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Urban Computing: Concepts, Methodologies, and Applications
"... Urbanization’s rapid progress has modernized many people’s lives, but also engendered big issues, such as traffic congestion, energy consumption, and pollution. Urban computing aims to tackle these issues by using the data that has been generated in cities, e.g., traffic flow, human mobility and geo ..."
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Cited by 14 (7 self)
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Urbanization’s rapid progress has modernized many people’s lives, but also engendered big issues, such as traffic congestion, energy consumption, and pollution. Urban computing aims to tackle these issues by using the data that has been generated in cities, e.g., traffic flow, human mobility and geographical data. Urban computing connects urban sensing, data management, data analytics, and service providing into a recurrent process for an unobtrusive and continuous improvement of people’s lives, city operation systems, and the environment. Urban computing is an interdisciplinary field where computer sciences meet conventional city-related fields, like transportation, civil engineering, environment, economy, ecology, and sociology, in the context of urban spaces. This article first introduces the concept of urban computing, discussing its general framework and key challenges from the perspective of computer sciences. Secondly, we classify the applications of urban computing into seven categories, consisting of urban planning, transportation, the environment, energy, social, economy, and public safety & security, presenting representative scenarios in each category. Thirdly, we summarize the typical technologies that are needed in urban computing into four folds, which are about urban sensing, urban data management, knowledge fusion across heterogeneous data, and urban data visualization. Finally, we outlook the
A Fast O(N) Multiresolution Polygonal Approximation Algorithm for GPS Trajectory Simplification
, 2012
"... Recent advances in geopositioning mobile phones have made it possible for users to collect a large number of GPS trajectories by recording their location information. However, these mobile phones with built-in GPS devices usually record far more data than needed, which brings about both heavy data ..."
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Cited by 10 (3 self)
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Recent advances in geopositioning mobile phones have made it possible for users to collect a large number of GPS trajectories by recording their location information. However, these mobile phones with built-in GPS devices usually record far more data than needed, which brings about both heavy data storage and a computationally expensive burden in the rendering process for a Web browser. To address this practical problem, we present a fast polygonal approximation algorithm in 2-D space for the GPS trajectory simplification under the so-called integral square synchronous distance error criterion in a linear time complexity. The underlying algorithm is designed and implemented using a bottom–up multiresolution method, where the input of polygonal approximation in the coarser resolution is the polygonal curve achieved in the finer resolution. For each resolution (map scale), priority-queue structure is exploited in graph construction to construct the initialized approximated curve. Once the polygonal curve is initialized, two fine-tune algorithms are employed in order to achieve the desirable quality level. Experimental results validated that the proposed algorithm is fast and achieves a better approximation result than the existing competitive methods.
Compression of GPS Trajectories
- Data Compression Conference
, 2012
"... Abstract: Enormous amounts of GPS trajectories, which record users ' spatial and temporal information, are collected by geo-positioning mobile phones in recent years. The massive volumes of trajectory data bring about heavy burdens for both network transmission and data storage. To overcome the ..."
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Cited by 5 (1 self)
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Abstract: Enormous amounts of GPS trajectories, which record users ' spatial and temporal information, are collected by geo-positioning mobile phones in recent years. The massive volumes of trajectory data bring about heavy burdens for both network transmission and data storage. To overcome these difficulties, a number of compression algorithms have been proposed by reducing the number of points in the trajectory data. But these algorithms lack a rigorous investigation on how to encode the reduced trajectories. In this paper, we propose an algorithm that optimizes both the trajectory simplification and the coding procedure using the quantized data. The underlying algorithm is also compared with the existing methods across 640 trajectories from Microsoft Geolife dataset using synchronous Euclidean distance (SED) as the error metrics. Experimental results show that the proposed method saves 60 % of compression cost against the current state of the art compression algorithms. 1.
DirectionPreserving Trajectory Simplification
"... Trajectories of moving objects are collected in many applications. Raw trajectory data is typically very large, and has to be simplified before use. In this paper, we introduce the notion of directionpreserving trajectory simplification, and show both analytically and empirically that it can support ..."
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Cited by 2 (1 self)
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Trajectories of moving objects are collected in many applications. Raw trajectory data is typically very large, and has to be simplified before use. In this paper, we introduce the notion of directionpreserving trajectory simplification, and show both analytically and empirically that it can support a broader range of applications than traditional position-preserving trajectory simplification. We present a polynomial-time algorithm for optimal directionpreserving simplification, and another approximate algorithm with a quality guarantee. Extensive experimental evaluation with real trajectory data shows the benefit of the new techniques. 1.
Real Time Access to Multiple GPS Tracks
- in International Conference on Web Information Systems and Technologies (WEBIST'13
, 2013
"... Abstract: Increasing availability of mobile devices with GPS receiver gives users the possibility to record and share a variety of location-based data, including GPS tracks. We describe a complete real-time system for acquisition, storage, querying, retrieval and visualization of GPS tracks. The mai ..."
