3DGCN: 3-Dimensional Dynamic Graph Convolutional Network for Citywide Crowd Flow Prediction

2021 ◽  
Vol 15 (6) ◽  
pp. 1-21
Author(s):  
Tong Xia ◽  
Junjie Lin ◽  
Yong Li ◽  
Jie Feng ◽  
Pan Hui ◽  
...  
Keyword(s):  
Time Interval ◽  
Urban Structure ◽  
Dynamic Graph ◽  
Temporal Dependence ◽  
Convolutional Network ◽  
Urban Structures ◽  
Flow Prediction ◽  
Wide Range ◽  
Spatio Temporal ◽  
Real World Datasets

Crowd flow prediction is an essential task benefiting a wide range of applications for the transportation system and public safety. However, it is a challenging problem due to the complex spatio-temporal dependence and the complicated impact of urban structure on the crowd flow patterns. In this article, we propose a novel framework, 3- D imensional G raph C onvolution N etwork (3DGCN), to predict citywide crowd flow. We first model it as a dynamic spatio-temporal graph prediction problem, where each node represents a region with time-varying flows, and each edge represents the origin–destination (OD) flow between its corresponding regions. As such, OD flows among regions are treated as a proxy for the spatial interactions among regions. To tackle the complex spatio-temporal dependence, our proposed 3DGCN can model the correlation among graph spatial and temporal neighbors simultaneously. To learn and incorporate urban structures in crowd flow prediction, we design the GCN aggregator to be learned from both crowd flow prediction and region function inference at the same time. Extensive experiments with real-world datasets in two cities demonstrate that our model outperforms state-of-the-art baselines by 9.6%∼19.5% for the next-time-interval prediction.

scholarly journals Region-Based Message Exploration over Spatio-Temporal Data Streams

2019 ◽  
Vol 33 ◽  
pp. 873-880 ◽  
Author(s):  
Lisi Chen ◽  
Shuo Shang
Keyword(s):  
Spatial Distribution ◽  
Data Streams ◽  
High Efficiency ◽  
Temporal Data ◽  
Wide Range ◽  
Efficiency And Effectiveness ◽  
Spatial Attributes ◽  
Spatio Temporal ◽  
Real World Datasets ◽  
Text Content

Massive amount of spatio-temporal data that contain location and text content are being generated by location-based social media. These spatio-temporal messages cover a wide range of topics. It is of great significance to discover local trending topics based on users’ location-based and topicbased requirements. We develop a region-based message exploration mechanism that retrieve spatio-temporal message clusters from a stream of spatio-temporal messages based on users’ preferences on message topic and message spatial distribution. Additionally, we propose a region summarization algorithm that finds a subset of representative messages in a cluster to summarize the topics and the spatial attributes of messages in the cluster. We evaluate the efficacy and efficiency of our proposal on two real-world datasets and the results demonstrate that our solution is capable of high efficiency and effectiveness compared with baselines.

scholarly journals DeepSTN+: Context-Aware Spatial-Temporal Neural Network for Crowd Flow Prediction in Metropolis

2019 ◽  
Vol 33 ◽  
pp. 1020-1027 ◽  
Author(s):  
Ziqian Lin ◽  
Jie Feng ◽  
Ziyang Lu ◽  
Yong Li ◽  
Depeng Jin
Keyword(s):  
Traffic Control ◽  
State Of The Art ◽  
Real Life ◽  
Time Interval ◽  
Context Aware ◽  
Training Process ◽  
Flow Prediction ◽  
Wide Range ◽  
The City ◽  
Historical Flow

Crowd flow prediction is of great importance in a wide range of applications from urban planning, traffic control to public safety. It aims to predict the inflow (the traffic of crowds entering a region in a given time interval) and outflow (the traffic of crowds leaving a region for other places) of each region in the city with knowing the historical flow data. In this paper, we propose DeepSTN+, a deep learning-based convolutional model, to predict crowd flows in the metropolis. First, DeepSTN+ employs the ConvPlus structure to model the longrange spatial dependence among crowd flows in different regions. Further, PoI distributions and time factor are combined to express the effect of location attributes to introduce prior knowledge of the crowd movements. Finally, we propose an effective fusion mechanism to stabilize the training process, which further improves the performance. Extensive experimental results based on two real-life datasets demonstrate the superiority of our model, i.e., DeepSTN+ reduces the error of the crowd flow prediction by approximately 8%∼13% compared with the state-of-the-art baselines.

