# Special Issue on Transportation Big Data and Its Applications
###### Abstract  
The proposed method for the transportation of a vehicle, is based on a  
predict the trip distributions of tourists on suburban tourist railways. The model was able to accurately reflect the real trip distribution characteristics of tourists and can be used for the planning and construction of suburban tourist railways. [PERSON] et al. (contribution 2) reported a global assessment approach for bridge passage in relation to oversized and excessive road transport. They analyzed vehicle/vehicle combination parameters and assessed routes for heavy and oversized transportation in Slovakia. The authors proposed a new procedure for obtaining special permission for road use and introduced the concept of the cumulative axle load for assessing bridge passage. [PERSON] et al. (contribution 3) developed a new method for the density analysis of Automatic Identification System (AIS) data in the coastal waters of Korea. They used spatial-temporal density analysis and standard deviation-based stretch symbolization to calculate and visualize the density distribution of different ship types. The method allowed for the identification of major maritime traffic patterns and will be valuable for confirming maritime traffic patterns and density in the coastal areas of Korea. [PERSON] et al. (contribution 4) developed a ship classification method that integrated multiple base classifiers and effectively integrated the static and dynamic information of ships. The method outperformed individual base classifiers and achieved near real-time online classification. [PERSON] et al. (contribution 5) proposed a method for the automatic extraction of urban road intersections using trajectory line segment intersection points. The method achieved better recognition accuracy compared to the benchmark method and effectively identified intersections in low-traffic or low-sampling areas. They used a maximum reconstruction error method to extract straight-line segments and merged them to enhance the road network structure pattern. However, the method had limitations in terms of identifying different types of intersections and when the distances between adjacent intersections were less than 60 m. [PERSON] et al. (contribution 6) developed a short-term traffic state prediction model based on the data acquisition strategy of a mobile edge computing-assisted vehicle-to-everything (V2X) network. The model combined the advantages of a Graph Convolutional Network (GCN) and a Gated Recurrent Units (GRU) soft-attention mechanism to analyze the spatial-temporal characteristics of traffic data. The proposed model showed improved accuracy in predicting traffic flow compared to other models. Working on a similar issue, [PERSON] et al. (contribution 7) presented a traffic prediction model called Time-Evolving Graph Convolutional Recurrent Network (TEGCRN). The model utilized time-evolving adjacency graphs to capture dynamic internode dependency and achieved superior performance compared to the baseline models, especially in short-term prediction. [PERSON] et al. (contribution 8) proposed a hybrid model for vehicle acceleration prediction using Hidden Markov Models (HMM), Long Short-Term Memory (LSTM), and GRU. They used MHMM to divide the driving behavior semantics, evaluate the similarity, and group drivers accordingly. The results showed that the MHMM-based approach improved the prediction accuracy, and the GRU outperformed the LSTM in predicting vehicle acceleration. [PERSON] et al. (contribution 9) presented a tensor-based approach for road traffic anomaly detection. They used Speed Transition Matrices (STMs) to model the spatial-temporal traffic patterns and applied anomaly detection based on the center of mass computation. The proposed method achieved a precision score of 92.88% in detecting anomalies on urban road networks. [PERSON] et al. (contribution 10) analyzed the impact of urban built-environment variables on bus-metro-transfer ridership using the XGBoost model.