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Abstract: Increasing availability of mobile devices with GPS receiver gives users the possibility to record and share a variety of location-based data, including GPS tracks. We describe a complete real-time system for acquisition, storage, querying, retrieval and visualization of GPS tracks. The main problems faced are how to store the data, how to access and how to visualize large amount of data. We propose to reduce the quantity of the data to be visualized, without affecting visualization quality. In order to achieve this, our system uses a fast polygonal approximation algorithm for different map scales along with a bounding box solution. 1
An Online Compression Algorithm for Positioning Data Acquisition
, 2014
"... Positioning data are usually acquired periodically and uploaded to the server via wireless network in the location data acquisition systems. Huge communication overheads between the terminal and the server and heavy loads of storage space are needed when a large number of data points are uploaded. T ..."
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Positioning data are usually acquired periodically and uploaded to the server via wireless network in the location data acquisition systems. Huge communication overheads between the terminal and the server and heavy loads of storage space are needed when a large number of data points are uploaded. To this end, an online compression algorithm for positioning data acquisition is proposed, which compresses data by reducing the number of uploaded positioning points. Error threshold can be set according to users ’ needs. Feature points are extracted to upload real-timely by considering the changes of direction and speed. If necessary, an approximation trajectory can be obtained by using the proposed recovery algorithm based on the feature points on the server. Positioning data in three different travel modes, including walk, non-walk and mixed mode, are acquired to validate the efficiency of the algorithm. The experimental results show that the proposed algorithm can get appropriate compression rate in various road conditions and travel modes, and has better adaptability. Povzetek: Predstavljen je nov algoritem za zajemanje podatkov o realnem času, uporaben za sisteme za določanje položaja.
Compact Representation of GPS Trajectories over Vectorial Road Networks
"... Abstract. Many devices nowadays record traveling routes, of users, as sequences of GPS locations. With the growing popularity of smartphones, millions of such routes are generated each day, and many routes have to be stored locally on the device or transmitted to a remote database. It is, thus, esse ..."
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Abstract. Many devices nowadays record traveling routes, of users, as sequences of GPS locations. With the growing popularity of smartphones, millions of such routes are generated each day, and many routes have to be stored locally on the device or transmitted to a remote database. It is, thus, essential to encode the sequences, to decrease the volume of the stored or transmitted data. In this paper we study the problem of coding routes over a vectorial road network (map), where GPS locations can be associated with vertices or with road segments. We consider a three-step process of dilution, map-matching and coding. We present two methods to code routes. The first method represents the given route as a sequence of greedy paths. We provide two algorithms to generate a greedy-path code for a sequence of n vertices on the map. The first algorithm has O(n) time complexity, and the second one has O(n 2) time complexity, but it is optimal, meaning that it generates the shortest possible greedypath code. Decoding a greedy-path code can be done in O(n) time. The second method codes a route as a sequence of shortest paths. We provide a simple algorithm to generate a shortest-path code in O(kn 2 log n) time, where k is the length of the produced code, and we prove that this code is optimal. Decoding a shortest-path code also requires O(kn 2 log n) time. Our experimental evaluation shows that shortest-path codes are more compact than greedy-path codes, justifying the larger time complexity. 1
Chapter 8 Location-Based Social Networks: Users
"... Abstract In this chapter, we introduce and define the meaning of location-based social network (LBSN) and discuss the research philosophy behind LBSNs from the perspective of users and locations. Under the circumstances of trajectory-centric LBSN, we then explore two fundamental research points conc ..."
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Abstract In this chapter, we introduce and define the meaning of location-based social network (LBSN) and discuss the research philosophy behind LBSNs from the perspective of users and locations. Under the circumstances of trajectory-centric LBSN, we then explore two fundamental research points concerned with understanding users in terms of their locations. One is modeling the location history of an individual using the individual’s trajectory data. The other is estimating the similarity between two different people according to their location histories. The inferred similarity represents the strength of connection between two users in a locationbased social network, and can enable friend recommendations and community discovery. The general approaches for evaluating these applications are also presented.
unknown title
"... Abstract — Smartphones give users a possibility to georeference photos, follow their sport achievements and share them to peers. We describe a complete real-time system for storage, querying, retrieval and visualization of GPS tracks. I. ..."
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Abstract — Smartphones give users a possibility to georeference photos, follow their sport achievements and share them to peers. We describe a complete real-time system for storage, querying, retrieval and visualization of GPS tracks. I.
Compression of GPS Trajectories using Optimized Approximation
"... A large number of GPS trajectories, which include users ' spatial and temporal information, are collected by geo-positioning mobile phones in recent years. The massive volumes of trajectory data bring about heavy burdens for both network transmission and data storage. To overcome these difficul ..."
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A large number of GPS trajectories, which include users ' spatial and temporal information, are collected by geo-positioning mobile phones in recent years. The massive volumes of trajectory data bring about heavy burdens for both network transmission and data storage. To overcome these difficulties, GPS trajectory compression algorithm (GTC) was proposed recently that optimizes both the data reduction by trajectory simplification and the coding procedure using the quantized data. In this paper, instead of using greedy solution in GTC algorithm, the approximation process is optimized jointly with the encoding step via dynamic programming. In addition, Bayes ' theorem is applied to improve the robustness of probability estimation for encoded values. The proposed solution has the same time complexity with GTC algorithm in the decoding procedure and experimental results show that its bit-rate is around 80 % comparing with GTC algorithm. 1.
