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2014 | 24 | 3 | 611-619
Tytuł artykułu

Methods for estimating vehicle queues at a marine terminal: a computational comparison

Treść / Zawartość
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
A long queue of vehicles at the gate of a marine terminal is a common traffic phenomenon in a port-city, which sometimes causes problems in urban traffic. In order to be able to solve this issue, we firstly need accurate models to estimate such a vehicle queue length. In this paper, we compare the existing methods in a case study, and evaluate their advantages and disadvantages. Particularly, we develop a simulation-based regression model, using the micro traffic simulation software PARAMIC. In simulation, it is found that the queue transient process follows a natural logarithm curve. Then, based on these curves, we develop a queue length estimation model. In the numerical experiment, the proposed model exhibits better estimation accuracy than the other existing methods.
Rocznik
Tom
24
Numer
3
Strony
611-619
Opis fizyczny
Daty
wydano
2014
otrzymano
2013-08-01
poprawiono
2013-12-13
poprawiono
2014-02-18
Twórcy
autor
  • Transport Planning Institute, Dalian Maritime University, Linghai Road 1, Dalian, Liaoning Province, PR China
  • Transport Planning Institute, Dalian Maritime University, Linghai Road 1, Dalian, Liaoning Province, PR China
Bibliografia
  • Chen, G., Kannan, G., Yang, Z., Choi, T. and Jiang, L. (2013). Terminal appointment system design by non-stationary M(t)/Ek/c(t) queueing model and genetic algorithm, International Journal of Production Economics 146(2): 694-703.
  • Chen, G. and Yang, Z. (2010). Optimizing time windows for managing arrivals of export containers at Chinese container terminals, Maritime Economics and Logistics 2010(12): 111-126.
  • Chen, X., Zhou, X. and List, G.F. (2011). Using time-varying tolls to optimize truck arrivals at ports, Transportation Research, Part E: Logistics and Transportation Review 47(6): 965-982.
  • Chydziński, A. and Chróst, Ł. (2011). Analysis of AQM queues with queue size based packet dropping, International Journal of Applied Mathematics and Computer Science 21(3): 567-577, DOI: 10.2478/v10006-011-0045-7.
  • Cosmetatos, G.P. (1976). Some approximate equilibrium results for the multi-server queue (M/G/r), Operational Research Quarterly 27(3): 615-620.
  • Davidson, C. (1988). Equilibrium in service industries: An economic application of queuing theory, Journal of Business 61(3): 347-367.
  • Guan, C. Q. and Liu, R.F. (2009). Modeling maritime container terminal gate congestion, truck waiting cost, and optimization, Transportation Research Record: Journal of the Transportation Research Board 2100(7): 58-67.
  • Hartmann, S. (2004). Generating scenarios for simulation and optimization of container terminal logistics, OR Spectrum 26(2): 171-192.
  • Huynh, N., Walton, M.C. and Davis, J. (2004). Finding the number of yard cranes needed to achieve desired truck turn time at maritime container terminals, Transportation Research Record: Journal of the Transportation Research Board 1873(12): 99-108.
  • Kia, M., Shayan, E. and Ghotb, F. (2002). Investigation of port capacity under a new approach by computer simulation, Computers and Industrial Engineering 42(2): 533-540.
  • Kim, S. (2009). The toll plaza optimization problem: Design, operations, and strategies, Transportation Research, Part E: Logistics and Transportation Review 45(1): 125-137.
  • Liu, C.I., Jula, H. and Ioannou, P.A. (2002). Design, simulation, and evaluation of automated container terminals, IEEE Transactions on Intelligent Transportation Systems 3(1): 12-26.
  • Sacone, S. and Siri, S. (2009). An integrated simulation-optimization framework for the operational planning of seaport container terminals, Mathematical and Computer Modelling of Dynamical Systems 15(3): 275-293.
  • Shabayek, A. and Yeung, W. (2002). A simulation model for the Kwai Chung container terminals in Hong Kong, European Journal of Operational Research 140(1): 1-11.
  • Smith, J. (2010). Robustness of state-dependent queues and material handling systems, International Journal of Production Research 48(16): 4631-4663.
  • Srivatsan, N. and Kempf, K. (1995). Effective modeling of factory throughput times, 17th IEEE/CPMT International Electronics Manufacturing Technology Symposium, Omiya, Japan, pp. 377-383.
  • Taniguchi, E., Noritake, M., Yamada, T. and Izumitani, T. (1999). Optimal size and location planning of public logistics terminals, Transportation Research, Part E: Logistics and Transportation Review 35(3): 207-222.
  • Wu, J., Abbas-Turki, A. and Perronnet, F. (2013). Cooperative driving at isolated intersections based on the optimal minimization of the maximum exit time, International Journal of Applied Mathematics and Computer Science 23(4): 773-785, DOI: 10.2478/amcs-2013-0058.
  • Xie, Y., Chowdhury, M., Bhavsar, P. and Zhou, Y. (2012). An integrated modeling approach for facilitating emission estimations of alternative fueled vehicles, Transportation Research, Part D: Transport and Environment 17(1): 15-20.
  • Yang, C.H., Choi, Y.S. and Ha, T.Y. (2004). Simulation-based performance evaluation of transport vehicles at automated container terminals, OR Spectrum 26(2): 149-170.
Typ dokumentu
Bibliografia
Identyfikatory
Identyfikator YADDA
bwmeta1.element.bwnjournal-article-amcv24i3p611bwm
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