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

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

Carbohydrate-based nanostructured catalysts: applications in organic transformations

The requirement of green and sustainable materials to prepare heterogeneous catalysts has intensified for practical reasons over the past few decades. Carbohydrates are possibly the most plentiful and renewable organic materials in nature with inimitable physiochemical properties, plausible low-cost and large-scale production, and sustainability features could be exploited in the generation of nanostructured heterogeneous catalysts. This review article outlines the organic transformations catalyzed by diverse carbohydrate-based nanostructured catalysts in greener and environmentally friendly processes. Selected examples are highlighted for a variety of organic reactions exploiting the proposed catalysts' reactivity and reusability, and interactions with the intrinsic nature of the applied carbohydrate supports; advantages and speculated challenges of the introduced catalysts are deliberated as well.(c) 2022 Published by Elsevier Ltd.N