Planning sightseeing tours using crowdsensed trajectories

Brilhante, Igo Ramalho; Macêdo, José Antônio Fernandes de; Nardini, Franco Maria; Perego, Raffaele; Renso, Chiara

doi:10.1145/2782759.2782769

Cited by 6 publications

(5 citation statements)

References 4 publications

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“…Geospatial big data in social media is used for analyzing and visualizing various aspects related to the urban space. Studies have shown the contribution of crowdsourced geotagged photos for diverse applications, for example, mapping areas that are more calm and more exciting by using big data of geotagged images and audio samples [7], extracting landmarks from geotagged photos by using deep neural network [8], computing pleasant (beautiful and happy) routes in Boston and London [9], and tourism route planning [10,11]. Photography is strongly related to tourism experience [12], meaning that most tourists take photos for documentation purposes as a proof of consuming the experience of travelling a city [4], such that tourism photography is correlated with tourism development [13].…”

Section: Related Researchmentioning

confidence: 99%

3D Visibility Analysis for Evaluating the Attractiveness of Tourism Routes Computed from Social Media Photos

Mor

Fisher-Gewirtzman

Yosifof

et al. 2021

IJGI

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Social media is used nowadays for various location-based applications and services, aspiring to use the vast and timely potential of user-generated content. To evaluate the correctness, reliability and potential of these applications and services, they are mostly evaluated in terms of optimization or compared to existing authoritative data sources and services. With respect to route planning, criterion optimization is mostly implemented to evaluate the service effectiveness, in terms of, e.g., length, time or visited places. These evaluations are mostly limited in their effectiveness at presenting the complete experience of the route, since they are limited to a predefined criterion and are mostly implemented in two-dimensional space. In this research, we propose a comprehensive evaluation process, in which a tourism walking route is analyzed with respect to three-dimensional visibility that measures the attractiveness of the route relating to the user perception. To present our development, we showcase the use of Flickr, a social media photo-sharing online website that is popular among travelers that share their tourism experiences. We use Flickr photos to generate tourism walking routes and evaluate them in terms of the visible space. We show that the 3D visibility analysis identifies the various visible urban elements in the vicinity of the tourism routes, which are more attractive, scenery and include many tourism attractions. Since urban attractivity is often reflected in the photo-trails of Flickr photographers, we argue that using 3D visibility analysis that measures urban attractiveness and scenery should be considered for the purpose of analysis and evaluation of location-based services.

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Section: Related Researchmentioning

confidence: 99%

3D Visibility Analysis for Evaluating the Attractiveness of Tourism Routes Computed from Social Media Photos

Mor

Fisher-Gewirtzman

Yosifof

et al. 2021

IJGI

View full text Add to dashboard Cite

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“…Researchers aim to discover comprehensive tourism routes from user-generated information by using the calculated POI geographic information to guide photographers to locations that demonstrate tourism features (e.g., Lim (2015), Li et al, (2015), Choudury et al, (2010), andYahi et al, (2015)). Yahi et al (2015), Brilhante et al, (2015), and Mor and Dalyot (2018), for example, use Google Maps API to compute routes between POIs, while Sun et al (2015) use the Dijkstra algorithm with weighted popularity road matrix to compute the routes. Chen et al, (2017) enriched the road network with scenic score considering density and dominate direction of geotagged photos and check-ins near each segment.…”

Section: Related Workmentioning

confidence: 99%

“…Becker et al (2015) extracted photo trails for a single photographer, regardless of the time span, by comparing the extracted trail to a weighted POI geographic layer to explore tourists' patterns. Ali et al (2013) and Choudhury et al (2010) analyze photographers' pattern by a comprehensive study of their photo sequence to evaluate tourism routes, while Brilhante et al (2015) used Wikipedia POIs information to analyze crowdsourced tourism trajectories. To extend these approaches, we aim to generate comprehensive tourism routes by investigating and interpreting the accumulated trajectory patterns of the tourism-photographers, without relying on external POIs information.…”

