Modeling and Prediction of Freight Delivery for Blocked and Unblocked Street Using Machine Learning Techniques

Sustainable Multidimensional Performance Prediction by ANN-Based Supervised Machine Learning

“…Random forest XGBoost Robust Regression [11] A freight inspection volume forecasting approach using an aggregation/disaggregation procedure, machine learning and ensemble models Artificial neural networks Bayesian regularization Aggregation/disaggregation Time series inspection [12] Modeling and prediction of freight delivery for blocked and unblocked street using machine learning techniques Artificial neural network Support vector machine [13] Inventory management and cost reduction of supply chain processes using AI based time-series forecasting and ANN modeling Artificial neural network [14] Applying a random forest method approach to model travel mode choice behavior Random forest [15] Energy consumption forecasting in agriculture by artificial intelligence and mathematical models Support vector machine [16] Using artificial neural network for predicting and controlling the effluent chemical oxygen demand in wastewater treatment plant Artificial neural network [17] A paired neural network model for tourist arrival forecasting Neural network [18] Mobile demand forecasting via deep graph-sequence spatiotemporal modeling in cellular networks Deep learning [19] Customer demand prediction of service-oriented manufacturing using the least square support vector machine optimized by particle swarm optimization algorithm Support vector machine [20] Statistical modeling and prediction for tourism economy using dendritic neural network Neural network [21] A data mining based method for route and freight estimation Naive bayes multinomial updatable [22] Container sea-rail transport volume forecasting of Ningbo port based on combination forecasting model Artificial neural networks [23] Short-term load and wind power forecasting using neural network-based prediction intervals.…”

A neural network-based model for estimating the delivery time of oxygen gas cylinders during COVID-19 pandemic

“…Another study considered street blockage in their input to train the models predicting delivery times. The authors experimented on neural networks and support vector machines, and both provided accurate results comparing with actual delivery times [ 33 ]. Liao and Wang [ 34 ] used neural networks to predict delivery time of an automatic material handling system.…”

Using Machine Learning to Predict Freight Vehicles’ Demand for Loading Zones in Urban Environments

“…Their results, applied to the city of Melbourne, Australia, show that tree regression and neural networks provided the best estimates of availability and survival over the week, while reducing computational complexity and execution times. Pandya et al ( 23 ) use a combination of Support Vector Machines (SVM) and neural networks to predict and analyze the effect of freight delivery on the blockage of urban streets in Ahmedabad, India, based on the saturated flow rates. Overall, the neural network approach outperformed SVM in both root mean square error (RMSE) and mean absolute error (MAE) across the capacity and delay prediction.…”

“…Further applications of neural networks in freight demand parking are those of Low et al ( 21 ), Errousso et al ( 22 ), Pandya et al ( 23 ), and Hughes et al ( 24 ). Low et al ( 21 ) analyzed data imputation strategies via generative adversarial networks (GANs).…”