Network-Matched Trajectory-Based Moving-Object Database: Models and Applications

Ding, Zhiming; Yang, Bin; Güting, Ralf Hartmut; Li, Yaguang

doi:10.1109/tits.2014.2383494

Cited by 47 publications

(26 citation statements)

References 32 publications

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“…In this paper, the results are compared with a kinematics-based method in which the future location of a vehicle is estimated regarding the kinematics of the vehicle derived from its recent trajectory [8]. Applying the kinematics of the vehicles in location prediction is a conventional approach in moving object databases [23,24], but the accuracy of its results is limited in the arterial networks, as it neglects the dynamics of the intersections.…”

Section: Resultsmentioning

confidence: 99%

Short-Range Prediction of the Zone of Moving Vehicles in Arterial Networks

Jonaghani

Honarparvar

Khademi

2018

IJGI

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In many moving object databases, future locations of vehicles in arterial networks are predicted. While most of studies apply the frequent behavior of historical trajectories or vehicles' recent kinematics as the basis of predictions, consideration of the dynamics of the intersections is mostly neglected. Signalized intersections make vehicles experience different delays, which vary from zero to some minutes based on the traffic state at intersections. In the absence of traffic signal information (red and green times of traffic signal phases, the queue lengths, approaching traffic volume, turning volumes to each intersection leg, etc.), the experienced delays in traffic signals are random variables. In this paper, we model the probability distribution function (PDF) and cumulative distribution function (CDF) of the delay for any point in the arterial networks based on a spatiotemporal model of the queue at the intersection. The probability of the presence of a vehicle in a zone is determined based on the modeled probability function of the delay. A comparison between the results of the proposed method and a well-known kinematic-based method indicates a significant improvement in the precisions of the predictions.

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Section: Resultsmentioning

confidence: 99%

Short-Range Prediction of the Zone of Moving Vehicles in Arterial Networks

Jonaghani

Honarparvar

Khademi

2018

IJGI

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“…nearest neighbor in category C q+1 (lines [14][15][16][17]. Otherwise, if its size |p| is in the HT ≺C of v q , it means that another route with size |p| and smaller cost has been reached and extended at v q , so that p is dominated.…”

Section: A Dominance Based Algorithmmentioning

confidence: 99%

Finding Top-k Optimal Sequenced Routes

Liu

Jin

Yang

et al. 2018

2018 IEEE 34th International Conference on Data Engineering (ICDE)

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Motivated by many practical applications in logistics and mobility-as-a-service, we study the top-k optimal sequenced routes (KOSR) querying on large, general graphs where the edge weights may not satisfy the triangle inequality, e.g., road network graphs with travel times as edge weights. The KOSR querying strives to find the top-k optimal routes (i.e., with the top-k minimal total costs) from a given source to a given destination, which must visit a number of vertices with specific vertex categories (e.g., gas stations, restaurants, and shopping malls) in a particular order (e.g., visiting gas stations before restaurants and then shopping malls).To efficiently find the top-k optimal sequenced routes, we propose two algorithms PruningKOSR and StarKOSR. In Prun-ingKOSR, we define a dominance relationship between two partially-explored routes. The partially-explored routes that can be dominated by other partially-explored routes are postponed being extended, which leads to a smaller searching space and thus improves efficiency. In StarKOSR, we further improve the efficiency by extending routes in an A * manner. With the help of a judiciously designed heuristic estimation that works for general graphs, the cost of partially explored routes to the destination can be estimated such that the qualified complete routes can be found early. In addition, we demonstrate the high extensibility of the proposed algorithms by incorporating Hop Labeling, an effective label indexing technique for shortest path queries, to further improve efficiency. Extensive experiments on multiple real-world graphs demonstrate that the proposed methods significantly outperform the baseline method. Furthermore, when k = 1, StarKOSR also outperforms the state-of-the-art method for the optimal sequenced route queries. This is a full version of "Finding Top-k Optimal Sequenced Routes" [20], to appear in IEEE ICDE 2018.

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“…This trajectory model includes a data type for representing networks and for representing the relative location of static and moving points on the network [22]. In a recent work, network-matched trajectories are defined to avoid the need of a mobile map at the moving-object side [23].…”

Section: Models Of Trajectory and Types Of Queriesmentioning

confidence: 99%

A compact representation for trips over networks built on self-indexes

Brisaboa

Fariña

Galaktionov

et al. 2018

Information Systems

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Representing the movements of objects (trips) over a network in a compact way while retaining the capability of exploiting such data effectively is an important challenge of real applications. We present a new Compact Trip Representation (CTR) that handles the spatio-temporal data associated with users' trips over transportation networks. Depending on the network and types of queries, nodes in the network can represent intersections, stops, or even street segments.CTR represents separately sequences of nodes and the time instants when users traverse these nodes. The spatial component is handled with a data structure based on the well-known Compressed Suffix Array (CSA), which provides both a compact representation and interesting indexing capabilities. The temporal component is self-indexed with either a Hu-Tucker-shaped Wavelet-tree or a Wavelet Matrix that solve range-interval queries efficiently. We show how CTR can solve relevant counting-based spatial, temporal, and spatio-temporal queries over large sets of trips. Experimental results show the space requirements (around 50-70% of the space needed by a compact non-indexed baseline) and query efficiency (most queries are solved in the range of 1-1000 microseconds) of CTR.

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Network-Matched Trajectory-Based Moving-Object Database: Models and Applications

Cited by 47 publications

References 32 publications

Short-Range Prediction of the Zone of Moving Vehicles in Arterial Networks

Short-Range Prediction of the Zone of Moving Vehicles in Arterial Networks

Finding Top-k Optimal Sequenced Routes

A compact representation for trips over networks built on self-indexes

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