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From page 264... ... G-1 Appendix G: Technical Methodology for Impacts of Shared E-Scooters on Bus Ridership This Appendix compliments the analysis results in Chapter 7 of the report. G.1 Data Used in the E-Scooter Analysis This section discusses the data and methodology used for the study. Read the entire page →
From page 265... ... G-2 As in many cities nationwide, shared e-scooter usage has grown in Louisville. Figure G-1 presents the average daily ridership of shared e-scooters in Louisville over the period August 2018 through December 2019. Read the entire page →
From page 266... ... G-3 Figure G-2: Shared e-scooters service area and TARC transit routes The shared e-scooter trip data were cleaned by implementing several filters related to trip viability. Shared e-scooter trips were deemed viable if their origin and destination both fell within the shared e-scooter service area and if the trips had a non-negative, non-zero trip duration or distance. Read the entire page →
From page 267... ... G-4 G.1.3 Other Variables This study also used other variables that may affect transit ridership like population, income, employment, gas price, and weather. The one year American Community Survey (ACS) Read the entire page →
From page 268... ... G-5 Broadway) Read the entire page →
From page 269... ... G-6 local routes to assess the second research question. The variable shared e-scooter first-mile connector trip count was defined as the number of shared e-scooter trips that have destinations within the bus route catchment area. Read the entire page →
From page 270... ... G-7 G.2.1.2 Shared E-scooter Trips Assignment to Express Bus Routes Express bus routes serve longer bus trips that have one of their ends located outside the shared escooters service area in Louisville. Therefore, it is unlikely that express bus trips could be replaced by a shared e-scooter. Read the entire page →
From page 271... ... G-8 Figure G-5: Example of shared e-scooter trips assignment to an express TARC transit route (Route 65 Sellersburg Express) for August 10, 2018 Read the entire page →
From page 272... ... G-9 Figure G-6: Heatmap of the average daily shared e-scooters complement trip count for each express TARC route for the period August 2018 to December 2019 G.2.2 Summary Statistics Table G-1 presents the summary statistics of the different variables. First, the average bus ridership was 899 unlinked passenger trips per day per route with ridership ranging from 1 to 9,632 unlinked passenger trips per day per route. Read the entire page →
From page 273... ... G-10 Table G-1: Summary Statistics Variable Spatial Unit Temporal Unit Min Max Median Mean (Standard Deviation) Dependent variable Unlinked passenger trips a Route Daily 1 9,632 293 899 (1,451) Read the entire page →
From page 274... ... G-11 G.2.3 Modeling Framework This section discusses the modeling approach used in this analysis. This study evaluated the research questions by estimating several fixed effects regression models of the change in routelevel bus ridership as a function of the change in shared e-scooters trips within a catchment area near each route. Read the entire page →
From page 275... ... G-12 This study also used the clustered sandwich estimator to estimate cluster-robust standard errors (StataCorp LLC, 2017) Read the entire page →
From page 276... ... G-13 Model 2 shows the estimated weekly model with the same specification as the weekday model. This model was estimated since there is a high portion of shared e-scooter trips occurring on Saturdays and Sundays, which might have impacts on bus ridership. Read the entire page →
From page 277... ... G-14 The findings of Models 1-4 suggest that shared e-scooters have no significant effect on local bus ridership. They also suggest that e-scooters may increase ridership on express bus routes in Louisville. Read the entire page →
From page 278... ... G-15 Table G-3: Fixed Effects Regression Results (Models 5–12) Dependent variable: unlinked passenger trips per route (5) Read the entire page →
From page 279... ... G-16 which is likely different from typical shared e-scooter users, who are likely to be white and have higher household income, as suggested by surveys from other cities (Mobility Lab and Arlington County Commuter Services (ACCS) 2019; San Francisco Municipal Transportation Agency, 2019) Read the entire page →

From page 264...

... G-1 Appendix G: Technical Methodology for Impacts of Shared E-Scooters on Bus Ridership This Appendix compliments the analysis results in Chapter 7 of the report. G.1 Data Used in the E-Scooter Analysis This section discusses the data and methodology used for the study.