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Optimal Automated Demand Responsive Feeder Transit Operation and Its Impact Final Report Optimal Automated Demand Responsive Feeder Transit Operation and Its Impact Principal Investigator Young-Jae Lee, Ph.D. Associate Professor, Department of Transportation and Urban Infrastructure Studies, Morgan State University, 1700 E. Cold Spring Lane, Baltimore, MD 21251 Tel: 443-885-1872; Fax: 443-885-8218; Email: [email protected] Co-Principal Investigator Amirreza Nickkar Graduate Research Assistant, Department of Transportation and Urban Infrastructure Studies, Morgan State University, 1700 E. Cold Spring Lane, Baltimore, MD 21251 Email: [email protected] Date September 2018 Prepared for the Urban Mobility & Equity Center, Morgan State University, CBEIS 327, 1700 E. Coldspring Lane, Baltimore, MD 21251 1 ACKNOWLEDGMENT The authors would like to thank Mana Meskar and Sina Sahebi, Ph.D. students at Sharif University of Technology, for their contributions to this study. This research was supported by the Urban Mobility & Equity Center at Morgan State University and the University Transportation Center(s) Program of the U.S. Department of Transportation. Disclaimer The contents of this report reflect the views of the authors, who are responsible for the facts and the accuracy of the information presented herein. This document is disseminated under the sponsorship of the U.S. Department of Transportation’s University Transportation Centers Program, in the interest of information exchange. The U.S. Government assumes no liability for the contents or use thereof. ©Morgan State University, 2018. Non-exclusive rights are retained by the U.S. DOT. 2 1. Report No. 2. Government Accession No. 3. Recipient’s Catalog No. 4. Title and Subtitle 5. Report Date Optimal Automated Demand Responsive Feeder Transit September 2018 Operation and Its Impact 6. Performing Organization Code 7. Author(s) Young-Jae Lee https://orcid.org/0000-0002-1422- 8. Performing Organization Report No. 7965; Amirreza Nickkar https://orcid.org/0000-0002-1242-3778 9. Performing Organization Name and Address 10. Work Unit No. Morgan State University 1700 E. Cold Spring Lane, 11. Contract or Grant No. Baltimore, MD 21251-0001 69A43551747123 12. Sponsoring Agency Name and Address 13. Type of Report and Period Covered US Department of Transportation Final June 2017- September 2018 Office of the Secretary-Research UTC Program, RDT-30 14. Sponsoring Agency Code 1200 New Jersey Ave., SE Washington, DC 20590 15. Supplementary Notes 16. Abstract 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. 17. Key Words : Automated Transit, Demand Responsive 18. Distribution Statement Transit, Feeder Bus, Vehicle Routing Problem, Optimization No restrictions. 19. Security Classif. (of this 20. Security Classif. (of this page) 21. No. of Pages 22. Price report) : Unclassified Unclassified 62 3 TABLE OF CONTENTS 1. Introduction ................................................................................................................................. 9 2. Literature Review...................................................................................................................... 10 2.1. Feeder Bus Service ............................................................................................................. 11 2.2. Ridesharing......................................................................................................................... 13 2.3. Vehicle Routing Problem with Simultaneous Pickup and Delivery (VRPSPD) ............... 14 2.4. The Pickup and Delivery Problem with Time Windows (PDPTW) .................................. 14 2.5. The Dial-A-Ride Problem (DARP) .................................................................................... 15 3. Methodology ............................................................................................................................. 16 3.1. Algorithm ........................................................................................................................... 16 3.2. Hypothetical Network ........................................................................................................ 24 4. Analyses and Results ................................................................................................................ 28 5. Impact of Automation in Feeder Transit Network .................................................................... 36 6. Estimation of Transit Operating Costs ...................................................................................... 37 6.1 Conventional Bus Operating Cost ....................................................................................... 37 6.2 Expected Bus Operating Cost with Emerging Technology ................................................ 44 7. Analyses and Results with Various Transit Operating Costs ................................................... 50 8. Conclusions ............................................................................................................................... 55 9. References ................................................................................................................................. 58 4 LIST OF TABLES Table 1. Passenger information for each station and each train ................................................... 26 Table 2. Results of the model with Max DOC of 2.5 ................................................................... 31 Table 3. Results of the model with Max DOC of 3 ...................................................................... 32 Table 4. Results of the model with Max DOC of 3.5 ................................................................... 33 Table 5. Results of the model with Max DOC of 4 ...................................................................... 34 Table 6. Summary of the routings for the various circuities ......................................................... 35 Table 7. Bus operating cost per kilometer .................................................................................... 39 Table 8. Operating cost by agency size for conventional buses ................................................... 43 Table 9. Possible options of the evolution of current transit in the future with developing technologies. ................................................................................................................................. 45 Table 10. Estimated operating costs for some emerging transit technologies .............................. 46 Table 11. Potential impacts of electrification and automation on operating costs ....................... 48 Table 12. Estimated operating costs considering different emerging technologies ...................... 48 Table 13. Operating cost per revenue kilometer for buses by agency size for emerging technologies (2018 US dollars) ..................................................................................................... 49 Table 14. Passenger information for each station ........................................................................
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