یک سیستم پشتیبانی تصمیم گیری چند کاربره برای برنامه ریزی تور شهری آنلاین اتوبوس A multi-user decision support system for online city bus tour planning

1 Introduction Tourism is rapidly becoming a sustainable pathway toward economic prosperity for host countries and communities. Recent advances in information and communications technology (ICT), the smartphone, the Internet and Wi-Fi have given a boost to the tourism industry [1–5]. In the last thirty years, the energetic growth and development of the tourism industry has been mirrored by the growth of ICT [6], which adds value to tourism services and products throughout the value chain. Nowadays, all tourism-related activities, from hotel reservations and air tickets to visa applications, are pre-planned online (see Fig. 1). The capacity of mobile technology in assisting travelrelated decisions has substantially improved with respect to that offered by the early prototype of mobile tour-guides. From GPS-supported systems and camera phones to today’s smartphones with enhanced information and communication capabilities [7], we have witnessed an ICT escalation that has provided a very fertile ground to promote tourism. Sightseeing in cities is an indispensable part of almost all tourists’ plans. A large body of research is dedicated to identify the most attractive places and major landmarks for tourists to visit. Once identified, these places, usually referred to as places of interest (PoI), need to be reached and visited by the tourists, which is the problem we focus on in this study. City bus tour (CBT) service, that is, a specific bus service providing pre-planned tours to a number of PoI, is an alternative commonly preferred to public transportation. Thus, the problem becomes the design of CBT routes that allow the tourists to save time while reducing the operational costs of the city bus company. Due to the surge of interest shown by experts of different disciplines, city tour planning tools have proliferated, incorporating an array of useful services [8]. However, the existing tools still fail to address several practical aspects, hence compromising their utility in realistic tourist scenarios [9]. Formally speaking, the problem belongs to the class of minimum cost network flow problems [10, 11] that are in general intractable (NP-hard) and involve optimal and/or heuristic algorithms and methods characterized by different types of time complexity [12–19]. This is particularly true when dealing with networks of extremely large size. Note that while this can also be the case of urban networks, the road networks connecting PoI are usually small in size or composed of small size blocks. Thus, the main issue is to formulate a mathematical model that makes the problem tractable. From a practical viewpoint, the goal is to guarantee that a solution (minimum cost flow) exists and can be obtained using a specific optimization software with relatively good performance (time complexity: reasonably fast).