Nowadays, millions of people prefer traveling to another city to spend holidays. To navigate in an unfamiliar tourism destination (e.g., city or region), tourists usually visit its famous attractions via the popular travel routes. However, the popularity of attractions and travel routes is time-evolving and depends on many factors, such as seasons, time budgets and personal preferences. Consequently, the latest and most effective recommendations need to be explored for tourists to elevate the satisfaction and experience of their journeys.
In existing tourism studies, travel recommendation for tourists is generally classified into two categories: generic and personalized [1
]. The generic travel recommendation follows the paradigm of “trajectories → interesting locations → popular travel sequences → itinerary planning → activities recommendation” [2
]. In comparison, a personalized recommender offers locations matching an individual’s preferences, which are learned from the individual’s location history [3
]. To achieve the goal, a variety of methods is adopted to acquire people’s travel data, such as surveying tourist’s location histories [4
] and automatic location-sensing devices like global positioning systems (GPS) [5
]. The cost, scalability and privacy issues, however, hinder the effectiveness of these methods. Promisingly, social media platforms, such as Flickr, Twitter, Facebook, OpenStreetMap, etc., enable users to share their tourism-related information [6
]. In reality, the fact that users prefer to use social media apps, particularly photo sharing services, during tourism activities has been widely recognized [8
]. Those photo-sharing services have already led to enormous community-contributed photos with text tags, timestamps and geographic references on the Internet. More importantly, it turns out that the geo-tagged photos can tackle the aforementioned issues in previous methods and provide an effective solution to automatic tourist mobility analysis [10
As the most popular photo-sharing platform, Flickr has accumulated a large collection of photos with metadata (such as location, time, size, camera type with which each photo was taken) and textual information (such as title, tag, etc.) that partially capture individuals’ travel activities in space and time [11
]. These geo-tagged photos have been widely used to re-construct trajectories of tourists and to uncover the underlying tourist behavior patterns [12
]. With the assistance of geo-tagged photos from Flickr, significant advances have been achieved for our understanding of tourists’ travel behavior patterns. For instance, Pladino et al. quantified both the global and local attractiveness of several famous tourism destinations using information from geo-tagged photos [13
]. Arase et al. identified people’s frequent trip patterns, i.e., typical sequences of visited cities and stay durations, as well as descriptive tags that characterize the trip patterns [14
]. They defined six trip themes (i.e., Landmark, Nature, Event, Gourmet, Business, Local) and mined frequent trip patterns on each theme at the city level. Nonetheless, much of the existing research only focuses on the city level of locations for photo trip mining, whereas the scene level is more informative. Hence, Lu et al. extracted popular travel routes from geotagged photos by clustering and took into account a number of factors such as duration of the trip and traveling cost to help the tourist with trip planning [15
]. Zheng et al. investigated tourist’s movement patterns in relation to the regions of attractions and the topological characteristics of travel routes visited by different tourists [16
]. Sun et al. built a recommendation system that provides users with the optimal travel routes, of which the basic unit is the separate road segment instead of the GPS trajectory segment [17
]. Simply put, those exploratory investigations are making Flickr photograph data the most promising data source for tourism study in academic society.
From a methodological perspective, travel patterns and routes taken by tourists between main attractions are conventionally modeled by the Markov chain-based approach [18
] or collaborative filtering [20
]. It generally requires the detection of frequent travel sequences to gain a deep insight into the tourists’ travel behaviors. Recently, with the advance of complex network science, a tourist’s location history can be represented as a directed graph where a node is a location and an edge denotes the traveling sequence [22
]. Popular travel sequences in those graphs can be termed as motifs in analogy to motifs in complex network, which were originally defined as patterns of interconnections occurring in complex networks at numbers that are significantly higher than those in randomized networks [24
]. Jackson et al. systematically investigated motifs according to different shapes as feed-forward loop, chain, feedback loop, feedback with two mutual dyads, bi-fan, bi-parallel, fully-connected triad and uplinked mutual dyad [25
]. Motifs were further classified as hub-and-spoke, pairs, trees, chains, triads, cycles and stars. It is also noteworthy that Kovanen et al. introduced the framework of temporal motifs to study classes of similar event sequences, where the similarity refers not only to network topology, but also to the temporal order of the events [26
]. Stimulated by the aforementioned studies, Schneider et al. adopted the concept of motifs to study human mobility and found that only 17 unique motifs are present in daily mobility, which are sufficient to capture up to 90 percent of the population in surveys and mobile phone datasets for different countries [27
]. Those previous studies have confirmed that motifs possess great potential for analyzing travel patterns.
