A multiple clusteringalgorithm based on high-dimensional automatic identification system (AIS) data is proposed to extract the important waypoints in the ship's navigation trajectory based on selected AIS attribu...
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A multiple clusteringalgorithm based on high-dimensional automatic identification system (AIS) data is proposed to extract the important waypoints in the ship's navigation trajectory based on selected AIS attribute features and construct a route network using the waypoints. The algorithm improves the accuracy of route network planning by using the latitude and longitude of the historical voyage trajectory and the heading to the ground. Unlike the navigation clustering method that only uses ship latitude and longitude coordinates, the algorithm first calculates the major waypoints using clustering in QUEst (CLIQUE) and Balance Iterative Reducing and clustering Using Hierarchies (BIRCH) algorithms, and then builds the route network using network construction. Under the common PC specification (i5 processor), this algorithm forms 440 major waypoints from 220,133 AIS data and constructs a route network with directional features in 5 min, which is faster in computing speed and more suitable for complex ship trajectory differentiation and can extend the application boundary of ship route planning.
Vehicle evaluation parameters, which are increasingly of concern for governments and consumers, quantify performance indicators, such as vehicle performance, emissions, and driving experience to help guide consumers i...
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Vehicle evaluation parameters, which are increasingly of concern for governments and consumers, quantify performance indicators, such as vehicle performance, emissions, and driving experience to help guide consumers in purchasing cars. While past approaches for driving cycle prediction have been proven effective and used in many countries, these algorithms are difficult to use in China with its complex traffic environment and increasingly high frequency of traffic jams. Meanwhile, we found that the vehicle dataset used by the driving cycle prediction problem is usually unbalanced in real cases, which means that there are more medium and high speed samples and very few samples at low and ultra-high speeds. If the ordinary clusteringalgorithm is directly applied to the unbalanced data, it will have a huge impact on the performance to build driving cycle maps, and the parameters of the map will deviate considerable from actual ones. In order to address these issues, this paper propose a novel driving cycle map algorithm framework based on an ensemble learning method named multi-clustering algorithm, to improve the performance of traditional clusteringalgorithms on unbalanced data sets. It is noteworthy that our model framework can be easily extended to other complicated structure areas due to its flexible modular design and parameter configuration. Finally, we tested our method based on actual traffic data generated in Fujian Province in China. The results prove the multi-clustering algorithm has excellent performance on our dataset.
In this paper, Large-Scale Vehicle Routing Problem in Real Traffic Condition (LSVRPRTC) is studied. In this kind of problem the client demand and the presence of the client are assumed to certain. The service vehicle ...
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ISBN:
(纸本)9781424415304
In this paper, Large-Scale Vehicle Routing Problem in Real Traffic Condition (LSVRPRTC) is studied. In this kind of problem the client demand and the presence of the client are assumed to certain. The service vehicle returns to the depot whenever its capacity is attained or exceeded, and it is resumed that its collections along the planned route. After describing the preliminaries, a mathematical formulation is developed. multi-clustering algorithm (MC), as a kind of two-phase algorithm, is proposed for this intractable problem in order to obtain optimal or approximate optimal solutions with minimum total cost. Computational examples on a group of instances are given, showing the proposed approach is not only a simple but effective way to solve such problems.
In this paper, Large-Scale Vehicle Routing Problem in Real Traffic Condition (LSVRPRTC) is studied. In this kind of problem the client demand and the presence of the client are assumed to certain. The service vehicle ...
详细信息
ISBN:
(纸本)9781424415304;1424415306
In this paper, Large-Scale Vehicle Routing Problem in Real Traffic Condition (LSVRPRTC) is studied. In this kind of problem the client demand and the presence of the client are assumed to certain. The service vehicle returns to the depot whenever its capacity is attained or exceeded, and it is resumed that its collections along the planned route. After describing the preliminaries, a mathematical formulation is developed. multi-clustering algorithm (MC), as a kind of two-phase algorithm, is proposed for this intractable problem in order to obtain optimal or approximate optimal solutions with minimum total cost. Computational examples on a group of instances are given, showing the proposed approach is not only a simple but effective way to solve such problems.
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