A context-based geoprocessing framework for optimizing meetup location of multiple moving objects along road networks

TL;DR

This paper presents a novel context-based geoprocessing framework for optimizing meetup locations for multiple moving objects on road networks, integrating real-time data and spatial constraints to improve social and logistical decision-making.

Contribution

It introduces a formal spatial optimization model and a heuristic framework that effectively incorporates geographic context and real-time information for dynamic meetup location planning.

Findings

R* tree-based algorithm yields high-quality solutions with low computation time

Framework successfully integrates real-time traffic and geographic data in GIS environment

Applicable to trip planning, carpooling, and logistics management

Abstract

Given different types of constraints on human life, people must make decisions that satisfy social activity needs. Minimizing costs (i.e., distance, time, or money) associated with travel plays an important role in perceived and realized social quality of life. Identifying optimal interaction locations on road networks when there are multiple moving objects (MMO) with space-time constraints remains a challenge. In this research, we formalize the problem of finding dynamic ideal interaction locations for MMO as a spatial optimization model and introduce a context-based geoprocessing heuristic framework to address this problem. As a proof of concept, a case study involving identification of a meetup location for multiple people under traffic conditions is used to validate the proposed geoprocessing framework. Five heuristic methods with regard to efficient shortest-path search space have been tested. We find that the R* tree-based algorithm performs the best with high quality solutions and low computation time. This framework is implemented in a GIS environment to facilitate integration with external geographic contextual information, e.g., temporary road barriers, points of interest (POI), and real-time traffic information, when dynamically searching for ideal meetup sites. The proposed method can be applied in trip planning, carpooling services, collaborative interaction, and logistics management.