Abstract

Computing path queries such as the shortest path in public transport networks is challenging because the path costs between nodes change over time. A reachability query from a node at a given start time on such a network retrieves all points of interest (POIs) that are reachable within a given cost budget. Reachability queries are essential building blocks in many applications, for example, group recommendations, ranking spatial queries, or geomarketing. We propose an efficient solution for reachability queries in public transport networks. Currently, there are two options to solve reachability queries. (1) Execute a modified version of Dijkstra’s algorithm that supports time-dependent edge traversal costs; this solution is slow since it must expand edge by edge and does not use an index. (2) Issue a separate path query for each single POI, i.e., a single reachability query requires answering many path queries. None of these solutions scales to large networks with many POIs. We propose a novel and lightweight reachability index. The key idea is to partition the network into cells. Then, in contrast to other approaches, we expand the network cell by cell. Empirical evaluations on synthetic and real-world networks confirm the efficiency and the effectiveness of our index-based reachability query solution.

Highlights

  • We study the problem of scalable and efficient reachability querying in public transport networks

  • A reachability query retrieves all points of interest (POIs) reachable from a given query node at a specific start time within a given

  • The paper offers improved support for reachability queries in temporal graphs that retrieve all reachable points of interest (POIs) from a given query node at a specific start time within a given time budget

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Summary

Introduction

We study the problem of scalable and efficient reachability querying in public transport networks. A reachability query retrieves all points of interest (POIs) reachable from a given query node at a specific start time within a given. Consider a platform that recommends events to a group of people such that the group members like to attend the event together (Amer-Yahia et al, 2009; Jameson & Smyth, 2007). Group members are query nodes and events are POIs. When the group is given, the events must be evaluated by various criteria to optimize the benefit to the group. One important aspect is the location of the event relative to the group members. The start time and the travel time budget to reach an event may differ for each member. A single recommendation comprises multiple reachability queries, one for each group member

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