scholarly journals Large-Scale Station-Level Crowd Flow Forecast with ST-Unet

2019 ◽  
Vol 8 (3) ◽  
pp. 140 ◽  
Author(s):  
Yirong Zhou ◽  
Hao Chen ◽  
Jun Li ◽  
Ye Wu ◽  
Jiangjiang Wu ◽  
...  
Keyword(s):  
Spatial Dependence ◽  
Traffic Management ◽  
Large Scale ◽  
Weather Conditions ◽  
Temporal Dependence ◽  
Flow Information ◽  
Spatio Temporal ◽  
Real World Datasets ◽  
Temporal Events ◽  
Flow Forecast

High crowd mobility is a characteristic of transportation hubs such as metro/bus/bike stations in cities worldwide. Forecasting the crowd flow for such places, known as station-level crowd flow forecast (SLCFF) in this paper, would have many benefits, for example traffic management and public safety. Concretely, SLCFF predicts the number of people that will arrive at or depart from stations in a given period. However, one challenge is that the crowd flows across hundreds of stations irregularly scattered throughout a city are affected by complicated spatio-temporal events. Additionally, some external factors such as weather conditions or holidays may change the crowd flow tremendously. In this paper, a spatio-temporal U-shape network model (ST-Unet) for SLCFF is proposed. It is a neural network-based multi-output regression model, handling hundreds of target variables, i.e., all stations’ in and out flows. ST-Unet emphasizes stations’ spatial dependence by integrating the crowd flow information from neighboring stations and the cluster it belongs to after hierarchical clustering. It learns the temporal dependence by modeling the temporal closeness, period, and trend of crowd flows. With proper modifications on the network structure, ST-Unet is easily trained and has reliable convergency. Experiments on four real-world datasets were carried out to verify the proposed method’s performance and the results show that ST-Unet outperforms seven baselines in terms of SLCFF.

scholarly journals Capsules TCN Network for Urban Computing and Intelligence in Urban Traffic Prediction

2020 ◽  
Vol 2020 ◽  
pp. 1-15
Author(s):  
Dazhou Li ◽  
Chuan Lin ◽  
Wei Gao ◽  
Zeying Chen ◽  
Zeshen Wang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Traffic Flow ◽  
Geographical Location ◽  
Urban Traffic ◽  
Basic Unit ◽  
Urban Computing ◽  
Convolutional Network ◽  
Traffic Flow Prediction ◽  
Flow Prediction ◽  
Real World Datasets

Predicting urban traffic is of great importance to smart city systems and public security; however, it is a very challenging task because of several dynamic and complex factors, such as patterns of urban geographical location, weather, seasons, and holidays. To tackle these challenges, we are stimulated by the deep-learning method proposed to unlock the power of knowledge from urban computing and proposed a deep-learning model based on neural network, entitled Capsules TCN Network, to predict the traffic flow in local areas of the city at once. Capsules TCN Network employs a Capsules Network and Temporal Convolutional Network as the basic unit to learn the spatial dependence, time dependence, and external factors of traffic flow prediction. In specific, we consider some particular scenarios that require accurate traffic flow prediction (e.g., smart transportation, business circle analysis, and traffic flow assessment) and propose a GAN-based superresolution reconstruction model. Extensive experiments were conducted based on real-world datasets to demonstrate the superiority of Capsules TCN Network beyond several state-of-the-art methods. Compared with HA, ARIMA, RNN, and LSTM classic methods, respectively, the method proposed in the paper achieved better results in the experimental verification.

Jointly Modeling Spatio–Temporal Dependencies and Daily Flow Correlations for Crowd Flow Prediction

2021 ◽  
Vol 15 (4) ◽  
pp. 1-20
Author(s):  
Tianzi Zang ◽  
Yanmin Zhu ◽  
Yanan Xu ◽  
Jiadi Yu
Keyword(s):  
Traffic Management ◽  
Intelligent Transportation System ◽  
System Construction ◽  
Strong Correlations ◽  
Flow Prediction ◽  
Proposed Model ◽  
Spatio Temporal ◽  
Real World Datasets ◽  
Novel Model ◽  
Temporal Dependencies

Crowd flow prediction is a vital problem for an intelligent transportation system construction in a smart city. It plays a crucial role in traffic management and behavioral analysis, thus it has raised great attention from many researchers. However, predicting crowd flows timely and accurately is a challenging task that is affected by many complex factors such as the dependencies of adjacent regions or recent crowd flows. Existing models mainly focus on capturing such dependencies in spatial or temporal domains and fail to model relations between crowd flows of distant regions. We notice that each region has a relatively fixed daily flow and some regions (even very far away from each other) may share similar flow patterns which show strong correlations among them. In this article, we propose a novel model named Double-Encoder which follows a general encoder–decoder framework for multi-step citywide crowd flow prediction. The model consists of two encoder modules named ST-Encoder and FR-Encoder to model spatial-temporal dependencies and daily flow correlations, respectively. We conduct extensive experiments on two real-world datasets to evaluate the performance of the proposed model and show that our model consistently outperforms state-of-the-art methods.

