Ozgur Baskan

A new solution algorithm for improving performance of ant colony optimization

This study proposes an improved solution algorithm using ant colony optimization (ACO) for finding global optimum for any given test functions. The procedure of the ACO algorithms simulates the decision-making processes of ant colonies as they forage for food and is similar to other artificial intelligent techniques such as Tabu search, Simulated Annealing and Genetic Algorithms. ACO algorithms can be used as a tool for optimizing continuous and discrete mathematical functions. The proposed algorithm is based on each ant searches only around the best solution of the previous iteration with @b. The proposed algorithm is called as ACORSES, an abbreviation of ACO Reduced SEarch Space. @b is proposed for improving ACOs solution performance to reach global optimum fairly quickly. The ACORSES is tested on fourteen mathematical test functions taken from literature and encouraging results were obtained. The performance of ACORSES is compared with other optimization methods. The results showed that the ACORSES performs better than other optimization algorithms, available in literature in terms of minimum values of objective functions and number of iterations.

Determining Optimal Link Capacity Expansions in Road Networks Using Cuckoo Search Algorithm with Lévy Flights

During the last two decades, Continuous Network Design Problem (CNDP) has received much more attention because of increasing trend of traffic congestion in road networks. In the CNDP, the problem is to find optimal link capacity expansions by minimizing the sum of total travel time and investment cost of capacity expansions in a road network. Considering both increasing traffic congestion and limited budgets of local authorities, the CNDP deserves to receive more attention in order to use available budget economically and to mitigate traffic congestion. The CNDP can generally be formulated as bilevel programming model in which the upper level deals with finding optimal link capacity expansions, whereas at the lower level, User Equilibrium (UE) link flows are determined by Wardrop’s first principle. In this paper, cuckoo search (CS) algorithm with Levy flights is introduced for finding optimal link capacity expansions because of its recent successful applications in solving such complex problems. CS is applied to the 16-link and Sioux Falls networks and compared with available methods in the literature. Results show the potential of CS for finding optimal or near optimal link capacity expansions in a given road network.

Artificial Bee Colony-Based Algorithm for Optimising Traffic Signal Timings

This study proposed Artificial Bee Colony (ABC) algorithm for finding optimal setting of traffic signals in coordinated signalized networks for given fixed set of link flows. For optimizing traffic signal timings in coordinated signalized networks, ABC with TRANSYT-7F (ABCTRANS) model is developed. The ABC algorithm is a new population-based metaheuristic approach, and it is inspired by the foraging behavior of honeybee swarm. TRANSYT-7F traffic model is used to estimate total network performance index (PI). The ABCTRANS is tested on medium sized signalized road network. Results showed that the proposed model is slightly better in signal timing optimization in terms of final values of PI when it is compared with TRANSYT-7F in which Genetic Algorithm (GA) and Hill-climbing (HC) methods are exist. Results also showed that the ABCTRANS model improves the medium sized network’s PI by 2.4 and 2.7 % when it is compared with GA and HC methods.

An Ant Colony Optimization Algorithm for Area Traffic Control

The optimization of traffic signal control is at the heart of urban traffic control. Traffic signal control which encloses delay, queuing, pollution, fuel consumption is a multi-objective opti‐ mization. For a signal-controlled road network, using the optimization techniques in deter‐ mining signal timings has been discussed greatly for decades. Due to complexity of the Area Traffic Control (ATC) problem, new methods and approaches are needed to improve effi‐ ciency of signal control in a signalized road network. In urban networks, traffic signals are used to control vehicle movements so as to reduce congestion, improve safety, and enable specific strategies such as minimizing delays, improving environmental pollution, etc. [1]. Signal systems that control road junctions are operated according to the type of junction. Al‐ though the optimization of signal timings for an isolated junction is relatively easy, the opti‐ mization of signal timings in coordinated road networks requires further research due to the “offset” term. Early methods such as that of [2] only considered an isolated signalized junc‐ tion. Later, fixed time strategies were developed that optimizing a group of signalized junc‐ tions using historical flow data [3]. For the ATC, TRANSYT-7F is one of the most useful network study software tools for optimizing signal timing and also the most widely used program of its type. It consists of two main parts: A traffic flow model and a signal timing optimizer. Traffic model utilizes a platoon dispersion algorithm that simulates the normal dispersion of platoons as they travel downstream. It simulates traffic in a network of signal‐ ized intersections to produce a cyclic flow profile of arrivals at each intersection that is used to compute a Performance Index (PI) for a given signal timing and staging plan. The PI in TRANSYT-7F may be defined in a number of ways. One of the TRANSYT-7F’s PI is the Dis‐ utility Index (DI). The DI is a measure of disadvantageous operation; that is stops, delay, fuel consumption, etc. Optimization in TRANSYT-7F consists of a series of trial simulation

