## 3 Literature Review
The first model refers to a large shopping centre (GLA greater than 200,000 square feet) in which only GLA is found as an independent variable, and the second model is presented for a medium-sized shopping centre (usually for 50,000 to 150,000 square feet) in which GLA, the age of shopping centres and traffic on surrounding roads appear as independent variables that affect the volume of traffic generated by shopping centres. Through the work [31], an analysis of a travel generation study conducted for ITE and the International Council of Shopping Centres (ICSC) in 1994 and 1995 was presented. The study explored several different aspects of trip generation in shopping centres, including the relationship between trip generation and combinations of several independent variables, concluding that gross leasable area (GLA) is significant for predicting trip generation for a shopping centre. Also, in the paper [32], it was concluded that the gross leasable area (GLA) is significant for predicting the emergence of travel for shopping centres. [PERSON] et al. [33] developed a travel and parking generation model for shopping centres and found that gross area and the number of employees are the most significant independent variables. [PERSON] et al. [34] developed an alternative methodology based on artificial neural networks (artificial neural networks--ANN) to analyze the relationship between the percentage of transient trips generated by shopping centres and the factors affecting them and compare it with the regression model results from the American Institute of Transportation Engineers' (ITE) Travel Generation Handbook. Through their work, it was found that ANN-based models have the ability to more accurately represent the relationship between the percentage of transient trips and independent variables than traditional regression models. [PERSON] et al. [10] conducted tests in six shopping centres in the Dhanmondi area of Dhaka (Bangladesh) and concluded that the physical characteristics of the shopping centre (total number of parking spaces, gross area and number of stores) are significant for predicting the occurrence of travel. [PERSON] et al. [2] collected data from six medium and small shopping centres in Dhaka and developed two macroscopic models to generate travel. The first model has a gross available area (GFA), the number of parking spaces and the number of restaurants as significant independent variables in determining the attractiveness of trips to the mall, while the second model uses the total number of stores instead of the GFA. It is concluded that the total number of stores is a better predictor than the GFA for determining the attractiveness of trips to the shopping centre. [PERSON] et al. [35] conducted a study in 18 shopping centres in Delaware (USA) to determine travel attractiveness rates in shopping centres. This study adopted two approaches, microscopic and macroscopic, to calculate the rate of travel attraction. The microscopic approach deals with the relationship between the travel attractiveness rates of individual stores and the shopping centre as a whole, while the macroscopic approach connects the attractiveness of shopping centre travel as a function of the physical characteristics of the shopping centre itself (total number of parking spaces, total area of establishments and number of establishments). The conclusion is that the microscopic model gives more precise results compared to the macroscopic approach. In their study, [PERSON] and [PERSON] [30] developed models for generating trips for shopping centres in Mumbai Metropolitan (India). In total, eighteen shopping centres were selected to develop a travel generation model using regression analysis. It was found that the generation of trips is related to five parameters: built-up area, number of screens in the multiplex, number of seats in the multiplex, number of kiosks and number of stores in shopping centres.