Monitoring path nearest neighbor in road networks

Abstract

This paper addresses the problem of monitoring the k nearest neighbors to a dynamically changing path in road networks. Given a destination where a user is going to, this new query returns the k -NN with respect to the shortest path connecting the destination and the user's current location, and thus provides a list of nearest candidates for reference by considering the whole coming journey. We name this query the k -Path Nearest Neighbor query (k -PNN). As the user is moving and may not always follow the shortest path, the query path keeps changing. The challenge of monitoring the k -PNN for an arbitrarily moving user is to dynamically determine the update locations and then refresh the k -PNN efficiently. We propose a three-phase Best-first Network Expansion (BNE) algorithm for monitoring the k -PNN and the corresponding shortest path. In the searching phase, the BNE finds the shortest path to the destination, during which a candidate set that guarantees to include the k -PNN is generated at the same time. Then in the verification phase, a heuristic algorithm runs for examining candidates' exact distances to the query path, and it achieves significant reduction in the number of visited nodes. The monitoring phase deals with computing update locations as well as refreshing the k -PNN in different user movements. Since determining the network distance is a costly process, an expansion tree and the candidate set are carefully maintained by the BNE algorithm, which can provide efficient update on the shortest path and the k -PNN results. Finally, we conduct extensive experiments on real road networks and show that our methods achieve satisfactory performance.

Collective Travel Planning in Spatial Networks

“…For example, the in-route nearest neighbor (IRNN) query [27] is designed for travelers following a fixed route. The path nearest neighbor (PNN) query [4] [23] [25] is an extension of the IRNN query that maintains an up-todate path nearest neighbor result as the user is moving along a predefined route. Moreover, the path nearby cluster query [26] further extends the PNN query to find the POI clusters spatially close to a given path.…”

Path oracles for spatial networks

“…In this paper we address how to find shortest paths in such a dynamically changing environment which is only the first step in being able to perform a wide variety of operations on spatial networks such as region searches [9,10], nearest neighbor finding [2,3,7,[9][10][11] and distance joins [9]. The problem is that finding shortest paths and distances invariably involve a search process (e.g., via use of a shortest path algorithm [5,6,17]), which takes quite a bit of time, and is not a satisfactory solution in terms of data that is organized using a relational database and is accessed via SELECT operations.…”

Collective Travel Planning in Spatial Networks

“…Here, we evaluate all possible subsets of cardinality from 1 to k (lines 1-2). For each potential i-subset A, we compute and record its lower bound GT C(A).lb and upper bound GT C(A).ub (lines [3][4]. If the value of GT C(A).ub is less than that of UB , the value of UB is set to GT C(A).ub (lines [5][6].…”

“…The EU targets a 50% reduction in CO 2 emissions by 2050. This work is motivated in part by the EU project Reduction 4 . Given the current locations Q of a set of travelers, a set of meeting points S, a destination d, and an integer threshold k (1 ≤ k ≤ min{|S|, |Q|}), we aim to identify a subset A of S with at most k elements that when used as meeting points results in the minimum global travel cost.…”

“…For example, for the traveler at q 1 , p 1 is the closest meeting point, so the traveler will follow the shortest path from q 1 to p 1 by private transport (local travel cost of q 1 ). A total of five travelers, q 1 , q 2 , q 3 , q 4 , and q 5 , meet at p 1 . They then follow the shortest path from p 1 to the destination d by collective transport (connection travel cost of p 1 ).…”