Abstract--Accurate taxi demand-supply forecasting is a challenging applicationof ITS (Intelligent Transportation Systems), due to the complex spatial and temporal patterns. We investigate the impact of different spatial partitioning techniques on the prediction performance of an LSTM (Long Short-Term Memory) network, in the context of taxi demand-supply forecasting. We consider two tessellation schemes: (i) the variable-sized Voronoi tessellation, and (ii) the fixed-sized Geohash tessellation. While the widely employed ConvLSTM (Convolutional LSTM) can model fixed-sized Geohash partitions, the standard convolutional filters cannot be applied on the variable-sized Voronoi partitions. To explore the Voronoi tessellation scheme, we propose the use of GraphLSTM (Graph-based LSTM), by representing the Voronoi spatial partitions as nodes on an arbitrarily structured graph. The GraphLSTM offers competitive performance against ConvLSTM, atlower computational complexity, across three realworld large-scale taxi demand-supply data sets, with different performance metrics. To ensure superior performance across diverse settings, a HEDGE based ensemble learning algorithm is applied over the ConvLSTM and the GraphLSTM networks. I. INTRODUCTION Spatiotemporal forecasting has a wide range of applications, rangingfrom epidemic detection [1], energy management [2], to cellular traffic [3], among others. Location-based taxi demand and supply forecasting, one of the key components ofITS (Intelligent Transportation Systems), also relies heavily on accurate spatiotemporal forecasting. Mobility-on- Demand services such as e-hailing taxis, which have gained tremendous popularity in the recent years, often face taxi demand-supply imbalances. During peak and off-peak hours, mismatches occur between the spatial distributions of the taxi demand and the available drivers, resulting in either scarcity or abundance of vacant taxis. For example, Figure 1 presents a case of demand-supply mismatch averaged over all Mondays near the city center in Bengaluru, India.

Past work in relation extraction mostly focuses on binary relation between entity pairs within single sentence. Recently, the NLP community has gained interest in relation extraction in entity pairs spanning multiple sentences. In this paper, we propose a novel architecture for this task: inter-sentential dependency-based neural networks (iDepNN). iDepNN models the shortest and augmented dependency paths via recurrent and recursive neural networks to extract relationships within (intra-) and across (inter-) sentence boundaries. Compared to SVM and neural network baselines, iDepNN is more robust to false positives in relationships spanning sentences. We evaluate our models on four datasets from newswire (MUC6) and medical (BioNLP shared task) domains that achieve state-of-the-art performance and show a better balance in precision and recall for inter-sentential relationships. We perform better than 11 teams participating in the BioNLP shared task 2016 and achieve a gain of 5.2% (0.587 vs 0.558) in F1 over the winning team. We also release the crosssentence annotations for MUC6.