Neighborhoods, cars, and commuting in New York City: A discrete choice approach

Salon, Deborah

doi:10.1016/j.tra.2008.10.002

Cited by 73 publications

(53 citation statements)

References 18 publications

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“…SEM allows for the parameterization of endogenous relations between variables, thus accounting explicitly for self-selection effects (Bagley and Mokhtarian 2002;Golob 2003b). More recent studies using simultaneous methods (e.g., SEM or joint choice models) tend to corroborate the existence of statistically significant effects on travel behavior due to land-use/built environment dimensions (Bhat and Guo 2007;Chen et al 2008;Pinjari et al 2007;Salon 2009;Cao et al 2007;Scheiner and Holz-Rau 2007;Aditjandra et al 2012). Studies using these methods, and in particular SEM, also highlight the mediating effects of several long-term decisions like car ownership (Van Acker and Witlox 2010;Chen et al 2008; de Abreu e Silva, Martinez, and Goulias 2012) on travel decisions.…”

Section: Literature Reviewmentioning

confidence: 91%

Spatial self-selection in land-use–travel behavior interactions: accounting simultaneously for attitudes and socioeconomic characteristics

Silva¹

2014

JTLU

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Spatial self-selection can be ascribed to two main factors: socioeconomic characteristics or attitudinal aspects towards travel and location choices. Several studies have investigated the influence of self-selection on the relations between travel behavior and land-use patterns. So far the results could be considered mixed.The model proposed herein uses data collected in 2009 for the Lisbon Metropolitan Area. A structural equations model was built to study the effects of spatial self-selection due to both socioeconomic and attitudinal effects. Three model specifications were considered: one treating attitudes toward travel as exogenous and two others considering them as endogenous. The preferred specification considered attitudes as exogenous. The land-use patterns are described by factors both at the residence and employment zones of each individual. The travel behavior variables included here are multidimensional and include commuting distance, car ownership, the number of trips by mode, and the total amount of time between the first and last trips. On account of the data characteristics and sample size, Bayesian estimation, as implemented in AMOS TM software, was used. The results obtained show that although the attitudinal variables significantly influence travel behavior, they do not annul the effects of land-use patterns in also contributing to shape the same behavior.Copyright 2014 João de Abreu e Silva. Licensed under the Creative Commons Attribution -NonCommercial License 3.0. IntroductionOne of the main theoretical criticisms of the use of land-use policies as a way of reducing the level of car use and changing mobility in the direction of a more intensive use of transit and nonmotorized modes is related to residential self-selection. Residential self-selection can be defined as the tendency of people to opt for residential locations based on their abilities, needs, and preferences (Mokhtarian and Cao 2008; Cao et al. 2009). This concept could be extended beyond residential to other locational choices and to other dimensions of travel behavior like car ownership, car type choice, and modal preferences (van Wee 2009). Not accounting for self-selection could result in biased model results.Residential self-selection is related to the importance and direction of the relations between travel behavior and land-use patterns, and it is connected with the existence of causal relations between them. Here the concept of self-selection is extended to both residential and work location characteristics. Therefore, it will be referred as spatial self-selection, since it encompasses more than residential self-selection. In this paper, the impact of spatial self-selection in several travel behavior dimensions, covering both long-and short-term decisions, is studied. A preliminary analysis of the endogeneity of attitudinal effects vis-à-vis socioeconomic characteristics is made and, proceeding from that, a structural equations model (SEM) is developed. It is based on a previous model, which used the same base...

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Section: Literature Reviewmentioning

confidence: 91%

Spatial self-selection in land-use–travel behavior interactions: accounting simultaneously for attitudes and socioeconomic characteristics

Silva¹

2014

JTLU

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“…Logistic regression method is commonly used in the mode choice modelings [7,8]. Binary logistic regression used the cumulative logistic probability function, which assumes the possibility of Y = 1 is P, then the other one of Y = 0 is (1 -P).…”

Section: Commuting Mode Choice Model Establishmentmentioning

confidence: 99%

“…In the 112 middle sized cities in Europe, the increase of the car ownership and cities' GDP will increase the car uses; the increase of the lengths of the bicycle lanes will increase the bicycle uses; and the increase of the bus number and population will increase the bus uses [7]. The commuting travel survey results in New York show that the effective way to reduce the car uses is increase of the bus frequency and speed and making the traffic congestions prolonging the travel time of using cars [8]. In Barcelona of Spain, increasing the fees of using cars will effectively control the increases of the car uses [9].…”

Section: Introductionmentioning

confidence: 99%

Commuting Mode Choice Behaviour Study and Policy Suggestions for Low-Carbon Emission Transportation in Xi’an (China)

Yang

Wang

2018

Teh. vjesn.

