Group Trip Planning Research Articles

Group trip planning and information seeking behaviours by mobile social media users: A study of tourists in Australia, Bangladesh and China

Social media plays an increasingly important role in travel information seeking and decision-making. However, there is limited understanding of how a group of tourists use social media to plan trips collaboratively and the different practices between countries. In this study, we investigated the collaborative information seeking (CIS) and sharing behaviours of mobile social media users from Australia, Bangladesh and China. Specifically, we surveyed a total of 219 participants to explore the differences in CIS behaviours when people were planning a group trip. The findings suggest significant differences among three countries in terms of the motivations of using social media, CIS activities and social interactions outside the group. Key findings include Bangladeshi and Chinese travellers preferred known contacts on social media, while Australian tourists intended to use both known contacts and user-generated contents for seeking information. The findings also show that social interactions employed by individuals are considered as an important complement of and are interwoven with in-group CIS; both contribute to tourism information seeking. Finally, we propose a framework for CIS research in the tourism domain.

Activity-aware Ridesharing Group Trip Planning Queries for Flexible POIs

In recent years, ridesharing has become a popular model that enables users to share their rides with others. In this article, we introduce a novel ridesharing service, an Activity-aware Ridesharing Group Trip Planning (ARGTP) query , in road networks that exhibits three novel features: (i) ensures a complete trip for visiting more than two locations, (ii) allows visiting both fixed and flexible locations, and (iii) provides true ridesharing services instead of taxilike ridesourcing services by matching a group of riders’ flexible trips with a driver’s fixed trip. A trip visits a point-of-interest (POI) like a bank, restaurant, or supermarket for an activity in between source and destination locations. In a fixed trip, the POI is predetermined (e.g., a specific branch of a bank) and in a flexible trip, the POI is a flexible one (e.g., any branch of a bank). Considering the spatial proximity of the riders’ trips with a driver’s trip, an ARGTP query returns an optimal ridesharing group that minimizes the group cost. We develop the first solution to process ARGTP queries in real time and extend our solution for generalized ARGTP queries with multiple POIs. The efficiency of ARGTP query processing algorithms depends on the number of candidate riders and POIs to be explored. We introduce novel pruning techniques to refine the riders and POI search space. We perform extensive experiments using both real and synthetic datasets to validate the efficiency and effectiveness of our approach and show that it outperforms two baseline approaches with a large margin.

Efficient Scheduling of Generalized Group Trips in Road Networks

In this article, we introduce generalized group trip scheduling (GGTS) queries that enable friends and families to perform activities at different points of interest (POIs), such as a shopping center, a restaurant and a pharmacy with the minimum total travel distance. Trip planning and scheduling for groups, an important class of location-based services (LBSs), have recently received attention from researchers. However, both group trip planning (GTP) and group trip scheduling (GTS) queries have restrictions: a GTP query assumes that all group members visit all required POIs together, whereas a GTS query requires that each POI is visited by a single group member. A GGTS query is more general and allows any number of group members to visit a POI together. We propose an efficient algorithm to evaluate the exact answers for GGTS queries in road networks. Since finding the answer for a GGTS query is an NP-hard problem, to reduce the processing overhead for a large group size or a large number of required POI types or a large POI dataset, we propose two heuristic solutions—trip-scheduling heuristic (TSH) and search region refinement heuristic (SRH)—for processing GGTS queries. Extensive experiments with real datasets show that our optimal algorithm is preferable for small parameter settings, and the heuristic solutions reduce the processing overhead significantly in return for sacrificing the accuracy slightly.

Tourists' social interactions on social media during a group trip planning

ABSTRACTThis paper explores the motivations for employing social interactions on social media during collaborative information seeking (CIS) in group trip planning. We conducted interviews with nine groups of tourists. The analysis identified three motivations for employing social interactions on social media while planning a group trip: finding and gathering information, scheduling the trip‐related activities, and cross‐checking of ideas. The findings also suggest that such interactions on social media contribute to group trip planning through improving the productivity of group information seeking and making better group trip planning.

Characteristics of collaborative information seeking activities during group trip planning: A preliminary study

ABSTRACTCollaborative work among tourists is a topic of ongoing interest in information seeking area, and such activities are constantly evolving as technology advances. This paper explores the characteristics of a tourists group's activities during tourism information seeking. We conducted interviews with nine groups of tourists. The analysis identified four main characteristics of collaborative information seeking (CIS) activities in group trip planning process: initial co‐planning on the trip, seeking and sharing of information, prioritization of choices, and collaborative decisions. The findings of CIS characteristics will help CIS developer and tourism service operators to integrate new customized features and strategies to support group trip planning.

Optimal group route query: Finding itinerary for group of users in spatial databases

The increasing popularity of location-based applications creates new opportunities for users to travel together. In this paper, we study a novel spatio-social optimization problem , i.e., Optimal Group Route, for multi-user itinerary planning. With our problem formulation, users can individually specify sources and destinations, preferences on the Point-of-interest (POI) categories, as well as the distance constraints. The goal is to find a itinerary that can be traversed by all the users while maximizing the group’s preference of POI categories in the itinerary. Our work advances existing group trip planning studies by maximizing the group’s social experience. To this end, individual preferences of POI categories are aggregated by considering the agreement and disagreement among group members. Furthermore, planning a multi-user itinerary on large road networks is computationally challenging. We propose two efficient greedy algorithms with bounded approximation ratio, one exact solution which computes the optimal itinerary by exploring a limited number of paths in the road network, and a scaled approximation algorithm to speed up the dynamic programming employed by the exact solution. We conduct extensive empirical evaluations on two real-world road network/POI datasets and our results confirm the effectiveness and efficiency of our solutions.

A Human Factors Approach to Exploring the Experience of Group Trip Planning from the Perspective of Intragroup Interaction

ABSTRACTPrevious studies have investigated the experiences and characteristics of holiday decision-making among groups of travelers. This study adds to the knowledge of group trip holiday planning through exploring influential factors (including the individual and group characteristics of travelers), and linking those with their intragroup interactions when planning a group trip. A total of 261 usable questionnaires were collected across two university campuses in the UK and China. The survey employed a retrospective approach, asking participants to recall one of their past group trip planning experiences within the previous 3 months. This study found that intragroup interactions during a group trip planning process are influenced both by tourists’ individual factors, such as age, gender, and nationality, and by group characteristics, such as group size, common interest, group type, and group travel style. This study shows that common interest is the most influential factor in terms of its positive impact on group collaboration, feeling of connectedness, strength of preparation, and flexibility and spontaneity during group trip planning process. Further, in general, Chinese groups tend to spend less time on planning their trips before departure, but focus more on the details of the itinerary. Finally, the implications for technologies that are designed to facilitate the group trip planning process, with a view to enhancing the level of group enjoyment, are discussed based on the findings in this study.