Actividad VI: Los Planetas como sistema de referencia no inercial

The study of the relationship between the built environment and travel behavior has been classified into three groups. First, simulation studies develop travel demand models in order to establish the impacts that changes on the built environment may have on different travel patterns. The simulation studies have been conducted mainly at the city, system and neighborhood levels. Second, aggregated data studies have been developed in order to establish correlations and associations between built environment attributes and travel within a specific geographic area. The aggregated study type has been developed to examine modal split, number of trips and levels

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of transit ridership. This type of studies has been also conducted at the station area level as part of direct ridership models. Third, disaggregate data studies seek to study relationships between built environment attributes with individual level travel patterns (Handy, Boarnet, Ewing, & Killingsworth, 2002).

These three categories of studies have also been conducted at different levels in order to assess the influence of the built environment on travel behavior. At the system level, researchers have conducted studies to determine the factors influencing ridership levels along main transport infrastructure investments and trunk corridors. At the station level, studies have been defining catchment areas around transit stations in order to measure built environment characteristics and to assess the influence on ridership levels. At the individual, level studies have been conducted to understand the relationships between individual travel behavior and the built environment (E. Guerra & Cervero, 2011; Handy, 2005).

Studies have been conducted in order to assess the influence of the built environment at the station level. Some studies have examined this relationship for metro stations by looking at built environment attributes within buffer areas between 500 meters and 800 meters. In a high density city like Taiwan, the study of metro stations ridership found a positive association with TOD features such as floor-space area (building density), density and pedestrian infrastructure, but a negative association with connectivity and mixed land use (Lin & Shin, 2008). Another study found a positive association between commercial and residential land uses with ridership in Hong Kong and a positive association between population density and metro stations in New York City (Loo, Chen, & Chan, 2010). In Seoul (Korea), two studies found positive associations between population densities, land use mix, residential, office and commercial land uses with ridership (Choi, Lee, Kim, & Sohn, 2012; Sung & Oh, 2011). In Montreal (Canada), the study of

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130 metro stations found positive associations between population density, commercial and institutional land uses with ridership (Chan & Miranda-Moreno, 2013). In Nanjing (China), the study of 55 metro stations found positive associations between population densities, office buildings, and the presence of schools with ridership (Zhao, Deng, Song, & Zhu, 2013).

Some studies have found positive associations between population density and light rail- transit (LRT) ridership in metropolitan areas in North America. The positive association

identified is not only for population density in terms of population per gross station area, but also in terms of average household within the catchment area. These studies have also found positive associations between terminal stations and ridership levels (Cervero, 2006; Kuby, Barranda, & Upchurch, 2004; Lane, DiCarlantonio, & Usvyat, 2006; Parsons Brinckerhoff Quade & Douglas, Cervero, Howard/Stein-Hudson Associates, & Zupan, 1996). Another study looking at the relationship in 67 LRT stations found positive associations between residential and retails uses with ridership as well as between the presence of facilities such as schools and hotels with ridership levels (Foletta, Vanderkwaak, & Grandy, 2013).

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Table 2 Selected studies relationship between the built environment and transit ridership, station level (aggregated type)

City (Country) Authors Data analysis Data Results R-square

Method Sample Buffer Selected variables Elasticities*

Rail (Metro)

Taipei

(Taiwan) (Lin, Shin, 2008) OLS (CS) N=46 500m Residential density Building density 8.120.0017 M1: 0.709 (-08) M2: 0.426 % retail/service floor level -6.47(-06) **

Land use variety -6.10(-06) ** New York City (USA)

Hong Kong

(Loo, Chen, Chan, 2010) OLS (CS) N=80 (HK) N= 468 (NYC)

500m Commercial residential floor area (Hong Kong)

0.01*** _{M1: 0.74} M2: 0.59 M3: 0.64 Off-street parking area (Hong Kong) 0.02***

