Search Results (3)

In modern times, people predominantly use personal vehicles as a means of transportation, and, as this trend has developed, services that enable consumers to rent vehicles instead of buying their own have emerged. These services have grown into an industry, and the demand for predicting rental prices has arisen with the number of consumers. This study addresses the challenge in accurately predicting rental prices using big data with numerous features, and presents the experiments conducted and results obtained by applying various machine learning (ML) algorithms to enhance the prediction accuracy. Our experiment was conducted in two parts: single- and multi-step forecasting. In the single-step forecasting experiment, we employed random forest regression (RFR), multilayer perceptron (MLP), 1D convolutional neural network (1D-CNN), long short-term memory (LSTM), and the autoregressive integrated moving average (ARIMA) model to predict car rental prices and compared the results of each model. In the multi-step forecasting experiment, rental prices after 7, 14, 21 and 30 days were predicted using the algorithms applied in single-step forecasting. The prediction performance was improved by applying Bayesian optimization hyperband. The experimental results demonstrate that the LSTM and ARIMA models were effective in predicting car rental prices. Based on these results, useful information could be provided to both rental car companies and consumers. Full article

Sharing new energy vehicles is crucial for addressing the issue of traditional vehicles’ carbon emissions, reducing urban traffic congestion, safeguarding the environment, and promoting citizens’ use of green transportation. However, the parking lot’s drawbacks—poor location, challenging parking, and difficulty finding a car—lead to a low popularity rate, few users, and infrequent use. How to scientifically choose parking outlets and maximize the advantages of sharing new energy vehicles has become an important topic in current urban traffic management. This paper constructed a “G-B-U” framework starting with quasi-public goods and stakeholders to analyze the factors influencing the location selection of these vehicles. On this basis, a three-stage location decision method of “market demand prediction—alternative network screening—location model solution” is proposed to optimize the location selection of shared new energy vehicles. The factors are analyzed, and numerical examples are studied, using the districts of Xuzhou City in China as examples: Gulou, Yunlong, and Quanshan. The findings indicate that the main variables influencing how frequently Xuzhou residents use shared new energy cars are network dispersion, rental and return convenience, and usage experience. After site selection optimization, the journey distance is nearly cut in half, saving users a significant amount of travel time. It may meet the travel needs of residents better based on the same number of parking lots. Full article

The unconstrained demand forecast for car rentals has become a difficult problem for revenue management due to the need to cope with a variety of rental vehicles, the strong subjective desires and requests of customers, and the high probability of upgrading and downgrading circumstances. The unconstrained demand forecast mainly includes repairing of constrained historical demand and forecasting of future demand. In this work, a new methodology is developed based on multiple discrete choice models to obtain customer choice preference probabilities and improve a known spill model, including a repair process of the unconstrained demand. In addition, the linear Holt–Winters model and the nonlinear backpropagation neural network are combined to predict future demand and avoid excessive errors caused by a single method. In a case study, we take advantage of a stated preference and a revealed preference survey and use the variable precision rough set to obtain factors and weights that affect customer choices. In this case study and based on a numerical example, three forecasting methods are compared to determine the car rental demand of the next time cycle. The comparison with real demand verifies the feasibility and effectiveness of the hybrid forecasting model with a resulting average error of only 3.06%. Full article