Deep Graph Convolutional Networks for Incident-Driven Traffic Speed Prediction

Xie, Qinge; Guo, Tiancheng; Yang, Chen; Xiao, Yu; Wang, Xin; Zhao, Ben Y.

doi:10.1145/3340531.3411873

Cited by 40 publications

(24 citation statements)

References 37 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…GPS (Konishi et al 2016), origin-destination records (Zhang, Zheng, and Yu 2018;Zhang et al 2019), trip survey (Wang et al 2021)) are commonly used as the underlying clues to infer both local and citywide anomalous events. On the other hand, mobility behavior is affected by these societal events conversely (Song et al 2014;Fan et al 2015;Jiang et al 2019;Xie et al 2020)…”

Section: Related Workmentioning

confidence: 99%

Event-Aware Multimodal Mobility Nowcasting

Wang¹,

Jiang²,

Han³

et al. 2021

Preprint

View full text Add to dashboard Cite

As a decisive part in the success of Mobility-as-a-Service (MaaS), spatio-temporal predictive modeling for crowd movements is a challenging task particularly considering scenarios where societal events drive mobility behavior deviated from the normality. While tremendous progress has been made to model high-level spatio-temporal regularities with deep learning, most, if not all of the existing methods are neither aware of the dynamic interactions among multiple transport modes nor adaptive to unprecedented volatility brought by potential societal events. In this paper, we are therefore motivated to improve the canonical spatio-temporal network (ST-Net) from two perspectives: (1) design a heterogeneous mobility information network (HMIN) to explicitly represent intermodality in multimodal mobility; (2) propose a memory-augmented dynamic filter generator (MDFG) to generate sequence-specific parameters in an on-the-fly fashion for various scenarios. The enhanced event-aware spatiotemporal network, namely EAST-Net, is evaluated on several real-world datasets with a wide variety and coverage of societal events. Both quantitative and qualitative experimental results verify the superiority of our approach compared with the state-of-the-art baselines. Code and data are published on https://github.com/underdoc-wang/EAST-Net.

show abstract

Section: Related Workmentioning

confidence: 99%

Event-Aware Multimodal Mobility Nowcasting

Wang¹,

Jiang²,

Han³

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…Therefore, it is not possible to proactively update the capacity plan. As an extension of this work, we will use deep learningbased algorithms, such as long short-term memory (LSTM) network [51] and graph convolutional network (GCN) [52], for predicting the future traffic flow and update the capacity plan accordingly. Second, our capacity planning method targets long-term fog node deployment.…”

Section: Advantages and Limitations Of Our Workmentioning

confidence: 99%

Data-Driven Capacity Planning for Vehicular Fog Computing

Mao

Akgül

Mehrabi

et al. 2022

IEEE Internet Things J.

Self Cite

View full text Add to dashboard Cite

The strict latency constraints of emerging vehicular applications make it unfeasible to forward sensing data from vehicles to the cloud for processing. To shorten network latency, vehicular fog computing (VFC) moves computation to the edge of the Internet, with the extension to support the mobility of distributed computing entities (a.k.a fog nodes). In other words, VFC proposes to complement stationary fog nodes co-located with cellular base stations with mobile ones carried by moving vehicles (e.g., buses). Previous works on VFC mainly focus on optimizing the assignments of computing tasks among available fog nodes. However, capacity planning, which decides where and how much computing resources to deploy, remains an open and challenging issue. The complexity of this problem results from the spatio-temporal dynamics of vehicular traffic, varying computing resource demand generated by vehicular applications, and the mobility of fog nodes. To solve the above challenges, we propose a data-driven capacity planning framework that optimizes the deployment of stationary and mobile fog nodes to minimize the installation and operational costs under the quality-of-service constraints, taking into account the spatiotemporal variation in both demand and supply. Using realworld traffic data and application profiles, we analyze the cost efficiency potential of VFC in the long term. We also evaluate the impacts of traffic patterns on the capacity plans and the potential cost savings. We find that high traffic density and significant hourly variation would lead to dense deployment of mobile fog nodes and create more savings in operational costs in the long term.

show abstract

“…Therefore, it is not possible to proactively update the capacity plan. As an extension of this work, we will use deep learning-based algorithms, such as the long short-term memory (LSTM) network [51] and graph convolutional network (GCN) [52], for predicting the future traffic flow and update the capacity plan accordingly. Second, our capacity planning method targets long-term FN deployment.…”

Section: Advantages and Limitations Of Our Workmentioning

confidence: 99%

Data-Driven Capacity Planning for Vehicular Fog Computing

Mao¹,

Akgül²,

Mehrabidavoodabadi³

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

The strict latency constraints of emerging vehicular applications make it unfeasible to forward sensing data from vehicles to the cloud for processing. To shorten network latency, Vehicular fog computing (VFC) moves computation to the edge of the Internet, with the extension to support the mobility of distributed computing entities. In other words, VFC proposes to complement stationary fog nodes co-located with cellular base stations with mobile ones carried by moving vehicles. Previous works of VFC mainly focus on optimizing the assignments of computing tasks among available fog nodes. However, capacity planning, which decides where and how much capacity to deploy, remains an open and challenging issue. The complexity of this problem comes from the mobility of vehicles, the spatio-temporal dynamics of vehicular traffic, and the computing resource demand generated by varying vehicular applications. To solve the above challenges, we propose a data-driven capacity planning framework that optimizes the deployment of stationary and mobile fog nodes to minimize the installation and operational costs under the quality-of-service constraints, taking into account the spatio-temporal variation in computing demand. Through real-world experiments, we analyze the cost efficiency potential of VFC in long term and demonstrate that the performance loss of VFC is below $6\%$ compared to stationary deployment with equal network capacity. We also analyze the impacts of traffic patterns on the potential cost saving. The results show when the traffic density is higher, more operational costs will be saved in the long run due to more dense deployment of mobile fog nodes.

show abstract

Deep Graph Convolutional Networks for Incident-Driven Traffic Speed Prediction

Cited by 40 publications

References 37 publications

Event-Aware Multimodal Mobility Nowcasting

Event-Aware Multimodal Mobility Nowcasting

Data-Driven Capacity Planning for Vehicular Fog Computing

Data-Driven Capacity Planning for Vehicular Fog Computing

Contact Info

Product

Resources

About