A Survey on Trajectory Data Mining: Techniques and Applications
TL;DR: This paper surveys various applications of trajectory data mining, e.g., path discovery, location prediction, movement behavior analysis, and so on, and reviews an extensive collection of existing trajectory datamining techniques and discusses them in a framework of trajectoryData Mining.
Abstract: Rapid advance of location acquisition technologies boosts the generation of trajectory data, which track the traces of moving objects. A trajectory is typically represented by a sequence of timestamped geographical locations. A wide spectrum of applications can benefit from the trajectory data mining. Bringing unprecedented opportunities, large-scale trajectory data also pose great challenges. In this paper, we survey various applications of trajectory data mining, e.g., path discovery, location prediction, movement behavior analysis, and so on. Furthermore, this paper reviews an extensive collection of existing trajectory data mining techniques and discusses them in a framework of trajectory data mining. This framework and the survey can be used as a guideline for designing future trajectory data mining solutions.
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TL;DR: Polaris, a system for analyzing and predicting users’ sentimental trajectories for events analyzed in real time out of the massive social media contents, is proposed and the results of preliminary validation work are shown.
Abstract: With the influence and social ripple effect of social media sites, diverse studies are in progress to analyze the contents generated by users. Numerous contents generated in real time contain information about social issues and events such as natural disasters. In particular, users show not only information about the events that occurred but also their sentiments. In this paper, we propose Polaris, a system for analyzing and predicting users’ sentimental trajectories for events analyzed in real time out of the massive social media contents, and show the results of preliminary validation work that we have done. We show both trajectory analysis and sentiment analysis so that users can obtain the insight at a glance. Also, we increased the accuracy in sentiment analysis and prediction by making use of the latest deep-learning technique.
100 citations
TL;DR: The survey can help the partitioners to understand existing AQP techniques and select appropriate methods in their applications and provide research challenges and opportunities of AQP.
Abstract: Online analytical processing (OLAP) is a core functionality in database systems. The performance of OLAP is crucial to make online decisions in many applications. However, it is rather costly to support OLAP on large datasets, especially big data, and the methods that compute exact answers cannot meet the high-performance requirement. To alleviate this problem, approximate query processing (AQP) has been proposed, which aims to find an approximate answer as close as to the exact answer efficiently. Existing AQP techniques can be broadly categorized into two categories. (1) Online aggregation: select samples online and use these samples to answer OLAP queries. (2) Offline synopses generation: generate synopses offline based on a-priori knowledge (e.g., data statistics or query workload) and use these synopses to answer OLAP queries. We discuss the research challenges in AQP and summarize existing techniques to address these challenges. In addition, we review how to use AQP to support other complex data types, e.g., spatial data and trajectory data, and support other applications, e.g., data visualization and data cleaning. We also introduce existing AQP systems and summarize their advantages and limitations. Lastly, we provide research challenges and opportunities of AQP. We believe that the survey can help the partitioners to understand existing AQP techniques and select appropriate methods in their applications.
99 citations
TL;DR: The necessity of mobility prediction, together with its intrinsic characteristics in terms of movement predictability, prediction outputs, and performance metrics is discussed and an overview of the state-of-the-art approaches is provided.
Abstract: Recently, mobility has gathered tremendous interest as the users’ desire for consecutive connections and better quality of service has increased. An accurate prediction of user mobility in mobile networks provides efficient resource and handover management, which can avoid unacceptable degradation of the perceived quality. Therefore, mobility prediction in wireless networks is of great importance and many works have been dedicated to this issue. In this paper, the necessity of mobility prediction, together with its intrinsic characteristics in terms of movement predictability, prediction outputs, and performance metrics is discussed. Moreover, the learning perspective of solutions to mobility prediction has been studied. Specifically, an overview of the state-of-the-art approaches is provided, including Markov chain, hidden Markov model, artificial neural network, Bayesian network, and data mining based on different kinds of knowledge. At last, this paper also explores the open research challenges due to the advent of the fifth-generation mobile system and puts forward some potential trends in the near future.
