High-density land uses cause high-intensity traffic demand. Metro as an urban mass transit mode is considered as a sustainable strategy to balance the urban high-density land uses development and the high-intensity traffic demand. However, the capacity of the metro cannot always meet the traffic demand during rush hours. It calls for traffic agents to reinforce the operation and management standard to improve the service level. Passenger flow prediction is the foremost and pivotal technology in improving the management standard and service level of metro. It is an important technological means in ensuring sustainable and steady development of urban transportation. This paper uses mathematical and neural network modeling methods to predict metro passenger flow based on the land uses around the metro stations, along with considering the spatial correlation of metro stations within the metro line and the temporal correlation of time series in passenger flow prediction. It aims to provide a feasible solution to predict the passenger flow based on land uses around the metro stations and then potentially improving the understanding of the land uses around the metro station impact on the metro passenger flow, and exploring the potential association between the land uses and the metro passenger flow. Based on the data source from metro line 2 in Qingdao, China, the perdition results show the proposed methods have a good accuracy, with Mean Absolute Percentage Errors (MAPEs) of 11.6%, 3.24%, and 3.86 corresponding to the metro line prediction model with Categorical Regression (CATREG), single metro station prediction model with Artificial Neural Network (ANN), and single metro station prediction model with Long Short-Term Memory (LSTM), respectively.

The decision-making process of travel behaviors under uncertainty and risk shall be analyzed in order to solve the emergency traffic management or evacuation problem under sudden fire disaster in a high-density urban environment. Firstly, this paper attempts to acquire the travel risk attitude thought online survey questionnaires. In the questionnaire, we focused on obtaining the traveler’s response thought set a scene and obtain the traveler’s risk attitude. Secondly, we explore the relationship between traveler’s personal attributes and risk attitudes through questionnaires. Finally, the questionnaire data were used to calibrate and adjust the parameters in the proposed prospect theory (PT) based model. Subsequently, the K-T model and Wang’s model were used to compare and verify the accuracy and validity of the proposed model. The results presented that the proposed model is more accurate and the largest prediction error of travel route selection behavior is only nine percent.