The daily dynamic potential accessibility by car in London on Wednesdays

Moya-Gómez, Borja; Palomares, Juan Carlos García

doi:10.1080/17445647.2017.1305301

Cited by 9 publications

(3 citation statements)

References 26 publications

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“…In this study the authors decided to analyse data on travel times at 8 a.m. However, analyses using GTFS data may be interpreted in many ways (Ting & Church 2010) and presented on highly detailed maps (Stępniak & Goliszek 2017;Moya-Gómez & García-Palomares 2017). Szczecin is a city that has already been analysed in the context of accessibility by means of public transport (Goliszek 2017;Goliszek & Połom 2016;Poelman & Dijkstra 2015;Stępniak & Goliszek 2017;Stępniak et al 2019).…”

Section: Literature Reviewmentioning

confidence: 99%

The potential accessibility to workplaces and working-age population by means of public and private car transport in Szczecin

Goliszek

2022

Miscellanea Geographica

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Commuting to work is one of the key motivations for people to move around cities or other regions. For the purpose of this study, the city of Szczecin in Poland has been selected, where the modal division of transport includes public transport (bus, tram) and private transport (car). The maps present the spatial distribution of citizens within a working area as well as precise locations of workplaces registered in the REGON database. The main objective of this study is to analyse the potential accessibility of workplaces and the working-age population of the city using the abovementioned modes of transport, and to indicate the places less accessible by public transport and car for commuting purposes. The study presented herein is based on data from 2018. The calculated average accessibility by different means of public and private transport as well as the potential accessibility quotient are presented in the form of box plots.

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Section: Literature Reviewmentioning

confidence: 99%

The potential accessibility to workplaces and working-age population by means of public and private car transport in Szczecin

Goliszek

2022

Miscellanea Geographica

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“…Typically, commercial traffic data providers such as TomTom, Here, and Google also offer aggregated historical and real-time traffic information [21]. Some researchers have used real-time speed data from TomTom and Google to estimate dynamic accessibility or congestion level [21,22]. To exploit the power of citizen science, the crowdsensing approach has been recently used to collect travel data.…”

Section: Introductionmentioning

confidence: 99%

Uber Movement Data: A Proxy for Average One-way Commuting Times by Car

Sun

Ren

Sun

2020

IJGI

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Recently, Uber released datasets named Uber Movement to the public in support of urban planning and transportation planning. To prevent user privacy issues, Uber aggregates car GPS traces into small areas. After aggregating car GPS traces into small areas, Uber releases free data products that indicate the average travel times of Uber cars between two small areas. The average travel times of Uber cars in the morning peak time periods on weekdays could be used as a proxy for average one-way car-based commuting times. In this study, to demonstrate usefulness of Uber Movement data, we use Uber Movement data as a proxy for commuting time data by which commuters’ average one-way commuting time across Greater Boston can be figured out. We propose a new approach to estimate the average car-based commuting times through combining commuting times from Uber Movement data and commuting flows from travel survey data. To further demonstrate the applicability of the commuting times estimated by Uber movement data, this study further measures the spatial accessibility of jobs by car by aggregating place-to-place commuting times to census tracts. The empirical results further uncover that 1) commuters’ average one-way commuting time is around 20 min across Greater Boston; 2) more than 75% of car-based commuters are likely to have a one-way commuting time of less than 30 min; 3) less than 1% of car-based commuters are likely to have a one-way commuting time of more than 60 min; and 4) the areas suffering a lower level of spatial accessibility of jobs by car are likely to be evenly distributed across Greater Boston.

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“…As a result of the increasing number of studies on accessibility, the measurement of accessibility has seen significant progress. In addition to investigating the spatial disparity of accessibility itself, accessibility analyses focused on land use (Hansen, 1959), employment (Shen, 1998), population and economic impacts (Jin, Jiao, Qi, & Yang, 2017;Ozbay, Ozmen-Ertekin, & Berechman, 2003), transport infrastructure development (Hou & Li, 2011;Jiao, Wang, Jin, & Dunford, 2014;Li & Shum, 2001), transport modes (Arranz-López, Soria-Lara, López-Escolano, & Campos, 2017;Moya-Gómez & García-Palomares, 2017;Pérez, Quintana, & Pastor, 2011), human behavior (Neutens, Delafontaine, Scott, & De Maeyer, 2012;Shaw & Yu, 2009), service facilities (McGrail & Humphreys, 2014;Wang, 2012), land value (Giannopoulou, Vavatsikos, & Lykostratis, 2016), and social exclusion (Lucas et al, 2016;Preston & Raje, 2007). Although accessibility has become an important concept that is widely used in various fields, the measurement of accessibility is often operationalized in different ways.…”

Section: Introductionmentioning

confidence: 99%

Integrating land surface conditions and transport networks to quantify the spatial accessibility of cities in China

Chen

Liu

2019

Journal of Maps

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Although there is a need to determine the spatial accessibility of cities in China, the limitations in available data and methods hinder research in this area. The main objective of this study was to quantify the accessibility of cities in China. We developed a comprehensive analytical method to quantify spatial accessibility at a spatial resolution of one kilometer by integrating both land surface conditions and transport networks. The results show that the spatial patterns of accessibility of Chinese cities at the prefecture level and above are characterized by significant spatial heterogeneity, consistent with the patterns in physical geography, population density, and economic activities. The results improve our understanding of deficiencies in the accessibility of cities in China and provide a foundation for transportation infrastructure planning.

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The daily dynamic potential accessibility by car in London on Wednesdays

Cited by 9 publications

References 26 publications

The potential accessibility to workplaces and working-age population by means of public and private car transport in Szczecin

The potential accessibility to workplaces and working-age population by means of public and private car transport in Szczecin

Uber Movement Data: A Proxy for Average One-way Commuting Times by Car

Integrating land surface conditions and transport networks to quantify the spatial accessibility of cities in China

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