Topology evaluation of Tehran subway network utilizing a bi-level mixed index for subway networks ranking

Document Type : Research Article

Authors

1 Transportation Planning Department, Faculty of Civil and Environmental Engineering, Tarbiat Modares University, Tehran, Iran

2 Transportation Planning Dept., Civil & Envi. Eng. Faculty, Tarbiat Modares University

3 Groove School of Engineering, City College of New York, United States of America

Abstract

As an essential infrastructure of cities, public transit networks have special importance in decreasing traffic congestion and air pollution and subway system is considered as the most efficient mode of public transit due to being green and mass transit. In this study, a mixed evaluation index composed of two components of shape and service points is proposed. The shape point is calculated utilizing network length, topology characteristics, station density, and average edge length (integer value between zero and ten). Annual passenger and passenger per unit length are used to calculate the service point (between zero and one). The study evaluated and compared subway networks for 52 cities around the world where according to this analysis New York city subway system is ranked 1, with a score of 8.506, and Tehran is ranked 29, with the score of 4.39. We also classified subway networks into three groups based on their connectivity and complexity indices using fuzzy c-means (FCM) clustering method and Tehran’s subway system is classified as partially accessibility network. Results of proposed classification based on network complexity and connectivity using fuzzy c-means methods indicate that the Tehran subway is the developed subway system but London, Tokyo, and New York are the more developed subway system. Results of regression models based on the world trend for primary predicting of the needed number of stations and length of a network show that currently, length and number of stations of the Tehran subway network should be equal to 206.3 km (31.1 km deficiency) and 147 (8 stations deficiency), respectively.

Keywords

Main Subjects

References

[1] D. Pulido, G. Darido, R. Munoz-Raskin, J. Moody. The urban rail development handbook. World Bank Group Press, (2018).
[2] W. L. Garrison, D. F. Marble. Factor-analytic study of the connectivity of a transportation network. Papers in Regional Science, 12(1) (1964) 231-238.
[3] W. L. Garrison, D. F. Marble. A prolegomenon to the forecasting of transportation development. Evanston, IL: Transportation Center Northwestern University (1965)
[4] J. Lin, Y. Ban. Complex network topology of transportation systems. Transport reviews, 33(6) (2013) 658-685.
[5] S. Derrible, C. Kennedy. Applications of graph theory and network science to transit network design. Transport reviews, 31(4) (2011) 495-519.
[6] K. J. Kansky. Structure of transportation networks: relationships between network geometry and regional characteristics. Chicago, IL: University of Chicago Press (1963).
[7] G. Laporte, J. A. Mesa, F. A. Ortega. Assessing the efficiency of rapid transit configurations. Top, 5(1) (1997) 95-104.
[8] G. Laporte, J. A. Mesa, F. A. Ortega, F. Perea. Planning rapid transit networks. Socio-Economic Planning Sciences, 45(3) (2011) 95-104.
[9] J. Zhang, M. Zhao, H. Liu, X. Xu. Networked characteristics of the urban rail transit networks. Physica A: Statistical Mechanics and its Applications, 392(6) (2013) 15381546.
[10] S. Derrible, C. Kennedy. Network analysis of world subway systems using updated graph theory. Transportation Research Record, 2112(1) (2009) 17-25.
[11] N. Sharav, S. Bekhor, Y. Shiftan. Network Analysis of the Tel Aviv Mass Transit Plan. Urban Rail Transit, 4(1) (2018) 23-34.
[12] D. Levinson. Network structure and city size. PloS one, 7(1) (2012) e29721.
[13] X. Wang, Y. Koç, S. Derrible, S. N. Ahmad, W. J. Pino, R. E. Kooij. Multi-criteria robustness analysis of metro networks. Physica A: Statistical Mechanics and its Applications, 474, (2017) 19-31.
[14] J. Feng, X. Li, B. Mao, Q. Xu, Y. Bai. Weighted complex network analysis of the Beijing subway system: Train and passenger flows. Physica A: Statistical Mechanics and its Applications, 474, (2017) 213-223.
[15] A. R. Mamdoohi, H. Zarei. An Analysis of Public Transit Connectivity Index in Tehran. The Case Study: Tehran Multi-Modal Transit Network. Tema. Journal of Land Use, Mobility and Environment, Special Issue (2016) 59-76.
[16] S. Derrible, C. Kennedy. Characterizing metro networks: state, form, and structure. Transportation, 37(2), (2010) 275-297.
[17] S. Derrible, C. Kennedy. Evaluating, Comparing, and Improving Metro Networks: Application to Plans for Toronto, Canada. Transportation Research Record, 2146(1), (2010) 43-51.
[18] S. Derrible, C. Kennedy. The complexity and robustness of metro networks. Physica A: Statistical Mechanics and its Applications, 389(17), (2010) 3678-3691.
[19] M. R. Haghi, M. Pouralikhani, S. Sedaghatnia. Assessment of Citizens’ Satisfaction about Subway Station Design and Location, Case Study: Subway Station of Elmo Sanat in Tehran. Transportation Engineering Journal, Online Published, (2018) (In Persian).
[20] F. A. Nosratian, Evaluation of Stability Degree of Tehran Metro Stations Locations, MSc Thesis, Khaje Nasirodin Toosi University, (2014).
[21] M. Mousivand, H, Haghshenas. Structure and Evaluation Indices of World Metro Network Case Study of Isfahan Metro Network, 3th International Conference on Recent Advances in Railway Engineering, Tehran, Iran, (2013) .7-1
Amirkabir Journal of Civil Engineering
Volume 52, Issue 12
March 2021
Pages 3003-3014

Files

Share

How to cite

Statistics