Evaluating the Ability of Transit Direct Ridership Models to Forecast Medium-Term Ridership Changes: Evidence from San Francisco

Mucci, Richard Alexander; Erhardt, Gregory D.

doi:10.1177/0361198118758632

Cited by 23 publications

(11 citation statements)

References 11 publications

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“…These detailed data are important, because the location and timing of TNC trips directly affects the modes with which they compete (Roy et al 2020). Previous work in Toronto and at San Francisco airport leveraged detailed TNC data (Li 2019;Sturgeon 2019;Young et al 2020), while other work that used spatially detailed transit ridership data but did not consider TNC data (Mucci and Erhardt 2018;Berrebi and Watkins 2020). TNC trips concentrate in city centers where transit ridership is highest (Schaller 2018;Feigon and Murphy 2018;Fehr and Peers 2019), and in the biggest cities during peak travel times (Gehrke et al 2018;San Francisco County Transportation Authority 2017).…”

Section: Previous Research and Contribution Of This Workmentioning

confidence: 99%

Do transportation network companies increase or decrease transit ridership? Empirical evidence from San Francisco

et al. 2021

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Transportation network companies (TNCs), such as Uber and Lyft, have been hypothesized to both complement and compete with public transit. Existing research on the topic is limited by a lack of detailed data on the timing and location of TNC trips. This study overcomes that limitation by using data scraped from the Application Programming Interfaces of two TNCs, combined with Automated Passenger Count data on transit use and other supporting data. Using a panel data model of the change in bus ridership in San Francisco between 2010 and 2015, and confirming the result with a separate time-series model, we find that TNCs are responsible for a net ridership decline of about 10%, offsetting net gains from other factors such as service increases and population growth. We do not find a statistically significant effect on light rail ridership. Cities and transit agencies should recognize the transit-competitive nature of TNCs as they plan, regulate and operate their transportation systems.

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Section: Previous Research and Contribution Of This Workmentioning

confidence: 99%

Do transportation network companies increase or decrease transit ridership? Empirical evidence from San Francisco

et al. 2021

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“…A preliminary study using TNC data for New York reinforced the theorized substitution between taxis and TNCs and showed their ability to complement transit ( 18,20 ). The study in Austin identified hotels and airports as ridesharing hotspots in the city.…”

Section: Literature Reviewmentioning

confidence: 92%

Evolution of Transportation Network Companies and Taxis through 2013–2018 in Chicago

Roy

Komanduri

Proussaloglou

2020

Transportation Research Record: Journal of the Transportation R

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The objective of this paper is to highlight important differences between taxis and transportation network companies (TNCs) in a large urban area. We analyze the publicly available dataset from Chicago which includes taxi and transportation network company (TNC) utilization and the level of service measures from five months in 2013–2014 and the same five months in 2018–2019. We compare and contrast the data from these two points in time to document utilization of taxis and TNCs and to measure differences in travel times, travel distances, fares, destinations served, and the spatial and temporal distribution of these trips. Travel to and from airports has been evaluated separately owing to the exceptionally high number of trips they generate. Striking differences between pooled and unpooled TNC trip volumes and other travel metrics have been assessed to highlight their operational diversity despite being considered as the same mode. The exploratory analysis has been carried out across the shared-ride, time, and mode dimensions. The study revealed both similarities and differences in taxi trip characteristics between the two evaluation periods and also outlined how the ridehailing market has grown over the years despite the near stagnation in population and employment in the city. We believe that assessing how taxis have fared through this time and highlighting the intrinsic differences between how the old and new mode of on-demand ride services coexist is important. This study aims to help understand how new-age mobility services are impacting transportation in one of the largest cities in the U.S.

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“…erefore, the choice of spatial analysis unit affects the results of the direct ridership model. Studies using CT as the spatial analysis unit include [6,10], and studies using CBG as the spatial analysis unit include [11,12].…”

Section: Literature Reviewmentioning

confidence: 99%

“…For example, the data weight is 1 when the analysis unit is completely covered by the station service coverage area. In existing literature, two basic spatial analysis units are often used: census block groups and census tracts [6,[10][11][12]. Both census block groups (CBGs) and census tracts (CTs) are geographic units used by the U.S. Census Bureau.…”

Section: Introductionmentioning

confidence: 99%

Effects of Coverage Area Treatment, Spatial Analysis Unit, and Regression Model on the Results of Station-Level Demand Modeling of Urban Rail Transit

Yang

et al. 2021

Journal of Advanced Transportation

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Direct ridership models can predict station-level urban rail transit ridership. Previous research indicates that the direct modeling of urban rail transit ridership uses different coverage overlapping area processing methods (such as naive method or Thiessen polygons), area analysis units (such as census block group and census tract), and various regression models (such as linear regression and negative binomial regression). However, the selection of these methods and models seems arbitrary. The objective of this research is to suggest methods of station-level urban rail transit ridership model selection and evaluate the impact of this selection on ridership model results and prediction accuracy. Urban rail transit ridership data in 2010 were collected from five cities: New York, San Francisco, Chicago, Philadelphia, and Boston. Using the built environment characteristics as the independent variables and station-level ridership as the dependent variable, an analysis was conducted to examine the differences in the model performance in ridership prediction. Our results show that a large overlap of circular coverage areas will greatly affect the accuracy of models. The equal division method increases model accuracy significantly. Most models show that the generalized additive models have lower mean absolute percentage errors (MAPE) and higher adjusted R 2 values. By comparison, the Akaike information criterion (AIC) values of the negative binomial models are lower. The influence of different basic spatial analysis unit on the model results is marginal. Therefore, the selection of basic area unit can use existing data. In terms of model selection, advanced models seem to perform better than the linear regression models.

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Evaluating the Ability of Transit Direct Ridership Models to Forecast Medium-Term Ridership Changes: Evidence from San Francisco

Cited by 23 publications

References 11 publications

Do transportation network companies increase or decrease transit ridership? Empirical evidence from San Francisco

Do transportation network companies increase or decrease transit ridership? Empirical evidence from San Francisco

Evolution of Transportation Network Companies and Taxis through 2013–2018 in Chicago

Effects of Coverage Area Treatment, Spatial Analysis Unit, and Regression Model on the Results of Station-Level Demand Modeling of Urban Rail Transit

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