Traffic congestion is a problem facing today's world, especially in smart cities where the economy is booming. Solving this issue by upgrading the traffic infrastructure of the city might be very cost‐inefficient as well as time‐consuming. With the help of recent technologies, traffic can be predicted to give the authorities the time to react before congestion evolves. As traffic is affected by several external factors, such as weather and anomalies (accidents, not expected road closures etc.), understanding the relationship between traffic and these factors can improve the prediction even further. In this study, a new method, the weather‐based traffic analysis (hereafter WBTA), is utilised to investigate the temporal correlations between the traffic flow and the exogenous weather factors at different frequencies and time intervals. In addition, a novel method, the wavelet‐attention‐based calculation (hereafter WABC) is introduced to help to understand the importance of each external factor, compared with the others. Five weather factors (temperature, wind speed, rain, visibility, and humidity) are analysed, weighted, and merged with each other as one auxiliary input to improve traffic prediction accuracy. Based on that, the wavelet‐attention‐based prediction model is introduced, where the mean squared error is reduced by 32.3% and 24.52% for one future time step prediction, and 14.9% and 18.22% for five, compared with using the traffic time series alone, and with external factors without weights, respectively.
In recent years, Intelligent Transportation Systems (ITS) have gained increased attention. ITS [1] [2]provide opportunities for traffic engineers and decision-makers to deal with problems related to highway traffic operation and congestion management. This paper deals with building a device for collecting data about Doha traffic and storing this data on a central server. The server maintains a traffic information database that can be used for traffic analysis and prediction.
A newly developed tool for analysing and visualizing data based on python. The main aim of this tool is saving the data analysis time by automating the data analysis process and creating a Graphical User Interface (GUI) to facilitate the usage of this tool regardless how much they experience programming with Python. This paper describes the architecture of the developed software tool and provides a variety of examples to show the developed tool’s advanced features and functionalities. Moreover, in this paper, an industrial problem related to data analysis is discussed, and then a solution of this problem is presented. This tool is developed using open source libraries (Pandas, Matplotlib, Tkinter, and SciPy) to manage multi data files at once using one tool, as well as visualizing and storing these data in one file in a clean and organized way.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.