United States Department of Transportation
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Quantifying the Impact of New Mobility on Transit Ridership
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2023-03-01
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[PDF-4.18 MB]
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Corporate Contributors:Transit – Serving Communities Optimally, Responsively, and Efficiently (T-SCORE) Center [UTC] ; United States. Department of Transportation. Office of the Assistant Secretary for Research and Technology ; United States. Department of Transportation. University Transportation Centers (UTC) Program ; Tennessee. Department of Transportation
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Edition:Final Report Aug 2020-Jan 2023
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Abstract:This Final Report presents the outcomes of Community Analysis Research Project C3 that analyzed the impacts of new mobility modes – particularly micromobility – on transit ridership. Micromobility includes modes such as bicycles, electric bicycles (e-bikes) and electric scooters (e-scooters). This research focused specifically on shared electric scooters (e-scooters) in Nashville, Tennessee because of the availability of detailed e-scooter trip and device location data that were obtained through a data request to Nashville’s Metropolitan Planning Organization. T-SCORE Project C3 was divided into two primary parts. The first part of the research performed an empirical analysis to quantify the impacts of the shared e-scooters on bus ridership in Nashville, Tennessee. Fixed effects regression models were estimated to explore six hypotheses about the relationship between bus ridership and shared e-scooters using both infrastructure-based and trip-based measures. The findings suggest that utilitarian shared e-scooter trips are associated with a decrease of 0.94% in bus ridership in Nashville on a typical weekday, whereas shared e-scooter social trips are associated with an increase of 0.86% in bus ridership in Nashville on a typical weekday. These findings suggest that shared e-scooters were associated with a net decrease of about 0.08% of total bus ridership on a typical weekday in Nashville, which is a minimal impact. The second part of T-SCORE Project C3 proposed a mixed methods approach to select locations to place shared e-scooter corrals near transit stops to encourage the use of shared e-scooters connecting to transit using Nashville, Tennessee as a case study. The method first used machine learning techniques to identify shared e-scooters trips that complement transit. Then, a multi-criteria scoring system was applied to rank bus stops based on shared e-scooter activity and bus service characteristics. Based on this scoring system, bus stops with the 50 highest scores were selected as potential locations for shared e-scooter corrals. Then, the capacity for the potential parking locations was estimated based on the hourly shared e-scooter usage. The results suggest that the 50 proposed corral locations could capture about 44% of shared e-scooter demand. The findings of this part of the research project could guide the implementation of shared e-scooter corrals in Nashville and inform other cities about how to select locations for shared e-scooter corrals near transit.
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