###### Abstract
A deep learn-based travel time estimation model that neighbors for travel time estimation (Nei-TTE) was used for the first time by [PERSON] et al. [15]. It can effectively simulate the topological information of the surrounding space by using the velocity characteristics and assign an exact start time to each section. Experiments on large-scale real data sets show that the proposed method achieves better estimation results and is obviously superior to existing models. [PERSON] et al. [16] proposed a statistical model to estimate the travel time of the urban road network by taking the vehicle track acquired by low-frequency global position system (GPS) probe as the observation value and dividing the model travel time into road segment travel time and intersection delay time. Based on the Spatial Moving Average (SMA) structure, the correlation between travel time on different network links is allowed. Then, the model was applied to the Stockholm network in Sweden. The results showed that road attributes and travel conditions (including recent snowfall) had a significant impact on travel time. There is a significant positive correlation between each stage. [PERSON] et al. [17] studied a generalized Markov chain method, which weighted the travel time distribution of Markov paths according to its probability of occurrence to obtain the probability distribution of travel time. The link travel time distribution depends on current link traffic and can be estimated from historical observations. The algorithm, based on moment generation function, is used to approximate the sum of the travel time distributions of related links to the travel time distributions of Markov paths under traffic conditions. The method is applied to examine a traffic case study using automatic vehicle location data. [PERSON] et al. [18] considered a robust travel time distribution estimation method, using emerging low-frequency floating car data to estimate the mean and variance of travel time, and designed a distribution estimation algorithm to estimate random turn delay. A case study of Wuhan, China, was taken to verify the applicability of this method. The research of post-earthquake transportation systems abroad in wide-ranging, including post-earthquake road emergency repair, post-earthquake road network reliability and vulnerability analysis, and post-earthquake recovery process. [PERSON] et al. [19]believed that spatial connectivity is an effective spatial index for monitoring and evaluating the post-earthquake recovery process, which integrates the variables of distance, travel time, and quality of public transport service after assuming the relationship between the connectivity of the urban center and the location of the new resettlement sites assigned to the homeless during the recovery process. [PERSON] et al. [20] found out the areas along with the quake route which were most likely to be disrupted by earthquakes. [PERSON] et al. [21] studied the planning of post-earthquake emergency repair, especially the re-opening of damaged roads, and produced an accurate algorithm of mixed integer programming (MIP). By clustering trips with the same starting point in a specific time interval, [PERSON] et al. [22] developed a temporal graph neural network with heterogeneous features (TGCNHF) with heterogeneous characteristics, thereby introducing network trip risk (NTR) to evaluate the reliability of this region. There were relatively few studies on the reliability of post-earthquake travel time. [PERSON] et al. [23] creatively extracted spatial-temporal traffic features by using gray level co-occurrence matrix (GLCM) and introduced them into the pattern matching method of travel time. Different from previous methods, this method used large-scale spatial-temporal traffic patterns to predict multi-step travel time. [PERSON] et al.