Authors: Chro H. Ahmed1 & Hardy K. Karim2 & Hirsh M. Majid3
1,2&3University of Sulaimani, Iraq
Abstract: Optimizing rail transit station locations is a very complex engineering problem. The requirements and constraints that should be considered in locating rail transit stations are complex and interrelated. Although several optimization models have been developed to solve the rail transit station location problem, most of them focus on a single objective and only yield a suboptimal solution to the problem. Multiple-objective models for optimizing rail transit station locations are rare in the literature and their capabilities are very limited. This paper, addresses the limitations in the existing models by developing an evolutionary model, taking into account various local conditions and the multiple planning requirements that arise from passenger, operator and the community to optimize station locations. The model uses an evolutionary solution algorithm (a search algorithm that imitates the natural evolution process) based on genetic algorithm (GA) integrated with geographic information system (GIS) tools to perform the optimal search. The model was applied to an artificial case study and the results demonstrate that the model can optimally locate stations that satisfied the identified planning requirements and constraints.
Keywords: Rail Transit Station, Optimization, Genetic algorithm, GIS
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Ahmed, C. (2016). GIS and genetic algorithm based integrated optimization for rail transit system planning. Ph.D. dissertation, Imperial College London, London, UK.
Carrizosa, E., Harbering, J., & Schöbel, A. (2016). Minimizing the passengers’ traveling time in the stop location problem. Journal of the Operational Research Society, 67(10),1325-1337.
Goldberg , D. E. & Deb, K. (1991). A comparison analysis of selection schemes used in genetic algorithms. In G.J. E. Rawlins (Eds.), Foundations of genetic algorithm. California: Morgan Kaufmann.
Goldberg, D. E. (1989). Genetic algorithms in search, optimization, and machine learning. Massachusetts: AddisonWesley.
Hamacher, H. W., Horn, S. and Schöbel, A. (2009). Stop location design in public transportation networks: Covering and accessibility objectives. TOP, 17(2), 335-346.
Jha, M. K., & Oluokun, C. (2004). Optimizing station locations along transit rail lines with geographic information systems and artificial intelligence. In Allan, J., Brebbia, C.A., Hill, R.J., Sciutto, G., Sone, S. (Eds.), Computers in railways IX. Southampton: WIT Press.
Kikuchi, S., & Vuchic, V. R. (1982). Transit vehicle stopping regimes and spacings. Transportation Science, 16 (3), 311-331.
Laporte, G., Mesa, J. A., & Ortega, F. A. (2002). Locating stations on rapid transit lines. Computers & Operations Research, 29 (6), 741-759.
Samanta, S. & Jha, M., (2008). Identifying feasible locations for rail transit stations: Two-stage analytical model. Journal of the Transportation Research Board, 2063, 81-88.
Schöbel, A., Hamacher, H., Liebers, A., & Wagner, D. (2002). The continuous stop location problem in public transportation networks. Technical Report 81, Wirtschaftsmathematik, University of Kaiserslautern, Germany.
Vuchic, V. R. & Newell, G. F., (1968). Rapid transit interstation spacings for minimum travel time. Transportation Science, 2(4), 303-309.
Vuchic, V. R. (1969). Rapid transit interstation spacings for maximum number of passengers. Transportation Science, 3(3), 214-232.
Wirasinghe, S. C., & Vandebona., U., (1987). Some aspects of the location of subway stations and routes. Presented at the Fourth International Symposium on Locational Decisions (ISOLDE). Namur, Belgium.