Optimal Automated Demand Responsive Feeder Transit Operations and Its Impact

Although demand responsive feeder bus operation is possible with human-driven vehicles, it has not been very popular and mostly available as a special service because of the high operating costs due to the intensive labor costs as well as advanced real-time information technology and complicated operation. However, once automated vehicles become available, small-sized flexible door-to-door feeder bus operation will become more realistic, thanks to recent technological advances and business innovations by the transportation network companies (TNCs). So, preparing for the automated flexible feeder service is necessary to catch the rapid improvement of automated vehicle technology. Therefore, this research developed an algorithm for the optimal flexible feeder bus routing, which considers relocation of buses for multi-stations and multi-trains, using a simulated annealing (SA) algorithm for future automated vehicle operation. An example was developed and tested to demonstrate the developed algorithm. The algorithm successfully handled relocating the buses when the optimal bus routings were not feasible with the available buses at certain stations. Furthermore, the developed algorithm limited the maximum Degree of Circuity for each passenger while minimizing total cost, including total vehicle operating costs and total passenger in-vehicle travel time costs. Unlike fixed route mass transit, small vehicle demand responsive service uses flexible routing, which means lower unit operating costs not only decrease total operating costs and total costs but also can affect routing and impact network characteristics. In the second part of this research, optimal flexible demand responsive feeder transit networks were generated with various unit transit operating costs using the developed routing optimization algorithm. Then network characteristics of those feeder networks were examined and compared. The results showed that when unit operating costs decline, total operating costs and total costs obviously decline. Furthermore, when unit operating costs decline, the average passenger travel distance and total passenger travel costs decline while the ratio of total operating costs per unit operating costs increases. That means if unit operating costs decrease, the portion of passenger travel costs in total costs increases, and the optimization process tends to reduce passenger costs more while reducing total costs. Assuming that automation of the vehicles reduces the operating costs, it will reduce total operating costs, total costs and total passenger travel costs as well.

Language

  • English

Project

  • Status: Completed
  • Funding: $95519
  • Contract Numbers:

    69A43551747123

  • Sponsor Organizations:

    Morgan State University

    1700 E. Coldspring Lane
    Baltimore, Maryland  United States  21251

    Office of the Assistant Secretary for Research and Technology

    University Transportation Centers Program
    Department of Transportation
    Washington, DC  United States  20590
  • Managing Organizations:

    Urban Mobility and Equity Center

    Morgan State University
    Baltimore, MD  United States  21251
  • Project Managers:

    Tucker-Thomas, Dawn

  • Performing Organizations:

    Urban Mobility and Equity Center

    Morgan State University
    Baltimore, MD  United States  21251
  • Principal Investigators:

    Lee, Young-Jae

  • Start Date: 20170525
  • Expected Completion Date: 20180524
  • Actual Completion Date: 20180918

Subject/Index Terms

Filing Info

  • Accession Number: 01637675
  • Record Type: Research project
  • Source Agency: Urban Mobility and Equity Center
  • Contract Numbers: 69A43551747123
  • Files: UTC, RiP
  • Created Date: Jun 2 2017 10:07AM