Ant Colony Optimization Approach for Optimizing Traffic Signal Timings

In urban networks, traffic signals are used to control vehicle movements so as to reduce congestion, improve safety, and enable specific strategies such as minimizing delays, improving environmental pollution, etc (Teklu et al., 2007). Due to the increasing in the number of cars and developing industry, finding optimal traffic signal parameters has been an important task in order to use the network capacity optimally. Through the last decade, developments in communications and information technologies have improved the classical methods for optimising the traffic signal timings toward the intelligent ones. There is an important interaction between the signal timings and the routes chosen by individual road users in road networks controlled by fixed time signals. The mutual interaction leads to the framework of a leader-follower or Stackelberg game, where the supplier is the leader and the user is the follower (Fisk, 1984). Network design problem (NDP) that it may contain the signal setting problem is characterized by the so called bilevel structure. Bi-level programming problems generally are difficult to solve, because the evaluation of the upper-level objective involves solving the lower level problem for every feasible set of upper level decisions (Sun et al., 2006). On the upper level, a transport planner designs the network. Road users respond to that design in the lower level. This problem is known to be one of the most attractive mathematical problems in the optimization field because of non-convexity of feasible region that it has multiple local optima (Baskan, 2009). Moreover, the driver’s behaviours on the network should be taken into account when the traffic signal timings are optimised. When drivers follow the Wardrop’s (1952) first principle, the problem is called the “user equilibrium” (UE). On the other hand, it turns to the stochastic user equilibrium (SUE) in the case that the users’ face with the decision of route choice between the each Origin-Destination (O-D) pair for a given road network according to perceived travel time. The difference between SUE and UE approaches is that in SUE models each driver is meant to define ‘travel costs’ individually instead of using a single definition of costs applicable to all drivers. SUE traffic assignment takes into account the variability in driver’s perception of cost. This is done by treating the perceived cost on

Determining On-Street Parking Places in Urban Road Networks Using Meta-Heuristic Harmony Search Algorithm

This study aims to develop a simulation/optimization model for the solution to the problem of determining on-street parking places in urban road networks. The problem is dealt within the Discrete Network Design (DND) context due to the binary decision variables and the bi-level programming technique is used for the solution of the problem. The upper level represents the determination of on-street parking places while the reaction of drivers’ to the design is handled in user equilibrium manner in the lower level. The upper level problem is formulized as a non-linear mixed integer programming problem and the meta-heuristic Harmony Search (HS) optimization technique is employed for the solution. In the proposed model, VISUM traffic analysis software is utilized as the simulation tool for solving the lower level problem. The performance of the proposed model is tested on Sioux-Falls road network which has widely been used on DND studies in the previous works. Results show that determining optimal or near-optimal on-street parking places may be achieved by using the proposed model.

Estimating Transport Energy Demand Using Ant Colony Optimization

Abstract This study proposes a heuristic algorithm based on ant colony optimization for estimating the transport energy demand (TED) of Turkey using gross domestic product, population, and vehicle-km. Three forms of the improved ant colony optimization transport energy demand estimation (IACOTEDE) models are used for improving estimating capabilities of TED models. Performance of IACOTEDE is compared with the Ministry of Energy and Natural Resources (MENR) projections. Sensitivity analysis is also carried out for testing the effects of the parameters. The quadratic form provided a better-fit solution to the observed data, and it underestimates Turkeys TED by about 28% less than the MENR projection in year 2025. Thus, it may be used with a highest correlation coefficient and considerably lower relative error according as the MENR projection in the testing period. It is also expected that this study will be helpful in developing highly applicable and productive planning for transport energy policies.

