Transit ridership forecasting at station level: an approach based on distance-decay weighted regression

Gutiérrez, Javier; Cardozo, Osvaldo Daniel; Palomares, Juan Carlos García

doi:10.1016/j.jtrangeo.2011.05.004

Cited by 260 publications

(172 citation statements)

References 28 publications

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“…Direct ridership models try to estimate ridership as a function of station environments and transit services features, using multiple regression Chu, 2004, Gutiérrez et al, 2011.…”

Section: Boletín De La Asociación De Geógrafos Españoles 76 102-130mentioning

confidence: 99%

“…Ewing and Cervero (2001) define 6 types of street system, which they introduce as dummy variables into OLS regression models. Gutiérrez et al (2011) include street system density within the service area as an explanatory variable in entries to Metro stations. Chu (2004) includes several variables related to the pedestrian environment in a direct model using a Poisson regression.…”

Section: Boletín De La Asociación De Geógrafos Españoles 76 102-130mentioning

confidence: 99%

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

Palomares

Ribeiro

Puebla

et al. 2018

BAGE

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Published under the terms and conditions of an Attribution-NonCommercial 4.0 International license. AbstractThe proximity of homes and employment to public transport networks is a key factor in explaining ridership. One of the most frequent strategies for increasing proximity to public transport is to densify the immediate station environment. However, proximity can also be created through the design of the street network in new urban developments. This article analyses the impact of urban design on public transport network coverage and its potential demand by comparing the real situation of the Madrid Metro network with four hypothetical scenarios representing different types of street network: irregular with high density, irregular with low density, orthogonal and station-oriented.By keeping the distribution of population and employment constant, the differences between the real scenario and each of the hypothetical ones can be explained entirely by the role played by the design of the urban fabric. A series of indicators have been calculated to measure the extent of the impact of street network design on the proximity to public transport: surface of the coverage areas, population and employment covered according to proximity bands, access quality and potential demand. The results obtained show that the station-oriented street system would lead to a substantial increase in population and employment in the first coverage bands, thereby generating a highly significant increase in the potential demand for public transport.Key words: urban design; public transport; accessibility to stations; coverage analysis; potential demand. ResumenLa proximidad de los hogares y el empleo a las redes de transporte público es un factor clave en el uso final de las mismas. Una de las estrategias más frecuentes para aumentar la proximidad al transporte público es densificar el entorno de las estaciones. Sin embargo, también se pueden generar proximidad en los nuevos desarrollos mediante el diseño de la trama urbana. En este artículo se analiza el impacto del diseño de la trama urbana en la cobertura de la redes de transporte público y en su demanda potencial mediante la comparación de la situación real de la red de Metro de Madrid con cuatro escenarios hipotéticos, que representan diferentes tipos de red viaria: una red irregular de alta densidad; irregular de baja densidad, ortogonal y un viario orientado a la estación. Para ver el efecto de la trama urbana, se mantienen las distribuciones de la población y el empleo constante, de manera que las diferencias entre el escenario real y cada uno de los hipotéticos se explican únicamente por el papel desempeñado por el diseño de la trama urbano. Para medir la magnitud del impacto del diseño de la trama urbana en la proximidad al transporte público se han calculado una serie de indicadores: la superficie de las áreas de cobertura, la población y el empleo cubiertos de acuerdo a las franjas de distancia, la calidad de acceso y la demanda potencial. Los resultados obtenidos muestran que ...

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“…Direct ridership models try to estimate ridership as a function of station environments and transit services features, using multiple regression Chu, 2004, Gutiérrez et al, 2011.…”

Section: Boletín De La Asociación De Geógrafos Españoles 76 102-130mentioning

confidence: 99%

Section: Boletín De La Asociación De Geógrafos Españoles 76 102-130mentioning

confidence: 99%

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

Palomares

Ribeiro

Puebla

et al. 2018

BAGE

Self Cite

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“…Recent studies [33,34] raise a question about the justification of any particular station catchment area. Cervero found that residents within the 0.8 km of transit catchment area were more likely to use transit than those living further from stations [35].…”

Section: Discussionmentioning

confidence: 99%

Sustainable Mobility: Longitudinal Analysis of Built Environment on Transit Ridership

Kim

Ahn

Choi³

et al. 2016

Sustainability

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Abstract:Given the concerns about urban mobility, traffic congestion, and greenhouse gas (GHG) emissions, extensive research has explored the relationship between the built environment and transit ridership. However, the nature of aggregation and the cross-sectional approach of the research rarely provide essential clues on the potential of a transit system as a sustainable mobility option. From the perspective of longitudinal sustainability, this paper develops regression models for rail transit stations in the Los Angeles Metro system. These models attempt to identify the socio-demographic characteristics and land use features influencing longitudinal transit ridership changes.Step-wise ordinary least square (OLS) regression models are used to identify factors that contribute to transit ridership changes. Those factors include the number of dwelling units, employment-oriented land uses such as office and commercial land uses, and land use balance. The models suggest a negative relationship between job and population balance with transit ridership change. They also raise a question regarding the 0.4 km radius commonly used in transit analysis. The models indicate that the 0.4 km radius is too small to capture the significant influence of the built environment on transit ridership.

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“…In recent years, Clayton lane and AICP come up with the concept of "exclusive station area" in order to solve the problem of catchment area overlapping [19]; Javier Gutié rrez presented distance-decay weighted regression around transit stations in order to reflect the relationship between passenger flow and land use more precisely [20].…”

Section: Catchment Area Delineating Methods In Sketch-level Forecastingmentioning

confidence: 99%

Transit Traffic Analysis Zone Delineating Method Based on Thiessen Polygon

et al. 2014

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A green transportation system composed of transit, busses and bicycles could be a significant in alleviating traffic congestion. However, the inaccuracy of current transit ridership forecasting methods is imposing a negative impact on the development of urban transit systems. Traffic Analysis Zone (TAZ) delineating is a fundamental and essential step in ridership forecasting, existing delineating method in four-step models have some problems in reflecting the travel characteristics of urban transit. This paper aims to come up with a Transit Traffic Analysis Zone delineation method as supplement of traditional TAZs in transit service analysis. The deficiencies of current TAZ delineating methods were analyzed, and the requirements of Transit Traffic Analysis Zone (TTAZ) were summarized. Considering these requirements, Thiessen Polygon was introduced into TTAZ delineating. In order to validate its feasibility, Beijing was then taken as an example to delineate TTAZs, followed by a spatial analysis of office buildings within a TTAZ and transit station departure passengers. Analysis result shows that the TTAZs based on Thiessen polygon could reflect the transit travel characteristic and is of in-depth research value.

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Transit ridership forecasting at station level: an approach based on distance-decay weighted regression

Cited by 260 publications

References 28 publications

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

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

Sustainable Mobility: Longitudinal Analysis of Built Environment on Transit Ridership

Transit Traffic Analysis Zone Delineating Method Based on Thiessen Polygon

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