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.

Location prediction for facility placement by incorporating multi-characteristic information

2021 ◽  
Vol 25 (5) ◽  
pp. 1187-1210
Author(s):  
Pu Wang ◽  
Wei Chen ◽  
Jinjing Huang ◽  
Yuyang Wei ◽  
Junhua Fang ◽  
...  
Keyword(s):  
Location Prediction ◽  
Feature Evaluation ◽  
Temporal Features ◽  
Proposed Model ◽  
The Social ◽  
Efficiency And Effectiveness ◽  
Facility Placement ◽  
Spatio Temporal ◽  
Real World Datasets ◽  
Novel Model

In the course of recommending locations for establishing new facilities on urban planning or commercial programming, the location prediction offers the optimal candidates, which maximizes the number of served customers or minimize customer inconvenience, therefore brings the maximum profits. In most existing studies, only the spatial-temporal features are recognized to evaluate the location popularity, where social relationships of customers, which are significant factors for popularity assessing, have been ignored. Additionally, current researches also fail to take capacities and categories of the facilities into consideration. To overcome the drawbacks, we introduce a novel model of Multi-characteristic Information based Top-k Location Prediction (MITLP), it captures the spatio-temporal behaviors of customers based on historical trajectories, exploits the social relevancy from their friend relationships, as well as examines the category competitiveness of specific facilities thoroughly. Subsequently, by drawing on the feature evaluation and popularity quantization, MITLP will be implemented within a hybrid B-tree-liked recommending framework, Constrained Location and Social-Trajectory Clustered forest (CLSTC-forest), which can not only produce better performance in practice but also address the facility service constraints. Finally, extensive experiments conducted on real-world datasets demonstrate the higher efficiency and effectiveness of the proposed model.

scholarly journals A novel information diffusion model based on psychosocial factors with automatic parameter learning

2020 ◽  
pp. 50-50
Author(s):  
Sabina-Adriana Floria ◽  
Florin Leon
Keyword(s):  
Online Social Networks ◽  
Information Diffusion ◽  
Gregarious Behavior ◽  
Proposed Model ◽  
Transmission Of Information ◽  
Two Factors ◽  
Information Diffusion Model ◽  
Real World Datasets ◽  
Sociological Factor ◽  
Novel Model

Online social networks are the main choice of people to maintain their social relationships and share information or opinions. Estimating the actions of a user is not trivial because an individual can act spontaneously or be influenced by external factors. In this paper we propose a novel model for imitating the evolution of the information diffusion in a network as well as possible. Each individual is modeled as a node with two factors (psychological and sociological) that control its probabilistic transmission of information. The psychological factor refers to the node?s preference for the topic discussed, i.e. the information diffused. The sociological factor takes into account the influence of the neighbors? activity on the node, i.e. the gregarious behavior. A genetic algorithm is used to automatically tune the parameters of the model in order to fit the evolution of information diffusion observed in two real-world datasets with three topics. The reproduced diffusions show that the proposed model imitates the real diffusions very well.

scholarly journals Incomplete Label Multi-Task Deep Learning for Spatio-Temporal Event Subtype Forecasting

2019 ◽  
Vol 33 ◽  
pp. 3638-3646 ◽  
Author(s):  
Yuyang Gao ◽  
Liang Zhao ◽  
Lingfei Wu ◽  
Yanfang Ye ◽  
Hui Xiong ◽  
...  
Keyword(s):  
Deep Learning ◽  
Spatial Heterogeneity ◽  
Complex Problem ◽  
Strongly Coupled ◽  
Social Risks ◽  
Proposed Model ◽  
Future Events ◽  
Spatio Temporal ◽  
Real World Datasets ◽  
Spatial Locations

Due to the potentially significant benefits for society, forecasting spatio-temporal societal events is currently attracting considerable attention from researchers. Beyond merely predicting the occurrence of future events, practitioners are now looking for information about specific subtypes of future events in order to allocate appropriate amounts and types of resources to manage such events and any associated social risks. However, forecasting event subtypes is far more complex than merely extending binary prediction to cover multiple classes, as 1) different locations require different models to handle their characteristic event subtype patterns due to spatial heterogeneity; 2) historically, many locations have only experienced a incomplete set of event subtypes, thus limiting the local model’s ability to predict previously “unseen” subtypes; and 3) the subtle discrepancy among different event subtypes requires more discriminative and profound representations of societal events. In order to address all these challenges concurrently, we propose a Spatial Incomplete Multi-task Deep leArning (SIMDA) framework that is capable of effectively forecasting the subtypes of future events. The new framework formulates spatial locations into tasks to handle spatial heterogeneity in event subtypes, and learns a joint deep representation of subtypes across tasks. Furthermore, based on the “first law of geography”, spatiallyclosed tasks share similar event subtype patterns such that adjacent tasks can share knowledge with each other effectively. Optimizing the proposed model amounts to a new nonconvex and strongly-coupled problem, we propose a new algorithm based on Alternating Direction Method of Multipliers (ADMM) that can decompose the complex problem into subproblems that can be solved efficiently. Extensive experiments on six real-world datasets demonstrate the effectiveness and efficiency of the proposed model.

