Sürdürülebilir Kalkınma Amaçları
Yayınlar
- MakaleYalnız künye2018TRIP OPTIMIZATION FOR PUBLIC TRANSPORTATION SYSTEMS WITH LINEAR GOAL PROGRAMMING (LGP) METHOD
Tekin, Sezgin; Köfteci, Sevil; Aydin, Metin Mutlu; Yildirim, Mehmet Sinan
Determination of the optimum trip schedules is an important problem for public transportation systems. It is complex task to assign optimum number of vehicles and determine the trip schedules for a public transport systems which consist of many routes. In the case of taking infrequent trip schedules, the existing passenger demand is not satisfied. Therefore waiting times are increased in the bus stops. In contrary, with more frequent intervals, unutilized capacity and higher operational costs are expected. Also, intense traffic density and environmental pollution are associated with the frequent trips. The optimum trip frequencies of the passenger demands varies during the hours of a day and is important for passenger satisfaction and operation efficiency of the system. Trip scheduling and vehicle assignment studies take attention in the current literature assisted with different optimization techniques and artificial intelligence method. In this study, only 10 different bus routes which is operated privately, were considered in the city center of Antalya and the Linear Goal Programming (LGP) was used to determine the optimum number of vehicles operated on the routes. The study results showed that the existing system performance can be preserved by reducing the frequency of specific trips and LGP is stated as an efficient algorithm for determining the optimum trip frequencies and number of vehicles in a public transportation systems.
- MakaleAçık Erişim2015Prediction of pedal cyclists and pedestrian fatalities from total monthly accidents and registered private car numbers
Ghasemlou, Kiarash; Aydin, Metin Mutlu; Yildirim, Mehmet Sinan
Accident prevention is relatively a complex issue considering the effectiveness of the injury prevention technologies as well as more detailed assessment of the complex interactions between the road condition, vehicle and human factor. For many years, highway agencies and vehicle manufacturers showed great efforts to reduce the injuries resulting from the vehicle crashes. Many researchers used a broad range of methods to evaluate the impact of several factors on traffic accidents and injuries. Recent developments lead up to capable for determining the effects of these factors. According to World Health Organization (WHO), cyclists and pedestrians comprise respectively 1.6% and 16.3% in traffic crash fatalities in 2013. Also in Turkey crash fatalities for pedestrian and cyclists are respectively 20.6% and 3% according to Turkish Statistical Institute data in 2013. The relationship between cycling and pedestrian rates and injury rates over time is also unknown. This paper aims to predict the crash severity with the traffic injury data of the Konya City in Turkey by implementing the Artificial Neural Networks (ANN), Regression Trees (RT) and Multiple Linear Regression modelling (MLRM) method.