98 citations
Cites background from "A Survey on Trajectory Data Mining:..."
...[56] S. Qiao, N. Han, W. Zhu, and L. A. Gutierrez, ‘‘TraPlan: An effective three-in-one trajectory-prediction model in transportation networks,’’ IEEE Trans....
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...[25] Z. Feng and Y. Zhu, ‘‘A survey on trajectory data mining: Techniques and applications,’’ IEEE Access, vol. 4, pp. 2056–2067, 2016....
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...[43] Y. Zhuang et al., ‘‘A survey of positioning systems using visible LED lights,’’ IEEE Commun....
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...[57] S. Qiao, D. Shen, X. Wang, N. Han, and W. Zhu, ‘‘A self-adaptive parameter selection trajectory prediction approach via hidden Markov models,’’ IEEE Trans....
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...Similarly, Feng and Zhu [25] and Zheng [26] also surveyed various applications of large-scale data mining but focused on providing a quick understanding of the field of trajectory data mining....
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01 Jan 2020
TL;DR: A comprehensive survey of the trajectory distance measures is conducted, classified into four categories according to the trajectory data type and whether the temporal information is measured.
Abstract: The proliferation of trajectory data in various application domains has inspired tremendous research efforts to analyze large-scale trajectory data from a variety of aspects. A fundamental ingredient of these trajectory analysis tasks and applications is distance measures for effectively determining how similar two trajectories are. We conduct a comprehensive survey of the trajectory distance measures. The trajectory distance measures are classified into four categories according to the trajectory data type and whether the temporal information is measured. In addition, the effectiveness and complexity of each distance measure are studied. The experimental study is also conducted on their effectiveness in the six different trajectory transformations.
97 citations
Cites background from "A Survey on Trajectory Data Mining:..."
...Edit distance with projections (EDwP) [8,25,54,71,84,93] uses dynamic interpolation to match sample points and calculates how far two trajectories are based on their edit distances, i....
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TL;DR: A light-weighted yet efficient map matcher, namely, Spatial-Directional Matching (SD-Matching), to align the noisy and sparse GPS points upon the underlying road network, which fully explores the usage of vehicle heading direction collected from the GPS trajectory data.
Abstract: Massive and redundant vehicle trajectory data are continuously sent to the data center via vehicle-mounted GPS devices, causing a number of sustainable issues, such as storage, communication, and computation. Online trajectory compression becomes a promising way to alleviate these issues. In this paper, we present an online trajectory compression framework running under the mobile environment. The framework consists of two phases, i.e., online trajectory mapping and trajectory compression. In the phase of online trajectory mapping, we develop a light-weighted yet efficient map matcher, namely, Spatial-Directional Matching (SD-Matching), to align the noisy and sparse GPS points upon the underlying road network, which fully explores the usage of vehicle heading direction collected from the GPS trajectory data. In the phase of online trajectory compression, we propose a novel compressor based on the heading change at intersections, namely, Heading Change Compression (HCC), aiming at finding a concise and compact trajectory representation. Finally, we conduct experiments to evaluate the effectiveness and efficiency of the proposed framework using real-world datasets in the city of Beijing, China. We further deploy the system in the real world in the city of Chongqing, China. The experimental results demonstrate that: 1) the SD-Matching algorithm achieves a higher mean accuracy but consumes less time than the state-of-the-art algorithm, namely, Spatial-Temporal Matching (ST-Matching) and 2) the HCC algorithm also outperforms baselines in trading-off compression ratio and computation time.
94 citations
Cites methods from "A Survey on Trajectory Data Mining:..."
...In the overview of trajectory data mining, [15], [58] briefly summarize some popular trajectory compression algorithms....
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References
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TL;DR: In this article, the authors study the trajectory of 100,000 anonymized mobile phone users whose position is tracked for a six-month period and find that the individual travel patterns collapse into a single spatial probability distribution, indicating that humans follow simple reproducible patterns.
Abstract: The mapping of large-scale human movements is important for urban planning, traffic forecasting and epidemic prevention. Work in animals had suggested that their foraging might be explained in terms of a random walk, a mathematical rendition of a series of random steps, or a Levy flight, a random walk punctuated by occasional larger steps. The role of Levy statistics in animal behaviour is much debated — as explained in an accompanying News Feature — but the idea of extending it to human behaviour was boosted by a report in 2006 of Levy flight-like patterns in human movement tracked via dollar bills. A new human study, based on tracking the trajectory of 100,000 cell-phone users for six months, reveals behaviour close to a Levy pattern, but deviating from it as individual trajectories show a high degree of temporal and spatial regularity: work and other commitments mean we are not as free to roam as a foraging animal. But by correcting the data to accommodate individual variation, simple and predictable patterns in human travel begin to emerge. The cover photo (by Cesar Hidalgo) captures human mobility in New York's Grand Central Station. This study used a sample of 100,000 mobile phone users whose trajectory was tracked for six months to study human mobility patterns. Displacements across all users suggest behaviour close to the Levy-flight-like pattern observed previously based on the motion of marked dollar bills, but with a cutoff in the distribution. The origin of the Levy patterns observed in the aggregate data appears to be population heterogeneity and not Levy patterns at the level of the individual. Despite their importance for urban planning1, traffic forecasting2 and the spread of biological3,4,5 and mobile viruses6, our understanding of the basic laws governing human motion remains limited owing to the lack of tools to monitor the time-resolved location of individuals. Here we study the trajectory of 100,000 anonymized mobile phone users whose position is tracked for a six-month period. We find that, in contrast with the random trajectories predicted by the prevailing Levy flight and random walk models7, human trajectories show a high degree of temporal and spatial regularity, each individual being characterized by a time-independent characteristic travel distance and a significant probability to return to a few highly frequented locations. After correcting for differences in travel distances and the inherent anisotropy of each trajectory, the individual travel patterns collapse into a single spatial probability distribution, indicating that, despite the diversity of their travel history, humans follow simple reproducible patterns. This inherent similarity in travel patterns could impact all phenomena driven by human mobility, from epidemic prevention to emergency response, urban planning and agent-based modelling.
5,514 citations
12 Aug 2012
TL;DR: This paper proposes a framework (titled DRoF) that Discovers Regions of different Functions in a city using both human mobility among regions and points of interests (POIs) located in a region.
Abstract: The development of a city gradually fosters different functional regions, such as educational areas and business districts. In this paper, we propose a framework (titled DRoF) that Discovers Regions of different Functions in a city using both human mobility among regions and points of interests (POIs) located in a region. Specifically, we segment a city into disjointed regions according to major roads, such as highways and urban express ways. We infer the functions of each region using a topic-based inference model, which regards a region as a document, a function as a topic, categories of POIs (e.g., restaurants and shopping malls) as metadata (like authors, affiliations, and key words), and human mobility patterns (when people reach/leave a region and where people come from and leave for) as words. As a result, a region is represented by a distribution of functions, and a function is featured by a distribution of mobility patterns. We further identify the intensity of each function in different locations. The results generated by our framework can benefit a variety of applications, including urban planning, location choosing for a business, and social recommendations. We evaluated our method using large-scale and real-world datasets, consisting of two POI datasets of Beijing (in 2010 and 2011) and two 3-month GPS trajectory datasets (representing human mobility) generated by over 12,000 taxicabs in Beijing in 2010 and 2011 respectively. The results justify the advantages of our approach over baseline methods solely using POIs or human mobility.
1,050 citations
"A Survey on Trajectory Data Mining:..." refers background in this paper
...[50], [51] address a problem of discovering regions of different functions in a city based on a large scale of trajectory data....
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...[50], [51] focus on discovering regions of different functions in a city....
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11 Apr 2011
TL;DR: This work investigates the problem of discovering the Most Popular Route (MPR) between two locations by observing the traveling behaviors of many previous users, and proposes a Maximum Probability Product algorithm to discover the MPR from a transfer network based on the popularity indicators in a breadth-first manner.
Abstract: The booming industry of location-based services has accumulated a huge collection of users' location trajectories of driving, cycling, hiking, etc. In this work, we investigate the problem of discovering the Most Popular Route (MPR) between two locations by observing the traveling behaviors of many previous users. This new query is beneficial to travelers who are asking directions or planning a trip in an unfamiliar city/area, as historical traveling experiences can reveal how people usually choose routes between locations. To achieve this goal, we firstly develop a Coherence Expanding algorithm to retrieve a transfer network from raw trajectories, for indicating all the possible movements between locations. After that, the Absorbing Markov Chain model is applied to derive a reasonable transfer probability for each transfer node in the network, which is subsequently used as the popularity indicator in the search phase. Finally, we propose a Maximum Probability Product algorithm to discover the MPR from a transfer network based on the popularity indicators in a breadth-first manner, and we illustrate the results and performance of the algorithm by extensive experiments.
368 citations
"A Survey on Trajectory Data Mining:..." refers background in this paper
...When planning a trip in an unfamiliar area, people usually try to find the most frequent path between two locations [59]....
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10 Dec 2012
TL;DR: This work analyzes about 35 million check-ins made by Foursquare users in over 5 million venues across the globe, and proposes a set of features that aim to capture the factors that may drive users' movements, finding that the supervised methodology based on the combination of multiple features offers the highest levels of prediction accuracy.
Abstract: Mobile location-based services are thriving, providing an unprecedented opportunity to collect fine grained spatio-temporal data about the places users visit. This multi-dimensional source of data offers new possibilities to tackle established research problems on human mobility, but it also opens avenues for the development of novel mobile applications and services. In this work we study the problem of predicting the next venue a mobile user will visit, by exploring the predictive power offered by different facets of user behavior. We first analyze about 35 million check-ins made by about 1 million Foursquare users in over 5 million venues across the globe, spanning a period of five months. We then propose a set of features that aim to capture the factors that may drive users' movements. Our features exploit information on transitions between types of places, mobility flows between venues, and spatio-temporal characteristics of user check-in patterns. We further extend our study combining all individual features in two supervised learning models, based on linear regression and M5 model trees, resulting in a higher overall prediction accuracy. We find that the supervised methodology based on the combination of multiple features offers the highest levels of prediction accuracy: M5 model trees are able to rank in the top fifty venues one in two user check-ins, amongst thousands of candidate items in the prediction list.
341 citations
"A Survey on Trajectory Data Mining:..." refers background in this paper
...[66] focus on a problem of predicting the next place that a user will visit, by exploring human mobility patterns....
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TL;DR: This paper introduces the concept of latent activity trajectory (LAT), which captures socioeconomic activities conducted by citizens at different locations in a chronological order, and develops a topic-modeling-based approach to cluster the segmented regions into functional zones leveraging mobility and location semantics mined from LAT.
Abstract: The step of urbanization and modern civilization fosters different functional zones in a city, such as residential areas, business districts, and educational areas. In a metropolis, people commute between these functional zones every day to engage in different socioeconomic activities, e.g., working, shopping, and entertaining. In this paper, we propose a data-driven framework to discover functional zones in a city. Specifically, we introduce the concept of latent activity trajectory (LAT), which captures socioeconomic activities conducted by citizens at different locations in a chronological order. Later, we segment an urban area into disjointed regions according to major roads, such as highways and urban expressways. We have developed a topic-modeling-based approach to cluster the segmented regions into functional zones leveraging mobility and location semantics mined from LAT. Furthermore, we identify the intensity of each functional zone using Kernel Density Estimation. Extensive experiments are conducted with several urban scale datasets to show that the proposed framework offers a powerful ability to capture city dynamics and provides valuable calibrations to urban planners in terms of functional zones.
335 citations
"A Survey on Trajectory Data Mining:..." refers background in this paper
...[50], [51] address a problem of discovering regions of different functions in a city based on a large scale of trajectory data....
[...]
...[50], [51] focus on discovering regions of different functions in a city....
[...]