<p class="MsoNormal" style="text-align: left; margin: 0cm 0cm 0pt; mso-layout-grid-align: none;" align="left"><span style="mso-bidi-font-weight: bold;"><span style="font-size: x-small;"><span style="font-family: Times New Roman;">Pool segmentation is an essential step in the logistics management process for large-scale car rental business. Its main function involves dynamic decisions about pools clustering and regional logistics management centers selecting, whose goal is to optimize fleet utilization and improve the logistics management efficiency. According to developing mode of car rental enterprises and layout of leasing sites, a three-tier structure is presented to describe the logistics management relations among each leasing sites. Based on the logistics operation characteristics and practical administration demand, a dynamic model and its algorithm are proposed for pool segmentation in the car rental industry. A case study shows that the proposed methodology is feasible and effective. <em></em></span></span></span></p>

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%.

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Large scale and networked car rental companies are emerging continuously along with the rapid development of car rental industry. In order to improve the efficiency of logistics management, it is imperative to find a method which can implement the pool segmentation assignment. According to the developing mode of car rental enterprises and distribution of car rental locations, proposed a three-tier tower structure for enterprise logistics management. A method was obtained to solve the problem of pool segmentation and regional management center choice synchronously based on the study of car rental industry development and its logistics operation characteristic. The results of a case study show that the proposed method can achieve objectives effectively.

<abstract> <p>Amid the impact of COVID-19, the public's willingness to travel has changed, which has had a fundamental impact on the ridership of urban public transport. Usually, travel willingness is mainly analyzed by questionnaire survey, but it needs to reflect the accurate psychological perception of the public entirely. Based on Weibo text data, this paper used natural language processing technology to quantify the public's willingness to travel in the post-COVID-19 era. First, web crawler technology was used to collect microblog text data, which will discuss COVID-19 and travel at the same time. Then, based on the Naive Bayes classification algorithm, travel sentiment analysis was carried out on the data, and the relationship between public travel willingness and urban public transport ridership was analyzed by Spearman correlation analysis. Finally, the LDA topic model was used to conduct content topic research on microblog text data during and after COVID-19. The results showed that the mean values of compelling travel emotion were -0.8197 and -0.0640 during and after COVID-19, respectively. The willingness of the public to travel directly affects the ridership of urban public transport. Compared with the COVID-19 period, the public's fear of travel infection in the post-COVID-19 era has significantly improved, but it still exists. The public pays more attention to the level of COVID-19 prevention and control and the length of travel time on public transport.</p> </abstract>