The eCOMPASS multimodal tourist tour planner

Gavalas, Damianos; Kasapakis, Vlasios; Konstantopoulos, Charalampos; Pantziou, Grammati; Vathis, Nikolaos; Zaroliagis, Christos

doi:10.1016/j.eswa.2015.05.046

Cited by 107 publications

(42 citation statements)

References 27 publications

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“…In the model given by Eqs. (7) to (12), the degree constraints, that is, Constraints (2) and (3), are removed and replaced by two weaker ones, namely Constraints (8) and (9). As a consequence, a feasible path does not need to be Hamiltonian, that is, visit each node exactly once.…”

Section: Mathematical Model and Solution Methodsmentioning

confidence: 99%

“…This constraint represents what is formally called a flow conservation constraint. At the same time, Constraint (9) guarantees that the outgoing degree of each node is at most one, while Constraint (10) is still necessary in order to guarantee the elementarily of the solution path and prevent sub-tours. Recall that a path is elementary if each node is visited at the most once.…”

Section: Mathematical Model and Solution Methodsmentioning

confidence: 99%

“…Several studies tend to offer the maximum number of PoI based on the availability of public transport without taking into consideration the stochastic component of the services such as bus/train/tram arrival times [9,42]. The alternative to public transportation is given by a convenient and relatively affordable option known as city bus tour (CBT) service, that is, a specific bus service providing pre-planned tours to a number of PoI.…”

Section: The City Bus Tour (Cbt) Alternativementioning

confidence: 99%

“…However, the existing tools still fail to address several practical aspects, hence compromising their utility in realistic tourist scenarios [9].…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

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

et al. 2017

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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.

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Section: Mathematical Model and Solution Methodsmentioning

confidence: 99%

Section: Mathematical Model and Solution Methodsmentioning

confidence: 99%

Section: The City Bus Tour (Cbt) Alternativementioning

confidence: 99%

“…However, the existing tools still fail to address several practical aspects, hence compromising their utility in realistic tourist scenarios [9].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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

et al. 2017

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“…Further, the orienteering problem is solved using the iterative tabu search algorithm, which creates tourist routes that must contain all the mandatory places and maximize the overall satisfaction from the route when using additional daily visited places, taking into account access times and hours of operation, restrictions on tour time, user's budget and other restrictions. The paper [13] presents the eCOMPASS multimodal tourist tour planner. This tourist tour planner provides a navigational aid and incorporates multimodality within its routing logic aiming at deriving near-optimal sequencing of points of interest (POI) along recommended tours to utilize the time available for sightseeing and minimize waiting time at transit stops.…”

Section: Related Workmentioning

confidence: 99%

Smartphone-based tourist trip planning system: a context-based approach to offline attraction recommendation

Mikhailov

Kashevnik

2018

MATEC Web Conf.

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Abstract. The paper presents a tourist trip planning system that is aimed at offline attractions recommendation for a tourist based on current situation in the region and user profiles. Authors present the related work analysis in this area and based on this analysis develop the reference model of the proposed system and describe implementation that is based on previously developed Tourist Assistant -TAIS.

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