Traffic flow prediction plays an important role in intelligent transportation systems and is of great significance in the applications of traffic control and urban planning.Due to the complexity of road traffic flow d...Traffic flow prediction plays an important role in intelligent transportation systems and is of great significance in the applications of traffic control and urban planning.Due to the complexity of road traffic flow data,traffic flow prediction has been one of the challenging tasks to fully exploit the spatiotemporal characteristics of roads to improve prediction accuracy.In this study,a combined flow direction level traffic flow prediction graph convolutional network(GCN)and long short-term memory(LSTM)model based on spatiotemporal characteristics is proposed.First,a GCN model is employed to capture the topological structure of the data graph and extract the spatial features of road networks.Additionally,due to the capability to handle long-term dependencies,the longterm memory is used to predict the time series of traffic flow and extract the time features.The proposed model is evaluated using real-world data,which are obtained from the intersection of Liuquan Road and Zhongrun Avenue in the Zibo High-Tech Zone of China.The results show that the developed combined GCNLSTM flow direction level traffic flow prediction model can perform better than the single models of the LSTM model and GCN model,and the combined ARIMA-LSTM model in traffic flow has a strong spatiotemporal correlation.展开更多
Many applications in geodesy, hydrography and engineering require determining heights linked to the geoid. Direct leveling, which is the traditional method of obtaining these elevations, is slow, time consuming and ex...Many applications in geodesy, hydrography and engineering require determining heights linked to the geoid. Direct leveling, which is the traditional method of obtaining these elevations, is slow, time consuming and expensive. The contribution of space techniques can make it possible to overcome these constraints provided that we have a precision geoid model compatible with that obtained by the GNSS method. There are today relatively precise regional geoid models, at least outside of mountain ranges, in all developed countries, which is not yet the case in developing countries like Senegal. An alternative is to use local models restricted to a small area. Thus, this study aims to produce a geoid model by combining multi-source data for the city of Thies intended mainly to support leveling operations by GNSS. To achieve this objective, direct precision leveling and GNSS leveling (static mode) were carried out covering the study area. The reference points used are, among others, those of the RRS04 (Reference Network of Senegal 2004) and the NGAO53 (General Leveling of West Africa 1953). Additionally, gravimetric measurements were conducted using the Sensor Play-Data Recorder application. The calculation of the model was carried out by the SRBF (Spherical Radial Basis Function) method using the PAGravf4.5 software. The SRBF method uses EGM08 to first calculate height and gravity anomalies. These are then compared with the raw data in order to determine the residuals which will allow the model to be refined. In order to validate our model, control points (GNSS/leveled) were chosen based on a homogeneous geographical distribution in the area in order to evaluate their altitude. An accuracy of less than 2 cm was obtained. Comparing our model with the existing local model GGSV12v1 shows that our model is more accurate.展开更多
The spatial representation of the Earth’s surface,as well as the surfaces of other geometric objects,involves a series of representation models.Digital Terrain Models(DTMs)represent the natural surface of the land wi...The spatial representation of the Earth’s surface,as well as the surfaces of other geometric objects,involves a series of representation models.Digital Terrain Models(DTMs)represent the natural surface of the land without the structures or vegetation that cover it.Today,photogrammetry,with the advent of drone technology,has advanced working methods in precision topography and allows for much more efficient DTMs.However,there are several sources of errors in positioning due to instrumental,procedural,and environmental factors that arise during the process.The objective of this study is to validate the relevance of new drone technology in its application to topography,particularly in the production of DTMs.For this purpose,a DJI Phantom 4 Pro RTK drone was used to acquire 392 images in an area with difficult topography(8 ha),supported by 12 previously established ground control points(GCPs)whose altitudes were determined by a precision direct leveling operation.The processing was carried out using two photogrammetric processing software(Pix4D and Agisoft Metashape)to compare their performance on DTM accuracy.Then,GNSS surveys in real-time mode(RTK)were carried out to produce three-dimensional models to be used for comparison and validation purposes.The vertical error of the three digital terrain models(DTMs)was evaluated by comparing them with direct leveling campaign data on the ground.We found that the result from the GNSS processing method achieved the best performance in terms of absolute error evaluation.Between the results provided by the two software programs,Agisoft offers slightly better results than those obtained with Pix4D.展开更多
基金supported by the National Natural Science Foundation of China (Grant Nos.71901134&51878165)the National Science Foundation for Distinguished Young Scholars (Grant No.51925801).
