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Urban Traffic Prediction from Spatio-Temporal Data Using Deep Meta Learning

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Urban Traffic Prediction from Spatio-Temporal Data Using Deep Meta Learning Urban Traffic Prediction from Spatio-Temporal Data Using Deep Meta Learning Zheyi Pan1, Yuxuan Liang4, Weifeng Wang1, Yong Yu1, Yu Zheng2;3;4, Junbo Zhang2;3;5∗ 1Department of Computer Science and Engineering, Shanghai Jiaotong University, China 2JD Intelligent Cities Research, China 3JD Intelligent Cities Business Unit, China 4School of Computer Science and Technology, Xidian University, China 5Institute of Artificial Intelligence, Southwest Jiaotong University, China {zhpan,wfwang,yyu}@apex.sjtu.edu.cn;{yuxliang,msyuzheng,msjunbozhang}@outlook.com ABSTRACT 1 INTRODUCTION Predicting urban traffic is of great importance to intelligent trans- Recent advances in data acquisition technologies and mobile com- portation systems and public safety, yet is very challenging because puting lead to a collection of large amounts of traffic data, such of two aspects: 1) complex spatio-temporal correlations of urban as vehicle trajectories, enabling much urban analysis and related traffic, including spatial correlations between locations along with applications [30]. Urban traffic prediction has become a mission- temporal correlations among timestamps; 2) diversity of such spatio- critical work for the development of a smart city, as it can provide temporal correlations, which vary from location to location and insights for urban planning and traffic management to improve the depend on the surrounding geographical information, e.g., points efficiency of public transportation, as well as give early warnings of interests and road networks. To tackle these challenges, we pro- for public safety emergency management [29]. However, predicting posed a deep-meta-learning based model, entitled ST-MetaNet, to urban traffic is very challenging because of complicated spatio- collectively predict traffic in all location at once. ST-MetaNet em- temporal (ST) correlations. ploys a sequence-to-sequence architecture, consisting of an encoder Complex composition of ST correlations: Since traffic changes to learn historical information and a decoder to make predictions on the spatial domain (e.g. along road networks), we employ a geo- step by step. In specific, the encoder and decoder have the same net- graph to describe the spatial structure, where each node denotes a work structure, consisting of a recurrent neural network to encode location and each edge represents the relation between locations, the traffic, a meta graph attention network to capture diverse spatial as shown in Figure 1 (a). As urban traffic is highly dynamic, we correlations, and a meta recurrent neural network to consider di- consider such correlations from the following two factors. verse temporal correlations. Extensive experiments were conducted based on two real-world datasets to illustrate the effectiveness of Time Business District S2 Residence ST-MetaNet beyond several state-of-the-art methods. S S3 1 S R1 4 R2 CCS CONCEPTS • Information systems → Spatial-temporal systems; Data min- ing; • Computing methodologies → Neural networks. Business District KEYWORDS R3 Urban traffic; Spatio-temporal data; Neural network; Meta learning Location Spatial correlations Flows in Flows in Edge Temporal correlations the morning the evening ACM Reference Format: (a) Composition of ST correlations (b) Distribution of geo-attributes Zheyi Pan, Yuxuan Liang, Weifeng Wang, Yong Yu, Yu Zheng, Junbo Zhang. 2019. Urban Traffic Prediction from Spatio-Temporal Data Using Deep Meta Morning Evening rush hour rush hour Learning. In The 25th ACM SIGKDD Conference on Knowledge Discovery & Data Mining (KDD’19), August 4–8, 2019, Anchorage, AK, USA. ACM, NY, NY, USA, 11 pages. https://doi.org/10.1145/3292500.3330884 Inflow ∗Junbo Zhang is the corresponding author. Time Permission to make digital or hard copies of all or part of this work for personal or (c) Diversity of ST correlations classroom use is granted without fee provided that copies are not made or distributed Figure 1: Challenges of urban traffic prediction. for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM • Spatial correlations. As the red arrow shown in Figure 1 (a), the must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a traffic of some locations affects mutually and changes all thetime. fee. Request permissions from [email protected]. For example, traffic on road networks has strong spatial dependen- KDD ’19, August 4–8, 2019, Anchorage, AK, USA cies [14]. When a car accident occurs near S3, the traffic jam could © 2019 Association for Computing Machinery. ACM ISBN 978-1-4503-6201-6/19/08...