Identifying and recommending taxi hotspots in spatio-temporal space

Abstract

The GPS-driven mobile application-based ride-hailing systems, e.g., Uber and Ola, have become integral to daily life and natural transport choices for urban commuters. However, there is an imbalance between demand or pick-up requests and supply or drop-off requests in any area. The city planners and the researchers are working hard to balance this gap in demand and supply situation for taxi requests. The existing approaches have mainly focused on clustering the spatial regions to identify the hotspots, which refer to the locations with a high demand for pick-up requests. This study determined that if the hotspots focus on clustering high demand for pick-up requests, most of the hotspots pivot near the city center or in the two-three spatial regions, ignoring the other parts of the city. This paper (An earlier version of this paper was presented at the Australasian Database Conference and was published in its Proceedings: https://link.springer.com/chapter/10.1007/978-3-030-69377-0_10) presents a hotspot detection method that uses a dominating set problem-based solution in spatial-temporal space, which covers high-density taxi pick-up demand regions and covers those parts of the city with a moderate density of taxi pick-up demands during different hours of the day. The paper proposes algorithms based on k-hop dominating set; their performance is evaluated using real-world datasets and proves the edge over the existing state-of-the-art methods. It will also reduce the waiting time for customers and drivers looking for their subsequent pick-up requests. Therefore, this would maximize their profit and help improve their services.