Joint Modeling of Dense and Incomplete Trajectories for Citywide Traffic Volume Inference

Tang, Xianfeng; Gong, Boqing; Yu, Yanwei; Yao, Huaxiu; Li, Yandong; Xie, Haiyong; Wang, Xiaoyu

doi:10.1145/3308558.3313621

Cited by 32 publications

(26 citation statements)

References 36 publications

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“…Other existing work aim to infer traffic volume values of road segments using loop detector [6], [37], [38], surveillance cameras [39], [40], or float car trajectories [4], [7], [8]. [41] tries to model the characteristics of urban vehicular mobility using camera vehicular mobility images.…”

Section: Related Workmentioning

confidence: 99%

Citywide Traffic Volume Inference with Surveillance Camera Records

Tang

Yao

et al. 2021

IEEE Trans. Big Data

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Real-time traffic monitoring becomes an essential part of an intelligent city. In recent years, the adoption of surveillance cameras is rapidly growing because they are helpful to manage and control the traffic. However, it is impossible to install cameras on every road in a city due to the high costs of deployment and maintenance. Given the information from limited surveillance cameras, can we infer the citywide traffic volume accurately? This is a challenging question because we have no historical data on the roads without cameras. It requires us to design a method that goes beyond the inference using nearby traffic data. Moreover, a nice property of surveillance camera data is that these AI-equipped cameras can recognize individual vehicles. So we can recover incomplete trajectories for vehicles using plate numbers in surveillance camera records. However, for road segments without cameras, we do not know whether those vehicles pass through them or not. How can such incomplete trajectories be effectively used to help citywide traffic inference? In this paper, we propose a framework named CityVolInf to infer citywide traffic volume based on surveillance camera records. Our framework combines a semi-supervised learning-based similarity module with a novel simulation module to address the above challenges. While the similarity module focuses on spatiotemporal correlations of traffic volume between road segments, the simulation module utilizes incomplete trajectories to model transitions of traffic volume between adjacent road segments. Our framework bridges the conventional data-driven approach and transportation domain knowledge from the simulator. We conduct extensive experiments on a real-world dataset, containing 405, 370, 631 camera records collected from 1, 704 surveillance cameras over a period of 31 days in a provincial capital in China. The experimental results demonstrate the effectiveness of CityVolInf compared with existing methods.

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Section: Related Workmentioning

confidence: 99%

Citywide Traffic Volume Inference with Surveillance Camera Records

Tang

Yao

et al. 2021

IEEE Trans. Big Data

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“…6b, and the major breakthroughs of DRL's achievement include Deep Q-network (Mnih et al 2015) and AlphaGo (Silver et al 2016). In traffic-related studies, DRL models are implemented for traffic prediction (Li et al 2016a), traffic signal control (Wei et al 2018), data recovery (Tang et al 2019) and resource deployment (Li et al 2020).…”

Section: Deep-learning Models For Itsmentioning

confidence: 99%

“…They first identified non-Euclidean correlations of ride-hailing demand in different regions and then modeled these correlations with multi-graph convolution for demand forecasting. To infer the citywide traffic volume with biased GPS trajectories, Tang et al (2019) presented the JMDI framework to jointly model the dense and incomplete trajectories for citywide traffic volume inference, using dense trajectory data from GPS and incomplete trajectory data from the camera surveillance system.…”

Section: Deep Learning For Traffic Prediction With Miscellaneous Tasksmentioning

confidence: 99%

Deep learning for intelligent traffic sensing and prediction: recent advances and future challenges

Fan

Xiang

Gong

et al. 2020

CCF Trans. Pervasive Comp. Interact.

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With the emerging concepts of smart cities and intelligent transportation systems, accurate traffic sensing and prediction have become critically important to support urban management and traffic control. In recent years, the rapid uptake of the Internet of Vehicles and the rising pervasiveness of mobile services have produced unprecedented amounts of data to serve traffic sensing and prediction applications. However, it is significantly challenging to fulfill the computation demands by the big traffic data with ever-increasing complexity and diversity. Deep learning, with its powerful capabilities in representation learning and multi-level abstractions, has recently become the most effective approach in many intelligent sensing systems. In this paper, we present an up-to-date literature review on the most advanced research works in deep learning for intelligent traffic sensing and prediction.

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“…Representation learning on network data (e.g., social networks, citation networks and E-commerce networks) has become a pragmatic research field and been applied to many online services, such as advertising, E-commerce and social media platforms. At its core is to learn low-dimensional vector representations of nodes while preserving network topological structure and intrinsic characteristics, which could facilitate various downstream network analytical tasks, e.g., link prediction [6], node classification [27], spatial-temporal data modeling [23,29] and user behavior analysis [15,30].…”

Section: Introductionmentioning

confidence: 99%

Fast Attributed Multiplex Heterogeneous Network Embedding

Liu

Huang²,

et al. 2020

Proceedings of the 29th ACM International Conference on Information &Amp; Knowledge Management

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In recent years, heterogeneous network representation learning has attracted considerable attentions with the consideration of multiple node types. However, most of them ignore the rich set of network attributes (attributed network) and different types of relations (multiplex network), which can hardly recognize the multimodal contextual signals across different relations. While a handful of network embedding techniques are developed for attributed multiplex heterogeneous networks, they are significantly limited to the scalability issue on large-scale network data, due to their heavy computation and memory cost. In this work, we propose a Fast Attributed Multiplex heterogeneous network Embedding framework (FAME) for large-scale network data, by mapping the units from different modalities (i.e., network topological structures, various node features and relations) into the same latent space in an efficient way. Our FAME is an integrative architecture with the scalable spectral transformation and sparse random projection, to automatically preserve both attribute semantics and multi-type relations in the learned embeddings. Extensive experiments on four real-world datasets with various network analytical tasks, demonstrate that FAME achieves both effectiveness and significant efficiency over state-of-the-art baselines. The source code is available at: https://github.com/ZhijunLiu95/FAME. CCS CONCEPTS • Mathematics of computing → Graph algorithms; • Computing methodologies → Learning latent representations.

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Joint Modeling of Dense and Incomplete Trajectories for Citywide Traffic Volume Inference

Cited by 32 publications

References 36 publications

Citywide Traffic Volume Inference with Surveillance Camera Records

Citywide Traffic Volume Inference with Surveillance Camera Records

Deep learning for intelligent traffic sensing and prediction: recent advances and future challenges

Fast Attributed Multiplex Heterogeneous Network Embedding

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