multinomial logit model

As one of the largest and most profitable sub-sectors of tourism worldwide, Medical Tourism is considered an economic activity involving service exchanges, representing the link between at least medicine and tourism sectors. Therefore, this study sought to investigate the factors affecting the demands for medical tourism in Mashhad using the Multinomial Logit Model, which was estimated by Stata 16 software. The statistical population of the study comprised of domestic medical tourists who visited Mashhad in 2021, out of whom 150 people were selected as the participants. The dependent variable of the study was the demand for medical tourism in Mashhad, that was divided into low, high, and medium classes, and the required data was collected by administering a questionnaire. Accordingly, it was found that an increase in the quality of medical and urban services, low medical costs, and an increase in the tourists’ income would increase the demand for medical tourism in Mashhad. Moreover, the quality of medical services’ sub-indices and urban factors were also evaluated separately by the tourists who participated in this study.

Pedestrian-vehicle accidents have a higher level of severity compared to other types of accidents. Therefore, the potential contributing factors at different severity levels of this type of accident should be identified so that appropriate measures can be taken for each of these factors.

In this paper, the multinomial logit (MNL) model is used to identify the factors affecting the severity of pedestrian-vehicle accidents. The Pedestrian-vehicle accident data from Highway Safety Information System (HSIS) of California from 2015 to 2017 is used in this article. The severity of injury is defined using the KABCO scale and is classified into five levels: fatal injury (K) and 4 injury levels including: severe or disabling injury (level 1, (A)), evident injury (level 2, (B)), minor or possible injury (Level 3, (C)) and no injury (Level 4, (O)).

The results show that the factors that significantly increase the risk of death and injury are: Drivers’ age (26 to 65 and over 65 years), weekday, low and medium annual average daily traffic (AADT), morning peak and daytime off peak and daylight.

The developed model and the results of the analysis offer effective solutions to reduce the severity of pedestrian traffic accidents and improve the safety performance of the traffic system. The results of this study can be useful for identifying the potential contributing factors to pedestrian-vehicle accidents in the real world. In addition, road safety experts and officials can use the results of this study to improve pedestrian safety.

Shopping is one of the most important activities for tourists. The purpose of this study is to describe and predict tourist shopping route choice behavior in Tabriz historical bazaar. A model of tourist shopping behavior is estimated to investigate differences in route choice behavior of various types of tourist shoppers. The results indicate that shopping supply and accessibility, some physical characteristics, and the history of the route followed are importantfactors influencing route choice behavior. Furthermore, it can be concluded that shopping motivations, familiarity with the area and planning of the route, affect tourist route choice behavior. The model allows investigating the effects of environmental characteristics on route choice behavior and assessing various future planning scenarios, such as changes in physical aspects of links in the downtown area, or changes in supply of shops.