Browsing by Author "Aydin, MM"

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    TRIP OPTIMIZATION FOR PUBLIC TRANSPORTATION SYSTEMS WITH LINEAR GOAL PROGRAMMING (LGP) METHOD
    Tekin, S; Köfteci, S; Aydin, MM; Yildirim, MS
    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.
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    Multimethod simulation approach for capacity design of a truck parking area in city ports
    Yildirim, MS; Aydin, MM; Gökkus, Ü
    The port induced freight can cause traffic congestion problems in city ports if road freight transportation is used. The secondary congestion problem arises from the pooling of the trucks at the port gates because of the delays of the port operations. The absence of the truck parking areas inside the port causes additional truck trips between the port and auxiliary truck servicing areas around the port. For reducing the impacts of the associated problems of the port induced truck traffic, truck parking areas can be used as buffer zones between the port and city. The purpose of this study is to develop a Decision Support System (DSS) with using multimethod simulation and cost optimization model for the capacity design of a truck parking area for a city port. The preliminary design of the parking area is used to estimate the development cost and outputs of the simulation model is coupled for the capacity optimization for truck arrival scenarios. The methodology is implemented for a case study of the Izmir city port in Turkey. The results of the study indicated that significantly different parking area capacities are required for different truck dwell times for the time restricted and unrestricted truck arrivals.
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    Station Capacity Analysis of a Metro Line with Discrete Event Simulation
    Yildirim, MS; Aydin, MM
    This paper demonstrates the utilization of discrete event simulation for the capacity assessment of an existing metro line using performance metrics of train utilization and passenger waiting queues at the stations. The metro line was modelled with using Arena Simulation model blocks of queues and train routing delays and the simulation model was executed with using the hourly passenger arrival schedules, an origin-destination matrix scenario and variable train time headways. The results indicated the significant deviations of the waiting passenger numbers prior to train boarding with failed train boarding resulted from system congestion. The study indicated that the train time headways can influence the system equilibrium and significant congestions are especially prominent for the intermediate stations with high passenger traffic. The characteristics of the O-D matrix was also a significant contributor to the individual station congestion since the train capacity is highly occupied with the passengers of the popular stations.
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    Simulation optimization of the berth allocation in a container terminal with flexible vessel priority management
    Yildirim, MS; Aydin, MM; Gökkus, Ü
    Solving the berth allocation problem (BAP) in ports is not trivial where the berth resources are limited and various sizes of vessels arrive with dramatically dissimilar loads. Especially in real scenarios, arriving vessels are accepted for a berth with the first come first served (FCFS) priority rule. This study proposes a decision support system coupled with a simulation optimization module based on the swarm-based Artificial Bee Colony optimization algorithm for solving the BAP. The proposed methodology was implemented for the Izmir port in Turkey. To investigate the influences of the vessel priorities on the BAP, four different experimental scenarios based on the single (SQM) and multiple queue models (MQM) were coupled with FCFS and proposed hybrid queue priority (HQP) rule. The results indicated that SQM scenarios were superior to MQM scenarios in a manner of minimizing the average vessel waiting times and the implementation of a dynamic berth allocation strategy for the MQM significantly decreases the vessel waiting times. Results of the SQM also imply that utilization of the HQP approach further minimizes the average vessel waiting times and increases the berth utilization and port throughput without yielding excessive waiting times for the larger vessels compared with the FCFS priority rule.
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    Estimation of Container Traffic at Seaports by Using Several Soft Computing Methods: A Case of Turkish Seaports
    Gökkus, Ü; Yildirim, MS; Aydin, MM
    Container traffic forecasting is important for the operations and the design steps of a seaport facility. In this study, performances of the novel soft computing models were compared for the container traffic forecasting of principal Turkish seaports (Istanbul, Izmir, and Mersin seaports) with excessive container traffic. Four forecasting models were implemented based on Artificial Neural Network with Artificial Bee Colony and Levenberg-Marquardt Algorithms (ANN-ABC and ANN-LM), Multiple Nonlinear Regression with Genetic Algorithm (MNR-GA), and Least Square Support Vector Machine (LSSVM). Forecasts were carried out by using the past records of the gross domestic product, exports, and population of the Turkey as indicators of socioeconomic and demographic status. Performances of the forecasting models were evaluated with several performance metrics. Considering the testing period, the LSSVM, ANN-ABC, and ANN-LM models performed better than the MNR-GA model considering overall fitting and prediction performances of the extreme values in the testing data. The LSSVM model was found to be more reliable compared to the ANN models. Forecasting part of the study suggested that container traffic of the seaports will be increased up to 60%, 67%, and 95% at the 2023 for the Izmir, Mersin, and Istanbul seaports considering official growth scenarios of Turkey.