Integrating Demand Hotspots and Adjusted Spatial Indexing for Urban Taxi Demand Prediction

Accurate demand prediction in target areas is critical for an optimal taxi placement systems. This accuracy relies heavily on the correctness of the spatial index because predictions are directly runned on this spatial representation. However, conventional spatial index methods often fail to fully capture the dynamic patterns of urban demand, leading to inefficiencies in fleet management and service delivery. In this paper, we propose a novel methodology that integrates demand hotspots into an adjusted spatial grid. We demonstrate the implementation of this approach in a simulated environment and evaluate it against conventional spatial index. The results show that our proposed spatial index has more accurate representation of demand while having acceptable performance, leading to better taxi placement. This strategy is scalable and adaptable to various urban settings in the field of modern transportation.