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Optimizing bus services with variable directional and temporal demand using genetic algorithm

  • Geological, Civil, Energy and Traffic Engineering
  • Published:
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Abstract

As a major mode choice of commuters for daily travel, bus transit plays an important role in many urban and metropolitan areas. This work proposes a mathematical model to optimize bus service by minimizing total cost and considering a temporally and directionally variable demand. An integrated bus service, consisting of all-stop and stop-skipping services is proposed and optimized subject to directional frequency conservation, capacity and operable fleet size constraints. Since the research problem is a combinatorial optimization problem, a genetic algorithm is developed to search for the optimal result in a large solution space. The model was successfully implemented on a bus transit route in the City of Chengdu, China, and the optimal solution was proved to be better than the original operation in terms of total cost. The sensitivity of model parameters to some key attributes/variables is analyzed and discussed to explore further the potential of accruing additional benefits or avoiding some of the drawbacks of stop-skipping services.

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References

  1. ERCOLANO J M. Limited-stop bus operations: An evaluation [J]. 7, 1984(994): 24–29.

  2. CASELLO J M, HELLINGA B. Impacts of express bus service on passenger demand [J]. Journal of Public Transportation, 2008, 11(4): 1–23.

    Article  Google Scholar 

  3. EL-GENEIDY A M, SURPRENANT-LEGAULT J. Limited-stop bus service: An evaluation of an implementation strategy [J]. Public Transport, 2010, 2(4): 291–306.

    Article  Google Scholar 

  4. VUCHIC V R. Skip-stop operation as a method for transit speed increase [J]. Traffic Quarterly, 1973, 27(2): 307–327.

    Google Scholar 

  5. NIU H. Determination of the skip-stop scheduling for a congested transit line by bi-level genetic algorithm [J]. International Journal of Computational Intelligence Systems, 2011, 4(6): 1158–1167.

    Article  Google Scholar 

  6. TZENG G H, SHIAU T A. Multiple objective programming for bus operation: A case study for Taipei city [J]. Transportation Research Part B: Methodological, 1988, 22(3): 195–206.

    Article  Google Scholar 

  7. CHIEN S. Optimization of headway, vehicle size and route choice for minimum cost feeder service [J]. Transportation Planning and Technology, 2005, 28(5): 359–380.

    Article  MathSciNet  Google Scholar 

  8. ZHAO F, ZENG X. Optimization of transit network layout and headway with a combined genetic algorithm and simulated annealing method [J]. Engineering Optimization, 2006, 38(6): 701–722.

    Article  MathSciNet  Google Scholar 

  9. HAFEZI M H, ISMAIL A. Balancing between headway and frequency scheduling for bus service [C]// Applied Mechanics and Materials. 2011, 97: 669–673.

    Article  Google Scholar 

  10. VERBAS I Ö, FREI C, MAHMASSANI H S, CHAN R. Stretching resources: Sensitivity of optimal bus frequency allocation to stop-level demand elasticities [J]. Public Transport, 2015, 7(1): 1–20.

    Article  Google Scholar 

  11. FURTH P G, DAY F B. Transit routing and scheduling strategies for heavy demand corridors [J]. Transportation Research Record, 1985(1011): 23–26.

    Google Scholar 

  12. FURTH P G. Short turning on transit routes [J]. Transportation Research Record, 1987(1108): 42–52.

    Google Scholar 

  13. CEDER A. Optimal design of transit short-turn trips [J]. Transportation Research Record, 1989(1221): 8–22.

    Google Scholar 

  14. CEDER A, STERN H I. Deficit function bus scheduling with deadheading trip insertions for fleet size reduction [J]. Transportation Science, 1981, 15(4): 338–363.

    Article  Google Scholar 

  15. FURTH P G. Alternating deadheading in bus route operations [J]. Transportation Science, 1985, 19(1): 13–28.

    Article  Google Scholar 

  16. ULUSOY Y, CHIEN S, WEI C H. Optimal all-stop, short-turn, and express transit services under heterogeneous demand [J]. Transportation Research Record, 2010(2197): 8–18.

    Article  Google Scholar 

  17. TIRACHINI A, CORTÉS C E, JARA-DÍAZ S R. Optimal design and benefits of a short turning strategy for a bus corridor [J]. Transportation, 2011, 38(1): 169–189.

    Article  Google Scholar 

  18. CORTÉS C E, JARA-DÍAZ S, TIRACHINI A. Integrating short turning and deadheading in the optimization of transit services [J]. Transportation Research Part A: Policy and Practice, 2011, 45(5): 419–434.

    Google Scholar 

  19. FU L, LIU Q, CALAMAI P. Real-time optimization model for dynamic scheduling of transit operations [J]. Transportation Research Record, 2003(1857): 48–55.

    Article  Google Scholar 

  20. CHIEN S I J, ULUSOY Y Y, WEI C H. Integrated transit services for minimum cost operation considering heterogeneous demand [J]. Transportation Planning and Technology, 2010, 33(7): 605–624.

    Article  Google Scholar 

  21. ULUSOY Y Y, CHIEN S I J. Optimal bus service patterns and frequencies considering transfer demand elasticity with genetic algorithm [J]. Transportation Planning and Technology, 2015, 38(4): 409–424.

