Modeling of BRT System Travel Time Prediction Using AVL Data and ANN Approach

Improving the quality of public transportation systems and encouraging passengers to use them are effective solutions for reducing transportation problems in metropolitan. Prediction of travel time and providing information to passengers are significant factors in this process. In this research not only the travel time components in Bus Rapid Transit (BRT) system were investigated but also an Artificial Neural Network (ANN) model and a regression model for travel time prediction were presented. To enhance this aim, data was collected by AVL data and field observation and after investigating the primary independent variables, the significant ones were determined using statistical analysis, then ANN development was done. Moreover, linear regression method was used for this purpose. The results prove that although both models have high level of prediction accuracy, ANN model outperform the regression model and the accuracy for the route sections with no signalized intersections is higher than the others

Using Immune Genetic Algorithm in ATPG

Abstract: In this paper, an immune genetic based algorithm (IGA) for random test pattern generation was proposed. Genetic algorithms (GA) solve many search and optimization problems, effectively. However, they may drop into local optimal solutions; or they may find the optimal solution by low convergence speed. To overcome these problems, we used the immune concept and GA algorithm for random-based test generation. In the proposed algorithm, some of the main characteristics of the immune system were used to enhance the GA algorithm. As a result, a new random-based test pattern generation technique based on immune genetic algorithm (IGA) was presented. Experimental results showed that the proposed algorithm improved the ability of global search, avoided dropping into the local optimal solutions and increased the speed of computation convergence with respect to previously proposed non-immune GA algorithms. The proposed algorithm improved the test size with a factor of about 25 % in comparison with non-immune algorithms

Prediction of roadheaders' performance using artificial neural network approaches (MLP and KOSFM)

Application of mechanical excavators is one of the most commonly used excavation methods because it can bring the project more productivity, accuracy and safety. Among the mechanical excavators, roadheaders are mechanical miners which have been extensively used in tunneling, mining and civil industries. Performance prediction is an important issue for successful roadheader application and generally deals with machine selection, production rate and bit consumption. The main aim of this research is to investigate the cutting performance (instantaneous cutting rates (ICRs)) of medium-duty roadheaders by using artificial neural network (ANN) approach. There are different categories for ANNs, but based on training algorithm there are two main kinds: supervised and unsupervised. The multi-layer perceptron (MLP) and Kohonen self-organizing feature map (KSOFM) are the most widely used neural networks for supervised and unsupervised ones, respectively. For gaining this goal, a database was primarily provided from roadheaders' performance and geomechanical characteristics of rock formations in tunnels and drift galleries in Tabas coal mine, the largest and the only fully-mechanized coal mine in Iran. Then the database was analyzed in order to yield the most important factor for ICR by using relatively important factor in which Garson equation was utilized. The MLP network was trained by 3 input parameters including rock mass properties, rock quality designation (RQD), intact rock properties such as uniaxial compressive strength (UCS) and Brazilian tensile strength (BTS), and one output parameter (ICR). In order to have more validation on MLP outputs, KSOFM visualization was applied. The mean square error (MSE) and regression coefficient (R) of MLP were found to be 5.49 and 0.97, respectively. Moreover, KSOFM network has a map size of 8 × 5 and final quantization and topographic errors were 0.383 and 0.032, respectively. The results show that MLP neural networks have a strong capability to predict and evaluate the performance of medium-duty roadheaders in coal measure rocks. Furthermore, it is concluded that KSOFM neural network is an efficient way for understanding system behavior and knowledge extraction. Finally, it is indicated that UCS has more influence on ICR by applying the best trained MLP network weights in Garson equation which is also confirmed by KSOFM

Modeling of BRT System Travel Time Prediction Using AVL Data and ANN Approach

Improving the quality of public transportation systems and encouraging passengers to use them are effective solutions for reducing transportation problems in metropolitan. Prediction of travel time and providing information to passengers are significant factors in this process. In this research not only the travel time components in Bus Rapid Transit (BRT) system were investigated but also an Artificial Neural Network (ANN) model and a regression model for travel time prediction were presented. To enhance this aim, data was collected by AVL data and field observation and after investigating the primary independent variables, the significant ones were determined using statistical analysis, then ANN development was done. Moreover, linear regression method was used for this purpose. The results prove that although both models have high level of prediction accuracy, ANN model outperform the regression model and the accuracy for the route sections with no signalized intersections is higher than the others