Traffic travel pattern recognition based on sparse Global Positioning System trajectory data

Abstract

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%.

Grid-Based Anomaly Detection of Freight Vehicle Trajectory considering Local Temporal Window

“…In Figure 4, TW 8 is a special window. Time Step 4 is followed by time Step 7 in TW 8 in the test trajectory, which means that time steps (5,6,7) are in the same grid. ere is 30 seconds between time Step 4 and time Step 7.…”

“…In particular, during major events such as the ceremony, concert, and games, real-time monitoring and anomaly detection of the logistics vehicles trajectories have attracted a large amount of security concern since the tragedy in Nice (86 dead and 458 injured) [1] and Barcelona (15 dead and 100 injured). As the vehicle trajectory data can be conveniently collected nowadays by digital sensors, such as various navigation systems, smart cellphones, and RFID devices, researchers can study the reliability of trajectories and the invoked transportation risk issues, i.e., driving behavior detection [2,3], travel pattern recognition [4][5][6], and anomaly tracking in practice [4][5][6][7][8][9][10].…”

Individual Travel Knowledge Graph-Based Public Transport Commuter Identification: A Mixed Data Learning Approach

“…In addition, the rapid evolution of artificial intelligence and machine learning technology also provides methodological support for datadriven PT passenger classification. Some previous studies identified the PT commuters based on the intelligent algorithm including association rules algorithm [10], convolutional neural networks (CNNs) [8], Naïve Bayes probabilistic model [11,12], support vector machine and decision tree [12], and statistical analysis model [13]. Zhang et al identified the commuters among numerous bus passengers by using the IC data with the cluster analysis [14].…”