Short-Term Traffic Speed Prediction of Urban Road With Multi-Source Data

Abstract

In recent years, the convolutional and recurrent neural networks are widely applied in traffic prediction tasks. Traffic speed prediction is an important and challenging topic in intelligent transportation systems. In this case, this paper proposes a hybrid deep learning structure for short-term traffic speed prediction, which combines convolutional neural networks and long short-term memory neural networks together. External factors such as weather condition and air quality can also affect the driving behavior of travelers and cause fluctuation of traffic speed. Based on theories in traffic engineering, we propose a data-fusion method to measure the impact of environmental factors. To enhance the performance of our model, we introduced attention mechanism to our model. With convolutional block attention module, our network could emphasize important channels and pixels of input features and suppress unnecessary ones. Comparing with several deep learning methods and hybrid deep learning structures, an experiment in one region of Suzhou which contains 909 links shows the outperformance of our model. Under different time steps, the prediction error of our model is lower than any other methods in urban expressway, primaryarterial, secondary-arterial, and branch-road. The results indicate that the spatial dependencies, the temporal correlations, and environmental impact should not be ignored in traffic speed prediction tasks.

[Retracted] Application of Multisource Data Fusion Technology in the Construction of Land Ecological Index

“…Once the cluster head nodes of each site receive the data collected from each sensor in the region, they first use the weighted average method of adaptation to melt the data from the same sensor in the region and then aggregate it locally using the BP neural network method. This is the aggregation of nonhomogeneous sensor data in each region [20]. The first-level smelting result is then sent to the second-level smelting output node.…”

A comprehensive study of speed prediction in transportation system: From vehicle to traffic

“…The first layer aimed to extract the holiday and weather features, and the other was used to map features to high dimensions. A Multilayer Perceptron (MLP) was used to capture the features of road properties, weather, and air quality in ( Yang et al., 2020 ), and the traditional traffic theory was applied in the feature merge layer. Reference ( Qu et al., 2021 ) fed the weather and time information as a feature matrix, and an Autoencoder is employed to extract the features in parallel.…”

“…The bad weather conditions (e.g., fog, rain, and snow) significantly affect the visibility and road friction, such that change the driving behaviors ( Ahmed and Ghasemzadeh, 2018 ) and vehicle stability ( Yang et al., 2020 ). For example, the low visibility caused by heavy fog or rain heavily impact the perception and control of vehicles ( Ahmed and Ghasemzadeh, 2018 ; Fridman et al., 2019 ).…”

Spatial Analysis of Territorial Connectivity and Accessibility in the Province of Coclé in Panama

“…Road Condition: The condition of the road infrastructure affects the macroscopic parameters of volume, speed, and density considered in the study of traffic phenomena [34,35]; according to the geometric characteristics of the road, the condition of the pavement, and complementary works, users (drivers and pedestrians) will define their preferences when making any trip, which, in turn, will affect the behavior of vehicular and pedestrian flows, the speeds developed by vehicles, and the results of the analysis of the values obtained for the aforementioned parameters [36,37].…”