Decision support for inner city public transportation system

Savaşaneril S., Bayındır Z. P., Çetin U., İnce N.

International Conference on Operations Research (OR 2011), Zürich, Switzerland, 30 August - 02 September 2011, pp.128

  • Publication Type: Conference Paper / Summary Text
  • City: Zürich
  • Country: Switzerland
  • Page Numbers: pp.128
  • Middle East Technical University Affiliated: Yes


Decision support for inner city public transportation system Secil Savasaneril, Department of Industrial Engineering, Middle East Technical University, Z. Pelin Bayindir, Ugur Cetin, Nurbanu Ince In Turkey, a major portion of the inner city public transportation is carried out by municipalities. The decisions taken by the municipalities regarding public transporation include strategical (intermodal transportation network design, determining the fleet size), tactical (allocation of vehicles to counties,constructing the routes) and operational decisions (pick-ups and deliveries, maintenance). Since the municipalities have to provide transportation service to every resident, and at a low price, they are often left struggling with negative balance and operational inefficiencies. Due to low price policy, the revenues could only be increased through increased demand fulfilment. This implies operating under low costs and improving the service level is essential for survivability. We conduct a study to analyze the inefficiencies in the public transportation in Ankara. Due to large-scale of the problem, we focus on a single county in Ankara. We first identify the problems that lead to inefficiencies. Analysis of hourly transportation demand and supply reveal that there exist inefficiencies due to fleet allocation, routing and dispatch frequency of the vehicles. This leads to unsatisfied customers, especially in the peak hours. We provide decision support to the decision makers at the tactical level. Considering the daily and hourly transportation needs of the users, we determine routes and dispatch frequencies through a mathematical model. Alternative solutions are developed, and compared under performance measures such as; unmet demand, transport duration, or cost. Finally, the effect of factors such as fleet size, increase in fares and/or fuel prices, developing residential areas, on the robustness of the decisions are analyzed.