Travel time reliability assessment has been widely used in recent years to evaluate the performance of transportation networks and measure the operation level of transportation systems. Weather, as one of the important factors influencing travel time reliability, affects the relationship between the supply and requirement of urban road networks. Considering the traffic characteristics under different traffic conditions, a study on the influence of weather on travel time reliability under different conditions is proposed to predict the probability of travelers completing their trips within the expected time under different weather conditions. Based on the urban road network data and cab trajectory data of Harbin city, this paper correlates the floating vehicle location with the road network information through a hidden Markov model to reduce the influence of vehicle trajectory errors on the calculation results of path travel time. To analyze the entire distribution of extreme travel time and its impact on the reliability of travel time under various traffic situations, it captures the tail features of the travel time distribution based on extreme value theory. Then, to increase the predictability of each quantile, it combines a deep-learning LSTM model and a quantile regression model to create a probabilistic travel time prediction model utilizing combined layers. The proposed model is compared with the linear quantile regression and neural network quantile regression models, and the model is evaluated in terms of point prediction results and probabilistic prediction results, respectively, to ensure the accuracy of predictions from the model. As a result, the prediction accuracy of the model in this paper is greatly improved, and the degree of violating quantile constraints is greatly reduced.

Tunnel structures account for la large proportion of the structures in mountain highway transportation systems. Most tunnels are located in remote areas in which the geological conditions are complex and harsh and in which the layout of the management facilities along the way is complex. Thus, the management and maintenance costs of various facilities are expensive, the cost of fire safety operations and management is high, and disaster prevention and rescue have a difficult time meeting the objective requirements. Therefore, it is urgent to carry out research on quasi-unmanned operations and intelligent remote monitoring. This study firstly proposes a fire safety intelligent monitoring framework for quasi-unattended tunnels. By making full use of various intelligent sensor monitoring data in the tunnel, the tunnel operation status can be grasped in real time. Then, a fire safety evaluation model can be established through the analytic hierarchy process (AHP), and, based on the monitoring data, the AHP model parameters can be evaluated to realize the real-time evaluation and management of tunnel fire safety. Finally, on the basis of the fire safety intelligent monitoring system and the fire safety evaluation system, an adaptive fire rescue plan formulation scheme is proposed for the quasi-unattended tunnels so as to provide guaranteed support for the rapid automatic response of tunnel fire protection and to provide technical data support for the design and realization of intelligent and efficient tunnel management