DeepPTP: A Deep Pedestrian Trajectory Prediction Model for Traffic Intersection

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

doi:10.3837/tiis.2021.07.002

Cited by 8 publications

(1 citation statement)

References 16 publications

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“…Next, predicting the population density of key areas of the city is crucial [14]. The complexity of traffic scenarios and the spatial-temporal feature correlations pose higher challenges for traffic prediction research [15]. In order to ensure the smooth operation of the traffic network, it is necessary for the intelligent transportation system to have dynamic adjustment ability due to the changes in peak and low periods of urban traffic demand.…”

Section: Resultsmentioning

confidence: 99%

A survey of sustainable development of intelligent transportation system based on urban travel demand

Yan,

2024

Sustain. Social. Dev.

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<p>This paper provides a comprehensive exploration of urban travel demand forecasting and its implications for intelligent transportation systems, emphasizing the crucial role of intelligent transportation systems in promoting sustainable urban development. With the increasing challenges posed by traffic congestion, environmental pollution, and diverse travel needs, accurate prediction of urban travel demand becomes essential for optimizing transportation systems, fostering sustainable travel methods, and creating opportunities for business development. However, achieving this goal involves overcoming challenges such as data collection and processing, privacy protection, and information security. To address these challenges, the paper proposes a set of strategic measures, including advancing intelligent transportation technology, integrating intelligent transportation systems with urban planning, enforcing policy guidance and market supervision, promoting sustainable travel methods, and adopting intelligent transportation technology and green energy solutions. Additionally, the study highlights the role of intelligent transportation systems in mitigating traffic congestion and environmental impact through intelligent road condition monitoring, prediction, and traffic optimization. Looking ahead, the paper foresees an increasingly pivotal role for intelligent transportation systems in the future, leveraging advancements in deep learning and information technology to more accurately collect and analyze urban travel-related data for better predictive modeling. By combining data analysis, public transportation promotion, shared travel modes, intelligent transportation technology, and green energy adoption, cities can build more efficient, environmentally friendly transportation systems, enhancing residents’ travel experiences while reducing congestion and pollution to promote sustainable urban development. Furthermore, the study anticipates that intelligent transportation systems will be intricately integrated with urban public services and management, facilitating efficient and coordinated urban functions. Ultimately, the paper envisions intelligent transportation systems playing a vital role in supporting urban traffic management and enhancing the overall well-being of urban construction and residents’ lives. In conclusion, this research not only enhances our understanding of urban travel demand forecasting and the evolving landscape of intelligent transportation systems but also provides valuable insights for future research and practical applications in related fields. The study encourages greater attention and investment from scholars and practitioners in the research and practice of intelligent transportation systems to collectively advance the progress of urban transportation and sustainable development.<strong></strong></p>

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Section: Resultsmentioning

confidence: 99%

A survey of sustainable development of intelligent transportation system based on urban travel demand

Yan,

2024

Sustain. Social. Dev.

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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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Spatio-Temporal Heterogeneous Graph-Based Convolutional Networks for Traffic Flow Forecasting

Ma,

Lv,

Xin

et al. 2023

Transportation Research Record: Journal of the Transportation R

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Traffic flow forecasting plays a crucial role in the construction of intelligent transportation. The aims of this paper are to fully exploit the spatial correlation between nodes in a traffic network and to compensate for the inability of graph-based deep learning methods to model multiple relationship types, resulting in inadequate extraction of spatially correlated information about the traffic network. In this paper, we propose a deep spatio-temporal recurrent evolution network based on the graph convolution network (STREGCN) for heterogeneous graphs. Specifically, we transform the traffic network into a multi-relational heterogeneous graph to improve the information representation of the graph. This allows our model to capture multiple types of spatially relevant information. In the temporal dimension, we use one-dimensional causal convolution based on the gated linear unit to extract the temporal correlation information of the traffic flow. In addition, we designed the output of the spatio-temporal convolution module to obtain the final traffic flow predictions after a fully connected layer. Experiments on real datasets illustrate the effectiveness of the proposed STREGCN model and show the importance of representing information through heterogeneous graphs for the task of traffic flow prediction.

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DeepPTP: A Deep Pedestrian Trajectory Prediction Model for Traffic Intersection

Cited by 8 publications

References 16 publications

A survey of sustainable development of intelligent transportation system based on urban travel demand

A survey of sustainable development of intelligent transportation system based on urban travel demand

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

Spatio-Temporal Heterogeneous Graph-Based Convolutional Networks for Traffic Flow Forecasting

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