Deep Multi-View Spatial-Temporal Network for Taxi Demand Prediction

Abstract

Taxi demand prediction is an important building block to enabling intelligent transportation systems in a smart city. An accurate prediction model can help the city pre-allocate resources to meet travel demand and to reduce empty taxis on streets which waste energy and worsen the traffic congestion. With the increasing popularity of taxi requesting services such as Uber and Didi Chuxing (in China), we are able to collect large-scale taxi demand data continuously. How to utilize such big data to improve the demand prediction is an interesting and critical real-world problem. Traditional demand prediction methods mostly rely on time series forecasting techniques, which fail to model the complex non-linear spatial and temporal relations. Recent advances in deep learning have shown superior performance on traditionally challenging tasks such as image classification by learning the complex features and correlations from large-scale data. This breakthrough has inspired researchers to explore deep learning techniques on traffic prediction problems. However, existing methods on traffic prediction have only considered spatial relation (e.g., using CNN) or temporal relation (e.g., using LSTM) independently. We propose a Deep Multi-View Spatial-Temporal Network (DMVST-Net) framework to model both spatial and temporal relations. Specifically, our proposed model consists of three views: temporal view (modeling correlations between future demand values with near time points via LSTM), spatial view (modeling local spatial correlation via local CNN), and semantic view (modeling correlations among regions sharing similar temporal patterns). Experiments on large-scale real taxi demand data demonstrate effectiveness of our approach over state-of-the-art methods.

Graph Deep Factors for Probabilistic Time-series Forecasting

“…As an early work, Seo et al [67] introduced graph convolutional recurrent network (GCRN) to predict structured sequential data. Other recent work is mostly limited in spatio-temporal study, such as traic prediction [82] and ride-hailing demand forecasting [80,81]. All these methods are incorporated with a graph structure.…”

Event-Aware Multimodal Mobility Nowcasting

“…Besides, as illustrated in Figure 2a, temporal covariates can be used as auxiliary input (Yao et al 2018;Zonoozi et al 2018) to equip ST-Net with time and holiday awareness. In this case, X…”

“…the winter storm Jonas or COVID-19 pandemic. Following the common practice (Zonoozi et al 2018;Yao et al 2018), we encode temporal covariates of each time slot (i.e. time-of-day, day-of-week, month-of-year, whether-holiday) in an one-hot manner as auxiliary sequence input.…”

“…The volatility brought by societal events, on the other hand, is by far downplayed and usually handled by simple rectifications, such as incorporating temporal covariates (e.g. time-of-day, day-of-week, whether-holiday) as auxiliary input (Yao et al 2018;Zonoozi et al 2018), adding a memory bank to reuse similar patterns in history (Yao et al 2019;Tang et al 2020). These manipulations to a certain degree bring time and holiday awareness, but they mainly help with the periodic and precedent parts and would still fail under more extreme scenarios like unprecedented events (e.g.…”

Travel Time Prediction Method Based on Spatial-Feature-based Hierarchical Clustering and Deep Multi-input Gated Recurrent Unit

“…The convolutional neural network (CNN) [19] has remarkable performance in extracting local features of images. Since the traic network can be easily transformed into images, recent studies [9,36,45] had adopted the CNN to capture the spatial features. Since the traic network is naturally a graph, the graph neural networks (GNNs) have been the most popular way used to learn the spatial features of traic network.…”