Optimizing first- and last-mile public transit services leveraging transportation network companies (TNC) View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2022-06-23

AUTHORS

Rick Grahn, Sean Qian, Chris Hendrickson

ABSTRACT

First-mile last-mile (FMLM) mobility services that connect riders to public transit can lead to improved transit accessibility and network efficiency if such services are convenient and reliable. However, many current FMLM services are inefficient and costly because they are inflexible (e.g., fixed supply of shuttles) and do not leverage collected data for optimized decision making. At the same time, new forms of shared mobility can provide added flexibility and real-time analytics to FMLM systems when carefully integrated. This study evaluates performance and cost implications of public/private coordination between transit shuttles and transportation network companies (TNC) in the FMLM context. A real-time operations model was developed to simulate daily operations for an existing FMLM system using real-world demand data. Three supply strategies were tested with varying levels of flexibility: (1) Status Quo (two 23-passenger on-demand shuttles), (2) Hybrid (one 23-passenger on-demand shuttle + TNC), and (3) TNC Only (exclusively use TNC services). Results indicated that the added flexibility of the Hybrid service design (using shuttles and TNCs) improved service performance (a 7.7% improvement), reduced daily operating costs (− 6.0%), and improved service reliability (95th percentile travel times decreased by up to 40% during peak periods). In addition, the Hybrid service design was more robust to variations in demand. The Hybrid service was significantly cheaper to operate (− 31.6%) at reduced demand levels (50% of normal), and improved service performance (a 10.2% improvement) when demand levels were increased (150% of normal). These findings emphasize the importance of flexibility in FMLM service designs, especially when demand is sparse and variable. More... »

PAGES

1-28

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s11116-022-10301-z

DOI

http://dx.doi.org/10.1007/s11116-022-10301-z

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1148907934


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