Halim Ceylan

TRAFFIC SIGNAL TIMING OPTIMISATION BASED ON GENETIC ALGORITHM APPROACH, INCLUDING DRIVERS' ROUTING

The genetic algorithm approach to solve traffic signal control and traffic assignment problem is used to tackle the optimisation of signal timings with stochastic user equilibrium link flows. Signal timing is defined by the common network cycle time, the green time for each signal stage, and the offsets between the junctions. The system performance index is defined as the sum of a weighted linear combination of delay and number of stops per unit time for all traffic streams, which is evaluated by the traffic model of TRANSYT [User guide to TRANSYT, version 8, TRRL Report LR888, Transport and Road Research Laboratory, Crowthorne, 1980]. Stochastic user equilibrium assignment is formulated as an equivalent minimisation problem and solved by way of the Path Flow Estimator (PFE). The objective function adopted is the network performance index (PI) and its use for the Genetic Algorithm (GA) is the inversion of the network PI, called the fitness function. By integrating the genetic algorithms, traffic assignment and traffic control, the GATRANSPFE (Genetic Algorithm, TRANSYT and the PFE), solves the equilibrium network design problem. The performance of the GATRANSPFE is illustrated and compared with mutually consistent (MC) solution using numerical example. The computation results show that the GA approach is efficient and much simpler than previous heuristic algorithm. Furthermore, results from the test road network have shown that the values of the performance index were significantly improved relative to the MC.

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.

Energy Demand Estimation Based on Two-Different Genetic Algorithm Approaches

Energy modeling is a subject of widespread current interest among engineers and scientists concerned with problems of energy production and consumption. Energy planning is not possible without a reasonable knowledge of past and present energy consumption and likely future demands. In this study, two forms of the energy demand equations are developed in order to improve energy demand estimation efficiency for future projections based on the genetic algorithm (GA) notion. The genetic algorithm energy demand (GAEDM) model is used to estimate Turkeys future energy demand based on gross domestic product, population, import, and export figures. Both equations proposed here are non-linear, of which one is exponential and the other is quadratic. The quadratic form of the GAEDM model provided a slightly better fit solution to the observed data and can be used with a high correlation coefficient for Turkeys future energy projections. It is expected that this study will be helpful in developing highly applicable and productive planning for energy policies.

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.

RESERVE CAPACITY FOR A ROAD NETWORK UNDER OPTIMIZED FIXED TIME TRAFFIC SIGNAL CONTROL

In this article, a two-stage approach to the calculation of reserve capacity for a network is presented. The first stage uses a genetic algorithm to find signal timings that optimize network performance taking traffic reassignment into account. The genetic optimizer, referred to as Genetic Algorithm Transyt Path Flow Estimator (GATRANSPFE), combines the Traffic Network Study Tool (TRANSYT) model, used to estimate performance, with the Path Flow Estimator (PFE) logit assignment tool, used to predict traffic reassignment. In the second stage, the largest common multiplier that can be applied to the Origin-Destination (OD) matrix given the optimized signal timings of stage one is found, again taking reassignment into account. This study deals with the spare capacity available to accommodate the inevitable day-to-day fluctuations in demand. The application of the two-stage procedure is illustrated for a small network with two-signal controlled junctions taken from the literature.

Estimating Energy and Exergy Production and Consumption Values Using Three Different Genetic Algorithm Approaches. Part 2: Application and Scenarios

Abstract The main objective of the present study is to investigate the application of the genetic algorithm (GA) method with various scenarios for the future estimation of the energy and exergy production and consumption values. The methodology developed and presented in detail in Part 1 of this study was applied to Turkeys energy and exergy utilization values. Good correlations were obtained in all cases, indicating the validity of the models proposed that can be used to estimate total energy and exergy production and consumption of Turkey for the period of 2000–2020. It may be concluded that the models reported here will provide the investigators with knowledge about how a country can model its natural resources in terms of energy and exergy utilizations.

Modeling hydraulic and thermal electricity production based on genetic algorithm-time series (GATS)

Abstract This study deals with the estimation of electricity production from hydraulic and thermal sources using the Genetic Algorithm (GA) with time series (TS) approach. Two forms of the mathematical models are developed, of which one is exponential and the second is polynomial. The power form of the Genetic Algorithm-Time Series (GATS) model is used for the thermal electricity production. The polynomial form of the GATS is used for the electricity production from the hydraulic sources. The GATS weighting parameters are obtained by minimizing the Sum of Squared Error (SSE) between observed and estimated electricity production from both sources. Therefore, the fitness function adapted is the minimization of the SSE for use in the GA process. The application of the GATS model is correspondingly presented. Some future scenarios are made to increase the electricity production from hydraulic sources. Variations of the electricity production from thermal and hydraulic energy sources are analyzed. Future prospects of electricity production are dealt with in terms of policy changes. The GATS models are used for making scenarios for future electricity planning policy. Results also show if current trend continues, the thermal electricity production amounts to 75% of the total electricity production, which is undesirable for environmental concerns. Results also shows that if new policy is to move from the thermal to hydraulic electricity production, the hydraulic sources will meet the demand until 2020. #Contributed by the Organizing Committee for the First International Exergy, Energy and Environment Symposium (IEEES-1). Paper presented at IEEES-1, Izmir, Turkey, 13–17 July 2003. Manuscript received by IJGE on 2004-12-27; final revision received on 2004-04-07. Corresponding guest editors: I. Dincer and A. Hepbasli.

Estimating Energy and Exergy Production and Consumption Values Using Three Different Genetic Algorithm Approaches. Part 1: Model Development

Abstract The present study, consisting of two parts, proposes new models for estimating energy and exergy production and consumption values using the genetic algorithm approach. Part 1 of this study deals with the model development, while the application and testing with various scenarios will be treated in Part 2. In this regard, the genetic algorithm energy (GAEN) and genetic algorithm exergy (GAEX) estimating models have been proposed. During the energy and exergy estimation, independent variables are the GDP, population, and the ratio of export to import. The three forms of the GAEN and GAEX are developed, of which one is linear, second is exponential and the third is a mix of the exponential and linear form of the equations. Among them, the best fit models in terms of average relative errors and for the testing period are selected for future estimation and proposed both for GAEN and GAEX. It may be concluded that the models proposed here can be used as an alternative solution and estimation techniques to available estimation techniques.

Harmony Search Algorithm for Transport Energy Demand Modeling

The transport sector is one of the major consumers of energy production throughout the world. Thus, the estimation of medium and long-term energy consumption based on socio-economic and transport related indicators is a critical issue on a global scale. This chapter reviews the harmony search (HS) applications to transport energy modeling problems. The models reviewed in this chapter are in the form of linear, exponential and quadratic mathematical expressions, and they are applied to transportation sector energy consumption. Convergence behavior of the HS during the modeling is tested. Performance of each HS model is compared according to an absolute error between observed data and predicted data. Results showed the HS method can be applied to the transport energy modeling issues.

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.