Hybrid predictive control strategy for a public transport system with uncertain demand

Transportmetrica

Volumn 8, Issue 1, 2012, Pages 61-86

  Sáez, Doris ^a   Cortés, Cristián E ^a   Milla, Freddy ^a   Núñez, Alfredo ^a   Tirachini, Alejandro ^b   Riquelme, Marcela ^a  

^a UNIVERSITY OF CHILE   (Chile)

^b UNIVERSITY OF SYDNEY   (Australia)

Author keywords

holding; hybrid predictive control; public transport; station skipping

Indexed keywords

AD HOC IMPLEMENTATION; AUTOMATIC PASSENGER COUNTERS; AUTOMATIC VEHICLE LOCATIONS; BUS STOP; BUS SYSTEMS; COMPUTATION TIME; CONTROL ACTIONS; DISCRETE TIME; EFFICIENT ALGORITHM; HOLDING; NP-HARD; OBJECTIVE FUNCTIONS; OFFLINE; ON-LINE INFORMATION; OPTIMAL CONTROLS; OPTIMISATIONS; PASSENGER DEMANDS; PREDICTIVE CONTROL; PREDICTIVE CONTROL STRATEGY; PREDICTIVE CONTROLLER; PREDICTIVE MODELS; PUBLIC TRANSPORT; PUBLIC TRANSPORT SYSTEMS; REAL-TIME IMPLEMENTATIONS; STATION SKIPPING; TRANSPORT TECHNOLOGY; UNCERTAIN DEMAND; WAITING-TIME;

ALGORITHMS; BUS TRANSPORTATION; BUSES; OPTIMIZATION; REAL TIME CONTROL; TRANSPORTATION;

BEHAVIORAL RESEARCH;

EID: 84859408519     PISSN: 18128602     EISSN: None     Source Type: Journal    
DOI: 10.1080/18128601003615535     Document Type: Article

References (29)

1. Adamski, A. 1996. "Flexible dispatching control tools in public transport". In Advanced methods in transportation analysis, Edited by: Bianco, L and Toth, P. 481-506. Heidelberg, Germany: Springer.
2. Adamski, A and Turnau, A. 1998. Simulation support tool for real-time dispatching control in public transport. Transportation Research Part A: General, 32(2): 73-87.
3. ATC-Australian Transport Council. 2006. National guidelines for transport system management in Australia, Urban Transport: Volume 4. Available from: http://www.atcouncil.gov.au/documents/NGTSM.aspx [Retrieved on January 2009]
4. Barnett, A. 1974. On controlling randomness in transit operations. Transportation Science, 8: 102-116.
5. Bemporad, A and Morari, M. 1999. Control of systems integrating logic, dynamics and constraints. Automatica, 35: 407-427.
6. Cortés, CE, Núñez, A and Sáez, D. 2008. Hybrid adaptive predictive control for a dynamic pickup and delivery problem including traffic congestion. International Journal of Adaptive Control and Signal Processing, 22(2): 103-123.
7. Eberlein, X.J., (1995). Real-time control strategies in transit operations: models and analysis. Thesis (PhD). Massachusetts Institute of Technology, Cambridge, MA.
8. Eberlein, XJ, Wilson, NHM and Bernstein, D. 1999. Modeling real-time control strategies in public transit operations. Lecture Notes in Economics and Mathematical Systems, 471: 325-346.
9. Eberlein, XJ, Wilson, NHM and Bernstein, D. 2001. The holding problem with real-time information available. Transportation Science, 35(1): 1-18.
10. Fu, L., Liu, Q., and Calamai, P., 2003. Real-time optimization model for dynamic scheduling of transit operations. Transportation Research Record, 1857, 48-55.
11. Furth, P. 1986. Zonal route design for transit corridors. Transportation Science, 20(1): 1-12.
12. Furth, PG and Day, FB. 1984. Transit routing and scheduling strategies for heavy demand corridors. Transportation Research Record, 1011: 23-26.
13. Hegyi, A., 2004. Model predictive control for integrating traffic control measures. Thesis (PhD). Delft University of Technology, Delft Center for Systems and Control.
14. Hegyi, A, De Schutter, B and Hellendoorn, H. 2005. Model predictive control for optimal coordination of ramp metering and variable speed limits. Transportation Research Part C: Emerging Technologies, 13: 185-209.
15. Hickman, M. 2001. An analytic stochastic model for the transit vehicle holding problem. Transportation Science, 35(3): 215-237.
16. Jordan, WC and Turnquist, MA. 1979. Zone scheduling of bus routes to improve service reliability. Transportation Science, 13(3): 242-268.
17. Karer, G. 2007a. Hybrid fuzzy modeling for model predictive control. Journal of Intelligent and Robotic Systems, 50: 297-319.
18. Karer, G. 2007b. Hybrid fuzzy model-based predictive control of temperature in a batch reactor. Computers and Chemical Engineering, 31: 1552-1564.
19. Lin, G., et al., 1995. Adaptive control of transit operations. Report No. MD-26-7002, US Department of Transportation, Federal Transit Administration, November 15.
20. Man, K, Tang, K and Kwong, S. 1999. Genetic algorithms, concepts and designs, Heidelberg: Springer.
21. Osuna, EE and Newell, GF. 1972. Control strategies for an idealized public transportation system. Transportation Science, 6: 52-72.
22. Rudolph, G. 1994. Convergence analysis of canonical genetic algorithms. IEEE Transactions on Neural Networks, 5(1): 96-101.
23. Sáez, D, Cortés, CE and Núñez, A. 2008. Hybrid adaptive predictive control for the multi-vehicle dynamic pick-up and delivery problem based on genetic algorithms and fuzzy clustering. Computers and Operations Research, 35(11): 3412-3438.
24. Sun, A. and Hickman M., 2004. The holding problem at multiple holding stations [online]. In: 9th International conference on computer-aided scheduling of public transport, San Diego, CA: EE.UU. Available from:http://fugazi.engr.arizona.edu/caspt/sun.pdf
25. Sun, A and Hickman, M. 2005. The real-time stop-skipping problem. Journal of Intelligent Transportation Systems, 9(2): 91-109.
26. Turnquist, MA and Blume, SW. 1980. Evaluating potential effectiveness of headway control strategies for transit systems. Transportation Research Record, 746: 25-29.
27. Welding, PI. 1957. The instability of a close-interval service. Operational Research Quarterly, 8(3): 133-148.
28. Yu, B and Yang, Z. 2007. A dynamic holding strategy in public transit systems with real-time information. Applied Intelligence, 31(1): 69-80.
29. Zolfaghari, S, Azizi, N and Jaber, M. 2004. A model for holding strategy in public transit systems with real-time information. International Journal of Transport Management, 2: 99-110.