Traffic speed forecasting in the short term is one of the most critical parts of any intelligent transportation system (ITS). Accurate speed forecasting can support travelers’ route choices, traffic guidance, and traffic control. This study proposes a deep learning approach using long short-term memory (LSTM) network with tuning hyper-parameters to forecast short-term traffic speed on an arterial parallel multi-lane road in a developing country such as Vietnam. The challenge of mishandling the location data of vehicles on small and adjacent multi-lane roads will be addressed in this study. To test the accuracy of the proposed forecasting model, its application is illustrated using historical voyage GPS-monitored data on the Le Hong Phong urban arterial road in Haiphong city of Vietnam. The results indicate that in comparison with other models (e.g., traditional models and convolutional neural network), the best performance in terms of root mean square error (RMSE), mean absolute error (MAE), and median absolute error (MDAE) is obtained by using the proposed model.

No abstract

No abstract

This study aims to explore the applications of Geographical Information System (GIS) technology in managing and analysing the railway networks in Vietnam in a scientific and rational manner, developing the railway industry in a sustainable manner to keep pace with the development speed in the region and in the world. A process of building a GIS project and designing a geodatabase for a GIS project was proposed. From that point, it is suggested to build an experimental database for the railway networks of Vietnam through ArcGIS software 10.2. After that, the authors explored some typical applications of GIS technology for railway network management such as management, safety and security, and selecting optimum routes. The results showed that GIS application in managing railway system brings lots of benefit to not only in the railway sector but also in the transportation field.

No abstract