Maximum Gain Round Trips with Cost Constraints

TL;DR

This paper introduces a new class of cost-gain networks where edges have both costs and gains, and presents algorithms to find maximum gain round trips within a budget, demonstrated on real spatial networks.

Contribution

It extends traditional cost networks to include gain metrics and develops algorithms for maximum gain round trips under cost constraints.

Findings

Algorithms effectively find optimal round trips in real spatial networks.

Bidirectional search improves efficiency over unidirectional methods.

The approach is applicable to scenic or sightseeing trip planning.

Abstract

Searching for optimal ways in a network is an important task in multiple application areas such as social networks, co-citation graphs or road networks. In the majority of applications, each edge in a network is associated with a certain cost and an optimal way minimizes the cost while fulfilling a certain property, e.g connecting a start and a destination node. In this paper, we want to extend pure cost networks to so-called cost-gain networks. In this type of network, each edge is additionally associated with a certain gain. Thus, a way having a certain cost additionally provides a certain gain. In the following, we will discuss the problem of finding ways providing maximal gain while costing less than a certain budget. An application for this type of problem is the round trip problem of a traveler: Given a certain amount of time, which is the best round trip traversing the most scenic landscape or visiting the most important sights? In the following, we distinguish two cases of the problem. The first does not control any redundant edges and the second allows a more sophisticated handling of edges occurring more than once. To answer the maximum round trip queries on a given graph data set, we propose unidirectional and bidirectional search algorithms. Both types of algorithms are tested for the use case named above on real world spatial networks.