Traffic Incident Detection: A Trajectory-based Approach

Han, Xiaolin; Grubenmann, Tobias; Cheng, Reynold; Wong, Stephen C. P.; Li, Xiaodong; Sun, Wenya

doi:10.1109/icde48307.2020.00190

Cited by 22 publications

(9 citation statements)

References 10 publications

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“…Over the past decades, massive efforts [15]- [20] have been made to address the traffic analysis problem. For instance, Han et al [21] developed a Filter-Discovery-Match framework to learn incident patterns from vehicle trajectories for traffic incident detection, while Sun et al [9] employed spatial graph convolution to build a multi-view graph convolutional network for crowd flow prediction.…”

Section: A Traffic Flow Analysismentioning

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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Section: A Traffic Flow Analysismentioning

confidence: 99%

Road Network Guided Fine-Grained Urban Traffic Flow Inference

Liu¹,

Liu²,

Li³

et al. 2021

Preprint

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“…In [20] Filter-Discovery-Match (FDM) technique is proposed which uses speed patterns to identify incident scenes by partitioning a road network into segments. Speed vectors are generated for consecutive sequences of road segments traveled by a vehicle based on average speed.…”

Section: B Trajectory Segmentationmentioning

confidence: 99%

Road Speed Signatures from C-ITS messages

Moso

Cormier

Fouchal

et al. 2021

ICC 2021 - IEEE International Conference on Communications

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Cooperative Intelligent Transport Systems(C-ITS) focus on improving safety, comfort, traffic and energy efficiency. Vehicle speed and other speed based indicators are commonly used parameters in traffic research for generation of driving profiles.The main goal for studying speed variation is to gain a better understanding on why drivers respond in certain ways to road/traffic conditions and to discover factors which affect their actions. The aim of this paper is to use a real dataset of Cooperative Awareness Messages generated in a C-ITS environment to generate speed signatures. We apply a segmentation technique and statistical analysis in generation and evaluation of road speed signatures. Based on our approach, interesting characteristics on the evolution of driving behavior are revealed from speed signatures.

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“…Therefore, monitoring the traffic accidents is essential to avoid property loss and save life. Many researches focus on directions such as detecting traffic incidents [46], predicting traffic accidents from social media data [47], predicting the injury severity of traffic accidents [48], [49].…”

Section: A Traffic Problemsmentioning

confidence: 99%

“…4) Traffic Incident Detection: Major incidents can cause fatal injuries to travelers and long delays on a road network. Therefore, understanding the main cause of incidents and their impact on a traffic network is crucial for a modern transportation management system [46], [48], [49].…”

Section: B Research Directionsmentioning

confidence: 99%

How to Build a Graph-Based Deep Learning Architecture in Traffic Domain: A Survey

Ye,

Zhao,

et al. 2020

Preprint

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In recent years, various deep learning architectures have been proposed to solve complex challenges (e.g., spatial dependency, temporal dependency) in traffic domain, which have achieved satisfactory performance. These architectures are composed of multiple deep learning techniques in order to tackle various challenges in traffic data. Traditionally, convolution neural networks (CNNs) are utilized to model spatial dependency by decomposing the traffic network as grids. However, many traffic networks are graph-structured in nature. In order to utilize such spatial information fully, it's more appropriate to formulate traffic networks as graphs mathematically. Recently, various novel deep learning techniques have been developed to process graph data, called graph neural networks (GNNs). More and more works combine GNNs with other deep learning techniques to construct an architecture dealing with various challenges in a complex traffic task, where GNNs are responsible for extracting spatial correlations in traffic network. These graphbased architectures have achieved state-of-the-art performance. To provide a comprehensive and clear picture of such emerging trend, this survey carefully examines various graph-based deep learning architectures in many traffic applications. We first give guidelines to formulate a traffic problem based on graph and construct graphs from various traffic data. Then we decompose these graph-based architectures to discuss their shared deep learning techniques, clarifying the utilization of each technique in traffic tasks. What's more, we summarize common traffic challenges and the corresponding graph-based deep learning solutions to each challenge. Finally, we provide benchmark datasets, open source codes and future research directions in this rapidly growing field.

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Traffic Incident Detection: A Trajectory-based Approach

Cited by 22 publications

References 10 publications

Road Network Guided Fine-Grained Urban Traffic Flow Inference

Road Network Guided Fine-Grained Urban Traffic Flow Inference

Road Speed Signatures from C-ITS messages

How to Build a Graph-Based Deep Learning Architecture in Traffic Domain: A Survey

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