scholarly journals AddGraph: Anomaly Detection in Dynamic Graph Using Attention-based Temporal GCN

2019 ◽  
Author(s):  
Li Zheng ◽  
Zhenpeng Li ◽  
Jian Li ◽  
Zhao Li ◽  
Jun Gao
Keyword(s):  
Anomaly Detection ◽  
Dynamic Graphs ◽  
Dynamic Graph ◽  
Short Term ◽  
Convolutional Network ◽  
Attention Model ◽  
Temporal Features ◽  
Structural Content ◽  
Real World Datasets

Anomaly detection in dynamic graphs becomes very critical in many different application scenarios, e.g., recommender systems, while it also raises huge challenges due to the high flexible nature of anomaly and lack of sufficient labelled data. It is better to learn the anomaly patterns by considering all possible features including the structural, content and temporal features, rather than utilizing heuristic rules over the partial features. In this paper, we propose AddGraph, a general end-to-end anomalous edge detection framework using an extended temporal GCN (Graph Convolutional Network) with an attention model, which can capture both long-term patterns and the short-term patterns in dynamic graphs. In order to cope with insufficient explicit labelled data, we employ the negative sampling and margin loss in training of AddGraph in a semi-supervised fashion. We conduct extensive experiments on real-world datasets, and illustrate that AddGraph can outperform the state-of-the-art competitors in anomaly detection significantly.

A Multi-graph Convolutional Network Framework for Tourist Flow Prediction

2021 ◽  
Vol 21 (4) ◽  
pp. 1-13
Author(s):  
Wei Wang ◽  
Junyang Chen ◽  
Yushu Zhang ◽  
Zhiguo Gong ◽  
Neeraj Kumar ◽  
...  
Keyword(s):  
Data Science ◽  
Service Providers ◽  
Tourism Industry ◽  
Convolutional Network ◽  
Flow Prediction ◽  
Challenges And Opportunities ◽  
Real World Datasets ◽  
Relation Graph ◽  
Smart Tourism ◽  
Tourist Flow

With the advancement of Cyber Physic Systems and Social Internet of Things, the tourism industry is facing challenges and opportunities. We can now able to collect, store, and analyze large amounts of travel data. With the help of data science and artificial intelligence, smart tourism enables tourists with great autonomy and convenience for an intelligent trip. It is of great significance to make full use of these massive data to provide better services for smart tourism. However, due to the skewed and imbalanced visiting for point of interest located at different places, it is of great significance to predict the tourist flow of each place, which can help the service providers for designing a better schedule visiting strategy in advance. Against this background, this article proposes a multi-graph convolutional network framework, named AMOUNT, for tourist flow prediction. To capture the diverse relationships among POIs, AMOUNT first constructs three subgraphs, including the geographical graph, interaction graph, and the co-relation graph. Then, a multi-graph convolution network is utilized to predict the future tourist flow. Experimental results on two real-world datasets indicate that the proposed AMOUNT model outperforms all other baseline tourist flow prediction approaches.

scholarly journals Cross-Attention Fusion Based Spatial-Temporal Multi-Graph Convolutional Network for Traffic Flow Prediction

Sensors ◽  
2021 ◽  
Vol 21 (24) ◽  
pp. 8468
Author(s):  
Kun Yu ◽  
Xizhong Qin ◽  
Zhenhong Jia ◽  
Yan Du ◽  
Mengmeng Lin
Keyword(s):  
Traffic Flow ◽  
Prediction Errors ◽  
Temporal Data ◽  
Convolutional Network ◽  
Smart Transportation ◽  
Traffic Flow Prediction ◽  
Flow Prediction ◽  
Spatio Temporal ◽  
Regional Similarity ◽  
The Impact

Accurate traffic flow prediction is essential to building a smart transportation city. Existing research mainly uses a given single-graph structure as a model, only considers local and static spatial dependencies, and ignores the impact of dynamic spatio-temporal data diversity. To fully capture the characteristics of spatio-temporal data diversity, this paper proposes a cross-Attention Fusion Based Spatial-Temporal Multi-Graph Convolutional Network (CAFMGCN) model for traffic flow prediction. First, introduce GCN to model the historical traffic data’s three-time attributes (current, daily, and weekly) to extract time features. Second, consider the relationship between distance and traffic flow, constructing adjacency, connectivity, and regional similarity graphs to capture dynamic spatial topology information. To make full use of global information, a cross-attention mechanism is introduced to fuse temporal and spatial features separately to reduce prediction errors. Finally, the CAFMGCN model is evaluated, and the experimental results show that the prediction of this model is more accurate and effective than the baseline of other models.

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