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

Journal of Modern Transportation

Madjid Tavana¹  Mohsen Asadi²  Saeed Asadi Bagloee³  Debora Di Caprio⁴  Mitra Heshmati⁵ 

[1] Business Systems and Analytics Department, Distinguished Chair of Business Analytics, La Salle University;Department of Civil and Environmental Engineering, Kharazmi University;Department of Infrastructure Engineering, Melbourne School of Engineering, University of Melbourne;Department of Mathematics and Statistics, York University;Faculty of Engineering, Monash University;

关键词: Decision support system;    Tourism;    Operations planning;    Information and communications technology;   

DOI  :  10.1007/s40534-017-0126-x

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown