Path Prediction of Moving Objects on Road Networks Through Analyzing Past Trajectories

Kim, Sang–Wook; Won, Jung-Im; Kim, Jong-Dae; Shin, Moo Whan; Lee, Junghoon; Kim, Hanil

doi:10.1007/978-3-540-74819-9_47

Cited by 32 publications

(21 citation statements)

References 10 publications

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“…Krumm and Horvitz [210] proposed a method, called predestination, to predict the driver's destination by using Bayesian inference based on the history of a driver's destination. In [211], considering the characteristics of a road network, a trajectory was represented as a series of road segments. A novel similarity function is devised to search similar trajectories for a given query trajectory.…”

Section: ) Mining Of Trajectory Patternsmentioning

confidence: 99%

Hierarchical and Networked Vehicle Surveillance in ITS: A Survey

Tian

Morris

Tang

et al. 2017

IEEE Trans. Intell. Transport. Syst.

109

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Traffic surveillance has become an important topic in intelligent transportation systems (ITSs), which is aimed at monitoring and managing traffic flow. With the progress in computer vision, video-based surveillance systems have made great advances on traffic surveillance in ITSs. However, the performance of most existing surveillance systems is susceptible to challenging complex traffic scenes (e.g., object occlusion, pose variation, and cluttered background). Moreover, existing related research is mainly on a single video sensor node, which is incapable of addressing the surveillance of traffic road networks. Accordingly, we present a review of the literature on the video-based vehicle surveillance systems in ITSs. We analyze the existing challenges in video-based surveillance systems for the vehicle and present a general architecture for video surveillance systems, i.e., the hierarchical and networked vehicle surveillance, to survey the different existing and potential techniques. Then, different methods are reviewed and discussed with respect to each module. Applications and future developments are discussed to provide future needs of ITS services.

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Section: ) Mining Of Trajectory Patternsmentioning

confidence: 99%

Hierarchical and Networked Vehicle Surveillance in ITS: A Survey

Tian

Morris

Tang

et al. 2017

IEEE Trans. Intell. Transport. Syst.

109

View full text Add to dashboard Cite

show abstract

“…We talk about three categories of prediction models; (i) Linearity-based prediction [4], [43], [47], [50], [52], where the underlying prediction function is based on a simple assumption that objects move in a linear function in time along the input velocity and direction, (ii) Historical-based prediction [6], [14], [26], [29], [33], [31], [48], where the predication function uses object historical trajectories to predict the object's next location, and (iii) complex prediction [25], [49], [59], [60], where more complicated prediction functions are employed to realize better prediction accuracy.…”

Section: B Part 2: Research Trendsmentioning

confidence: 99%

Spatial Predictive Queries

Hendawi

Ali

2015

2015 16th IEEE International Conference on Mobile Data Management

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In this seminar, we address spatial predictive queries both in Euclidian spaces and over road networks. We provide a definition for various types of spatial predictive queries, describe current research trends, and envision future directions. We present practical application scenarios and emphasize the roadblocks that are holding industry back from the commercialization of spatial predictive queries. This seminar targets audience in mobile data management, spatiotemporal query processing, mobile crowd sourcing, and tracking of moving objects. I. TUTORIALOUTLINE The next generation of location based services would offer and recommend services for users according to their current locations as well as their future destinations [20], [21], [23], [25], [26], [29], [34], [37], [38], thanks to the widespread of mobile devices [17]. Nowadays, search engines, e.g., Microsoft Bing and Google, offer satisfactory location-aware search results that are based on the user's current location. However, these search engines remain in an infancy phase developing search techniques that takes into consideration the user's future (or intended) destination. Experience tells that the advertisement that targets the user's current location (e.g., a coupon in a nearby shopping mall) is not very effective. More specifically, it is too late to attract the user's attention to a nearby service because most users, by then, are already heading to a preplanned destination. Industry believes that targeting the user with services that are around his future destination is more valuable to the user (from a relevance perspective) and more profitable to industry (from a market share perspective). However, experience tells that users are either reluctant to share their future destinations with search engines or are unaware of the value they may get by doing so. Consequently, the prediction of the user's future location combined with spatial query processing has been gaining tremendous interest in both the research and industrial communities. Entering this era of "future-location-aware" search engines requires the ability to process spatial predictive queries.In this seminar, we survey the existing research and envision the future of spatial predictive query processing and optimization. The seminar is organized in the following key sections: A. Part 1: Spatial Predictive Queries, What and Why?In this part, we provide basic definitions for different types of predictive queries. Then, we show the importance of this topic through real-world example applications and systems. In general, predictive queries [19], [21], [25], [26] aim at answering inquiries about the anticipated future locations of a set of moving objects, either in an Euclidean space or over a road network. The fundamental types of spatial predictive queries include: (a) predictive point query [19], [18], that finds out the objects that are most likely to show up around a specific location point in the space within a future time window, (b) predictive range query [26], [49], [60], where a user...

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“…Then, query processing techniques in this category, e.g., [7,15,24,27,33,30,55] are applied to trajectory of location points. Existing work in this category is based on either mobility model [24] or ordered historical routes [7,15,30]. The mobility model [24] is used to capture different possible turning patterns at different roads junctions, and the travel speed for each segment in the road network for each single object in the system.…”

Section: Prediction Functionsmentioning

confidence: 99%

“…The main concern of that model is to put more focus on the prediction of the object behavior in junctions based on historical data of objects trajectories. In the ordered historical routes, the stored past trajectories are ordered according to the similarity with the current time and location of the object and the top route is considered the most possible one [7,15,30,32,33]. Some of the existing work in this category is employed for predicting the current object trajectory in non-euclidian space [27] such as road-level granularity.…”

Section: Prediction Functionsmentioning

confidence: 99%

Predictive spatio-temporal queries

Hendawi

Mokbel

2012

Proceedings of the First ACM SIGSPATIAL International Workshop on Mobile Geographic Information Systems

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Predictive queries over spatio-temporal data proved to be vital in many location-based services including traffic management, ride sharing, and advertising. In the last few years, one of the most exciting work on spatio-temporal data management is about predictive queries. In this paper, we review the current research trends and present their related applications in the field of predictive spatiotemporal queries processing. Then, we discuss some basic challenges arising from new opportunities and open problems. The goal of this paper is to catch the interesting areas and future work under the umbrella of predictive queries over spatio-temporal data.

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Path Prediction of Moving Objects on Road Networks Through Analyzing Past Trajectories

Cited by 32 publications

References 10 publications

Hierarchical and Networked Vehicle Surveillance in ITS: A Survey

Hierarchical and Networked Vehicle Surveillance in ITS: A Survey

Spatial Predictive Queries

Predictive spatio-temporal queries

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