Deep Spatio-temporal Adaptive 3D Convolutional Neural Networks for Traffic Flow Prediction

2022 ◽  
Vol 13 (2) ◽  
pp. 1-21
Author(s):  
He Li ◽  
Xuejiao Li ◽  
Liangcai Su ◽  
Duo Jin ◽  
Jianbin Huang ◽  
...  
Keyword(s):  
Traffic Flow ◽  
Intelligent Transportation System ◽  
Historical Data ◽  
Temporal Correlation ◽  
Weather Conditions ◽  
External Factors ◽  
Traffic Flow Prediction ◽  
Flow Prediction ◽  
Spatio Temporal ◽  
The Impact

Traffic flow prediction is the upstream problem of path planning, intelligent transportation system, and other tasks. Many studies have been carried out on the traffic flow prediction of the spatio-temporal network, but the effects of spatio-temporal flexibility (historical data of the same type of time intervals in the same location will change flexibly) and spatio-temporal correlation (different road conditions have different effects at different times) have not been considered at the same time. We propose the Deep Spatio-temporal Adaptive 3D Convolution Neural Network (ST-A3DNet), which is a new scheme to solve both spatio-temporal correlation and flexibility, and consider spatio-temporal complexity (complex external factors, such as weather and holidays). Different from other traffic forecasting models, ST-A3DNet captures the spatio-temporal relationship at the same time through the Adaptive 3D convolution module, assigns different weights flexibly according to the influence of historical data, and obtains the impact of external factors on the flow through the ex-mask module. Considering the holidays and weather conditions, we train our model for experiments in Xi’an and Chengdu. We evaluate the ST-A3DNet and the results show that we have better results than the other 11 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.

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 Spatial-Temporal Synchronous Graph Convolutional Networks: A New Framework for Spatial-Temporal Network Data Forecasting

2020 ◽  
Vol 34 (01) ◽  
pp. 914-921 ◽  
Author(s):  
Chao Song ◽  
Youfang Lin ◽  
Shengnan Guo ◽  
Huaiyu Wan
Keyword(s):  
Traffic Management ◽  
Network Data ◽  
Temporal Data ◽  
Temporal Network ◽  
Convolutional Networks ◽  
Temporal Correlations ◽  
Real World Datasets ◽  
Temporal Graphs ◽  
Novel Model ◽  
New Framework

Spatial-temporal network data forecasting is of great importance in a huge amount of applications for traffic management and urban planning. However, the underlying complex spatial-temporal correlations and heterogeneities make this problem challenging. Existing methods usually use separate components to capture spatial and temporal correlations and ignore the heterogeneities in spatial-temporal data. In this paper, we propose a novel model, named Spatial-Temporal Synchronous Graph Convolutional Networks (STSGCN), for spatial-temporal network data forecasting. The model is able to effectively capture the complex localized spatial-temporal correlations through an elaborately designed spatial-temporal synchronous modeling mechanism. Meanwhile, multiple modules for different time periods are designed in the model to effectively capture the heterogeneities in localized spatial-temporal graphs. Extensive experiments are conducted on four real-world datasets, which demonstrates that our method achieves the state-of-the-art performance and consistently outperforms other baselines.

scholarly journals Adaptive Real-Time Routing Protocol for (m,k)-Firm in Industrial Wireless Multimedia Sensor Networks

Sensors ◽  
2020 ◽  
Vol 20 (6) ◽  
pp. 1633 ◽  
Author(s):  
Beom-Su Kim ◽  
Sangdae Kim ◽  
Kyong Hoon Kim ◽  
Tae-Eung Sung ◽  
Babar Shah ◽  
...  
Keyword(s):  
Real Time ◽  
Routing Protocol ◽  
Traffic Management ◽  
Research Area ◽  
Multimedia Data ◽  
Wireless Multimedia ◽  
Multimedia Sensor Networks ◽  
Proposed Model ◽  
Event Information ◽  
Firm Model

