Trajectory Based Optimal Segment Computation in Road Network Databases

TL;DR

This paper introduces a practical framework for identifying optimal road network segments for new facilities based on customer route data, using novel algorithms that efficiently prune search space and are validated with real data.

Contribution

It presents two new algorithms, AUG and ITE, for efficiently computing optimal segments in road networks considering customer movement, with empirical validation.

Findings

Algorithms achieve high performance on real datasets

Effective pruning reduces search space significantly

Framework successfully identifies optimal segments in realistic scenarios

Abstract

Finding a location for a new facility such that the facility attracts the maximal number of customers is a challenging problem. Existing studies either model customers as static sites and thus do not consider customer movement, or they focus on theoretical aspects and do not provide solutions that are shown empirically to be scalable. Given a road network, a set of existing facilities, and a collection of customer route traversals, an optimal segment query returns the optimal road network segment(s) for a new facility. We propose a practical framework for computing this query, where each route traversal is assigned a score that is distributed among the road segments covered by the route according to a score distribution model. The query returns the road segment(s) with the highest score. To achieve low latency, it is essential to prune the very large search space. We propose two algorithms that adopt different approaches to computing the query. Algorithm AUG uses graph augmentation, and ITE uses iterative road-network partitioning. Empirical studies with real data sets demonstrate that the algorithms are capable of offering high performance in realistic settings.