However, generic motifs do not take place semantics into consideration. Conventional tourism recommendation systems heavily rely on popular/frequent travel sequences of a group of tourists [28
]. Those sequences consist of a list of ad hoc attractions and cannot reveal the tastes/preferences of different kinds of tourism activities between users [29
]. For instance, tourists may travel distinctively in terms of the number and the category of attractions. In a sense, the generic motif can be regarded as the highly abstract form of popular/frequent travel sequences. It models the travel preference between a given number of potential attractions [27
]. Nonetheless, the generic motif does not takes spatio-temporal semantic information of the node and the edge of the underlying travel network into consideration [24
]. As a result, it fails to model tourists’ travel preferences between different types of attractions (such as natural, cultural attractions, and so on). However, understanding the frequent travel sequence with and without place semantics is both important and has distinct implications for tourism recommendation [30
]. For instance, under certain scenarios, tourists have to plan their itinerary by deciding which type of sites to visit with high priority, as well as the duration of their visitation according to their personal preferences and time budgets. The generic motif is only capable of suggesting a popular route between a given number of attractions and cannot address the requirements of the priority and the duration of visiting different kinds of attractions. Therefore, motifs with spatio-temporal semantics are an urgent need for detecting more specific travel behavior patterns to enhance the capability of existing travel recommender systems.
From this point of view, this research focuses on Flickr photos taken in New York and aims to mine tourists’ frequent travel patterns in the city. In Section 2
, we extend the concept of network motifs to unveil the spatio-temporal semantics of different travel motifs and quantify user similarity based on topological, temporal and semantic travel motifs to distinguish users with different preferences for assisting travel recommendation. In Section 3
, empirical results are described for our understanding of several typical tourists’ travel behavior patterns within the case study area. Section 4
discuss the limits and potentials of the proposed analytic framework. Section 5
highlights our contributions and concludes the paper.
As indicated by formal definitions and previous empirical results in Section 2
and Section 3
, Schneider’s topological motif, discrete topological motif and consecutive topological motif, as well as their corresponding temporal and semantic motifs, are different from each other. From a theoretical perspective, discrete motif unveil the “relative” priority for visiting different tourism attractions. Put simply, within a set of candidate landmarks, the discrete motif can assist tourists to determine to visit which first based on the collective intelligence of others. However, it cannot provide recommendation to tourists for deciding which landmark to visit immediately next to his/her current landmark. Hence, we developed a consecutive motif to address this “exact” priority problem. Compared with the discrete motif, the consecutive motif produces a continuous travel path between candidate landmarks. It thus enables us to recommend a candidate “next” destination for tourists. Additionally, as previously mentioned, Schneider’s motifs can be regarded as a subset of consecutive motifs.
It is also noteworthy that several improvements can be made in our future works. Among them, the three most important points are listed as follows. (1) Including textual information: This study only utilizes the location and captured time of photos to derive tourists’ mobility patterns. However, text data like tags and titles contain much information about trips. Therefore, a future direction can take text data into consideration for detecting additional contexts of trips, which are of great importance to enrich the knowledge of tourists’ behaviors. (2) Finer classification of attractions: This work classifies landmarks simply into three categories, which are very rough. A finer classification of landmarks will distinguish tourists better. For instance, two tourists sharing an interest in museums are more similar than others who like to visit other cultural places, such as churches. Therefore, constructing a semantic hierarchy with different granularities can be another direction of improvement to this research work. (3) Considering demographics of tourists: People from different countries usually have different preferences in many things, such as eating habits, styles of clothes and genres of art, just to name a few. Similarly, tourists with diverse cultural backgrounds are expected to behave differently in their tourism travel patterns. In this sense, by including the demographic background of tourists, we will be able to deepen our understanding of tourist behavior patterns.
Serving as the prerequisite to travel recommender systems, detecting and visualizing tourists’ frequent travel patterns is of great importance for effective travel itinerary planning. With the assistance of geo-tagged photos from Flickr, this research explored tourist behavior patterns based on a novel motif-based clustering framework. Enlightened by previous work on human mobility and network motifs, we extended the concept of travel motifs from topological spaces, to temporal spaces and to semantic spaces and unveiled tourist behavior patterns from different perspectives. In summary, the topological travel motif reveals typical travel patterns (e.g., popular/frequent travel sequence) of tourists between a given number of tourism attractions; the temporal travel motif indicates the time budgets (e.g., the duration of stay) of tourists spent based on the order of visited attractions; and the semantic travel motif differentiates the sightseeing tastes of tourists among different types (e.g., natural, cultural, business, and so on) of tourism attractions.
Our proposed analytic framework will enhance the state of the art tourism studies and recommendation systems in three directions. First, typical topological travel motifs enable tourists to determine the number of attractions to visit and the optimal route. Second, typical temporal travel motifs further enable tourists to determine the visitation duration based on the order of attractions. Third, typical semantic travel motifs enable tourists to determine which type of attraction to visit with high priority. More importantly, this kind of recommendation is different from the state of the art tourism studies and recommendation systems in that it requires no frequent travel sequence covering exactly the same attractions and travel routes. It therefore can remarkably enhance the flexibility of existing tourism recommendation systems to meet more general sightseeing preferences.
Additionally, empirical results derived from Manhattan Island, New York, indicate that tourists tend to travel in space and time with a limited collection of typical topological, temporal and semantic characteristics. These findings confirm that the proposed analytical framework for quantifying user similarity, which consists of representing travel trajectories by motifs and then calculating the closeness of transformed trajectories, provides a promising technique for understanding tourist behavior patterns with explicit spatial, temporal and semantic characteristics. It will enable tourists who want to visit New York to make travel arrangements in a more efficient manner. Last, but not least, concepts and methods introduced by this research can be applied to the study of other mobility datasets and cities and contribute to the development of tourism business in a general sense.