Performance of Financial Firms and Political Uncertainty

Jam Wan^*
*Correspondence: Jam Wan, Department of Civil Engineering, University of Infrastructure, Research and Management, Ahmedabad, India, Email:

Introduction

Setting shipping costs on a freight brokerage platform can be difficult if there isn't a precise cost standard. It can also be challenging to establish rates when transport brokers take advantage of their position in the market by charging excessive commissions. Additionally, fares are undervalued in relation to the labor input because there is no quantified fare policy in place. As a result, car owners are working for less money than they are worth. This study presents the recommended shipping cost provided by a machine learning-based price prediction model and the main variables that influence its setting. Four algorithms were used to construct the cost prediction model: XGBoost regression, LightGBM regression, multiple linear regression, and deep neural network regression. The performance evaluation index was R-squared. LightGBM was selected as the model with the highest explanatory power and the quickest processing speed in light of this study's findings. In addition, realistic usage patterns were taken into consideration when determining the range of the anticipated shipping costs. The range of the predicted shipping costs was calculated using the confidence interval, and the dataset was classified using the K-fold cross-validation method for this purpose. This paper could be used to raise utilization rates and set shipping costs on freight brokerage platforms. The logistics market is also becoming active as a result of an increase in internet transactions.

Description

The parcel forwarding service has expanded and numerous logistics centers have been constructed. The volume of general freight has been steadily rising. Even with the COVID-19 outbreak, domestic freight transportation via roads has been emphasized. After the COVID-19 outbreak, the traffic volume of small and medium-sized vehicles used for freight transportation increased in comparison. As a result, domestic road freight transportation is crucial to boosting the logistics market. However, there is no precise standard for the domestic freight industry's shipping costs. Currently, only distance and vehicle tonnage are taken into account when determining shipping costs. Because it does not take into account a variety of freight characteristics and is difficult to implement in practice, this is only intended as a guide for new market entrants. The shipper's expertise is used to determine shipping costs. The shipping costs are set by shippers based on the current market price and the shipping costs of similar freights in the past. Shipping costs are undervalued in comparison to labor, which is unreasonable from the point of view of vehicle owners. Additionally, some transportation agents make use of their high market position to charge excessive commissions. Current vehicle owners are extremely dissatisfied as a result of this circumstance, which is made up of intense disagreements between shippers and the owners of the vehicles [1,2].

Using data from a freight brokerage platform, a machine learning-based method for predicting shipping costs for domestic freight transportation is proposed in this paper. In addition, it compares predictive power to identify the best predictive model and demonstrates that predictive models are able to appropriately set shipping costs. For six months, we used data from the freight brokerage platform that related to transportation. New factors were added and various preprocessing techniques were used to identify the major factors. The major factors were determined using a step selection method and correlational analysis. After that, we used the derived factors and a machine learning algorithm to create a fare prediction model. Multiple linear regression (MLR), deep neural networks (DNNs), extreme gradient boosting (XGBoost) regression, and light gradient boosting machine (LightGBM) regression were the machine learning algorithms that we utilized. Compared to the XGBoost model, LightGBM's learning time is shorter [3,4].

We demonstrate a method for determining the range of predicted fares in light of actual usage patterns; The user should be able to see the fares as a range rather than as a single value. k-fold cross-validation was used to generate thirty training sets in total. For each iteration we trained the sets and predicted the test set. A confidence interval was calculated and an appropriate fare range was presented, assuming that the 30 derived predicted values have a normal distribution. The predictive model's performance evaluation index was R-squared. Predictive research on freight shipping costs currently requires additional analysis of additional algorithms and factors [5].

Conclusion

The characteristics of the freight as well as environmental factors need to be taken into account when determining freight shipping costs. As a result, this study takes into account factors like distance vehicle type and car volume as well as environmental factors like precipitation to determine factors that influence how shipping costs are determined. Additionally, the majority of studies on freight shipping cost prediction are currently carried out with conventional regression models. When looking at cost prediction studies in other fields, numerous studies employ AI algorithms. The accuracy of AI algorithms is typically higher than that of conventional models. As a result, it is essential to advance research by incorporating algorithms from artificial intelligence into the area of freight shipping cost prediction. Consequently, we utilize the derived factors and AI regression algorithm to construct a shipping prediction model. We use the confidence interval and k-fold crossvalidation to predict the range of shipping costs for this procedure and to make it more applicable in the real world.

Acknowledgement

None.

Conflict of Interest

No potential conflict of interest was reported by the authors.

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