK.

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“…Kriging has been an accepted tool in spatial statistics since it moved from geology and geochemistry into other applications in the 1990s. Although in the 1990s there were some studies in the literature on travel demand forecasting with applications of estimation techniques related to Kriging (Ickstadt, K. et al, 1998), its application is still recent and has not yet been fairly explored in the line of research of Transportation Planning (Yoon et al, 2014;Chen et al, 2015). However, it is possible to find diverse studies in "Transportation Engineering" field (Ciuffo et al, 2011;Mazzella et al, 2011;Zhang and Wang, 2013) and also Emission of vehicular gases (Pearcea et al, 2009;Kassteele and Velders, 2006;Kassteele and Stein, 2006).…”

Section: Introduction and Brief Literature Reviewmentioning

confidence: 99%

Proposal of a Geostatistical Procedure for Transportation Planning Field

2017

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Abstract:The main objective of this study is to estimate variables related to transportation planning, in particular transit trip production, by proposing a geostatistical procedure. The procedure combines the semivariogram deconvolution and Kriging with External Drift (KED). The method consists of initially assuming a disaggregated systematic sample from aggregate data. Subsequently, KED was applied to estimate the primary variable, considering the population as a secondary input. This research assesses two types of information related to the city of Salvador (Bahia, Brazil): an origin-destination dataset based on a home-interview survey carried out in 1995 and the 2010 census data. Besides standing out for the application of Geostatistics in the field of transportation planning, this paper introduces the concepts of semivariogram deconvolution applied to aggregated travel data. Thus far these aspects have not been explored in the research area. In this way, this paper mainly presents three contributions: 1) estimating urban travel data in unsampled spatial locations; 2) obtaining the values of the variable of interest deriving out of other variables; and 3) introducing a simple semivariogram deconvolution procedure, considering that disaggregated data are not available to maintain the confidentiality of individual data.

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Dynamic diurnal social taxonomy of urban environments using data from a geocoded time use activity-travel diary and point-based business establishment inventory

Cited by 13 publications

References 62 publications

Toward a spatial-temporal measure of land-use mix

Toward a spatial-temporal measure of land-use mix

A Conjoint Approach of Spatial Statistics and a Traditional Method for Travel Mode Choice Issues

Proposal of a Geostatistical Procedure for Transportation Planning Field

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