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This study established the commuting mode choice models in the typical Chinese city of Xi'an by using the Logistic regression method. Results show that commuters will transfer from the walking, bicycle, electric-bicycle/motor or bus to the car if the commuting distance, household income or the car availability increase; commuters will transfer from the walking, bicycle, electric-bicycle/motor to the car and transit if the commuting distance increases; compared with transit, the shorter driving time is the significant factor for the commuters choosing cars. The findings indicate that there is the necessity of great investment in the public transit with high-quality services to shorten the traveling time, combined with measures of car restriction, parking control, road congestion charging and transit priority lanes for the low-carbon emission transportation development in Chinese cities. The findings in the typical city of Xi'an will provide reference value for other cities in the world.

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“…The second approach in this study models car ownership and residential location simultaneously with a nested logit model, an approach used by Cervero (2007) and Salon (2009). Each household faces six different discrete choices: whether to live in a commercial housing development and whether to own zero, one, or two or more cars.…”

Section: H2: the Relationship Between Commercial Housing And Car Ownementioning

confidence: 99%

“…Cervero (2007) found a statistically significant unobserved correlation across households that live near rail transit and use it of almost 0.75. If the nest value is either greater than or not statistically different from one, as in Salon's (2009) study of car ownership and residential location within New York, it indicates that the nesting parameter should not be included in the estimation.…”

Section: H2: the Relationship Between Commercial Housing And Car Ownementioning

confidence: 99%

Has Mexico City’s shift to commercially produced housing increased car ownership and car use?

Guerra¹

2015

JTLU

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Mexico City's principal form of housing production has shifted over the past two decades. More households now purchase houses in large commercially built housing developments than move into informal settlements. Looking at 1500 households in two suburban municipalities from a 2007 metropolitan travel survey, this study is the first to quantify differences in car ownership and car use across households in informal settlements and commercial housing developments. Accounting for income, proxies for wealth, household composition, and geography, households living in commercial housing developments are likely to own more cars and drive more than similar households in neighboring informal settlements. A test for residential self-selection finds no unobserved correlations across households that own cars and live in commercial housing developments, suggesting that the included controls do a good job of capturing the effects of residential self-selection or that the effects are limited. Something about the local land use and design of new commercial housing developments appears conducive to car ownership and use. Differences between the two settlement types, including more parking, wider streets, less-connected street-grids, and less accessible transit stops in commercial settlements, likely play a role. IntroductionAt 35,000 feet-a typical cruising altitude for a long-distance commercial flight-Los Héroes Tecámac looks a bit like an urban airport. Two long, thick, near-perpendicular, gray lines intersect like massive runways providing access from northwest to southeast and northeast to southwest. To the north are the unmistakable tan and green rectangles of agricultural land. To the east and west are the equally unmistakable curving black, gray, and tan specks and lines of informal urbanization that is so characteristic of Mexico City's peripheral neighborhoods and many like them around the world (Figure 1). Located almost 20 miles to the northeast of the historic center, Los Héroes, despite its somewhat unusual appearance from the air, is typical of contemporary housing developments in Mexico City on the ground. Over the past two decades, Mexico City's principal form of new housing production has shifted from an informal process, where households built their own properties generally in informal settlements on the periphery, to a formal process, where private developers acquire large peripheral lots, build new housing speculatively, and sell completed homes in massive developments to households that qualify for publicly subsidized mortgages (Monkkonen 2011a, 2011b, Pardo and Velasco Sánchez 2006. What look like thick gray lines from the air are rows of identical one-, two-, and three-story attached houses and condominiums along intersecting rectangular grids of arterial roads and linear culs-de-sac. Small parking spaces separate the building facades from the sidewalk and street.

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Neighborhoods, cars, and commuting in New York City: A discrete choice approach

Cited by 73 publications

References 18 publications

Spatial self-selection in land-use–travel behavior interactions: accounting simultaneously for attitudes and socioeconomic characteristics

Spatial self-selection in land-use–travel behavior interactions: accounting simultaneously for attitudes and socioeconomic characteristics

Commuting Mode Choice Behaviour Study and Policy Suggestions for Low-Carbon Emission Transportation in Xi’an (China)

Has Mexico City’s shift to commercially produced housing increased car ownership and car use?

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