Toward equity in large-scale network-level pavement maintenance and rehabilitation scheduling using water cycle and genetic algorithms

Naseri, Hamed; Fani, Amirhossein; Golroo, Amir

doi:10.1080/10298436.2020.1790558

Cited by 29 publications

(7 citation statements)

References 38 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In this regard, non-dominated sorting genetic algorithm III (NSGA-III), a multi-objective metaheuristic algorithm, was used as the optimization tool. Genetic algorithms have been widely used to optimize several engineering problems [ 70 , 71 , 72 ]. NSGA-III was used for the optimization process since it is a multi-objective metaheuristic optimization technique, and metaheuristic techniques can sync with machine learning techniques [ 73 ].…”

Section: Methodsmentioning

confidence: 99%

Application of Machine Learning to Child Mode Choice with a Novel Technique to Optimize Hyperparameters

Naseri

Waygood

Wang

et al. 2022

IJERPH

Self Cite

View full text Add to dashboard Cite

Travel mode choice (TMC) prediction is crucial for transportation planning. Most previous studies have focused on TMC in adults, whereas predicting TMC in children has received less attention. On the other hand, previous children’s TMC prediction studies have generally focused on home-to-school TMC. Hence, LIGHT GRADIENT BOOSTING MACHINE (LGBM), as a robust machine learning method, is applied to predict children’s TMC and detect its determinants since it can present the relative influence of variables on children’s TMC. Nonetheless, the use of machine learning introduces its own challenges. First, these methods and their performance are highly dependent on the choice of “hyperparameters”. To solve this issue, a novel technique, called multi-objective hyperparameter tuning (MOHPT), is proposed to select hyperparameters using a multi-objective metaheuristic optimization framework. The performance of the proposed technique is compared with conventional hyperparameters tuning methods, including random search, grid search, and “Hyperopt”. Second, machine learning methods are black-box tools and hard to interpret. To overcome this deficiency, the most influential parameters on children’s TMC are determined by LGBM, and logistic regression is employed to investigate how these parameters influence children’s TMC. The results suggest that MOHPT outperforms conventional methods in tuning hyperparameters on the basis of prediction accuracy and computational cost. Trip distance, “walkability” and “bikeability” of the origin location, age, and household income are principal determinants of child mode choice. Furthermore, older children, those who live in walkable and bikeable areas, those belonging low-income groups, and short-distance travelers are more likely to travel by sustainable transportation modes.

show abstract

Section: Methodsmentioning

confidence: 99%

Application of Machine Learning to Child Mode Choice with a Novel Technique to Optimize Hyperparameters

Naseri

Waygood

Wang

et al. 2022

IJERPH

Self Cite

View full text Add to dashboard Cite

show abstract

“…Considering validation data accuracy is necessary to avoid over-fitting in the feature selection process and selecting the optimal features that increase the model's prediction power. Moreover, calibration weights are applied to investigate the optimal calibration weights according to the details provided by Naseri et al [50]. After running the model and obtaining the solutions, the testing data is applied to determine the calibration weight optimal value.…”

Section: Coa-qda Feature Selectionmentioning

confidence: 99%

“…However, Artificial Intelligence (AI) methods have the potential to improve the accuracy of the predictions, as well as the selection of the most important predictive variables. At the same time, when dealing with large numbers of variables, such as from the General Environmental Behavior questions (50), it is difficult to determine which combination of variables will provide the most accurate prediction model. Therefore, feature selection techniques are needed to select the most important predictors.…”

Section: Introductionmentioning

confidence: 99%

A Novel Feature Selection Technique to Better Predict Climate Change Stage of Change

et al. 2021

Self Cite

View full text Add to dashboard Cite

Indications of people’s environmental concern are linked to transport decisions and can provide great support for policymaking on climate change. This study aims to better predict individual climate change stage of change (CC-SoC) based on different features of transport-related behavior, General Ecological Behavior, New Environmental Paradigm, and socio-demographic characteristics. Together these sources result in over 100 possible features that indicate someone’s level of environmental concern. Such a large number of features may create several analytical problems, such as overfitting, accuracy reduction, and high computational costs. To this end, a new feature selection technique, named the Coyote Optimization Algorithm-Quadratic Discriminant Analysis (COA-QDA), is first proposed to find the optimal features to predict CC-SoC with the highest accuracy. Different conventional feature selection methods (Lasso, Elastic Net, Random Forest Feature Selection, Extra Trees, and Principal Component Analysis Feature Selection) are employed to compare with the COA-QDA. Afterward, eight classification techniques are applied to solve the prediction problem. Finally, a sensitivity analysis is performed to determine the most important features affecting the prediction of CC-SoC. The results indicate that COA-QDA outperforms conventional feature selection methods by increasing average testing data accuracy from 0.7 to 5.6%. Logistic Regression surpasses other classifiers with the highest prediction accuracy.

show abstract

“…Single-objective optimization pavement M&R programming either maximizes or minimizes the most important objective and uses the other criteria as constraints, which may lead to suboptimal programs at the network level. To capture the multi-objective nature of the pavement M&R programming problem at the network level many researchers adopted multi-objective optimization of pavement M&R programming (14,18,(23)(24)(25)(26)(27)(28)(29). However, the majority of the research efforts with multi-objective optimization only considered maximization of performance and minimization of costs and tried to trade off between performance and cost.…”

Section: Introductionmentioning

confidence: 99%

Pavement Maintenance Program at the Network Level: Mixed-Integer Programming with Multiple Objectives

Amin

2022

Transportation Research Record: Journal of the Transportation R

View full text Add to dashboard Cite

Pavement network conditions deteriorate over the years of use. To keep pavement conditions at acceptable levels, highway agencies plan pavement maintenance and rehabilitation (M&R) programs and perform accordingly. Highway agencies usually face budget variability for pavement M&R activities because of limited resources, economic conditions, and changes in policies. The situation makes it difficult for highway agencies to keep an acceptable pavement condition at the network level. Therefore, it is important for highway agencies to adopt M&R policies that can maximize the network condition as well as handle the deviation of the network condition considering the available maintenance funds. In this paper, a multi-period multi-objective linear integer programming model is proposed. Two objectives, maximization of the average network condition and minimization of deviation of the network condition from an idealized network condition trend, are considered in the formulation. The model is formulated for fixed M&R budgets, as well as for variable M&R budgets. The proposed model provides an M&R program for the pavement network that helps decision makers to manage pavement maintenance programs considering budgetary constraints. A case study examining a network of 45 pavement sections is conducted. The solutions of the fixed-budget and variable-budget model are presented. In addition, the values of the system to the decision maker are discussed. Results show that the proposed model is an attractive way to manage pavement maintenance programs at the network level.

show abstract

Toward equity in large-scale network-level pavement maintenance and rehabilitation scheduling using water cycle and genetic algorithms

Cited by 29 publications

References 38 publications

Application of Machine Learning to Child Mode Choice with a Novel Technique to Optimize Hyperparameters

Application of Machine Learning to Child Mode Choice with a Novel Technique to Optimize Hyperparameters

A Novel Feature Selection Technique to Better Predict Climate Change Stage of Change

Pavement Maintenance Program at the Network Level: Mixed-Integer Programming with Multiple Objectives

Contact Info

Product

Resources

About