Influence of weather conditions on transit ridership: A statistical study using data from Smartcards

Arana, P.; Cabezudo, Sara; Peñalba, M.

doi:10.1016/j.tra.2013.10.019

Cited by 94 publications

(87 citation statements)

References 11 publications

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“…The datasets are coupled with local weather data for analysis. One group of studies has shown that higher wind speeds result in a decrease in the number of public transit trips [40][41][42]. Another group suggested that wind negatively impacts bicycling [43][44][45][46][47][48].…”

Section: Introductionmentioning

confidence: 99%

Does Wind Discourage Sustainable Transportation Mode Choice? Findings from San Francisco, California, USA

Kim

Macdonald

2016

Sustainability

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This paper explores whether and to what extent wind discourages sustainable transportation mode choice, which includes riding public transportation, bicycling, and walking. A six month-long field study was carried out at four locations in San Francisco, a city that has been promoting sustainable transportation mode choice but that experiences high wind levels. It involved surveying pedestrians and on-site recording of microclimate data using various instruments. The survey adopted a mixed-method approach to collect both quantitative and qualitative data. Statistical analyses using Kruskal Wallis tests and ordinal logistic regression models identified the significant effect of wind speed on San Francisco's residents in estimating their discouragement for waiting at transit stop without shelter, bicycling, and walking. Qualitative data revealed a deeper understanding of how wind influences their sustainable transportation mode choice. This research argues for the need to adopt climate-based efforts in urban planning and policy and sheds light on the climate resilience of cities

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Section: Introductionmentioning

confidence: 99%

Does Wind Discourage Sustainable Transportation Mode Choice? Findings from San Francisco, California, USA

Kim

Macdonald

2016

Sustainability

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“…The relative importance of a given trip also concords with the results of studies in Scotland (Al Hassan & Barker, 1999) and Shenzhen, China (Zhou et al, 2017) that each show that weather is much more influential during weekends (Al Hassan and Barker (1999) and off-peak hours (Zhou et al, 2017) where travellers are more likely to be in a position to adjust their travel plans to suit the prevailing conditions. Weather has also been shown to exert an effect across various of modes of travel including active transport (Corcoran, Li, Rohde, Charles-Edwards, & Mateo-Babiano, 2014;Nankervis, 1999), bus (Arana, Cabezudo, & Peñalba, 2014;Tao, Corcoran, Hickman, & Stimson, 2016), train (Brazil et al, 2017), subway (Singhal, Kamga, & Yazici, 2014), general road traffic (Cools, Moons, Creemers et al, 2010), inter modal transfer (Gong, Currie, Liu, & Guo, 2017) as well as influence modal choice (Anta, Pérez-López, Martí-nez-Pardo, Novales, & Orro, 2016).…”

Section: Introductionmentioning

confidence: 99%

Mapping spatial patterns of bus usage under varying local temperature conditions

Corcoran

Sui

2017

Journal of Maps

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Weather influences our daily travel decisions in a number of important ways. At the individual scale, weather has been shown to influence trip-making behaviours where inclement conditions can induce re-scheduling, re-routing and cancellation of a planned journey. Furthermore at the transit system-level, poor weather can increase traffic congestion and reduce operational efficiencies. While some research has examined the weather-transit relationship, focus on the spatial dimension remain in their infancy. In this paper we adopt a visual analytic approach to spatially explore the complex weather-transit relationship at a micro geographical scale. We demonstrate that through spatially integrating a large disaggregate smart card database of bus ridership with hourly local weather measurements we can reveal how 'heat stress' changes the way in which passengers use the public transit system in subtropical Brisbane, Australia. Our approach has the potential for broader application across other public and private transport and climatic contexts to unveil the way in which weather influences our daily travel behaviour. ARTICLE HISTORY

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“…By following the use of a single card throughout the months, customers' loyalty to the transit network can be determined which is important for the operators . Impact of weather conditions on transit ridership has been studied by Arana et al (2014) recently. Ma et al (2013) applied Density-based Spatial Clustering of Applications with Noise (DBSCAN) algorithm to the SCD in order to reveal transit riders' historical travel patterns.…”

Section: Introductionmentioning

confidence: 99%

Travel behavior analysis using smart card data

2015

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Electronic fare payment systems have gained much popularity in large public transport systems across the world. Though their main purpose is to collect the revenue and improve passenger's comfort, they also generate huge amount of detailed and accurate data on onboard transactions which is of extreme importance for transit planners for efficient short-term and long-term transit operation and service planning. This paper attempts to utilize the smart card data as an input for large-scale activity based public transport simulation for analyzing travel behavior pertaining to transit users, using an open source agent-based transport simulation package, MATSim. Transit vehicles are optimized based on input demand generated by smart card and a high temporal resolution of waiting time and its components at different stations is extracted which is very important in managing the frequency and headway of transit vehicles. Since no card swipe is needed for transfers within subway system in Seoul, the data is not readily available to detect frequent transfer locations. However, after validating the boarding and alighting volumes at different stations, a methodology is presented to calculate within subway transfer volumes at different stations. We suggest that by improving the statistical analysis and utilizing advanced data mining techniques, smart cards can effectively generate the microsimulation travel demand models.

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Influence of weather conditions on transit ridership: A statistical study using data from Smartcards

Cited by 94 publications

References 11 publications

Does Wind Discourage Sustainable Transportation Mode Choice? Findings from San Francisco, California, USA

Does Wind Discourage Sustainable Transportation Mode Choice? Findings from San Francisco, California, USA

Mapping spatial patterns of bus usage under varying local temperature conditions

Travel behavior analysis using smart card data

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