Section: Related Workmentioning

confidence: 99%

“…Becker et al (2015) defined a cell size of 200X200 meters for Berlin, whereas Eli et al ( 2013) defined a cell size of 125X125 meters for Amsterdam. Additionally, Brilhante et al (2015) showed that a value of 200 meters represents the approximated distance among POIs in Wikipedia. Accordingly, we have decided to use a cell size of 250X250 meters, also taking into consideration GNSS localization errors in urban areas (inaccurate geo-tagging values of the photos).…”

Section: Poi Identificationmentioning

confidence: 99%

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Enriching Walking Routes With Tourism Attractions Retrieved From Crowdsourced User Generated Data

Mor

Dalyot

2020

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. It is always a tourism challenge – and aspiration – to discover scenery routes and tourism attractions in unfamiliar areas. Tourism information is getting more extensive, comprehensive and complex, so first-time tourists have to manage and mine large volumes of data to better plan their trip. Nowadays, geotagged photos are uploaded by users to social media photo-sharing online websites, which become more popular and commonly used by travelers to share their tourism experiences. Handling, mining and interpreting these user-generated ‘digital footprints’ can be used to reconstruct travel trajectories of users to recover their activity and knowledge. In this research, we showcase Flickr geotagged crowdsource photo database as a source for mining users’ trajectories to effectively compute walking tourism routes. Our methodology mines tourism context by conceptualizing a set of adaptive spatiotemporal descriptors to identify photographers that show tourism activity of first-time visitors. By implementing spatial clustering, we find popular locations that are traversed by these tourism-oriented photographers’ trajectories. To analyze our approach, we develop a greedy route computation algorithm that seeks the most popular traversed locations between origin and destination points defined by the user. Results for two cities are presented, proving the robust mining and retrieving of valuable tourism context and information from social media photos. We evaluate and validate our results by comparing the computed walking routes to recognized tourism information. The computed walking routes are scenery and pass through the main popular tourism sights and landmarks in the city, including additional attractive places that are frequently visited by tourism-photographers.

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“…While many studies recognize that explorations of meaningful spots in destinations are a primary task in itinerary planning and intelligent transportation [9][10][11], movement as a tour experience and pedestrian mobility based on qualitative factors, except for the shortest route principle, tend to be underestimated [12]. Some research on the automatic generation of a tour itinerary has proposed movement routes based on time saving or querying Google Maps [13][14][15][16]. From the perspective of walking tours, they only take access into account, not all factors in the policy of route selection.…”

Section: Introductionmentioning

confidence: 99%

Mobile Collaborative Heatmapping to Infer Self-Guided Walking Tourists’ Preferences for Geomedia

Sasaki

Sato

2023

IJGI

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This paper proposes a model-less feedback system driven by tourist tracking data that are automatically collected through mobile applications to visualize the gap between geomedia recommendations and the actual routes selected by tourists. High-frequency GPS data essentially make it difficult to interpret the semantic importance of hot spots and the presence of street-level features on a density map. Our mobile collaborative framework reorganizes tourist trajectories. This processing comprises (1) extracting the location of the user-generated content (UGC) recording, (2) abstracting the locations where tourists stay, (3) discarding locations where users remain stationary, and (4) simplifying the remaining points of location. Then, our heatmapping system visualizes heatmaps for hot streets, UGC-oriented hot spots, and indoor-oriented hot spots. According to our experimental study, this method can generate a trajectory that is more adaptable for hot street visualization than the raw trajectory and a simplified trajectory according to its geometry. This paper extends our previous work at the 2022 IEEE International Conference on Big Data, providing deeper discussions on application for local tourism. The framework allows us to derive insights for the development of guide content from mobile sensor data.

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Planning sightseeing tours using crowdsensed trajectories

Cited by 6 publications

References 4 publications

3D Visibility Analysis for Evaluating the Attractiveness of Tourism Routes Computed from Social Media Photos

3D Visibility Analysis for Evaluating the Attractiveness of Tourism Routes Computed from Social Media Photos

Enriching Walking Routes With Tourism Attractions Retrieved From Crowdsourced User Generated Data

Mobile Collaborative Heatmapping to Infer Self-Guided Walking Tourists’ Preferences for Geomedia

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