scholarly journals Improved Spatio-Temporal Residual Networks for Bus Traffic Flow Prediction

2019 ◽  
Vol 9 (4) ◽  
pp. 615 ◽  
Author(s):  
Panbiao Liu ◽  
Yong Zhang ◽  
Dehui Kong ◽  
Baocai Yin
Keyword(s):  
Traffic Flow ◽  
Traffic Congestion ◽  
Road Traffic ◽  
Transportation Network ◽  
Temporal Correlation ◽  
Traffic Flow Prediction ◽  
Temporal Correlations ◽  
Building Models ◽  
Spatio Temporal ◽  
Specific Scenario

Buses, as the most commonly used public transport, play a significant role in cities. Predicting bus traffic flow cannot only build an efficient and safe transportation network but also improve the current situation of road traffic congestion, which is very important for urban development. However, bus traffic flow has complex spatial and temporal correlations, as well as specific scenario patterns compared with other modes of transportation, which is one of the biggest challenges when building models to predict bus traffic flow. In this study, we explore bus traffic flow and its specific scenario patterns, then we build improved spatio-temporal residual networks to predict bus traffic flow, which uses fully connected neural networks to capture the bus scenario patterns and improved residual networks to capture the bus traffic flow spatio-temporal correlation. Experiments on Beijing transportation smart card data demonstrate that our method achieves better results than the four baseline methods.

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

2022 ◽  
Vol 13 (2) ◽  
pp. 1-21
Author(s):  
He Li ◽  
Xuejiao Li ◽  
Liangcai Su ◽  
Duo Jin ◽  
Jianbin Huang ◽  
...  
Keyword(s):  
Traffic Flow ◽  
Intelligent Transportation System ◽  
Historical Data ◽  
Temporal Correlation ◽  
Weather Conditions ◽  
External Factors ◽  
Traffic Flow Prediction ◽  
Flow Prediction ◽  
Spatio Temporal ◽  
The Impact

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.

scholarly journals GSTNet: Global Spatial-Temporal Network for Traffic Flow Prediction

2019 ◽  
Author(s):  
Shen Fang ◽  
Qi Zhang ◽  
Gaofeng Meng ◽  
Shiming Xiang ◽  
Chunhong Pan
Keyword(s):  
Traffic Flow ◽  
Public Transportation ◽  
Transportation Network ◽  
Temporal Network ◽  
Spatial Correlations ◽  
Traffic Flow Prediction ◽  
Temporal Correlations ◽  
Global Dynamic ◽  
Real World Datasets ◽  
Temporal Dependencies

Predicting traffic flow on traffic networks is a very challenging task, due to the complicated and dynamic spatial-temporal dependencies between different nodes on the network. The traffic flow renders two types of temporal dependencies, including short-term neighboring and long-term periodic dependencies. What's more, the spatial correlations over different nodes are both local and non-local. To capture the global dynamic spatial-temporal correlations, we propose a Global Spatial-Temporal Network (GSTNet), which consists of several layers of spatial-temporal blocks. Each block contains a multi-resolution temporal module and a global correlated spatial module in sequence, which can simultaneously extract the dynamic temporal dependencies and the global spatial correlations. Extensive experiments on the real world datasets verify the effectiveness and superiority of the proposed method on both the public transportation network and the road network.

scholarly journals Adaptive Correction Forecasting Approach for Urban Traffic Flow Based on Fuzzyc-Mean Clustering and Advanced Neural Network

2013 ◽  
Vol 2013 ◽  
pp. 1-7 ◽  
Author(s):  
He Huang ◽  
Qifeng Tang ◽  
Zhen Liu
Keyword(s):  
Neural Network ◽  
Traffic Flow ◽  
Traffic Congestion ◽  
Prediction Accuracy ◽  
Intelligent Transportation System ◽  
Prediction Models ◽  
Road Traffic ◽  
Urban Traffic ◽  
Traffic Flow Prediction ◽  
Adaptive Correction

Forecasting of urban traffic flow is important to intelligent transportation system (ITS) developments and implementations. The precise forecasting of traffic flow will be pretty helpful to relax road traffic congestion. The accuracy of traditional single model without correction mechanism is poor. Summarizing the existing prediction models and considering the characteristics of the traffic itself, a traffic flow prediction model based on fuzzyc-mean clustering method (FCM) and advanced neural network (NN) was proposed. FCM can improve the prediction accuracy and robustness of the model, while advanced NN can optimize the generalization ability of the model. Besides these, the output value of the model is calibrated by the correction mechanism. The experimental results show that the proposed method has better prediction accuracy and robustness than the other models.

Deep Spatio-Temporal Modified-Inception with Dilated Convolution Networks for Citywide Crowd Flows Prediction

2019 ◽  
Vol 34 (08) ◽  
pp. 2052003
Author(s):  
Yan Kang ◽  
Bing Yang ◽  
Hao Li ◽  
Tie Chen ◽  
Yachuan Zhang
Keyword(s):  
Traffic Flow ◽  
Traffic Safety ◽  
Road Traffic ◽  
Network Models ◽  
Traffic Data ◽  
Traffic Flow Prediction ◽  
Dilated Convolution ◽  
Flow Prediction ◽  
Traffic Capacity ◽  
Spatio Temporal

Traffic flow prediction has great significance for improving road traffic capacity and traffic safety. However, traffic flow in a certain area is usually affected by some factors such as weather, holidays and neighboring areas. So, traffic situation is complicated and traffic flow prediction is difficult. How to use existing traffic data information to predict future traffic flow is the key to this problem. In this paper, we develop an accurate prediction model based on dilated convolution — ST-MINet (Deep Spatio-Temporal Modified-Inception with Dilated convolution Networks). We fully consider the complexity, nonlinearity and uncertainly of traffic network by summarizing various network models such as ResNet and Inception. So, we use the deep space-time residual network to ensure the convolution accuracy of the information’s position distribution on the basis of existing networks. Then, we add the cavity convolution to the model, which can effectively control the field of view of the convolution kernel. In the experimental part, we compare ten classical algorithms with our ST-MINet, it shows that our model has higher accuracy than others.

MS-Net: Multi-Source Spatio-Temporal Network for Traffic Flow Prediction

2021 ◽  
pp. 1-14
Author(s):  
Shen Fang ◽  
Veronique Prinet ◽  
Jianlong Chang ◽  
Michael Werman ◽  
Chunxia Zhang ◽  
...  
Keyword(s):  
Traffic Flow ◽  
Temporal Network ◽  
Traffic Flow Prediction ◽  
Flow Prediction ◽  
Spatio Temporal
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