Unconstrained Estimation of Multitype Car Rental Demand

Abstract

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%.

Cross-Industry Statistical Law: Average Booking Curves Draw Exponential Functions

“…RM has become part of public research themes from the late 1980s, and many models and business instance reports have been illustrated over the past 30 years in various perishable assets industries. [3,4,5,33,6,8,9,12,13,15,11,7,10,14]. A study on airline tickets among them showed that a 20% improvement in the accuracy of quantity demand forecasting led to a 0.5-3% increase in revenue.…”

“…From Eqs. ( 8), (9), and (10), the function 𝜙 is uniquely determined as the exponential decay function 𝜙(𝑡; 𝛽) = exp(−𝛽𝑡) (see the supplemental document for an explanation), and we consequently obtain the formula about average booking curves: E[𝑋 (𝑡)] = 𝐴 exp(−𝛽𝑡). Note that a differential equation 𝑑 𝜙 (𝑡) 𝑑𝑡 = −𝛽𝜙(𝑡), which implies the property that an increase in the sums of bookings is proportional to the number of cumulative bookings, as is shown in the explanation document.…”

“…[1,2,3,4,5]. The application fields have been diverse [6,7] such as rental cars [8,9], golf [10,11], cruise lines [12,13], railway tickets [14], theme parks [15]. A booking curve is represented by the cumulative time series of bookings.…”

“…Therefore, the use of booking curves for forecasting in RM has been widely studied by many researchers and professionals in the perishable assets industries. This has led to the development of several advanced and highly accurate forecasting models [16,17,18,4,9,11,10,19,20,14,21], including exponential smoothing [22,23,24,25], stochastic process models [3,26], generalized linear mixed models [27], and methods utilizing neural nets that incorporate seasonal and day-of-week trends into their parameters [28,29].…”

Average booking curves draw exponential functions

“…For over 30 years, the perishable assets industry, comprising hotels and airline ticketing, has utilized revenue management (RM) to manage inventory over a finite sales horizon to maximize profits 1 – 5 . The application fields have been diverse 6 , 7 such as car rentals 8 , 9 , golf 10 , 11 , cruise lines 12 , 13 , railway tickets 14 , theme parks 15 . The cumulative time series of bookings represent a booking curve.…”