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.
In our previous work @cite_19 , 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.
@cite_19
2904724173 2565638783 2006227471 2591694916
In this paper, we investigate the significance of choosing an appropriate tessellation strategy for a spatio-temporal taxi demand-supply modeling framework. Our study compares (i) the variable-sized polygon based Voronoi tessellation, and (ii) the fixed-sized grid based Geohash tessellation, using taxi demand-supply GPS data for the cities of Bengaluru, India and New York, USA. Long Short-Term Memory (LSTM) networks are used for modeling and incorporating information from spatial neighbors into the model. We find that the LSTM model based on input features extracted from a variable-sized polygon tessellation yields superior performance over the LSTM model based on fixed-sized grid tessellation. Our study highlights the need to explore multiple spatial partitioning techniques for improving the prediction performance in neural network models. 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 this article, we introduce a new global optimization method to the field of multiview 3D reconstruction. While global minimization has been proposed in a discrete formulation in form of the maxflow-mincut framework, we suggest the use of a continuous convex relaxation scheme. Specifically, we propose to cast the problem of 3D shape reconstruction as one of minimizing a spatially continuous convex functional. In qualitative and quantitative evaluation we demonstrate several advantages of the proposed continuous formulation over the discrete graph cut solution. Firstly, geometric properties such as weighted boundary length and surface area are represented in a numerically consistent manner: The continuous convex relaxation assures that the algorithm does not suffer from metrication errors in the sense that the reconstruction converges to the continuous solution as the spatial resolution is increased. Moreover, memory requirements are reduced, allowing for globally optimal reconstructions at higher resolutions. We study three different energy models for multiview reconstruction, which are based on a common variational template unifying regional volumetric terms and on-surface photoconsistency. The three models use data measurements at increasing levels of sophistication. While the first two approaches are based on a classical silhouette-based volume subdivision, the third one relies on stereo information to define regional costs. Furthermore, this scheme is exploited to compute a precise photoconsistency measure as opposed to the classical estimation. All three models are compared on standard data sets demonstrating their advantages and shortcomings. For the third one, which gives the most accurate results, a more exhaustive qualitative and quantitative evaluation is presented. This paper presents a new approach for the challenging problem of geo-localization using image matching in a structured database of city-wide reference images with known GPS coordinates. We cast the geo-localization as a clustering problem of local image features. Akin to existing approaches to the problem, our framework builds on low-level features which allow local matching between images. For each local feature in the query image, we find its approximate nearest neighbors in the reference set. Next, we cluster the features from reference images using Dominant Set clustering, which affords several advantages over existing approaches. First, it permits variable number of nodes in the cluster, which we use to dynamically select the number of nearest neighbors for each query feature based on its discrimination value. Second, this approach is several orders of magnitude faster than existing approaches. Thus, we obtain multiple clusters (different local maximizers) and obtain a robust final solution to the problem using multiple weak solutions through constrained Dominant Set clustering on global image features, where we enforce the constraint that the query image must be included in the cluster. This second level of clustering also bypasses heuristic approaches to voting and selecting the reference image that matches to the query. We evaluate the proposed framework on an existing dataset of 102k street view images as well as a new larger dataset of 300k images, and show that it outperforms the state-of-the-art by 20 and 7 percent, respectively, on the two datasets.