Refine your search
Collections
Co-Authors
Journals
Year
A B C D E F G H I J K L M N O P Q R S T U V W X Y Z All
Parti, Raman
- Optimal generation of fast transit corridors in a city
Abstract Views :342 |
PDF Views:126
Authors
Aman Sharma
1,
Raman Parti
1
Affiliations
1 Department of Civil Engineering, National Institute of Technology, Hamirpur 177 005, IN
1 Department of Civil Engineering, National Institute of Technology, Hamirpur 177 005, IN
Source
Current Science, Vol 120, No 9 (2021), Pagination: 1500-1506Abstract
This study proposes a design methodology to generate optimal fast transit corridors in a city (Lucknow, Uttar Pradesh, India) integrated with a GIS platform. Population density distribution throughout the city was used for identification of nodes. Origin– destination (OD) distance matrix was generated between the nodes using Open Route Service. Centrality model consisting of connectivity and global integration centrality was used to generate an O–D demand matrix. Pre-defined number of clusters was generated to determine terminals using clustering algorithms. The optimal number of clusters was selected with an objective function to minimize the ‘total commuter time’ of the network. Ant colony optimization algorithm was used to generate fast transit corridors between the selected terminals that led to the generation of five such corridors for the study area.Keywords
Ant colony optimization, bus terminals, clustering, fast transit corridors.References
- Buba, A. T. and Lee, L. S., Differential evolution for urban transit routing problem. J. Comput. Commun., 2016, 4, 11–25.
- Buba, A. T. and Lee, L. S., A differential evolution for simultaneous transit network design and frequency setting problem. Expert Syst. Appl., 2018, 106, 277–289.
- Newell, G. F., Some issues relating to the optimal design of bus routes. Transp. Sci., 1979, 13, 20–35.
- Jayasinghe, A., Sano, K. and Rattanaporn, K., Application for developing countries: estimating trip attraction in urban zones based on centrality. J. Traffic Transp. Eng. (English Edn), 2017, 4, 464–476.
- Jayasinghe, A., Kasemsri, R., Abenayake, C. C. and Mahanama, P. K. S., Network centrality analysis of public transport systems: developing a strategic planning tool to assess passenger attraction. Int. J. Innov. Technol. Explor. Eng., 2019, 8, 472–476.
- Fan, W. and Machemehl, R. B., Optimal transit route network design problem with variable transit demand: genetic algorithm approach. J. Transp. Eng., 2006, 132, 40–51.
- Yang, Z., Yu, B. and Cheng, C., A parallel ant colony algorithm for bus network optimization. Comput. Civ. Infrastruct. Eng., 2007, 22, 44–55.
- Kechagiopoulos, P. N. and Beligiannis, G. N., Solving the urban transit routing problem using a particle swarm optimization based algorithm. Appl. Soft Comput. J., 2014, 21, 654–676.
- Daganzo, C. F., Structure of competitive transit networks. Transp. Res. Part B, 2010, 44, 434–446.
- Kelly, M. E. O., A clustering approach to the planar hub location. Ann. Oper. Res., 1992, 40, 339–353.
- Huang, D., Liu, Z., Fu, X. and Blythe, P. T., Multimodal transit network design in a hub- and spoke network framework. Transportmetrica. A, 2018, 14, 706–735.
- Parti, R., Marwah, B. R. and Kalra, P. K., Optimal selection of hubs for planning hub and spokes bus transit network. J. Inst. Eng. Civ. Eng. Div., 2005, 86, 49–52.
- Hillier, B. and Iida, S., Network and psychological effects: a theory of urban movement. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Ellicottville, NY, 2005, pp. 475–490.
- Kaufman, L. and Rousseuw, P. J., Finding Groups in Data: An Introduction to Cluster Analysis, Wiley Scr.Probab.Stat., John Wiley, 1990.
- Pal, N. R., Pal, K., Keller, J. M. and Bezdek, J. C., A possibilistic fuzzy C-means clustering algorithm. IEEE Trans. Fuzzy Syst., 2005, 13, 517–530.
- Cannon, R. L., Dave, J. V. and Bezdek, J. C., Efficient implementation of distinct the fuzzy C-means clustering algorithms. IEEE Trans. Pattern Anal. Mach. Intell., 1986, PAMI-8, 248–255.
- Bezdek, J. C., Ehrlich, R. and Full, W., FCM: the fuzzy C-means clustering algorithm. Comput. Geosci., 1984, 10, 191–203.
- Johnson, S. C. and Bell, Hierarchial clustering schemes. Psychometrika, 1967, 32, 241–254.
- Day, W. H. E. and Edelsbrunner, H., Efficient algorithms for agglomerative hierarchical clustering methods. J. Classif., 1984, 1, 7–24.
- Sasirekha, K. and Baby, P., Agglomerative hierarchical clustering algorithm – a review. Int. J. Sci. Res. Publ., 2013, 3, 2–4.
- Dorigo, M. and Di Caro, G., Ant colony optimization: a new metaheuristic. In Proceedings of the 1999 Congress on Evolutionary Computation, CEC1999, Washington DC, 1999, pp. 1470–1477.
- Talbi, E., Metaheuristics from Design to Implementation, John Wiley, 2009.
- Shi, X. and Xu, Y., Optimal design for urban mass transit network based on evolutionary algorithms. Adv. Nat. Comput., 2005, 3611, 1089–1100.
- Katiyar, S., Ibraheem and Ansari, A. Q., Ant colony optimization: a tutorial review. In National Conference on Advances in Power and Control, Faridabad, 2015.
- Kuan, S. N., Ong, H. L. and Ng, K. M., Solving the feeder bus network design problem by genetic algorithms and ant colony optimization. Adv. Eng. Softw., 2006, 37, 351–359.
- Li, P. and Zhu, H., Parameter selection for ant colony algorithm based on bacterial foraging algorithm. Math. Probl. Eng., 2016, 2016, 6469721.
- Gaertner, D. and Clark, K., On optimal parameters for ant colony optimization algorithms. In Proceedings of the 2005 International Conference on Artificial Intelligence, ICAI’05, Los Vegas, Nevada, 2005, pp. 83–89.
- Boeing, G., OSMnx: new methods for acquiring, constructing, analyzing, and visualizing complex street networks. Comput. Environ. Urban Syst., 2017, 65, 126–139.