Forecasting of Transportation cost for Logistics data

Anitha, P.; Patil, Malini M.

doi:10.1109/conecct52877.2021.9622576

Cited by 2 publications

(3 citation statements)

References 4 publications

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“…C OST PREDICTION is widely used in various fields, such as transportation [1], cybersecurity [2], construction [3], and healthcare [4]. Cost prediction is generally a method of studying historical data and predicting future costs.…”

Section: Introductionmentioning

confidence: 99%

“…Transportation cost prediction is one of the aspects of cost prediction. Anitha and Patil [1] predicted the transportation costs using a regression algorithm, which assisted the retail sector predict the cost incurred for logistics. In the freight company, predicting the costs of freight forwarding contracts by using historical data can help freight companies better understand the causes of costs and select profitable contracts.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

An Approach for Predicting the Costs of Forwarding Contracts using Gradient Boosting

Xiao¹,

Liu²,

Du³

et al. 2022

Annals of Computer Science and Information Systems

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Predicting the cost of forwarding contract is a severe challenge to road transport management system. The transportation cost of a forwarding contract often depends on many factors. It is hard for humans to evaluate the various factors in transportation and calculate the cost of forwarding contract. In this paper, we propose an approach to address such a problem by following the sequence of machine learning steps which consist of data analysis, feature engineering and model construction. First, we conduct a detailed analysis of the given data. Then, we generate effective features to characterize the cost of forwarding contract and eliminate redundant features. Finally, in the model construction phase, we propose a gradient boosting decision tree based method to train and predict the cost of forwarding contract. The proposed approach achieves RMSE scores of 0.1391 on the test set, which is the 2 nd final score in the competition.

show abstract

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An Approach for Predicting the Costs of Forwarding Contracts using Gradient Boosting

Xiao¹,

Liu²,

Du³

et al. 2022

Annals of Computer Science and Information Systems

View full text Add to dashboard Cite

show abstract

“…Cheng et al [18] presented an expanded RFM model by including the time since the first purchase and churn probability. Before the introduction of the RFM model, customer purchase behaviors were analyzed using real-time transactional and retail datasets to deploy data segmentation (for example, P. Anitha and Malini M. Patil [33]). Recently, Rahim et al [20] proposed the analysis of repurchase behaviors for customer classification using the RFM model; they focused on customer behavior modeling based on point-of-sale data.…”

Section: Introductionmentioning

confidence: 99%

Multi-Behavior RFM Model Based on Improved SOM Neural Network Algorithm for Customer Segmentation

et al. 2022

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Previous research on RFM (recency, frequency, and monetary value) models focused on only one type of user behavior data, i.e., the purchase behavior, without considering the interactions between users and items, such as clicking, favorite, and adding to cart. In this study, we propose a novel solution for deconstructing the multiple behaviors of consumers in a specific period and performing customer segmentation in an application promotion system called multi-behavior RFM (MB-RFM) based on the selforganizing map (SOM) algorithm. First, using the R, F, and M values, we analyzed the weight relationship between multiple behaviors of users and items using the superiority chart and entropy value methods. Each behavior ascribed to a customer was considered to be a part of MB-RFM model values, which were then used to classify customers using an improved SOM neural network. Subsequently, various promotion strategies were developed according to customer categories that can help application vendors in improving their application utilization and implementing targeted promotion strategies. To prove the effectiveness of the proposed method in sparse datasets, two real-world datasets were used to perform experiments, whose results demonstrated that the classification performance of our method was significantly more accurate.

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Forecasting of Transportation cost for Logistics data

Cited by 2 publications

References 4 publications

An Approach for Predicting the Costs of Forwarding Contracts using Gradient Boosting

An Approach for Predicting the Costs of Forwarding Contracts using Gradient Boosting

Multi-Behavior RFM Model Based on Improved SOM Neural Network Algorithm for Customer Segmentation

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