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This paper presents a transportation spatio-temporal system that efficiently converts traffic video data into vehicular motion information in spatio-temporal databases for a variety of transportation applications. The proposed transportation spatio-temporal system interpolates the vehicle trajectory data (i.e., time, location, and speed), which are extracted from video, and integrates them with spatial road information for storage of dynamic transportation environments. The proposed transportation spatio-temporal system can mitigate data storage and retrieval issues related to storing large amounts of traffic video. Moreover, users can manage and operate multiform and multidimensional traffic data in a spatio-temporal transportation environment. The proposed approach is demonstrated for typical transportation applications. The experimental results show that the proposed transportation spatio-temporal system has excellent potential for addressing issues related to storage of large amounts of traffic video data. [ABSTRACT FROM AUTHOR]

Route planning and recommendation have received significant attention in recent years. In this light, we study a novel problem of planning unobstructed paths in traffic-aware spatial networks (TAUP queries) to avoid potential traffic congestions. We propose two probabilistic TAUP queries: (1) a time-threshold query like 'what is the path from the check-in desk to the flight SK 1217 with the minimum congestion probability to take at most 45 minutes?', and (2) a probability-threshold query like 'what is the fastest path from the check-in desk to the flight SK 1217 whose congestion probability is less than 20 %?'. These queries are mainly motivated by indoor space applications, but are also applicable in outdoor spaces. We believe that these queries are useful in some popular applications, such as planning unobstructed paths for VIP bags in airports and planning convenient routes for travelers. The TAUP queries are challenged by two difficulties: (1) how to model the traffic awareness in spatial networks practically, and (2) how to compute the TAUP queries efficiently under different query settings. To overcome these challenges, we construct a traffic-aware spatial network G( V, E) by analyzing uncertain trajectories of moving objects. Based on G( V, E), two efficient algorithms are developed to compute the TAUP queries. The performances of TAUP queries are verified by extensive experiments on real and synthetic spatial data. [ABSTRACT FROM AUTHOR]

With the proliferation of mobile computing, the ability to index efficiently the movements of mobile objects becomes important. Objects are typically seen as moving in two-dimensional (x,y) space, which means that their movements across time may be embedded in the three-dimensional (x,y,t) space. Further, the movements are typically represented as trajectories, sequences of connected line segments. In certain cases, movement is restricted; specifically, in this paper, we aim at exploiting that movements occur in transportation networks to reduce the dimensionality of the data. Briefly, the idea is to reduce movements to occur in one spatial dimension. As a consequence, the movement occurs in two-dimensional (x,t) space. The advantages of considering such lower-dimensional trajectories are that the overall size of the data is reduced and that lower-dimensional data is to be indexed. Since off-the-shelf database management systems typically do not offer higher-dimensional indexing, this reduction in dimensionality allows us to use existing DBMSes to store and index trajectories. Moreover, we argue that, given the right circumstances, indexing these dimensionality-reduced trajectories can be more efficient than using a three-dimensional index. A decisive factor here is the fractal dimension of the network-the lower, the more efficient is the proposed approach. This hypothesis is verified by an experimental study that incorporates trajectories stemming from real and synthetic road networks. [ABSTRACT FROM AUTHOR]