Discovering popular routes from trajectories

Chen, Zaiben; Shen, Heng Tao; Zhou, Xiaofang

doi:10.1109/icde.2011.5767890

Cited by 297 publications

(207 citation statements)

References 25 publications

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“…These systems can be oriented to tourism, to customize the contents of a web page, or to recommend background music. Chen (2011) addressed the issue of finding the most popular route (PMR) between two sites based on the historical behavior of other tourists. First, a network with all possible routes between the two sites is built; and then the PMR is created from the popularity of each of the nodes in the network.…”

Section: Related Workmentioning

confidence: 99%

Recognition of highly frequented sets of tourist sites

2015

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In this paper we propose an algorithm to identify sets of the most frequently visited tourist sites. We do this by examining the trajectories followed by tourists and by considering their visits to these sites. We propose a second algorithm that recommends a specific order to visit these sites. To accomplish this task, we consider variables such as tourist preferences, departure and arrival locations, and time constraints. To validate our proposal, a prototype website application was developed, which experiments with real vehicle trajectories in Rio de Janeiro. Although more exhaustive experiments will be required to deal with different possible scenarios, preliminary results show the usefulness of our proposal for displaying sets of neighborhoods frequented by vehicles as they move about a city.Keywords: Tourism, routes, tourist recommender, heuristic algorithms, trajectories, frequent routes. RESUMENEn este artículo se propone un algoritmo para identificar los conjuntos de sitios turísticos más frecuentemente visitados. Para ello se examinan las trayectorias seguidas por turistas y se consideran sus visitas a los sitios turísticos. Se propone un segundo algoritmo que sugiere el orden en el que deben ser visitados los sitios identificados por el primer algoritmo. Para lograr esto se consideran variables como preferencias turísticas, lugar de partida y de llegada y restricciones de tiempo. La propuesta se validó mediante una aplicación web prototipo y se experimentó con trayectorias reales de vehículos en Río de Janeiro. Aunque se requieren experimentos más exhaustivos y se deben considerar otros escenarios, los resultados preliminares mostraron la utilidad de la propuesta al identificar conjuntos de barrios que son frecuentados por los vehiculos a medida que estos se desplazan por la ciudad.

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Section: Related Workmentioning

confidence: 99%

Recognition of highly frequented sets of tourist sites

2015

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“…Since the rapidly increased satellites and GPS (Global Position System) technologies have developed, it is possible to collect a large amount of trajectory data of moving objects such as the vehicle position data, hurricane track data, and animal movement data [1,2,16,17]. The analysis over these trajectory data is becoming important for many applications, such as meteorological observation and forecast, animal habits observation, road traffic situation analysis, and navigation in transportations [3][4][5][6]8].…”

Section: Introductionmentioning

confidence: 99%

“…The analysis over these trajectory data is becoming important for many applications, such as meteorological observation and forecast, animal habits observation, road traffic situation analysis, and navigation in transportations [3][4][5][6]8]. According to the recorded trajectory data and road networks, the moving pattern, traffic situation and road recommendation services can be supported [1,2,12,15,18]. Recently, with the continuously increasing mobile devices and vehicles, the route recommendation service is becoming more and more important [1,5,6,9,17,19,20].…”

Section: Introductionmentioning

confidence: 99%

A Clustering Scheme for Discovering Congested Routes on Road Networks

He¹,

Bok²,

Lim³

et al. 2015

Journal of Electrical Engineering and Technology

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-On road networks, the clustering of moving objects is important for traffic monitoring and routes recommendation. The existing schemes find out density route by considering the number of vehicles in a road segment. Since they don't consider the features of each road segment such as width, length, and directions in a road network, the results are not correct in some real road networks. To overcome such problems, we propose a clustering method for congested routes discovering from the trajectories of moving objects on road networks. The proposed scheme can be divided into three steps. First, it divides each road network into segments with different width, length, and directions. Second, the congested road segments are detected through analyzing the trajectories of moving objects on the road network. The saturation degree of each road segment and the average moving speed of vehicles in a road segment are computed to detect the congested road segments. Finally, we compute the final congested routes by using a clustering scheme. The experimental results showed that the proposed scheme can efficiently discover the congested routes in different directions of the roads.

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“…One way is to find routes among the data that are popular. This information can be used to make route recommendations to users [9] or to help in road network improvements and urban planning [10,11]. The service can be provided to the clients in the form of Software as a Service (SaaS), where the users can look for popular routes matching their interests near their location, e.g., running tracks that lasted at least 30 min, were longer than 5 km and were recorded in June.…”

Section: Introductionmentioning

confidence: 99%

Generating Heat Maps of Popular Routes Online from Massive Mobile Sports Tracking Application Data in Milliseconds While Respecting Privacy

Sainio

Westerholm

Oksanen

2015

IJGI

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The breakthrough of GPS-equipped smartphones has enabled the collection of track data from human mobility on massive scales that can be used in route recommendation, urban planning and traffic management. In this work we present a fast map server that can generate and visualize heat maps of popular routes online from massive sports track data based on client preferences, e.g., running routes lasting less than an hour. The heat maps shown respect user privacy by not showing routes with less than a predefined number of different users, for instance five. The results are represented to the client using a dynamic tile layer. The current implementation uses data collected by the Sports Tracker mobile application with over 800,000 different tracks and 2.8 billion GPS data points. Stress tests indicate that the server can handle hundreds of simultaneous client requests in a single server configuration.

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Discovering popular routes from trajectories

Cited by 297 publications

References 25 publications

Recognition of highly frequented sets of tourist sites

Recognition of highly frequented sets of tourist sites

A Clustering Scheme for Discovering Congested Routes on Road Networks

Generating Heat Maps of Popular Routes Online from Massive Mobile Sports Tracking Application Data in Milliseconds While Respecting Privacy

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