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Labeling plays an important role in map production, attaching specific texts to related geographic elements to provide clear environmental references. In three-dimensional geographical information systems (3DGISs), however, cluttering happens fairly commonly because of the unexpected overlapping and occlusion among labels and related objects, and results in an ambiguous and obscure environment. It generally also takes large computing power and memory to visualize spatial entities. Aimed at both unambiguous and efficient 3D map display, this article proposes an adaptive multi-resolution labeling method to deal with point, polyline, and polygon features labeling in a 3D landscape. It implements adaptive placement and view-driven label filtering without obscuring other visual features. The experiments indicated that the display of overlapping labels and label popping are reduced significantly with less computation burden while retaining the rendering quality.

In this paper, the expressway traffic flow prediction model based on the Bi-LSTM is designed, and four sections of expressway are applied to the model for training and evaluation. Through training and verification, the results show that the average prediction accuracy, MAPE, RMSE, and MAE of the proposed model is 89.54%, 10.46%, 31.55, and 24.58, respectively. In addition, in order to evaluate the effect of the proposed model, this paper introduces the ARIMA model for comparison. It is found that the prediction accuracy of the Bi-LSTM is 18.30% higher than ARIMA, the RMSE is reduced by 31.85, and the MAE is reduced by 26.32. The results show that the proposed Bi-LSTM model exhibits higher prediction performance. Afterwards, this paper makes a comparative analysis of the predicted value and the original value of each road section. The results show that the proposed model has a certain lag, and the forecast value of traffic flow is low for rush hours, however, the forecast value of traffic flow is higher in the low peak hours.

This study focuses on the economic benefits of railway transportation from the aspect of data mining using convolution neural network (CNN), long short-term memory (LSTM), and multiple linear regression (MLRA) models. Improved CNN and LSTM (CNN-LSTM), and CNN and bidirectional LSTM (CNN-Bi-LSTM) models have been developed to enhance learning. The case data sets include operating mileage, and passenger and freight turnover for four transportation modes (railway, highway, aviation, and water transportation) from 1952 to 2020 in China; various evaluation indexes are used to verify model effectiveness. The CNN and LSTM model prediction error rates are 29% and 22%, respectively, verifying the role of an integrated transportation system in railway transportation and the time effect as a national economic benefit of railway transportation, respectively. The CNN-LSTM model prediction error rate is 12%, indicating that the economic benefits of railway transportation depend on the structure of various transportation modes and the time stage of transportation resource allocation. The prediction error rate of the CNN-Bi-LSTM model is 14%, suggesting the irreversibility of the impact of railway transportation resources on economic benefits. The MLRA model error rate is lower than those of the CNN and LSTM models, at 19%, but higher than those of the other models. The CNN-LSTM model is recommended to quantify the economic benefits of railway transportation. This study illustrates the systematic nature, periodicity, time lag, and irreversibility of railway transportation and economic development, providing a theoretical basis for the formulation of transportation development and investment plans.