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Genetic algorithm approach to estimate transport energy demand in Turkey

Transport energy modeling is a subject of current interest among transport engineers and scientists concerned with problems of sustainable transport. Transport energy planning is not possible without a reasonable knowledge of past and present energy consumption and likely future demands. In this study, three forms of the energy demand equations are developed in order to improve transport energy demand estimation efficiency for future projections based on genetic algorithm (GA) notion. The Genetic Algorithm Transport Energy Demand Estimation (GATEDE) model is developed using population, gross domestic product and vehicle-km. All equations proposed here are linear and non-linear, of which one is linear, second is exponential and third is quadratic. The quadratic form of the GATEDE model provided better-fit solution to the observed data and can be used with a high correlation coefficient for Turkeys future transport energy projections. It is expected that this study will be helpful in developing highly applicable and productive planning for transport energy policies. The GATEDE gives transport energy demand in comparison with the other transport energy demand projections. The GATEDE model plans the sectoral energy demand of Turkey until 2020.

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.

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

Transport Demand Management in Turkey: A Genetic Algorithm Approach

Abstract This article proposes new models for estimating transport demand using a genetic algorithm (GA) approach. Based on population, gross national product and number of vehicles, four forms of the genetic algorithm transport planning (GATP) model are developed – one exponential and the others taking quadratic forms – and applied to Turkey. The best fit models in terms of minimum total average relative errors in the test period are selected for future estimation. Demand management strategies are proposed based on three scenarios: restricting private car use, restricting truck use and the simultaneous management of private car use and goods movement. Results show that the GATP model may be used to estimate transport demand in terms of passenger-kilometers traveled (pass-km), vehicle-kilometers traveled (veh-km) and ton-kilometers completed (ton-km). Results also show that the third scenario – simultaneous restrictions on private car use and goods movement – could reduce total veh-km by about 35% by 2025 in this study of Turkish rural roads.

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.

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.