Population size (NYC) 0.30*** Commercial floor area in sqmt (NYC) 0.00*** Seoul

(Korea) (Sung, Oh, 2011) Log-LR (CS) N=214 500m Residential land use density 0.10

***(1) _{M1: 0.779} M2: 0.700 Office land use density 0.03***(1)

Land use mix 0.15***

Seoul (Korea)

(Choi, Lee, Kim, et.al., 2012) Multiplicative Model (CS) N=251 500m Population -origin 0.33*** _{M1: 0.769} M2: 0.793 M3: 0.772 Population -destination 0.12**

Commercial area - destination 0.04** Montreal

(Canada) (Chan, Miranda-Moreno, 2013) OLS / Log-LR (CS) N=130 1000m 500m Population in buffer area** 0.74

*** _{M1: 0.679} M2: 0.552 Density of households Not significant

Commercial land use (area) 0.52*** Institutional land use (area) 0.67*** Nanjing

(China) (Zhao, Deng, Song, Zhu, 2013) OLS (CS) N=55 800m Population in buffer area 0.15

** _{M1: 0.979}

Building office area 0.08**

# education facilities 0.17** # shopping centers 0.03** Light-rail transit LRT 11 metropolitan areas (USA, Canada) (Parsons, Brinckerhoff, et.al., 1996) Log-LR Log-log (CS) N=261 LRT N=526 Rail 1/2 mile

2miles†††† Population density –LRT 0.59

*** _{M1: 0.536} M2: 0.343

Centrality – LRT -0.60***

Population density – commuter rail 0.25*** 9 metropolitan areas

(USA) (Kuby et. al., 2004) OLS (CS) N=268 1 ½ mile Population within walking distance 0.11

* _{M1: 0.727}

Centrality -0.95***

11 metropolitan areas

(USA, Canada) (Cervero, 2006) Log-LR (CS) N=225 1/2 mile Population density

††† _0.19** _{M1: 0.771}

Centrality -0.21**

11 metropolitan areas (USA)

(Lane et.al., 2006) Log-LR (CS) N=348 1/4 mile 1/2 mile 1 mile 2 miles

Ln(Household ½ mile) – LRT 0.18*** _{M1: 0.760} M2: 0.571 Ln(population 2 mile) –comm. rail 0.26***

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Tacoma (USA) Grandy, 2013) Retail NA M2: 0.79

M3: 0.77

Education facility NA

Bus rapid transit BRT

Bogota

(Colombia) (Estupiñan, Rodriguez, 2008) 2SLS (CS) N=68 250m BE factor: Barrier to car use

†† 0.14**(2) _{M1: 0.45} Los Angeles County

(USA) (Cervero, Murakami, Miller, 2009) OLS (CS) N=69 1/2 mile Population density 0.32

*** _{M1: 0.952} Global (119 cities)† _{(Cervero, Dai, 2014) Log-LR (CS) N=119 City level Population density 0.39}*** _{M1: 0.286} For detailed information please refer to each study

OLS=ordinary least squares. Log-LR=log-linear regression; 2SLS=two stage least squares. CS=cross sectional study. HK=Hong Kong. NYC=New York City.

*Elasticities were calculated based on coefficients and mean values reported by the authors in their respective papers, except for one of the papers about Seoul (Choi, Lee, Kim, et al., 2012). Some of the elasticities for the studies in 11 metropolitan areas in the US and Canada (Kuby et al., 2004; Cervero, 2006) were obtained from the paper “Travel and the Built Environment: A Meta-Analysis” (Ewing, Cervero, 2010).

**The elasticity provided by the author corresponds to population in the buffer area (1000s) but not to population density. The variable measuring density of household was not significant. (1)_{Mean value adjusted to square kilometers.}

(2)_{Assumption: mean value of built environment factor equal to one.}

†Included for comparison purposes due to this study on BRT includes a point of reference in terms of population density. ††Built environment factor based on the loading of seven variables including population density.

††† After controlling for stations located at CBD †††† Oblong area

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