Traffic prediction is the core task of intelligent transportation system (ITS) and accurate traffic prediction can greatly improve the utilization of public resources. Dynamic interaction of multiple spatial relationships will influence the accuracy of traffic prediction. However, many existing methods only consider static spatial relationships, which restricts the accuracy of the prediction. To address the above problem, in this article, we propose the Dynamic Multi-Graph Fusion Network (DMGF-Net) to model the spatial-temporal correlations in traffic network. In the DMGF-Net, the fusion graph is designed to leverage and extract the various spatial correlations between different regions by fusing spatial graph, semantic graph, and spatial-semantic graph. Further, to dynamically learn the importance of different neighbors, we design the Dynamic Spatial-Temporal Unit (DSTU), which can adjust the aggregation weights of different neighbors by combining the convolution operation and the attention mechanism. It can selectively aggregate spatial-temporal features from different neighbors. Extensive experiments on three datasets demonstrate that effectiveness of our model, especially on PEMS08, our model achieves an increase of about 8.55% and 7.55% in terms of MAE and RMSE than the static model STGCN.

DMGF-Net: An Efficient Dynamic Multi-Graph Fusion Network for Traffic Prediction

The rapid urbanization and continuous improvement of road traffic equipment result in massive daily production of traffic data. These data contain the long-term evolution of traffic flow and dynamic changes in the traffic road network. Due to the complex topology of the traffic road network, traffic flow prediction is challenging as it contains complex, multi-periodic patterns, and is often affected by sudden events. In this paper, we propose a Multi-View Dynamic Graph Convolution Network (MVDGCN) that captures different levels of spatial–temporal dependencies to predict traffic flow. Firstly, we use the coupling graph convolution network to learn the relationship matrix among stations dynamically, capturing the spatial dependencies at different levels in the traffic network. Secondly, we establish three encoder–decoders, representing hourly, daily, and weekly views, to extract the evolution law of traffic flow from three different time periods. Finally, we use the dynamic fusion module to merge the spatial–temporal dependencies extracted from the multi-view encoder–decoders. We conducted experiments on two real datasets, NYCTaxi and NYCBike, and found that our proposed MVDGCN model outperformed the best baseline, improving the RMSE, MAE, PCC, and MAPE by 12.9%, 6.2%, 0.8%, and 6.5% respectively on the NYCBike dataset and 9.2%, 4.2%, 4.6%, and 3.0% respectively on the NYCTaxi dataset. These results show that the proposed MVDGCN model performs better than state-of-the-art algorithms. 

Multi-view dynamic graph convolution neural network for traffic flow prediction

—Contextual features are important data sources for building spatiotemporal crowd ﬂow prediction (STCFP) models. However, the difﬁculty of applying context lies in the unknown generalizability of both contextual features (e.g., weather, holiday, and points of interests) and context modeling techniques across different scenarios. In this paper, we build a benchmark composed of large-scale spatiotemporal crowd ﬂow data, contextual data, and state-of-the-art spatiotemporal prediction models. We conduct a comprehensive experimental study to quantitatively investigate the generalizability of different contextual features and modeling techniques in several urban crowd ﬂow prediction scenarios (including bike ﬂow, metro passenger ﬂow, electric vehicle charging demand and so on). In particular, we develop a general taxonomy of context modeling techniques based on extensive investigations in prevailing research. With millions of records and rich context data, we have trained and tested hundreds of different models. Our results reveal several important observations: (1) Using more contextual features may not always result in better prediction with existing context modeling techniques; in particular, the combination of holiday and temporal position can provide more generalizable beneﬁcial information than other contextual feature combinations. (2) In context modeling techniques, using a gated unit to incorporate raw contextual features into the state-of-the-art prediction model has good generalizability. Besides, we also offer several suggestions about incorporating contextual factors for practitioners who want to build STCFP applications. From our ﬁndings, we call for future research efforts devoted to developing new context processing and modeling solutions to fully exploit the potential of contextual features for STCFP.

Exploring the Context Generalizability in Spatiotemporal Crowd Flow Prediction: Benchmark and Guideline

Modeling multi-regional temporal correlation with gated recurrent unit and multiple linear regression for urban traffic flow prediction

The motto of developing the diagnosis assistant is to save time and money in almost possible situations. Nowadays people using the internet has become an hourly habit due to which their busy schedule leads them to avoid visiting the hospital so they can use a chatbot to get their medical queries.It’s a Rule based chatbot develop with assistance of network communication to diagnose skin and ortho diseases. The information is trained in the neural network-based system. The trained information helps doctors to identify the disease accurately. 

Chatbot based Virtual Diagnosis Assistant for Skin Diseases and Bone Fracture Detection using Neural network

Traffic flow prediction is the upstream problem of path planning, intelligent transportation system, and other tasks. Many studies have been carried out on the traffic flow prediction of the spatio-temporal network, but the effects of spatio-temporal flexibility (historical data of the same type of time intervals in the same location will change flexibly) and spatio-temporal correlation (different road conditions have different effects at different times) have not been considered at the same time. We propose the Deep Spatio-temporal Adaptive 3D Convolution Neural Network (ST-A3DNet), which is a new scheme to solve both spatio-temporal correlation and flexibility, and consider spatio-temporal complexity (complex external factors, such as weather and holidays). Different from other traffic forecasting models, ST-A3DNet captures the spatio-temporal relationship at the same time through the Adaptive 3D convolution module, assigns different weights flexibly according to the influence of historical data, and obtains the impact of external factors on the flow through the ex-mask module. Considering the holidays and weather conditions, we train our model for experiments in Xi’an and Chengdu. We evaluate the ST-A3DNet and the results show that we have better results than the other 11 baselines.

Deep Spatio-temporal Adaptive 3D Convolutional Neural Networks for Traffic Flow Prediction


 Traffic prediction has attracted a lot of attention in recent years. However, it is challenging due to the dynamic and heterogeneous spatial–temporal correlations. Most of the existing methods adopt the attention mechanism to capture the dynamic spatial features, but it is difficult for the attention mechanism to learn the real-time change of spatial dependencies, which restrict accurate spatial dependencies learning. Furthermore, most methods are out at elbows when solving heterogeneous spatial–temporal data. To overcome these problems, we propose a novel traffic prediction model called Dynamic spatial–temporal Heterogeneous Graph Convolution Network. Different from the existing methods, we have designed a dynamic localized graph and a corresponding adaptive localized graph convolution network, which are capable of simultaneously capturing dynamic and heterogeneous spatial correlations. We propose a gated adaptive temporal convolution network to capture the temporal heterogeneity of traffic data and enjoy global receptive fields. Finally, a global correlations fusion network is provided to incorporate the global spatial–temporal correlations. Compared with 11 baselines, our proposed model achieves state-of-the-art performance in the accuracy of prediction.

D. Jin

Papers

Deep Spatio-temporal Adaptive 3D Convolutional Neural Networks for Traffic Flow Prediction

DMGF-Net: An Efficient Dynamic Multi-Graph Fusion Network for Traffic Prediction

A Dynamic Heterogeneous Graph Convolution Network For Traffic Flow Prediction