Many applications are able to obtain enriched information by employing a wireless multimedia sensor network (WMSN) in industrial environments, which consists of nodes that are capable of processing multimedia data. However, as many aspects of WMSNs still need to be refined, this remains a potential research area. An efficient application needs the ability to capture and store the latest information about an object or event, which requires real-time multimedia data to be delivered to the sink timely. Motivated to achieve this goal, we developed a new adaptive QoS routing protocol based on the (m,k)-firm model. The proposed model processes captured information by employing a multimedia stream in the (m,k)-firm format. In addition, the model includes a new adaptive real-time protocol and traffic handling scheme to transmit event information by selecting the next hop according to the flow status as well as the requirement of the (m,k)-firm model. Different from the previous approach, two level adjustment in routing protocol and traffic management are able to increase the number of successful packets within the deadline as well as path setup schemes along the previous route is able to reduce the packet loss until a new path is established. Our simulation results demonstrate that the proposed schemes are able to improve the stream dynamic success ratio and network lifetime compared to previous work by meeting the requirement of the (m,k)-firm model regardless of the amount of traffic.

scholarly journals A spatio-temporal specification language and its completeness & decidability

2020 ◽  
Vol 9 (1) ◽  
Author(s):  
Tengfei Li ◽  
Jing Liu ◽  
Haiying Sun ◽  
Xiang Chen ◽  
Lipeng Zhang ◽  
...  
Keyword(s):  
Intelligent Transportation System ◽  
Cyber Physical Systems ◽  
Specification Language ◽  
Finite Model ◽  
Critical Systems ◽  
Physical Systems ◽  
Spatial Objects ◽  
Temporal Properties ◽  
Spatio Temporal ◽  
Temporal Specification

AbstractIn the past few years, significant progress has been made on spatio-temporal cyber-physical systems in achieving spatio-temporal properties on several long-standing tasks. With the broader specification of spatio-temporal properties on various applications, the concerns over their spatio-temporal logics have been raised in public, especially after the widely reported safety-critical systems involving self-driving cars, intelligent transportation system, image processing. In this paper, we present a spatio-temporal specification language, STSL PC, by combining Signal Temporal Logic (STL) with a spatial logic S4 u, to characterize spatio-temporal dynamic behaviors of cyber-physical systems. This language is highly expressive: it allows the description of quantitative signals, by expressing spatio-temporal traces over real valued signals in dense time, and Boolean signals, by constraining values of spatial objects across threshold predicates. STSL PC combines the power of temporal modalities and spatial operators, and enjoys important properties such as finite model property. We provide a Hilbert-style axiomatization for the proposed STSL PC and prove the soundness and completeness by the spatio-temporal extension of maximal consistent set and canonical model. Further, we demonstrate the decidability of STSL PC and analyze the complexity of STSL PC. Besides, we generalize STSL to the evolution of spatial objects over time, called STSL OC, and provide the proof of its axiomatization system and decidability.

scholarly journals Spatio-Temporal Visual Analysis for Urban Traffic Characters Based on Video Surveillance Camera Data

2021 ◽  
Vol 10 (3) ◽  
pp. 177
Author(s):  
Haochen Zou ◽  
Keyan Cao ◽  
Chong Jiang
Keyword(s):  
Video Surveillance ◽  
Traffic Management ◽  
Visual Analysis ◽  
Urban Traffic ◽  
License Plate ◽  
Road Vehicle ◽  
Urban Road ◽  
Surveillance Camera ◽  
Vehicle Density ◽  
Spatio Temporal

Urban road traffic spatio-temporal characters reflect how citizens move and how goods are transported, which is crucial for trip planning, traffic management, and urban design. Video surveillance camera plays an important role in intelligent transport systems (ITS) for recognizing license plate numbers. This paper proposes a spatio-temporal visualization method to discover urban road vehicle density, city-wide regional vehicle density, and hot routes using license plate number data recorded by video surveillance cameras. To improve the accuracy of the visualization effect, during data analysis and processing, this paper utilized Internet crawler technology and adopted an outlier detection algorithm based on the Dixon detection method. In the design of the visualization map, this paper established an urban road vehicle traffic index to intuitively and quantitatively reveal the traffic operation situation of the area. To verify the feasibility of the method, an experiment in Guiyang on data from road video surveillance camera system was conducted. Multiple urban traffic spatial and temporal characters are recognized concisely and efficiently from three visualization maps. The results show the satisfactory performance of the proposed framework in terms of visual analysis, which will facilitate traffic management and operation.

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