Blind Travel Prediction Based on Obstacle Avoidance in Indoor Scene

Lv, Zhiqiang; Li, Jianbo; Li, Haoran; Xu, Zhihao; Wang, Yue

doi:10.1155/2021/5536386

Cited by 36 publications

(8 citation statements)

References 23 publications

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“…After continuous development and improvement, this method has been greatly applied and expanded in the United States. Lv et al added the relationship between people on the basis of problem search method, emphasizing the value of people [8].…”

Section: Literature Reviewmentioning

confidence: 99%

Building a Space Model Method Based on the Big Data Map Visual Design

Yang

2022

Wireless Communications and Mobile Computing

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In order to explore how to realize visual design of building space model, this paper proposes a method of building space model based on big data map visual design. This method explores the research on how to realize the visual design of map by building space model through the key technical problems and solutions of information recommendation based on big data map. The research shows that the building space model method based on big data map visual design can effectively solve the shortcomings of traditional methods, and the work efficiency is about 65% higher than that of traditional methods. From the perspective of big data, apply the thinking and technology of big data to the field of architectural planning, and promote the innovation and development of architectural planning through the data acquisition, analysis, feedback, and evaluation technology of big data.

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Section: Literature Reviewmentioning

confidence: 99%

Building a Space Model Method Based on the Big Data Map Visual Design

Yang

2022

Wireless Communications and Mobile Computing

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“…Figure 2 shows the overview of the steps for the development and evaluation of the model. Overall, the design plan for the study involves four steps 76,77 : data collection, data preprocessing, model development, and model evaluation. First, we present the data used for the development of the model.…”

Section: Overview Of the Model Development And Evaluationmentioning

confidence: 99%

A spatial–temporal graph deep learning model for urban flood nowcasting leveraging heterogeneous community features

Farahmand

Mostafavi

2023

Sci Rep

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Flood nowcasting refers to near-future prediction of flood status as an extreme weather event unfolds to enhance situational awareness. The objective of this study was to adopt and test a novel structured deep-learning model for urban flood nowcasting by integrating physics-based and human-sensed features. We present a new computational modeling framework including an attention-based spatial–temporal graph convolution network (ASTGCN) model and different streams of data that are collected in real-time, preprocessed, and fed into the model to consider spatial and temporal information and dependencies that improve flood nowcasting. The novelty of the computational modeling framework is threefold: first, the model is capable of considering spatial and temporal dependencies in inundation propagation thanks to the spatial and temporal graph convolutional modules; second, it enables capturing the influence of heterogeneous temporal data streams that can signal flooding status, including physics-based features (e.g., rainfall intensity and water elevation) and human-sensed data (e.g., residents’ flood reports and fluctuations of human activity) on flood nowcasting. Third, its attention mechanism enables the model to direct its focus to the most influential features that vary dynamically and influence the flood nowcasting. We show the application of the modeling framework in the context of Harris County, Texas, as the study area and 2017 Hurricane Harvey as the flood event. Three categories of features are used for nowcasting the extent of flood inundation in different census tracts: (i) static features that capture spatial characteristics of various locations and influence their flood status similarity, (ii) physics-based dynamic features that capture changes in hydrodynamic variables, and (iii) heterogeneous human-sensed dynamic features that capture various aspects of residents’ activities that can provide information regarding flood status. Results indicate that the ASTGCN model provides superior performance for nowcasting of urban flood inundation at the census-tract level, with precision 0.808 and recall 0.891, which shows the model performs better compared with other state-of-the-art models. Moreover, ASTGCN model performance improves when heterogeneous dynamic features are added into the model that solely relies on physics-based features, which demonstrates the promise of using heterogenous human-sensed data for flood nowcasting. Given the results of the comparisons of the models, the proposed modeling framework has the potential to be more investigated when more data of historical events are available in order to develop a predictive tool to provide community responders with an enhanced prediction of the flood inundation during urban flood.

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“…In recent years, it has received increasing attention from researchers. In the field of urban transportation, more and more researchers use deep-learning methods ( 19 , 20 ), especially convolutional neural networks (CNN) and recurrent neural networks (RNN) ( 21 ). Huang et al ( 22 ) proposed the VMD-LSTM model, which uses variational mode decomposition (VMD) to decompose the time-series passenger flow data into intrinsic mode functions (IMFs) at different time scales to reduce the impact of data noise on the passenger flow prediction model.…”

Section: Related Workmentioning

confidence: 99%

Will the Order Be Canceled? Order Cancellation Probability Prediction Based on Deep Residual Model

Sun

et al. 2023

Transportation Research Record: Journal of the Transportation R

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Online ride-hailing is gradually becoming one of the indispensable travel modes for people. Online ride-hailing platforms receive tens of thousands of taxi travel orders every day. However, some orders are canceled by the user after the driver answers. User cancellation of orders will disrupt the regular operation of the taxi platform, reduce the efficiency and income of drivers on the online ride-hailing platform, and affect the ride experience of other users. To capture the relationship between the cancellation probability of online taxi orders and users, we first analyze the factors that affect users’ cancellation of online taxi orders. We find that the cancellation probability of online ride-hailing orders is highly correlated with the travel time of the online taxi, the distance between vehicle and passengers, road congestion conditions, and alternative transportation resources around the passengers. Second, a deep residual network-based ride-hailing order cancellation probability prediction model (DeepOCP) is designed to predict the probability of a user canceling an answered online-hailing order. Finally, the Didi Chengdu Express public data set is used to test the model’s effectiveness. This paper is the first study to use a deep-learning model to predict the cancellation probability of online ride-hailing orders.

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Blind Travel Prediction Based on Obstacle Avoidance in Indoor Scene

Cited by 36 publications

References 23 publications

Building a Space Model Method Based on the Big Data Map Visual Design

Building a Space Model Method Based on the Big Data Map Visual Design

A spatial–temporal graph deep learning model for urban flood nowcasting leveraging heterogeneous community features

Will the Order Be Canceled? Order Cancellation Probability Prediction Based on Deep Residual Model

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