Predicting travel time within catchment area using Time Travel Voronoi Diagram (TTVD) and crowdsource map features. Issue 3 (May 2022)

• Record Type: Journal Article
• Authors: Adhinugraha, Kiki; Taniar, David; Phan, Thanh; Beare, Richard
• Is Part Of: Information processing & management. Volume 59:Issue 3(2022)
• Publication Date: 2022-05
• Subjects: 00-01 -- 99-00; Time Travel Voronoi Diagram -- Crowdsource -- Speed reduction -- Road obstacles; Information storage and retrieval systems -- Periodicals; Information science -- Periodicals; Systèmes d'information -- Périodiques; Sciences de l'information -- Périodiques; Information science; Information storage and retrieval systems; Periodicals; 658.4038
• DOI: 10.1016/j.ipm.2022.102922
• Languages: English
• ISSNs: 0306-4573

Abstract

Abstract: A catchment is a geographical area from which a business, service or organisation attracts its customers. A catchment area is a common way to ensure equal access to services such as hospitals, schools, libraries, ambulances, fire brigades, and shopping centres. Users will usually go to the service provider which is closest to their location instead of going further afield. In a time-sensitive environment where travelling time is limited, an incorrect decision might lead to serious consequences. In ambulance management, an incorrect dispatch that causes a unit late to arrive may lead to life and death situation. In this paper, we propose a Computational Geometry-based approach in determining catchment area, named Time Travel Voronoi Diagram (TTVD), not only by calculating the geographical location as used in most earlier work, but also through predicting the time travel to destination. This method can be used as a predictive analytic tool to support emergency dispatching, such as ambulance services. We utilise road features and the associated speed restrictions from crowdsource map platform in our prediction. Our simulation shows that a realistic catchment can be predicted using time-based distance with road features. Highlights: Propose a Computational Geometry approach to predict the time-based catchment area. Evaluate realistic road obstacles and crowdsource trajectories for realistic estimation model. Evaluate the model using ambulance stations and road data in metropolitan Melbourne.