This article mainly uses sparse Global Positioning System trajectory data to identify traffic travel pattern. In this article, the data are preprocessed and the eigenvalues are calculated. Then, the Global Positioning System track points are identified and extracted by walking and non-walking segments. Finally, the three machine learning models of support-vector machine, decision tree, and convolutional neural network are used for comparison experiments. The innovation of this article is to propose a walking and non-walking identification method based on density-based spatial clustering of applications with noise clustering. The method takes into account the continuous state between the geographical distributions, and it has better noise immunity against the influence of external factors. In this process, this article directly achieves better conversion point recognition results through the Global Positioning System track point information, which lays a good foundation for the accuracy of travel pattern recognition. The experimental results of this article show that compared with threshold-based and multi-layer perceptron–based methods, the recognition method based on density-based spatial clustering of applications with noise clustering has the highest accuracy, reaching 82.20%. Then, support-vector machine, decision tree, and convolutional neural network are used for traffic travel pattern recognition. The F1-score of support-vector machine is the highest, reaching 0.84, and the F1-scores of decision tree and convolutional neural network are 0.78 and 0.80, respectively. Finally, the support-vector machine was used for the recognition test to achieve an accuracy of 76.83%.

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The rapid development of unmanned aerial vehicles (UAVs) in recent years has promoted their application in various fields, such as precise agriculture, formation flight, etc. In these applications, the accurate and reliable real-time position and attitude determination between each moving device in the same platform system are the key issue for safe and effective cooperative works. In traditional ways, static reference stations should be set up near the platform to keep the stable position datum of the platform system. In this paper, we abandoned the static stations and expected to achieve stable position datums with the platform system itself. To achieve this goal, we proposed an improved method based on both the Global Positioning System (GPS)/Beidou Navigation Satellite System (BDS) data and the inertial navigation system (INS) data to obtain precise positions of the moving devices. The time-differenced carrier phase (TDCP) was used to get the position variations and update the positions over time, and then, the INS data was integrated to further improve the accuracy and reliability of the updated positions; thus, this method is denoted as the TDCP/INS method. To evaluate the performance of this method and compare it with the traditional single-point positioning (SPP) method and the Kalman filtered SPP (KFSPP) method, a field vehicle experiment was conducted, and the position results achieved from these three methods were compared with those from the tightly combined real-time kinematic positioning (RTK)/INS method, where centimeter-level accuracy was obtained and regarded as the reference. The quantitative analysis where the position variations were evaluated and the qualitative analysis where the vehicle trajectories in three typical urban driving scenarios were discussed were both made for the three methods. The numerical results showed that the accuracy of the position variations from the SPP, KSPP, and TDCP methods was at the meter level, while that from the TDCP/INS method improved to the centimeter level, and the accuracies were 1.9 cm, 2.9 cm, and 3.1 cm in the east, north, and upward directions. The trajectory results also demonstrated a perfect consistency of the driving positions between the TDCP/INS method and the reference. As a contrast, the trajectories from the SPP and KFSPP methods had frequent jumps or sways when the vehicle drove along a large, curved road, turned at a crossroad, and passed under an urban viaduct.

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