Walking Accessibility to Public Transport: An Analysis Based on Microdata and GIS

Abstract

In this paper we analyse the role of walking accessibility to transit facilities. Microdata and GIS tools have been used to calculate distances walked by different population groups in accessing Metro stations. Distances walked by the population were used to determine the threshold distances of the station service areas and calculate the population covered by the Metro network. With respect to Metro ridership, different distance-decay functions were adjusted and the sensitivity of the population groups to the distance was measured. Two indicators were proposed, based on the distance-decay functions, to measure access quality and potential demand. The Madrid Metro network was used as the study area. Results show that young people and adults, men, immigrants, and public transit captives are willing to walk longer distances and are less sensitive to the effect of distance. When walking distances have been used in order to fix the limit of catchment areas, the amount of the population covered is lower than when a standard threshold (0.5 miles) is used, but overestimations affect each age group in a different way. The access quality indicator shows that the population group in the worst situation is children and that stations in the centre of the network have higher access quality values. However, the synthetic accessibility indicator shows that potential demand is lower for the most central and most peripheral stations than for the stations located in the intermediate areas. It has been proved that both indicators are sensitive to changes in the spatial distribution of population groups within the catchment areas. These results demonstrate some of the advantages of the proposed methodology and argue in favour of its use in public transport planning.

Analysing proximity to public transport: the role of street network design

“…In Madrid, the ratio of rides per person in Metro from home gradually declines with distance, from 0.55 rides per person in the first 100 metres to scarcely 0.06 at 1500 metres. Decrease is lineal and shows that the ratio of rides per inhabitant falls 6.9% with each additional 100 metres of distance (García-Palomares et al, 2013).…”

“…As most users access public transport on foot, the more people who live and/or are employed in close proximity to transit, the greater the likelihood the service will be used (Murray et al, 1998). In fact, literature shows there is a significant decrease in the use of public transport as walking distance to bus stops or stations increases (O'Sullivan & Morrall, 1996;Talavera-García & Valenzuela-Montes, 2012;García-Palomares, 2013). Researchers and planners have tried to estimate potential demand, both at existing and proposed stations, from coverage analysis.…”

Caída De La Demanda Con La Distancia en El Uso Del Transporte Público: El Caso Del Acceso Al Campus Universitario De Cáceres

“…En los estudios de demanda del transporte, el uso de los SIG, es una herramienta básica y esencial para analizar la movilidad entre dos o más puntos y los factores que influyen en la misma, así como facilitar la representación gráfica del análisis efectuado (Gutiérrez Puebla et al 2008). Por tanto, el uso de los SIG constituye la herramienta de análisis principal de este tipo de estudios, como puede comprobarse en numerosos trabajos (Mora Aliseda et al 2003, Cardozo et al 2010, Rodríguez Díaz 2011, García-Palomares et al 2013, Pitarch Garrido 2015.…”

“…El concepto de "caída de la demanda con la distancia" (distance decay) se define como la relación entre la probabilidad de uso del servicio y la distancia al mismo. Este concepto se utiliza para determinar el área de influencia de las paradas, como puede evidenciarse en distintos estudios (Mora Aliseda et al 2003, Gutiérrez Puebla et al 2008, García-Palomares et al 2013, Martínez y Viegas 2013.…”

Cycling willingness: Investigating distance as a dependent variable in cycling behavior among college students

“…Further, in plotting select distance decay functions, we gained a likely indication on the span of distance and insights into the willingness of bike users to bike farther distances. We calculated distance bands of 1000ft in using the proportion of bike users per band over the total respondents and plotted normalized values with a fitted curve similar to that utilized by Zhao et al (2003) and García-Palomares et al (2013). Figure 3 shows graph of distance decay in the case of transportation for biking (r 2 =.74) and, in comparison, car owners (r 2 =.74) that used their bikes as well.…”