Impact of weather on urban transit ridership

Singhal, Abhishek; Kamga, Camille; Yazici, Anil

doi:10.1016/j.tra.2014.09.008

Cited by 112 publications

(89 citation statements)

References 21 publications

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“…Research has also shown that variance in daily weather can impact transit ridership (e.g., Stover and McCormack, 2012;Singhal et al, 2014;Arana et al, 2014). Therefore, weather data were gathered from the National Oceanic and Atmospheric Administration (NOAA) for New York, NY.…”

Section: External Factorsmentioning

confidence: 99%

The impact of real-time information on bus ridership in New York City

Brakewood

Macfarlane

Watkins

2015

Transportation Research Part C: Emerging Technologies

119

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Section: External Factorsmentioning

confidence: 99%

The impact of real-time information on bus ridership in New York City

Brakewood

Macfarlane

Watkins

2015

Transportation Research Part C: Emerging Technologies

119

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“…Lower patronage is expected, as studies of ticketing data in various cities over periods of up to 2 years conclude that ridership usually decreases in ‘bad’ weather and increases in ‘good’ weather; even with small percentage changes, many tests have had statistically significant results (Guo et al ., ; Kalkstein et al ., ; Stover and McCormack, ; Singhal et al ., ). Public transport passengers are thought to respond to a range of direct and indirect weather impacts (Guo et al ., ; Adler and van Ommeren, ).…”

Section: Impacts On Bus Travelmentioning

confidence: 97%

Better by bus? Insights into public transport travel behaviour during Storm Doris in Reading, UK

Budnitz

Chapman

Tranos

2017

Weather

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This case study uses datasets from Reading Buses’ electronic ticketing system to gain insights into the reactions of public transport passengers to the disruption to bus and rail services in Reading, UK during Storm Doris on 23 February 2017. The analysis generally supports previous research findings that there are fewer public transport trips in adverse weather conditions. However, an increase in bus trips on services parallel to the more severely disrupted train services suggests that some travellers respond flexibly by using the bus as an alternative, more resilient mode.

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“…This paper summarizes the related studies on direct demand models for ridership estimation (see Figure A1 in A.1). As summarized in Figure A1, the most widely used method is Ordinary Least applied OLS regression to model transit ridership and its influencing factors [3,7,[10][11][12][13][14][15][16][17][18][19]. However, the limitation of OLS models is that they assume the transit ridership is affected by various factors but has nothing to do with the spatial location, without considering the spatial autocorrelation, in other words, OLS models estimate ridership from a global perspective believing that calculated coefficients do not have significant differences in space.…”

Section: Transit Ridership Estimation Modellingmentioning

confidence: 99%

An adapted geographically weighted LASSO (Ada-GWL) model for predicting subway ridership

Zhao

Tsui

2020

Transportation

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Ridership estimation at station level plays a critical role in metro transportation planning.Among various existing ridership estimation methods, direct demand model has been recognized as an effective approach. However, existing direct demand models including Geographically Weighted Regression (GWR) have rarely included local model selection in ridership estimation.In practice, acquiring insights into metro ridership under multiple influencing factors from a local perspective is important for passenger volume management and transportation planning operations adapting to local conditions. In this study, we propose an Adapted Geographically Weighted Lasso (Ada-GWL) framework for modelling metro ridership, which performs regression-coefficient shrinkage and local model selection. It takes metro network connection intermedia into account and adopts network-based distance metric instead of Euclidean-based distance metric, making it so-called adapted to the context of metro networks. The real-world case of Shenzhen Metro is used to validate the superiority of our proposed model. The resultsshow that the Ada-GWL model performs the best compared with the global model (Ordinary Least Square (OLS), GWR, GWR calibrated with network-based distance metric and GWL in terms of estimation error of the dependent variable and goodness-of-fit. Through understanding the variation of each coefficient across space (elasticities) and variables selection of each station, * it provides more realistic conclusions based on local analysis. Besides, through clustering analysis of the stations according to the regression coefficients, clusters' functional characteristics are found to be in compliance with the facts of the functional land use policy of Shenzhen.These results of the proposed Ada-GWL model demonstrate a great spatial explanatory power in transportation planning.

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Impact of weather on urban transit ridership

Cited by 112 publications

References 21 publications

The impact of real-time information on bus ridership in New York City

The impact of real-time information on bus ridership in New York City

Better by bus? Insights into public transport travel behaviour during Storm Doris in Reading, UK

An adapted geographically weighted LASSO (Ada-GWL) model for predicting subway ridership

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