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Proceedings ArticleDOI

Most Diverse Near-Shortest Paths

TL;DR: In this paper, the authors investigate a variant of alternative routing, termed the k-Most Diverse Near-Shortest Paths (kMDNSP), which aims at maximizing the diversity of the recommended paths, while bounding their length based on a user-defined constraint.
Abstract: Computing the shortest path in a road network is a fundamental problem that has attracted lots of attention. However, in many real-world scenarios, determining solely the shortest path is not enough as users want to have additional, alternative ways of reaching their destination. In this paper, we investigate a novel variant of alternative routing, termed the k-Most Diverse Near-Shortest Paths (kMDNSP). In contrast to previous work, kMDNSP aims at maximizing the diversity of the recommended paths, while bounding their length based on a user-defined constraint. Our theoretical analysis proves the NP-hardness of the problem at hand. To compute an exact solution to kMDNSP, we present an algorithm which iterates over all paths that abide by the length constraint and generates k-subsets of them as candidate results. Furthermore, in order to achieve scalability, we also design three heuristic algorithms that trade the diversity of the result for performance. Our experimental analysis compares all proposed algorithms in terms of their runtime and the quality of the recommended paths.
Citations
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Journal ArticleDOI
TL;DR: This work investigates the problem of diversifying the top-k paths between an OD pair such that their similarities are under a threshold while their total length is minimal and proposes an edge deviation and concatenation-based method to avoid the expensive graph search in path enumeration.
Abstract: Route planning is ubiquitous and has a profound impact on our daily life. However, the existing path algorithms tend to produce similar paths between similar OD (Origin-Destination) pairs because they optimize query results without considering their influence on the whole network, which further introduces congestions. Therefore, we investigate the problem of diversifying the top-k paths between an OD pair such that their similarities are under a threshold while their total length is minimal. However, the current solutions all depend on the expensive graph traversal which is too slow to apply in practice. Therefore, we first propose an edge deviation and concatenation-based method to avoid the expensive graph search in path enumeration. After that, we dive into the path relations and propose a path similarity computation method with constant complexity, and propose a pruning technique to improve efficiency. Finally, we provide the completeness and efficiency-oriented solutions to further accelerate the query answering. Evaluations on the real-life road networks demonstrate the effectiveness and efficiency of our algorithm over the state-of-the-art.

4 citations

Proceedings ArticleDOI
01 Nov 2022
TL;DR: In this article , the authors investigate methods for route recovery in the absence of such historical data, and present methods for recovering the single most likely route that a vehicle has travelled, and introduce the region recovery problem that aims at determining a small region that is very likely to contain the traveled route.
Abstract: The availability of GPS sensors in vehicles has enabled the collection of trajectory data that can be utilized to improve the quality of location-based services. However, mostly due to privacy concerns, many data sets are published without containing entire trajectories but only the source location, the target location and the duration of recorded trips. In this paper, we study the problem of route recovery from trip data. In contrast to recent works that assume the availability of entire trajectories for past trips, we investigate methods for route recovery in the absence of such historical data, and we present methods for recovering the single most likely route that a vehicle has travelled. Furthermore, we introduce the region recovery problem that aims at determining a small region that is very likely to contain the traveled route. We also introduce region recovery methods for both single trips and trip groups. In a comprehensive experimental evaluation, we study the efficacy of our solutions for both the route and the region recovery problem. For the region recovery problem in particular, we demonstrate the pros and cons of each method along with the trade-off they offer between the size of the recovered region and the likelihood that the region contains the actual route.

