A Heuristic Method for Public Transportation Network Design using Route Generation Algorithm

Document Type : Scientific - Research

Authors

1 Assistant Professor, Department of Civil and Environmental Engineering, Tarbiat Modares University, Iran

2 MSc. Grad., Department of Civil and Environmental Engineering, Tarbiat Modares University, Iran

3 PhD. Student, Department of Civil and Environmental Engineering, Tarbiat Modares University, Iran

Abstract

Public transportation network design is one of the most complex issues in transportation engineering where researchers usually apply simple heuristic methods to solve the complicated problem. Route generation algorithm is one of the heuristic methods that use the shortest path between high transit demand O-D pairs and expand the shortest path, called route generation algorithm, to cover more transit demand. In this paper, the expanding route generation algorithm has been revised in order to consider restrictions on minimum route travel time and length to the primary route generation algorithm. The proposed algorithm for route generation has been coded in a computer programs and used for transit network design in Sioux Falls test network. The results show that the algorithm reduces the number of transit routes in the test network by 70 percent and route overlaps length by one-third compare with the previous route generation algorithms used for transit network design on Sioux Falls. 

Keywords


- افندی زاده، شهریار،  جوانشیر، حسن،  الیاسی، رضا ( 1389) ”طراحی خطوط شبکه اتوبوسرانی شهری با استفاده از روش جستجوی ممنوع “  ، مهندسی حمل و نقل ، سال اول ، شماره چهارم. ص 13-26.
- ذکایی آشتیانی، هدایت و حجازی، بهرنگ (1380)”  کاربرد روش گرم و سرد کردن شبیه­سازی شده در حل مسائل مکانیابی پایانه­های شبکه اتوبوس­رانی “،  استقلال، سال 20، شماره2، ص 125-140.
-Asadi Bagloee, S.  and Ceder, A. (2011 “Transit-network design methodology for actual-size road networks” Transportation Research, Part B: Methodological, Vol.45, No. 10,   pp.1787–1804.
-Baaj,  M. H. and Mahmassani, H. S. (1991) “An AI-based approach for transit route system planning and design” Journal of Advanced Transportation: Vol.25, No.2,  pp.187–210.
-Baxter, M., Elgindy, T., Ernst, A., Kalinowski,T. and Savelsbergh, M. (2014) “Discrete optimization incremental network design with shortest paths”,  European Journal of Operational Research: Vol. 238, No. 3,   pp.675–684.
-Cipriani, E.,   Gori, S. and Petrelli, M. (2012)Transit network design: A procedure and an application to a large urban area” Transportation Research, Part C: Emerging Technologies, Vol. 20, No. 1,  pp. 3–14.
-Fan, W. and Machemehl, R. (2006a) “Optimal transit route network design problem with variable transit demand: A genetic algorithm approach”, Journal of Transportation Engineering, Vol. 132, No. 1, pp.40–51.
-Fan, W. and  Machemehl, R. (2006b) “Using a simulated annealing algorithm to solve the transit route network design problem”, Journal of Transportation Engineering: Vol.132, No. 2,  pp.122–132.
-Lee, Y.-J. and Vuchic, V. R. (2005) “Transit network design with variable demand”, Journal of Transportation Engineering, Vol. 131, No. 1,  pp.1–10.
-Mauttone, A. and Urquhart, M. (2009) “A route set construction algorithm for the transit network design problem”, Computers & Operations Research, Vol. 36, No. 8, pp.2440-2449.
-Ngamchai, S. and Lovell, D. J. (2003) “Optimal time transfer in bus transit route network design using an genetic
 algorithm” Journal of Transportation Engineering, Vol. 129, No. 5, pp.510–521.
-Tom, V. M. and Mohan, S. (2003) “Transit route network design using frequency coded genetic algorithm”, Journal of Transportation Engineering, Vol. 129, Nom. 2, pp. 186–195.
-Ukkusuri, V. and Patil, G. (2009)“Multi-period transportation network design under demand uncertainty”, Transportation Research Part B: Methodological, Vol. 43, No. 6, pp. 625–642.
-Zanjirani Farahani, R., Miandoabchi, E., Szeto,W.Y. and Rashidi, H. (2013)  “A review of  urban transportation network design problems”,   European Journal of Operational  Research, Vol. 229, No. 2,  pp.281–302.