A Simulation Model Indicated That Shared Automated Vehicles and Fully Automated Electric Shuttles Can Improve Transit Coverage in Large Cities by 13 to 315 Percent.

University Researchers Evaluated Automated Vehicle Technology and Transit Coverage in New York City, Chicago, Minneapolis, and Pittsburgh.

Date Posted
12/15/2022
Identifier
2022-B01699
TwitterLinkedInFacebook

Improving Access and Equity via Shared Automated Mobility in U.S. Public Transportation Systems

Summary Information

This study evaluated transit systems in various cities to identify opportunities for equitable improvement through shared automated mobility. For this purpose, locations of unmet transit demand among the transit-dependent population were identified and prioritized for future service via Shared fully Automated Vehicles (SAVs) or shared fully automated electric shuttles. Based on current transit and technology costs, this study estimated levelized operating costs for first- and last-mile service in transit systems in four US cities: New York City, Chicago, Pittsburgh, and Minneapolis-St. Paul. Analysis was performed at Census Block Group (CBG) level for each city, with 205 CBGs prioritized for new transit service in New York City, 118 for Chicago, five for Pittsburg, and eight for Minneapolis- St. Paul. Data used in this study were from different sources ranging from 2014-2017.

METHODOLOGY

In this study, transit stops, routes, and service frequency data from the standardized General Transit Feed Specification (GTFS) were used to determine the transit supply score for each CBG. The transit coverage score was determined using the transit supply score and transit need score revealing current service available to the transit dependent population. A subset of CBGs was prioritized for service improvement based on the lowest transit coverage score and greater than average low-income or minority population. These priority CBGs were used as origin points for calculating route distances to the nearest bus stop with adequate transit service, then used as inputs for cost analysis where three scenarios were developed: 

  1. Bus: (Base-case) This scenario added a transit stop in a priority CBG centroid served by conventional diesel buses connecting a priority CBG to an established transit stop with service to the central business district (CBD). 
  2. SAVs (Alternative 1): This scenario operated a fleet of fully automated vehicles (four-passenger gasoline SAVs) traveling from the priority CBG to transit stops with service to the CBD. 
  3. Electric Fully Automated Shuttles (Alternative 2): This scenario used electric shuttles (12-passenger electric fully automated shuttle) to serve priority CBGs with service to the nearest stops with service to the CBD. To compute separate estimates of direct costs for the implementation, both capital and operating costs were used.

FINDINGS

  • For New York City, adding electric fully automated shuttles services could increase transit coverage by an average of 13 percent across all the priority CBGs. 
  • For Chicago, adding electric fully automated shuttles could increase transit coverage by an average of 24 percent across all the priority CBGs. 
  • For Minneapolis-St. Paul, the cost-efficient scenario was determined as SAVs, with transit coverage increasing by an average of 18 percent across all the priority CBGs. 
  • For Pittsburg, the cost-efficient scenario was also determined as SAVs, with transit coverage increasing an average of 315 percent across all the priority CBGs.
  • While SAVs and shuttles were more cost efficient than buses in Minneapolis-St. Paul and Pittsburgh, for many priority CBGs in New York City and Chicago, buses were still the most cost-efficient.

     
Results Type
Deployment Locations