Spatio-Temporal Graph Convolutional Networks for Road Network Inundation Status Prediction during Urban Flooding

• URL: http://arxiv.org/abs/2104.02276v1
• Date: Tue, 6 Apr 2021 04:03:34 GMT
• Title: Spatio-Temporal Graph Convolutional Networks for Road Network Inundation Status Prediction during Urban Flooding
• Authors: Faxi Yuan, Yuanchang Xu, Qingchun Li, Ali Mostafavi
• Abstract summary: The objective of this study is to predict the near-future flooding status of road segments based on their own and adjacent road segments current status. Existing studies related to near-future prediction of road network flooding status at road segment level are missing.
• Score: 1.376408511310322
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The objective of this study is to predict the near-future flooding status of road segments based on their own and adjacent road segments current status through the use of deep learning framework on fine-grained traffic data. Predictive flood monitoring for situational awareness of road network status plays a critical role to support crisis response activities such as evaluation of the loss of access to hospitals and shelters. Existing studies related to near-future prediction of road network flooding status at road segment level are missing. Using fine-grained traffic speed data related to road sections, this study designed and implemented three spatio-temporal graph convolutional network (STGCN) models to predict road network status during flood events at the road segment level in the context of the 2017 Hurricane Harvey in Harris County (Texas, USA). Model 1 consists of two spatio-temporal blocks considering the adjacency and distance between road segments, while Model 2 contains an additional elevation block to account for elevation difference between road segments. Model 3 includes three blocks for considering the adjacency and the product of distance and elevation difference between road segments. The analysis tested the STGCN models and evaluated their prediction performance. Our results indicated that Model 1 and Model 2 have reliable and accurate performance for predicting road network flooding status in near future (e.g., 2-4 hours) with model precision and recall values larger than 98% and 96%, respectively. With reliable road network status predictions in floods, the proposed model can benefit affected communities to avoid flooded roads and the emergency management agencies to implement evacuation and relief resource delivery plans.

Related papers

• ICST-DNET: An Interpretable Causal Spatio-Temporal Diffusion Network for Traffic Speed Prediction [47.17205142864036]
  ICST-DENT consists of three parts, namely the Spatio-Temporal Causality Learning (STCL), Causal Graph Generation (CGG), and Speed Fluctuation Pattern Recognition (SFPR) modules. ICST-DENT can outperform all existing baselines, as evidenced by the higher prediction accuracy, ability to explain causality, and adaptability to different scenarios.
  arXiv  Detail & Related papers  (2024-04-22T03:35:19Z)
• Implicit Occupancy Flow Fields for Perception and Prediction in Self-Driving [68.95178518732965]
  A self-driving vehicle (SDV) must be able to perceive its surroundings and predict the future behavior of other traffic participants. Existing works either perform object detection followed by trajectory of the detected objects, or predict dense occupancy and flow grids for the whole scene. This motivates our unified approach to perception and future prediction that implicitly represents occupancy and flow over time with a single neural network.
  arXiv  Detail & Related papers  (2023-08-02T23:39:24Z)
• OpenLane-V2: A Topology Reasoning Benchmark for Unified 3D HD Mapping [84.65114565766596]
  We present OpenLane-V2, the first dataset on topology reasoning for traffic scene structure. OpenLane-V2 consists of 2,000 annotated road scenes that describe traffic elements and their correlation to the lanes. We evaluate various state-of-the-art methods, and present their quantitative and qualitative results on OpenLane-V2 to indicate future avenues for investigating topology reasoning in traffic scenes.
  arXiv  Detail & Related papers  (2023-04-20T16:31:22Z)
• Dynamic loss balancing and sequential enhancement for road-safety assessment and traffic scene classification [0.0]
  Road-safety inspection is an indispensable instrument for reducing road-accident fatalities contributed to road infrastructure. Recent work formalizes road-safety assessment in terms of carefully selected risk factors that are also known as road-safety attributes. We propose to reduce dependency on tedious human labor by automating recognition with a two-stage neural architecture.
  arXiv  Detail & Related papers  (2022-11-08T11:10:07Z)
• A Graph and Attentive Multi-Path Convolutional Network for Traffic Prediction [16.28015945020806]
  We propose a graph and attentive multi-path convolutional network (GAMCN) model to predict traffic conditions into the future. Our model focuses on the spatial and temporal factors that impact traffic conditions. Our model outperforms state-of-art traffic prediction models by up to 18.9% in terms of prediction errors and 23.4% in terms of prediction efficiency.
  arXiv  Detail & Related papers  (2022-05-30T16:24:43Z)
• Road Network Guided Fine-Grained Urban Traffic Flow Inference [108.64631590347352]
  Accurate inference of fine-grained traffic flow from coarse-grained one is an emerging yet crucial problem. We propose a novel Road-Aware Traffic Flow Magnifier (RATFM) that exploits the prior knowledge of road networks. Our method can generate high-quality fine-grained traffic flow maps.
  arXiv  Detail & Related papers  (2021-09-29T07:51:49Z)
• Predicting Road Flooding Risk with Machine Learning Approaches Using Crowdsourced Reports and Fine-grained Traffic Data [1.0554048699217669]
  The objective of this study is to predict road flooding risks based on topographic, hydrologic, and temporal precipitation features using machine learning models. The findings from Hurricane Harvey indicate that precipitation is the most important feature for predicting road inundation susceptibility. This study advances the emerging field of smart flood resilience in terms of predictive flood risk mapping at the road level.
  arXiv  Detail & Related papers  (2021-08-30T14:25:58Z)
• PSTN: Periodic Spatial-temporal Deep Neural Network for Traffic Condition Prediction [8.255993195520306]
  We propose a periodic deeptemporal neural network (PSTN) with three modules to improve the forecasting performance of traffic conditions. First, the historical traffic information is folded and fed into a module consisting of a graph convolutional network and a temporal convolutional network.
  arXiv  Detail & Related papers  (2021-08-05T07:42:43Z)
• A Graph Convolutional Network with Signal Phasing Information for Arterial Traffic Prediction [63.470149585093665]
  arterial traffic prediction plays a crucial role in the development of modern intelligent transportation systems. Many existing studies on arterial traffic prediction only consider temporal measurements of flow and occupancy from loop sensors and neglect the rich spatial relationships between upstream and downstream detectors. We fill this gap by enhancing a deep learning approach, Diffusion Convolutional Recurrent Neural Network, with spatial information generated from signal timing plans at targeted intersections.
  arXiv  Detail & Related papers  (2020-12-25T01:40:29Z)
• Road Network Metric Learning for Estimated Time of Arrival [93.0759529610483]
  In this paper, we propose the Road Network Metric Learning framework for Estimated Time of Arrival (ETA) It consists of two components: (1) a main regression task to predict the travel time, and (2) an auxiliary metric learning task to improve the quality of link embedding vectors. We show that our method outperforms the state-of-the-art model and the promotion concentrates on the cold links with few data.
  arXiv  Detail & Related papers  (2020-06-24T04:45:14Z)
• A Network Percolation-based Contagion Model of Flood Propagation and Recession in Urban Road Networks [2.5876546798940616]
  A network of urban roads resilient to flooding events is essential for provision of public services and for emergency response. This study presents a mathematical contagion model to describe the spatial-temporal spread and recession process of flood waters in urban road networks. The application of the proposed model was verified using high-resolution historical data of road flooding in Harris County during Hurricane Harvey in 2017.
  arXiv  Detail & Related papers  (2020-04-07T17:25:41Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.