A multi-user decision support system for online city bus tour planning

Abstract

Tourism is rapidly becoming a sustainable pathway toward economic prosperity for host countries and communities. Recent advances in information and communications technology, the smartphone, the Internet and Wi-Fi have given a boost to the tourism industry. The city bus tour (CBT) service is one of the most successful businesses in the tourism industry. However, there exists no smart decision support system determining the most efficient way to plan the itinerary of a CBT. In this research, we report on the ongoing development of a mobile application (app) and a website for tourists, hoteliers and travel agents to connect with city bus operators and book/purchase the best CBT both in terms of cost and time. Firstly, the CBT problem is formulated as an asymmetric sequential three-stage arc routing problem. All places of interest (PoI) and pickup/dropout points are identified with arcs of the network (instead of nodes), each of which can be visited at least once (instead of exactly once). Secondly, the resulting pure integer programming (IP) problem is solved using a leading optimization software known as General Algebraic Modeling System (GAMS). The GAMS code developed for this project returns: (1) the exact optimal solution identifying the footprints of the city bus relative to all the arcs forming the minimal cost network; (2) the augmenting paths corresponding to the pickup stage, the PoI visiting stage and the drop-off stage. Finally, we demonstrate the applicability of the mobile app/website via a pilot study in the city of Melbourne (Australia). All the computations relative to the initial tests show that the ability of the app to answer users' inquiries in a fraction of a minute.

Revisit Intention Model on Urban Tourism Basis by City Tour Bus with Destination Image as Intervening Variable

“…In this case, including the destination image of big cities in Indonesia which have the distinctive cultural characteristics of each region (Whang et al, 2016). When tourists visit a city, tourists feel the destination image, and expect transportation facilities that can be used to make it easier for them to visit accompanied by attractive marketing, service and route information and if this is found, the satisfaction that tourists feel can make tourists return to visit these destinations (Bagloee et al, 2017).…”

Tourist trip design with heterogeneous preferences, transport mode selection and environmental considerations

“…The great interest of the TTDP has attracted the attention of researchers, as witnessed by the amount of literature reviews published since 2009 (Gavalas et al, 2014 ; Gunawan et al, 2016 ; Lim et al, 2019 ; Ruiz-Meza & Montoya-Torres, 2020 ; Shcherbina & Shembeleva, 2014 ; Souffriau & Vansteenwegen, 2010 ; Vansteenwegen et al, 2009 ). Throughout the literature, tourism planning problems have been modeled through Operations Management/Operational Research techniques under several approaches, such as the Cruise Itinerary Problem (Leong & Ladany, 2001 ), the One-Period Routing Bus Problem (BTP) (Deitch & Ladany, 2000 , 2001 ), the City Bus Tour Problem (CTB) (Bagloee et al, 2017 ), the Generalized Maximum Coverage Problem (GMC) (Brilhante et al, 2013 , 2015 ), the Optimal Tourist Problem (OTP) (Yu et al, 2015 ), and the Orienteering problem (OP) (Vansteenwegen & Van Oudheusden, 2007 ). This last together with the Team Orientering Problem (TOP) are the most emplyed modeling appraoches (Ruiz-Meza & Montoya-Torres, 2020 ; Shcherbina & Shembeleva, 2014 ; Vansteenwegen & Van Oudheusden, 2007 ).…”

Progress in wireless technologies in hospitality and tourism

“…Interest in WiFi is diffuse, as this is a generic technology geared to connectivity regardless of the type of data transmitted and so is less specifically applicable, as occurs with sensor-based or more short-range technologies. The literature focusses chiefly on discussing its role in connectivity in tourism-related facilities such as airports (Nghiem-Phu and Suter, 2018), buses (Bagloee et al , 2017), car parks or coffee shops (Sanusi and Palen, 2008), trains (Carteni et al , 2017) and museums and cultural heritage environments (Borrero et al , 2015). As well as, given their wide availability, economic models such as social login have also recently been studied (Tanti and Buhalis, 2017).…”