文摘Traffic flow prediction plays an important role in intelligent transportation systems and is of great significance in the applications of traffic control and urban planning.Due to the complexity of road traffic flow data,traffic flow prediction has been one of the challenging tasks to fully exploit the spatiotemporal characteristics of roads to improve prediction accuracy.In this study,a combined flow direction level traffic flow prediction graph convolutional network(GCN)and long short-term memory(LSTM)model based on spatiotemporal characteristics is proposed.First,a GCN model is employed to capture the topological structure of the data graph and extract the spatial features of road networks.Additionally,due to the capability to handle long-term dependencies,the longterm memory is used to predict the time series of traffic flow and extract the time features.The proposed model is evaluated using real-world data,which are obtained from the intersection of Liuquan Road and Zhongrun Avenue in the Zibo High-Tech Zone of China.The results show that the developed combined GCNLSTM flow direction level traffic flow prediction model can perform better than the single models of the LSTM model and GCN model,and the combined ARIMA-LSTM model in traffic flow has a strong spatiotemporal correlation.
文摘Many applications in geodesy, hydrography and engineering require determining heights linked to the geoid. Direct leveling, which is the traditional method of obtaining these elevations, is slow, time consuming and expensive. The contribution of space techniques can make it possible to overcome these constraints provided that we have a precision geoid model compatible with that obtained by the GNSS method. There are today relatively precise regional geoid models, at least outside of mountain ranges, in all developed countries, which is not yet the case in developing countries like Senegal. An alternative is to use local models restricted to a small area. Thus, this study aims to produce a geoid model by combining multi-source data for the city of Thies intended mainly to support leveling operations by GNSS. To achieve this objective, direct precision leveling and GNSS leveling (static mode) were carried out covering the study area. The reference points used are, among others, those of the RRS04 (Reference Network of Senegal 2004) and the NGAO53 (General Leveling of West Africa 1953). Additionally, gravimetric measurements were conducted using the Sensor Play-Data Recorder application. The calculation of the model was carried out by the SRBF (Spherical Radial Basis Function) method using the PAGravf4.5 software. The SRBF method uses EGM08 to first calculate height and gravity anomalies. These are then compared with the raw data in order to determine the residuals which will allow the model to be refined. In order to validate our model, control points (GNSS/leveled) were chosen based on a homogeneous geographical distribution in the area in order to evaluate their altitude. An accuracy of less than 2 cm was obtained. Comparing our model with the existing local model GGSV12v1 shows that our model is more accurate.
文摘The spatial representation of the Earth’s surface,as well as the surfaces of other geometric objects,involves a series of representation models.Digital Terrain Models(DTMs)represent the natural surface of the land without the structures or vegetation that cover it.Today,photogrammetry,with the advent of drone technology,has advanced working methods in precision topography and allows for much more efficient DTMs.However,there are several sources of errors in positioning due to instrumental,procedural,and environmental factors that arise during the process.The objective of this study is to validate the relevance of new drone technology in its application to topography,particularly in the production of DTMs.For this purpose,a DJI Phantom 4 Pro RTK drone was used to acquire 392 images in an area with difficult topography(8 ha),supported by 12 previously established ground control points(GCPs)whose altitudes were determined by a precision direct leveling operation.The processing was carried out using two photogrammetric processing software(Pix4D and Agisoft Metashape)to compare their performance on DTM accuracy.Then,GNSS surveys in real-time mode(RTK)were carried out to produce three-dimensional models to be used for comparison and validation purposes.The vertical error of the three digital terrain models(DTMs)was evaluated by comparing them with direct leveling campaign data on the ground.We found that the result from the GNSS processing method achieved the best performance in terms of absolute error evaluation.Between the results provided by the two software programs,Agisoft offers slightly better results than those obtained with Pix4D.
