Fine-Grained Urban Flow Inference

Ouyang, Kun; Liang, Yuxuan; Liu, Ye; Tong, Zekun; Ruan, Sijie; Rosenblum, David S.; Zheng, Yu

doi:10.1109/tkde.2020.3017104

Cited by 32 publications

(17 citation statements)

References 36 publications

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“…Besides, we use zero matrices as history for those items without sufficient precedent records. To speed up the convergence of STRN, a unique data normalization method [19,25] for urban flow data is employed in our study. We test different hyperparameters for them all, finding the best setting for each over the two datasets separately.…”

Section: Evaluation 41 Experimental Settingsmentioning

confidence: 99%

Fine-Grained Urban Flow Prediction

Liang

Ouyang

Sun

et al. 2021

Proceedings of the Web Conference 2021

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Urban flow prediction benefits smart cities in many aspects, such as traffic management and risk assessment. However, a critical prerequisite for these benefits is having fine-grained knowledge of the city. Thus, unlike previous works that are limited to coarse-grained data, we extend the horizon of urban flow prediction to fine granularity which raises specific challenges: 1) the predominance of inter-grid transitions observed in fine-grained data makes it more complicated to capture the spatial dependencies among grid cells at a global scale; 2) it is very challenging to learn the impact of external factors (e.g., weather) on a large number of grid cells separately. To address these two challenges, we present a Spatio-Temporal Relation Network (STRN) to predict fine-grained urban flows. First, a backbone network is used to learn high-level representations for each cell. Second, we present a Global Relation Module (GloNet) that captures global spatial dependencies much more efficiently compared to existing methods. Third, we design a Meta Learner that takes external factors and land functions (e.g., POI density) as inputs to produce meta knowledge and boost model performances. We conduct extensive experiments on two real-world datasets. The results show that STRN reduces the errors by 7.1% to 11.5% compared to the state-of-the-art method while using much fewer parameters. Moreover, a cloud-based system called UrbanFlow 3.0 has been deployed to show the practicality of our approach.

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Section: Evaluation 41 Experimental Settingsmentioning

confidence: 99%

Fine-Grained Urban Flow Prediction

Liang

Ouyang

Sun

et al. 2021

Proceedings of the Web Conference 2021

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“…Due to its great social benefits, this problem has recently received increasing attention in both industry and academic communities. In most preliminary works [4]- [6], the city being studied is first divided into a coarse grid map and a fine grid map on the basis of latitude and longitude coordinates, as shown in Fig. 1-(b,c).…”

Section: Introductionmentioning

confidence: 99%

“…In this case, the road network can be regarded as an instructive prior knowledge for traffic flow inference. Nevertheless, most previous methods [4]- [6] were not aware of this knowledge. Second, how to model the road network is still an open problem.…”

Section: Introductionmentioning

confidence: 99%

Road Network Guided Fine-Grained Urban Traffic Flow Inference

Liu¹,

Liu²,

Li³

et al. 2021

Preprint

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Accurate inference of fine-grained traffic flow from coarse-grained one is an emerging yet crucial problem, which can help greatly reduce the number of traffic monitoring sensors for cost savings. In this work, we notice that traffic flow has a high correlation with road network, which was either completely ignored or simply treated as an external factor in previous works. To facilitate this problem, we propose a novel Road-Aware Traffic Flow Magnifier (RATFM) that explicitly exploits the prior knowledge of road networks to fully learn the roadaware spatial distribution of fine-grained traffic flow. Specifically, a multi-directional 1D convolutional layer is first introduced to extract the semantic feature of the road network. Subsequently, we incorporate the road network feature and coarse-grained flow feature to regularize the short-range spatial distribution modeling of road-relative traffic flow. Furthermore, we take the road network feature as a query to capture the long-range spatial distribution of traffic flow with a transformer architecture. Benefiting from the road-aware inference mechanism, our method can generate high-quality fine-grained traffic flow maps. Extensive experiments on three real-world datasets show that the proposed RATFM outperforms state-of-the-art models under various scenarios. Our source code and datasets would be released after peer review.

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“…Traditionally, estimating the urban traffic flow requires the deployment of numerous sensing devices on the road (e.g., loop detectors and cameras). However, processing these data needs huge communication and computation resources for transmission, processing, and maintenance, which are prohibitive in areas where grained observations from the massive edge nodes (e.g., taxis and bikes) in a PEC manner becomes an urgent problem [6,8]. As a variant of image SR in the traffic domain [9,10], FSR has practical significance in urban planning and traffic monitoring.…”

Section: Introductionmentioning

confidence: 99%

“…Previous works [6,8] have proposed to address the FSR problem with the deep ResNet architecture [11], while the spatial constraints are assured by a simple normalization scheme. However, these methods have the intensive computational overheads incurred by underlying ResNet -which requires significantly more memory cost and network parameters -making it hard to be optimized and inapplicable in restricted computational environments such as PEC with IoT.…”

Section: Introductionmentioning

confidence: 99%

Inferring High-Resolutional Urban Flow With Internet Of Mobile Things

Zhang

Jing

et al. 2021

ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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Monitoring urban flow timely and accurately is crucial for many industrial applications -from urban planning to traffic control in the smart cities. This work introduces a new method for inferring fine-grained urban flow with the internet of mobile things such as taxis and bikes. We tackle the problem from a new perspective and present a novel deep learning method UrbanODE (Urban flow inference with Neural Ordinary Differential Equations). Furthermore, UrbanODE provides a flexible balance between flow inference accuracy and computational efficiency, which is important in computation restricted scenarios such as pervasive edge computing. Extensive evaluations on real-world traffic flow data demonstrate the superiority of the proposed method.

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Fine-Grained Urban Flow Inference

Cited by 32 publications

References 36 publications

Fine-Grained Urban Flow Prediction

Fine-Grained Urban Flow Prediction

Road Network Guided Fine-Grained Urban Traffic Flow Inference

Inferring High-Resolutional Urban Flow With Internet Of Mobile Things

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