Predicting the price of second-hand vehicles using data mining techniques

The electronic commerce, known as “E-commerce”, has been boosted rapidly in recent years, and makes it possible to record all information such as price, location, customer’s review, search history, discount options, competitor’s price, and so on. Accessing to such rich source of data, companies can analyze their users’ behavior to improve the customer satisfaction as well as the revenue. This study aims to estimate the price of used light vehicles in a commercial website, Divar, which is a popular website in Iran for trading second-handed goods. At first, highlighted features were extracted from the description column using the three methods of Bag of Words (BOW), Latent Dirichlet Allocation (LDA), and Hierarchical Dirichlet Process (HDP). Second, a multiple linear regression model was fit to predict the product price based on its attributes and the highlighted features. The accuracy index of Actuals-Predictions Correlation, the min-max index, and MAPE methods were used to validate the proposed methods. Results showed that the BOW model is the best model with an Adjusted R-square of 0.7841

An AIS-Based Approach for Measuring Waterway Resiliency: A Case Study of Houston Ship Channel

Resiliency measurement is a great tool for evaluating system performance and proposing solutions to prevent damage and to recover from disruptive events. This study proposes an analytic approach to quantify narrow waterway systems’ resiliency during disasters. First, metrics are introduced to quantify the resiliency before, during, and after a disruption. The existing Key Performance Indicators (KPIs) for Maritime Transportation Systems (MTS) are examined, and two metrics, 1) the number of inbound and outbound vessels and 2) Total Stopped Vessel-Hours, are selected to measure the resiliency of a waterway system. Second, a heuristic method is developed to derive the KPIs from the Automatic Identification System (AIS) data. Finally, the proposed methodology is performed for the Houston Ship Channel (HSC) AIS data before, during, and after Hurricane Harvey, in August 2017. The results are presented for the entire channel and highlight useful information about the performance of individual docks, terminals, and waterway zones within HSC. This study helps decision-makers identify the weaknesses and potential bottlenecks in a waterway confronting a disruption and come up with remedies

Predicting the price of second-hand vehicles using data mining techniques

The electronic commerce, known as “E-commerce”, has been boosted rapidly in recent years, and makes it possible to record all information such as price, location, customer’s review, search history, discount options, competitor’s price, and so on. Accessing to such rich source of data, companies can analyze their users’ behavior to improve the customer satisfaction as well as the revenue. This study aims to estimate the price of used light vehicles in a commercial website, Divar, which is a popular website in Iran for trading second-handed goods. At first, highlighted features were extracted from the description column using the three methods of Bag of Words (BOW), Latent Dirichlet Allocation (LDA), and Hierarchical Dirichlet Process (HDP). Second, a multiple linear regression model was fit to predict the product price based on its attributes and the highlighted features. The accuracy index of Actuals-Predictions Correlation, the min-max index, and MAPE methods were used to validate the proposed methods. Results showed that the BOW model is the best model with an Adjusted R-square of 0.7841