How many places do you visit a day?

Papandrea, Michela; Zignani, Matteo; Gaito, Sabrina; Giordano, Silvia; Rossi, Gian Paolo

doi:10.1109/percomw.2013.6529485

Cited by 16 publications

(13 citation statements)

References 11 publications

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“…Remark: As this paper targets the travel route planning issue for tourists visiting a city for a day trip by renting a car, we thus assume that the number of user-specified venues is not big [28]. Therefore, the route search problem is just finding all the routes that meet the visiting order and time constraints, and the time spent on route search in a city scale is relatively short.…”

Section: A Phase I: Route Searchmentioning

confidence: 99%

TripPlanner: Personalized Trip Planning Leveraging Heterogeneous Crowdsourced Digital Footprints

Chen

Zhang

Guo

et al. 2015

IEEE Trans. Intell. Transport. Syst.

121

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Planning an itinerary before traveling to a city is one of the most important travel preparation activities. In this paper, we propose a novel framework called TRIPPLANNER, leveraging a combination of location-based social network (i.e., LBSN) and taxi GPS digital footprints to achieve personalized, interactive, and traffic-aware trip planning. First, we construct a dynamic point-of-interest network model by extracting relevant information from crowdsourced LBSN and taxi GPS traces. Then, we propose a two-phase approach for personalized trip planning. In the route search phase, TRIPPLANNER works interactively with users to generate candidate routes with specified venues. In the route augmentation phase, TRIPPLANNER applies heuristic algorithms to add user's preferred venues iteratively to the candidate routes, with the objective of maximizing the route score while satisfying both the venue visiting time and total travel time constraints. To validate the efficiency and effectiveness of the proposed approach, extensive empirical studies were performed on two real-world data sets from the city of San Francisco, which contain more than 391 900 passenger delivery trips generated by 536 taxis in a month and 110 214 check-ins left by 15 680 Foursquare users in six months.

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Section: A Phase I: Route Searchmentioning

confidence: 99%

TripPlanner: Personalized Trip Planning Leveraging Heterogeneous Crowdsourced Digital Footprints

Chen

Zhang

Guo

et al. 2015

IEEE Trans. Intell. Transport. Syst.

121

View full text Add to dashboard Cite

show abstract

“…The Third IEEE International Workshop on the Impact of Human Mobility in Pervasive Systems and Applications, 2014 useful to our analysis, we use a filling heuristic [1] to densify the trajectory points corresponding to the pause phase, while removing points belonging to users' movements. Furthermore, the dataset is highly fragmented.…”

Section: A Trajectories Datasetmentioning

confidence: 99%

“…According to the Definition 1, R is the percentage of days the user visits the PoI P . By the relevance we can capture how likely an individual will move towards a place or return to it according to his/her history According to the relevance values, we have shown [1] that the PoIs associated to each user can be automatically grouped in 3 classes: (i) Exceptionally Visited PoIs (EVP), PoIs unlikely visited more than very few times; (ii) Occasionally Visited PoIs (OVP), locations of interest for the user, but visited just occasionally; (iii) Mostly Visited PoIs (MVP), locations most frequently visited by the user (we can easily infer their semantic meaning, and associate them to home location, work place, gym). The histogram of the percentage of PoIs in the three classes in Figure 2 indicates that the PoIs partitioning in classes of relevance is common in both datasets.…”

Section: A Relevancementioning

confidence: 99%

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On the key features in human mobility: Relevance, time and distance

Zignani

Papandrea

Gaito

et al. 2014

2014 IEEE International Conference on Pervasive Computing and Communication Workshops (PERCOM WORKSHOPS)

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Our era of increased people mobility requires a better understanding of how people move, how many are the places that a person commonly visits, how people move among them. In our previous work we have shown that the number of visited points of interest (PoIs) of each person is regulated by some laws that are statistically similar among individuals, and we gave some classification of them in terms of their relevance. We investigate here the variables that characterize the way humans move among PoIs, and in particular the spatial and temporal distances between PoIs and the PoIs relevance. With this respect, most of the existing work on mobility, especially on its modeling, is based only on spatial distance, while we argue here that for human mobility the temporal distance and the PoIs relevance are essential factors. Also, by comparing the geographical and temporal distances between consecutive PoIs, we observe a smoother and more linear trend in the temporal case. The results suggest that the time, rather than physical distance, could represent a better measure of distance among PoIs, mainly due to the transportation facilities. We present here our analysis of this aspect, considering two different datasets, to show the validity of our results independently of the nature of the dataset under consideration, and to extract considerations on different scenario scales. We argue that these results can be seminal to novel mobility research.

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“…This is due mainly to the fact that they have been considered as points in an area and social aggregation places, without anchoring spatial features to the behavior of each single user. This paper, which represents an extension of our previous works [31,44], aims to fill the gap by providing a general framework for dealing with modeling locations from a per-user perspective. Also, it paves the way towards enabling the semantic interpretation of locations to be overlaid on their spatial distribution.…”

Section: Introductionmentioning

confidence: 99%

On the properties of human mobility

Papandrea

Jahromi

Zignani

et al. 2016

Computer Communications

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The current age of increased people mobility calls for a better understanding of how people move: how many places does an individual commonly visit, what are the semantics of these places, and how do people get from one place to another. We show that the number of places visited by each person (Points of Interest -PoIs) is regulated by some properties that are statistically similar among individuals. Subsequently, we present a PoIs classification in terms of their relevance on a per-user basis. In addition to the PoIs relevance, we also investigate the variables that describe the travel rules among PoIs in particular, the spatial and temporal distance. As regards the latter, existing works on mobility are mainly based on spatial distance. Here we argue, rather, that for human mobility the temporal distance and the PoIs relevance are the major driving factors. Moreover, we study the semantic of PoIs. This is useful for deriving statistics on people's habits without breaking their privacy. With the support of different datasets, our paper provides an in-depth analysis of PoIs distribution and semantics; it also shows that our results hold independently of the nature of the dataset in use. We illustrate that our approach is able to effectively extract a rich set of features describing human mobility and we argue that this can be seminal to novel mobility research.

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How many places do you visit a day?

Cited by 16 publications

References 11 publications

TripPlanner: Personalized Trip Planning Leveraging Heterogeneous Crowdsourced Digital Footprints

TripPlanner: Personalized Trip Planning Leveraging Heterogeneous Crowdsourced Digital Footprints

On the key features in human mobility: Relevance, time and distance

On the properties of human mobility

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