Modeling and Forecasting Car Ownership Based on Socio-Economic and Demographic Indicators in Turkey

Since car ownership is an important determinant to analyze car travel behavior especially in developing countries, this paper deals with modeling and forecasting car ownership in Turkey based on socio-economic and demographic indicators such as Gross Domestic Product (GDP) per capita, Gasoline Price (GP), car price and number of employees by using multiple nonlinear regression analysis. Although most of the studies on this subject prefer using annual data, we use monthly data for the analysis of car ownership since all explanatory variables and exchange rates used for the modeling are unstable and vary even in a short period in developing countries such as Turkey. Thus, it may be possible to reflect the effects of socio-economic and demographic indicators on car ownership more properly. During the modeling process, exponential and polynomial nonlinear regression models are set up and then tested to investigate their applicability for car ownership forecasting. Based on results of the Kolmogorov-Smirnov test, the polynomial models has been selected to forecast car ownership for the year 2035. In order to reveal the possible different trends of the independent variables in future, car ownership is forecasted along the scenarios which are related to the GDP per capita and GP. Results show that Turkey’s car ownership may vary between 230 and 325 per thousand capita in 2035 depending on economic achievements, global oil prices and national taxation policies. The lowest and the highest values of the car ownership may provide insight to car producers and transport planners in Turkey. Another significant result presented in this study is that car ownership rate will be substantially lower in Turkey than that in the European Union countries despite it has an increasing trend in the past two decades.

Modelling Domestic Air Transport Demand and Evaluating under Scenarios

Ulkemizde ulasim alt turler arasindaki dengesizlik ve entegrasyon en onemli problem olarak karsimiza cikmaktadir. Calismada son yillarda ichat hava tasimaciligi alt turunde gozlenen talebin aylik degisimini yansitabilen indeksleme yontemi kullanilarak modellemesi yapilmis, gelecege yonelik tahminler yapilarak alt turlerdeki dengesizligin giderilmesi yonunde onerilerde bulunulmustur. Modelleme calismasinda bagimsiz degiskenler olarak Satin alma Gucu Paritesi ve jet yakit fiyatlari kullanilmistir. Kullanilan yontem ile aylik ve mevsimsel degismelere duyarli bir model gelistirilmistir. Yapilan hesaplamalar sonucunda gelir seviyesinde iyimser gelismeler gozlenmesi ve jet yakit fiyatlarinda dusuk seyrin gozlenmesi durumunda karayolu ulasim sisteminin ardindan hava tasimaciliginin demiryolu sistemi ile ciddi bir rekabet icinde olacagi belirlenmistir. Bu nedenle ucret politikalarinda gerekirse jet yakit fiyatlarinda vergi duzenlemesi ile talep gelisiminin desteklenmesinin gerekli oldugu dusunulmektedir. The lack of balance and integration between transportation modes in Turkey is one of the main problems. In this study, domestic air transport demand is modeled and evaluated under scenarios. For this purpose, indexing method which is able to indicate observed monthly and seasonal variations in demand is used. Proposals are suggested in order to overcome the lack of balance between transportation modes. In modeling, purchasing power parity and jet fuel prices as independent variables are used. Results showed that the developed model using indexing method is substantially sensitive to observed monthly and seasonal variations in domestic air transport demand. Furthermore, in the event that there are optimistic an increase in the income level and a crawl in the jet fuel prices, domestic air transport can rival with railways for second place in the transportation modes behind highways. For this reason, it is considered regulation on wages policy and tax of jet fuel prices necessary to support development of domestic air transport demand. Anahtar kelimeler: Havayolu, Ulasim talebi, Indeksleme yontemi.