Examining Commuting Patterns

Sang, Sunhee; O’Kelly, Morton E.; Kwan, Mei‐Po

doi:10.1177/0042098010368576

Cited by 65 publications

(14 citation statements)

References 33 publications

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“…Pero estudios recientes en el ámbito nacional e internacional han tendido a relajar este debate, mostrando su complementariedad, incluida la propia Rosenbloom (2006). Estos trabajos tienden a integrar ambas hipótesis, introduciendo en el análisis variables socioeconómicas y sociodemográficas que muestran que el efecto de género puede ser tanto directo como indirecto, en la medida en que puede actuar a través de otras variables mediadoras y operar de manera diferente para distintos grupos (Preston y McLafferty 2016) y en distintos territorios (Sang, O'Kelly y Kwan 2011). Es, por tanto, necesario aislar los efectos de la ocupación y el género y analizar las diferencias entre hombres y mujeres, controlando a su vez por otras variables socio-económicas, demográficas y territoriales distintas de éstas (Kim et al 2012;McQuaid y Chen 2012;García-Palomares, 2008;Moss, Jack y Wallace 2004).…”

Section: Commuting Y Desigualdades De Género: La Teoría Del Enclaustrunclassified

“…Denominaremos commuting a la movilidad cotidiana por razón de trabajo, siguiendo la literatura internacional, en la que se suele emplear en sentido amplio, independientemente del ámbito territorial en que los movimientos residencia-trabajo se produzcan (Preston y McLafferty 2016;Sang, O'Kelly y Kwan 2011;Crane 2007, entre otros); aunque también en sentido restringido, referida a los movimientos entre unidades administrativas diferentes (Kim et al 2012; Artís, Romaní y Suriñach 2000; por ejemplo). El motivo por el que nos centramos en este tipo de movilidad es doble.…”

Section: Introductionunclassified

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Género y <em>commuting</em> en las regiones urbanas andaluzas

2018

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Las diferencias entre mujeres y hombres en las distancias recorridas en los desplazamientos residencia-trabajo son un objeto recurrente en la literatura sobre movilidad, al constatar que las mujeres trabajan más cerca de casa. Sin embargo, el modo en el que estas diferencias operan es objeto de debate entre dos perspectivas: la teoría del enclaustramiento femenino y la teoría tradicional. La primera afirma que las diferencias entre sexos en la movilidad se deben al género, en concreto, a la mayor carga de trabajo doméstico de las mujeres y a la relegación de su rol laboral. Mientras la segunda hace énfasis en cómo estas diferencias se deben a factores intermedios, principalmente socioeconómicos. Nuestro objetivo consiste en contrastar ambas hipótesis para el caso andaluz, a través de la Encuesta Social 2011 de Movilidad en las Regiones Urbanas de Andalucía. Los resultados obtenidos apuntan a la complementariedad de las mismas, aunque operando las desigualdades de género más de manera indirecta, por vía segregación ocupacional, que directa.

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Section: Commuting Y Desigualdades De Género: La Teoría Del Enclaustrunclassified

Section: Introductionunclassified

Género y <em>commuting</em> en las regiones urbanas andaluzas

2018

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“…Apparently, people of different person types are supposed to have different activity preferences (see Hanson 1982;Pas 1983;Sang et al 2011). For example, it is unlikely for the retired to conduct work activities, and the chance should be considerably high that full-time students will go for education activities.…”

Section: Problem Formulationsmentioning

confidence: 99%

Learning daily activity patterns with probabilistic grammars

Lee

2015

Transportation

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Daily activity pattern is the reflection and abstraction of actual individual activity participation on daily basis. It carries information on activity type, frequency and sequence. Preference of daily activity patterns varies among population, and thus can be interpreted as personal life styles. This paper advances studies on human daily activity patterns by providing new perspective and methodology in the modeling and learning of daily activity patterns using probabilistic context-free grammars. In this paper, similarities between daily activity pattern-which is defined as activity sequence-and language are explored. We developed context-free grammars to parse and generate daily activity patterns. To replicate people's heterogeneity in selecting daily activity patterns, we introduced probabilistic context-free grammars and proposed several formulations to estimate the probability of a context-free grammar with daily activity patterns observed in household travel survey. We conducted experiments on the proposed formulations, finding that under proper context-free grammar and problem formulation, the estimated probabilistic contextfree grammar is able to reproduce the observed pattern distribution in household travel survey with satisfactory precision. Practically, the proposed methodology sheds light on the issue of generating stochastic and accessibility-dependent choice sets for daily activity pattern models in certain activity-based modeling frameworks.

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“…Cities are concentrated areas of human activities, and urban spatial structures are closely related to the intracity travel patterns of their residents [1]. Identifying the patterns of intra-urban residents' trips will help us better understand urban structures and reveal the driving social factors, such as gender and occupation [2]. As questionnaire-based methods are limited by a lack of data, it is difficult to use this traditional method to explore human mobility deeply and accurately.…”

Section: Introductionmentioning

confidence: 99%

Revealing Spatial-Temporal Characteristics and Patterns of Urban Travel: A Large-Scale Analysis and Visualization Study with Taxi GPS Data

Huang

Zeng

2019

IJGI

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Mobility and spatial interaction data have become increasingly available due to the widespread adoption of location-aware technologies. Examples of mobile data include human daily activities, vehicle trajectories, and animal movements. In this study we focus on a special type of mobility data, i.e., origin–destination (OD) pairs, and propose a new adapted chord diagram plot to reveal the urban human travel spatial-temporal characteristics and patterns of a seven-day taxi trajectory data set collected in Beijing; this large scale data set includes approximately 88.5 million trips of anonymous customers. The spatial distribution patterns of the pick-up points (PUPs) and the drop-off points (DOPs) on weekdays and weekends are analyzed first. The maximum of the morning and the evening peaks are at 8:00–10:00 and 17:00–19:00. The morning peaks of taxis are delayed by 0.5–1 h compared with the commuting morning peaks. Second, travel demand, intensity, time, and distance on weekdays and weekends are analyzed to explore human mobility. The travel demand and high-intensity travel of residents in Beijing is mainly concentrated within the 6th Ring Road. The residents who travel long distances (>10 km) and for a long time (>60 min) mainly from outside the 6th Ring Road and the surrounding new towns of Beijing. The circular structure of the travel distance distribution also confirms the single-center urban structure of Beijing. Finally, a new adapted chord diagram plot is proposed to achieve the spatial-temporal scale visualization of taxi trajectory origin–destination (OD) flows. The method can characterize the volume, direction, and properties of OD flows in multiple spatial-temporal scales; it is implemented using a circular visualization package in R (circlize). Through the visualization experiment of taxi GPS trajectory data in Beijing, the results show that the proposed visualization technology is able to characterize the spatial-temporal patterns of trajectory OD flows in multiple spatial-temporal scales. These results are expected to enhance current urban mobility research and suggest some interesting avenues for future research.

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Examining Commuting Patterns

Cited by 65 publications

References 33 publications

Género y <em>commuting</em> en las regiones urbanas andaluzas

Género y <em>commuting</em> en las regiones urbanas andaluzas

Learning daily activity patterns with probabilistic grammars

Revealing Spatial-Temporal Characteristics and Patterns of Urban Travel: A Large-Scale Analysis and Visualization Study with Taxi GPS Data

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