Accessing Analysis of Passengers Covered by Payment Kiosks: A Case for the Gaziantep Public Transportation System

Yıl 2019, , 73 - 80, 01.07.2019

Eren Özceylan , Barış Özkan

https://doi.org/10.34248/bsengineering.556011

Öz

Recently, the use of public transport has become widespread with the increasing population. This brings with it many problems. Since the points of payment kiosks are not regularly distributed where the population is concentrated, the most important problem is accessing to these kiosks. The accessing analysis of passengers covered by payment kiosks in Gaziantep, Turkey is considered in this study. 177 different locations which have the densest population are considered as the demand points and 379 payment kiosks are considered as the source points. All spatial data are obtained using a geographic information system (GIS). The goal of this study is to maximize the rate of passengers (demand points) who try to access to the payment kiosks. To do so, two different location-allocation analysis which are set covering and p-median models were applied using mathematical modeling approach. As a result, the required number of kiosks and coverage distances were determined to satisfy all demand.

Anahtar Kelimeler

Case study, Geographic information system, P-median problem, Public transportation, Set covering

Kaynakça

• Amoroso S, Caruso L, Cassata G, Maritano L. 2015. Optimal location for bike parking lots: The case study of Palermo. Ingegneria Ferroviaria, 70(7-8): 599–610.
• Balabanov A, Stoilov T, Boneva Y. 2016. Linear-quadratic-gaussian optimization of urban transportation network with application to Sofia traffic optimization. Cybernetics Inform Technol, 16(3): 165–184.
• Beasley J. 1987. An algorithm for set covering problem. Eur J Operat Res, 31(1): 85–93.
• Brette O, Buhler T, Lazaric N, Marechal K. 2014. Reconsidering the nature and effects of habits in urban transportation behavior. J Instl Econ, 10(3): 399–426.
• Cheng YH, Chang YH, Lu IJ. 2015. Urban transportation energy and carbon dioxide emission reduction strategies. Applied Energy, 157: 953–973.
• Choi H, Nakagawa D, Matsunaka R, Oba T, Yoon J. 2013. Research on the causal relationship between urban density, travel behaviors, and transportation energy consumption by economic level. Int J Urban Sci, 17(3): 362–384.
• Farahani RZ, Fallah S, Ruiz R, Hosseini S, Asgari N. 2018. OR models in urban service facility location: A critical review of applications and future developments. Eur J Operat Res. doi: 10.1016/j.ejor.2018.07.036.
• Feng S, Hu B, Ma D, Wang D. 2016. Setting method of passenger site on inter-city expressway. J Harbin Instit Technol, 48(3): 71–76.
• He D, Meng F, Wang MQ, He K. 2011. Impacts of urban transportation mode split on CO2 emissions in Jinan, China. Energies, 4(4): 685–699.
• Koryagin M. 2018. Urban planning: A game theory application for the travel demand management. Periodica Polytechnica Transport Engin, 46(4): 171–178.
• Mete S, Cil ZA, Özceylan E. 2018. Location and coverage analysis of bike-sharing stations in university campus. Business Systems Res, 9(2): 80–95.
• Mladenović N, Labbé M, Hansen P. 2003. Solving the p-center problem with tabu search and variable neighborhood search networks. Networks: An Inter J, 42(1): 48–64.
• Murray AT. 2001. Strategic analysis of public transport coverage. Socio-Economic Planning Sciences, 35(3): 175–188.
• Pu H, Ma CR. 2014. Optimization analysis on urban transportation network of Lanzhou. Applied Mech Mater, 641-642: 685–689.
• Teixeira JC, Antunes AP. 2008. A hierarchical location model for public facility planning. Eur J Operat Res, 185(1): 92–104.
• Van Oudheusden DL, Ranjithan S, Singh KN. 1987. The design of bus route systems - An interactive location-allocation approach. Transport, 14(3): 253–270.