You are provided with the abstract of a scientific paper and the abstracts of the papers it references. Your task is to write a Related Work section for the main paper. This section should concisely summarize and integrate the key points from the cited abstracts, reflecting how they relate to the main paper's contributions and context. In your summary, use numerical citation markers like [1], [2], etc., to refer to the corresponding papers.

Main Abstract:
A key problem in location-based modeling and forecasting lies in identifying suitable spatial and temporal resolutions. In particular, judicious spatial partitioning can play a significant role in enhancing the performance of location-based forecasting models. In this paper, we investigate two widely used tessellation strategies for partitioning city space, in the context of real-time taxi demand forecasting. Our study compares 1) the Geohash tessellation and 2) the Voronoi tessellation, using two distinct taxi demand data sets, over multiple time scales. For the purpose of comparison, we employ classical time-series tools to model the spatio-temporal demand. Our study finds that the performance of each tessellation strategy is highly dependent on the city geography, spatial distribution of the data, and the time of the day, and that neither strategy is found to perform optimally across the forecast horizon. We propose a combining algorithm that selects the best tessellation strategy at each time step, based on their recent performance. Our algorithm is a non-stationary variant of the well-known HEDGE algorithm for choosing the best advice from multiple experts. We show that the proposed strategy performs consistently better than either of the two tessellation strategies across the data sets considered, at multiple time scales, and with different performance metrics. We achieved an average accuracy of above 80 per km2 for both data sets considered at 60 min aggregation levels.

Cited Abstract(s):
[1]: In this paper, we use time-series modeling to forecast taxi travel demand, in the context of a mobile application-based taxi hailing service. In particular, we model the passenger demand density at various locations in the city of Bengaluru, India. Using the data, we first shortlist time-series models that suit our application. We then analyse the performance of these models by using Mean Absolute Percentage Error (MAPE) as the performance metric. In order to improve the model performance, we employ a multi-level clustering technique where we aggregate demand over neighboring cells geohashes. We observe that the improved model based on clustering leads to a forecast accuracy of 80 per km2. In addition, our technique obtains an accuracy of 89 per km2 for the most frequently occurring use case.
In our previous work [1] , we tessellated the city of Bengaluru, India, into fixed-sized partitions known as geohashes. We observed that for those regions with low demand density, fixed sized partitions resulted in data scarcity, which led to low model accuracy. To improve the accuracy, we explored the spatial correlation between the neighbouring geohashes to enhance the performance of the models. The performance limitation due to the chosen tessellation strategy motivated us to conduct a comparison study of various tessellation strategies. In this scenario, a few significant questions arise: (i) How can one decide the tessellation scheme to be used?, (ii) How sensitive is the performance of the models to the tessellation strategy?, (iii) Can we arrive at a tessellation strategy that works for a broad range of datasets? We aim to address these questions through this paper. To the best of our knowledge, an extensive study of the tessellation techniques and their effects on the model performance have not been conducted in the past. In this work, we explore the relationships between tessellation strategies, demand densities and city geographies. This is one of the features that distinguishes our work from the existing literature.