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Constructing popular routes from uncertain trajectories.
- In KDD’12,
, 2012
"... ABSTRACT The advances in location-acquisition technologies have led to a myriad of spatial trajectories. These trajectories are usually generated at a low or an irregular frequency due to applications' characteristics or energy saving, leaving the routes between two consecutive points of a sin ..."
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ABSTRACT The advances in location-acquisition technologies have led to a myriad of spatial trajectories. These trajectories are usually generated at a low or an irregular frequency due to applications' characteristics or energy saving, leaving the routes between two consecutive points of a single trajectory uncertain (called an uncertain trajectory). In this paper, we present a Route Inference framework based on Collective Knowledge (abbreviated as RICK) to construct the popular routes from uncertain trajectories. Explicitly, given a location sequence and a time span, the RICK is able to construct the top-k routes which sequentially pass through the locations within the specified time span, by aggregating such uncertain trajectories in a mutual reinforcement way (i.e., uncertain + uncertain → certain). Our work can benefit trip planning, traffic management, and animal movement studies. The RICK comprises two components: routable graph construction and route inference. First, we explore the spatial and temporal characteristics of uncertain trajectories and construct a routable graph by collaborative learning among the uncertain trajectories. Second, in light of the routable graph, we propose a routing algorithm to construct the top-k routes according to a userspecified query. We have conducted extensive experiments on two real datasets, consisting of Foursquare check-in datasets and taxi trajectories. The results show that RICK is both effective and efficient.
Route Discovery from Mining Uncertain Trajectories
"... Abstract—Moving objects in the physical world usually generate many uncertain trajectories for some reasons such as the consideration of energy consumption, leaving the route passing two consecutive sampling points unknown. While such trajectories imply rich knowledge about the mobility of moving ob ..."
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Abstract—Moving objects in the physical world usually generate many uncertain trajectories for some reasons such as the consideration of energy consumption, leaving the route passing two consecutive sampling points unknown. While such trajectories imply rich knowledge about the mobility of moving objects, they are less useful individually. This paper introduces an online trip planning system that mines collective knowledge (i.e., most possible routes between given locations) from massive uncertain trajectories following a paradigm of “uncertain+uncertain→certain”. This system first builds a routable graph from uncertain trajectories, and then answers a user’s online query (a sequence of point locations) by searching top-k routes on the graph. Two large-scale datasets consisting of “check-in ” records from FourSquare and a trajectory dataset of taxis have been used to evaluate our system. As a result, our system provides a user with effective routes according to the user’s query efficiently. Keywords-spatial trajectories, uncertain trajectories, moving objects, trip planning I.
A generic data model for moving objects
- Geoinformatica 2013
"... Moving objects databases should be able to manage trips that pass through several real world environments, e.g., road network, indoor. However, the current data models only deal with the movement in one situation and cannot represent comprehensive trips for humans who can move inside a building, wal ..."
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Moving objects databases should be able to manage trips that pass through several real world environments, e.g., road network, indoor. However, the current data models only deal with the movement in one situation and cannot represent comprehensive trips for humans who can move inside a building, walk on the pavement, drive on the road, take the public vehicles (bus or train), etc. As a result, existing queries are solely limited to one environment. In this paper, we design a data model that is able to represent moving objects in multiple envi-ronments in order to support novel queries on trips in different surroundings and various transportation modes (e.g., Car, Walk, Bus). A generic and precise location representation is proposed that can apply in all environ-ments. The idea is to let the space for moving objects be covered by a set of so-called infrastructures each of which corresponds to an environment and defines the available places for moving objects. Then, the location is represented by referencing to the infrastructure. We formulate the concept of space and infrastructure and propose the methodology to represent moving objects in different environments with the integration of precise transportation modes. Due to different infrastructure characteristics, a set of novel data types is defined to rep-resent infrastructure components. To efficiently support new queries, we design a group of operators to access the data. We present how such a data model is implemented in a database system and report the experimental results. The new model is designed with attention to the data models of previous work for free space and road net-works to have a consistent type system and framework of operators. In this way, a powerful set of generic query operations is available for querying, together with those dealing with infrastructures and transportation modes. We demonstrate these capabilities by formulating a set of sophisticated queries across all infrastructures.
GeoSEMA: A Modelling Platform, Emerging “GeoSpatial-based Evolutionary and Mobile Agents”
"... Abstract—Spatial and mobile computing evolves. This paper describes a smart modeling platform called “GeoSEMA”. This approach tends to model multidimensional GeoSpatial Evolutionary and Mobile Agents. Instead of 3D and location-based issues, there are some other dimensions that may characterize spat ..."
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Abstract—Spatial and mobile computing evolves. This paper describes a smart modeling platform called “GeoSEMA”. This approach tends to model multidimensional GeoSpatial Evolutionary and Mobile Agents. Instead of 3D and location-based issues, there are some other dimensions that may characterize spatial agents, e.g. discrete-continuous time, agent behaviors. GeoSEMA is seen as a devoted design pattern motivating temporal geographic-based applications; it is a firm foundation for multipurpose and multidimensional special-based applications. It deals with multipurpose smart objects (buildings, shapes, missiles, etc.) by stimulating geospatial agents. Formally, GeoSEMA refers to geospatial, spatio-evolutive and mobile space constituents where a conceptual geospatial space model is given in this paper. In addition to modeling and categorizing geospatial agents, the model incorporates the concept of inter-agents event-based protocols. Finally, a rapid software-architecture prototyping GeoSEMA platform is also given. It will be implemented / validated in the next phase of our work.