2 citations

Proceedings ArticleDOI
17 Jul 2022
TL;DR: This paper extends a study on improving the performance of reduction-based solvers for the problem of multi-agent pathfinding by studying the effect of different choices of these paths.
Abstract: This paper extends a study on improving the performance of reduction-based solvers for the problem of multi-agent pathfinding. The task is to navigate a set of agents in a graph without collisions. Solvers that reduce this problem to other formalisms often have issues scaling to larger instances in terms of the graph size. A previous study suggests that pruning the graph of most vertices based on a randomly chosen shortest path for each agent. In this paper, we study the effect of different choices of these paths.
References
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Journal ArticleDOI
TL;DR: A tree is a graph with one and only one path between every two nodes, where at least one path exists between any two nodes and the length of each branch is given.
Abstract: We consider n points (nodes), some or all pairs of which are connected by a branch; the length of each branch is given. We restrict ourselves to the case where at least one path exists between any two nodes. We now consider two problems. Problem 1. Constrnct the tree of minimum total length between the n nodes. (A tree is a graph with one and only one path between every two nodes.) In the course of the construction that we present here, the branches are subdivided into three sets: I. the branches definitely assignec~ to the tree under construction (they will form a subtree) ; II. the branches from which the next branch to be added to set I, will be selected ; III. the remaining branches (rejected or not yet considered). The nodes are subdivided into two sets: A. the nodes connected by the branches of set I, B. the remaining nodes (one and only one branch of set II will lead to each of these nodes), We start the construction by choosing an arbitrary node as the only member of set A, and by placing all branches that end in this node in set II. To start with, set I is empty. From then onwards we perform the following two steps repeatedly. Step 1. The shortest branch of set II is removed from this set and added to

22,704 citations

Journal ArticleDOI
TL;DR: In this paper, the most important properties of network-based moving objects are presented and discussed and a framework is proposed where the user can control the behavior of the generator by re-defining the functionality of selected object classes.
Abstract: Benchmarking spatiotemporal database systems requires the definition of suitable datasets simulating the typical behavior of moving objects. Previous approaches for generating spatiotemporal data do not consider that moving objects often follow a given network. Therefore, benchmarks require datasets consisting of such “network-based” moving objects. In this paper, the most important properties of network-based moving objects are presented and discussed. Essential aspects are the maximum speed and the maximum capacity of connections, the influence of other moving objects on the speed and the route of an object, the adequate determination of the start and destination of an object, the influence of external events, and time-scheduled traffic. These characteristics are the basis for the specification and development of a new generator for spatiotemporal data. This generator combines real data (the network) with user-defined properties of the resulting dataset. A framework is proposed where the user can control the behavior of the generator by re-defining the functionality of selected object classes. An experimental performance investigation demonstrates that the chosen approach is suitable for generating large data sets.

889 citations

Journal ArticleDOI
TL;DR: Given a transportation network, the problem of finding a number of spatially dissimilar paths between an origin and a destination is considered and the generation of a large set of candidate paths and the selection of a subset using a dispersion model which maximizes the minimum dissimilarity in the selected subset is proposed.

192 citations

Journal ArticleDOI
TL;DR: The problem of finding alternative routes with a single via vertex is formally defined, efficient algorithms are developed, and they compare favorably with previous methods in both speed and solution quality.
Abstract: We study the problem of finding good alternative routes in road networks. We look for routes that are substantially different from the shortest path, have small stretch, and are locally optimal. We formally define the problem of finding alternative routes with a single via vertex, develop efficient algorithms for it, and evaluate them experimentally. Our algorithms are efficient enough for practical use and compare favorably with previous methods in both speed and solution quality.

104 citations

Journal ArticleDOI
01 Nov 2012
TL;DR: In this article, the authors propose a new, intuitive definition of diversity called DisC diversity, which is defined as a subset of a query result such that each object in the result is represented by a similar object in a diverse subset and the objects in the diverse subset are dissimilar to each other.
Abstract: Recently, result diversification has attracted a lot of attention as a means to improve the quality of results retrieved by user queries. In this paper, we propose a new, intuitive definition of diversity called DisC diversity. A DisC diverse subset of a query result contains objects such that each object in the result is represented by a similar object in the diverse subset and the objects in the diverse subset are dissimilar to each other. We show that locating a minimum DisC diverse subset is an NP-hard problem and provide heuristics for its approximation. We also propose adapting DisC diverse subsets to a different degree of diversification. We call this operation zooming. We present efficient implementations of our algorithms based on the M-tree, a spatial index structure, and experimentally evaluate their performance.

96 citations