    Article  Google Scholar 

  22. CHIRAPHADHANAKUL V, BARNHART C. Incremental bus service design: combining limited-stop and local bus services [J]. Public Transport, 2013, 5(1/2): 53–78.

    Article  Google Scholar 

  23. LEIVA C, MUÑOZ J C, GIESEN R, LARRAIN H. Design of limited-stop services for an urban bus corridor with capacity constraints [J]. Transportation Research Part B: Methodological, 2010, 44(10): 1186–1201.

    Article  Google Scholar 

  24. LARRAIN H, GIESEN R, MUÑOZ J. Choosing the right express services for bus corridor with capacity restrictions [J]. Transportation Research Record, 2010(2197): 63–70.

    Article  Google Scholar 

  25. ALGERS S, HANSEN S, TEGNER G. Role of waiting time, comfort, and convenience in modal choice for work trip [J]. Transportation Research Record, 1975(534): 38–51.

    Google Scholar 

  26. HAN A F. Assessment of transfer penalty to bus riders in taipei: A disaggregate demand modeling approach [J]. Transportation Research Record, 1987(1139): 8–14.

    Google Scholar 

  27. GUO Z, WILSON N. Assessment of the transfer penalty for transit trips geographic information system-based disaggregate modeling approach [J]. Transportation Research Record, 2004(1872): 10–18.

    Article  Google Scholar 

  28. GUO Z, WILSON N. Modeling effects of transit system transfers on travel behavior: case of commuter rail and subway in downtown boston, massachusetts [J]. Transportation Research Record, 2007(2006): 11–20.

    Article  Google Scholar 

  29. ULUSOY Y Y, CHIEN S I J. Optimizing integrated bus service for minimum cost operation with genetic algorithm–a case study in new jersey [C]// Transportation Research Board 90th Annual Meeting. 2011: No.11-1910.

    Google Scholar 

  30. HOLLAND J H. Adaptation in natural and artificial systems: An introductory analysis with applications to biology, control, and artificial intelligence [M]. Ann Arbor, USA: U Michigan Press, 1975.

    MATH  Google Scholar 

  31. GOLDBERG D E. Genetic algorithms in search optimization and machine learning [M]. Reading Menlo Park: Addison-Wesley, 1989.

    MATH  Google Scholar 

  32. GEN M, CHENG R. genetic algorithms and engineering design [M]. New York: John Wiley & Sons, 1997.

    Google Scholar 

  33. FAN W, MACHEMEHL R B. Optimal transit route network design problem with variable transit demand: Genetic algorithm approach [J]. Journal of Transportation Engineering, 2006, 132(1): 40–51.

    Article  Google Scholar 

  34. JHA M K, SCHONFELD P, SAMANTA S. Optimizing rail transit routes with genetic algorithms and geographic information system [J]. Journal of Urban Planning and Development, 2007, 133(3): 161–171.

    Article  Google Scholar 

  35. BALLING R J, TABER J T, BROWN M R, DAY, K. Multiobjective urban planning using genetic algorithm [J]. Journal of Urban Planning and Development, 1999, 125(2): 86–99.

    Article  Google Scholar 

  36. CHAKROBORTY P, DEB K, SUBRAHMANYAM P S. Optimal scheduling of urban transit systems using genetic algorithms [J]. Journal of Transportation Engineering, 1995, 121(6): 544–553.

    Article  Google Scholar 

  37. YU B, YANG Z, YAO J. Genetic algorithm for bus frequency optimization [J]. Journal of Transportation Engineering, 2009, 136(6): 576–583.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Transportation and Logistics, Southwest Jiaotong University, Chengdu, 610031, China

    He-zhou Qu  (瞿何舟), Xiao-bo Liu  (刘晓波) & Pei-tong Zhang  (张培桐)

  2. Sichuan Province Key Laboratory of Comprehensive Transportation, Chengdu, 610031, China

    He-zhou Qu  (瞿何舟)

  3. College of Automobile, Chang’an University, Xi’an, 710064, China

    Steven I. -Jy. Chien

  4. John A. Reif Jr. Department of Civil and Environmental Engineering, New Jersey Institute of Technology, Newark, 07102-1982, USA

    Steven I. -Jy. Chien

  5. Mechanical & Industrial Engineering, New Jersey Institute of Technology, Newark, 07102-1982, USA

    Athanassios Bladikas

Authors
  1. He-zhou Qu  (瞿何舟)
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  2. Steven I. -Jy. Chien
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  3. Xiao-bo Liu  (刘晓波)
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  4. Pei-tong Zhang  (张培桐)
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  5. Athanassios Bladikas
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Corresponding author

Correspondence to Xiao-bo Liu  (刘晓波).

Additional information

Foundation item: Project (B01B1203) supported by Sichuan Province Key Laboratory of Comprehensive Transportation, China; Project (SWJTU09BR141) supported by the Southwest Jiaotong University, China

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Qu, Hz., Chien, S.I.J., Liu, Xb. et al. Optimizing bus services with variable directional and temporal demand using genetic algorithm. J. Cent. South Univ. 23, 1786–1798 (2016). https://doi.org/10.1007/s11771-016-3232-8

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  • DOI: https://doi.org/10.1007/s11771-016-3232-8

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