$15.00 quickly diffuse to its neighbors, i.e., S1, S2, and S4, on the geo-graph. https://doi.org/10.1145/3292500.3330884 Such impact brings challenges to forecasting the traffic. Road POIs • Temporal correlations. Given a location, the current readings Networks of its traffic, such as inflows and outflows, are correlated withits Edge Node precedents. For instance, the taxi flow of a region is affected bythe Distance Characteristics Characteristics Location recent values, both near and far [27]. When a special event occurs Weight Historical traffic Generation Future traffic at S4 (e.g., a large concert), many people would go into the location Spatial Temporal and such location status would hold for a long time. Correlation Correlation Model for ST correlations Geographical Unobserved As both types of correlations affect urban traffic, it is necessary Models information factors to simultaneously model such spatial and temporal correlations. Figure 2: Insight of the framework. Diversity of ST correlations: In a city, characteristics of locations and their mutual relationship are diverse, depending on their own and nearby POIs. Likewise, the characteristic of an edge depends geo-graph attributes: 1) node attributes: the surrounding environ- on its attributes, e.g., road connectivity and the distance between ment of a location, namely, nearby points of interests (POIs) and locations. Based on these insights, ST-MetaNet first extracts the density of road networks (RNs); 2) edge attributes: the relationship meta knowledge (i.e. characteristics) of nodes and edges from their between two nodes, such as the connectivity of roads and the dis- attributes, respectively. The meta knowledge is then used to model tance between them. As shown in Figure 1 (b), District R1, R2 and the ST correlations, namely, generating weights of the prediction R3 have different distributions of POIs and road network structures. networks. The contributions of our work are four folds: Consequently, the characteristics of these districts are different, • We propose a novel deep meta learning based model, named ST- and the trends of their inflows are diverse, as shown in Figure 1 MetaNet, to predict urban traffic. ST-MetaNet leverages the meta (c), which demonstrates that districts with different characteristics knowledge extracted from geo-graph attributes to generate the always have different types of ST correlations. parameter weights of a graph attention network and a recurrent Nonetheless, locations with a similar combination of geo-graph network within sequence-to-sequence architecture. As a result, it attributes can lead to similar characteristics and analogous types can capture the inherent relation between geo-graph attributes and of ST correlations. In Figure 1 (b), District R1 and R3 contain nu- diverse types of ST correlations. merous office buildings that indicate business districts, whileR2 • We propose a meta graph attention network to model spatial contains many apartments, denoting a residential district. In gen- correlations. The attention mechanism can capture the dynamic eral, citizens usually commute from home to their workplaces in mutual relationship between locations, with regard to their current the morning, while return at night. Thus, business district R1 and states. In addition, the weights of attention network are generated R3 witness similar upward trends of inflows in the morning, while by meta knowledge of nodes and edges extracted from geo-graph the residential district R2 meets a different rush hour in the evening, attributes, such that it can model the diverse spatial correlations. as shown in Figure 1 (c). Therefore, modeling inherent relationship • We propose a meta gated recurrent neural network, which gen- between geo-graph attributes and ST correlations’ types plays an erates all weights of a normal gated recurrent unit from the meta essential role in urban traffic prediction. knowledge of each node. Thus each location has an unique model Recently, although there has been significant growth of works for its unique type of temporal correlations. for urban traffic prediction as well as analogous ST prediction tasks, • We evaluate ST-MetaNet on two tasks: taxi flow prediction and the aforementioned challenges are still not tackled well. First of traffic speed prediction. The experiment results verify that ST- all, some works [15, 22] focus on modeling ST correlations by a MetaNet can significantly improve urban traffic prediction, and single model for all locations. These methods cannot explicitly learn better traffic-related knowledge from geo-graph attributes. model the inherent relationships between geo-graph attributes and various types of ST correlations, as a result, such relationship is 2 PRELIMINARIES
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