The Impact of Urban Layout on Traffic Flow Prediction of Intersection

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.

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Machine Learning and Deep Learning Models for Traffic Flow Prediction: A Survey

10.21203/rs.3.rs-66048/v1 ◽

2020 ◽

Author(s):

Ayele Gobezie ◽

Marta Sintayehu Fufa

Keyword(s):

Machine Learning ◽

Deep Learning ◽

Traffic Flow ◽

Series Data ◽

Intelligent Transport System ◽

Learning Approaches ◽

Traffic Flow Prediction ◽

Traffic Regulation ◽

Flow Prediction ◽

The Impact

Abstract Traffic congestion is one of the problems for cities around the world due to the rapid increasing of vehicles in urbanization. Traffic flow prediction is of a great importance for Intelligent Transport System (ITS) which helps to optimize the traffic regulation of a transportation in the city. Nowadays, several researches have been studied so far on traffic flow prediction, accurate prediction has not yet been exploited by most of existing studies due to the impact of inability to effectively deal with spatial temporal features of the times series data. Traffic information in transportation system will also be affected by different factors. In this research we intended to study various models for Traffic flow prediction on the basis machine learning and deep learning approaches. Factors affecting the performance of traffic flow prediction intensity are studied as well. Benchmark performance evaluation metrics are also reviewed. Generally, this manuscript covers relevant methods and approaches, review the state-of-art works with respect to different traffic flow prediction technique help researchers in exploring future directions so as to realize robust traffic flow prediction.

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Cross-Attention Fusion Based Spatial-Temporal Multi-Graph Convolutional Network for Traffic Flow Prediction

Sensors ◽

10.3390/s21248468 ◽

2021 ◽

Vol 21 (24) ◽

pp. 8468

Author(s):

Kun Yu ◽

Xizhong Qin ◽

Zhenhong Jia ◽

Yan Du ◽

Mengmeng Lin

Keyword(s):

Traffic Flow ◽

Prediction Errors ◽

Temporal Data ◽

Convolutional Network ◽

Smart Transportation ◽

Traffic Flow Prediction ◽

Flow Prediction ◽

Spatio Temporal ◽

Regional Similarity ◽

The Impact

Accurate traffic flow prediction is essential to building a smart transportation city. Existing research mainly uses a given single-graph structure as a model, only considers local and static spatial dependencies, and ignores the impact of dynamic spatio-temporal data diversity. To fully capture the characteristics of spatio-temporal data diversity, this paper proposes a cross-Attention Fusion Based Spatial-Temporal Multi-Graph Convolutional Network (CAFMGCN) model for traffic flow prediction. First, introduce GCN to model the historical traffic data’s three-time attributes (current, daily, and weekly) to extract time features. Second, consider the relationship between distance and traffic flow, constructing adjacency, connectivity, and regional similarity graphs to capture dynamic spatial topology information. To make full use of global information, a cross-attention mechanism is introduced to fuse temporal and spatial features separately to reduce prediction errors. Finally, the CAFMGCN model is evaluated, and the experimental results show that the prediction of this model is more accurate and effective than the baseline of other models.

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Traffic Flow Prediction with Rainfall Impact Using a Deep Learning Method

Journal of Advanced Transportation ◽

10.1155/2017/6575947 ◽

2017 ◽

Vol 2017 ◽

pp. 1-10 ◽

Cited By ~ 33

Author(s):

Yuhan Jia ◽

Jianping Wu ◽

Ming Xu

Keyword(s):

Deep Learning ◽

Traffic Flow ◽

Short Term Memory ◽

Complex Model ◽

Urban Traffic ◽

Environmental Data ◽

Traffic Modeling ◽

Traffic Flow Prediction ◽

Flow Prediction ◽

The Impact

Accurate traffic flow prediction is increasingly essential for successful traffic modeling, operation, and management. Traditional data driven traffic flow prediction approaches have largely assumed restrictive (shallow) model architectures and do not leverage the large amount of environmental data available. Inspired by deep learning methods with more complex model architectures and effective data mining capabilities, this paper introduces the deep belief network (DBN) and long short-term memory (LSTM) to predict urban traffic flow considering the impact of rainfall. The rainfall-integrated DBN and LSTM can learn the features of traffic flow under various rainfall scenarios. Experimental results indicate that, with the consideration of additional rainfall factor, the deep learning predictors have better accuracy than existing predictors and also yield improvements over the original deep learning models without rainfall input. Furthermore, the LSTM can outperform the DBN to capture the time series characteristics of traffic flow data.

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