###### Abstract  
To carry out the estimation and reliability research of post-earthquake traffic travel time, which has the great influence for efficient allocation of relief materials. By analyzing the relationship among floating vehicle trajectory, target path and road network path, the intermediate parameters of converting floating vehicle trajectory data into target path travel time were defined and improved. In addition, the road damage identification method relying on lane detection is applied for evaluating the damage of road after the earthquake through the image information. Then, Bayesian average adaptive kernel density estimation method was used to estimate the distribution of post-earthquake road travel time, and a new formula for calculating the reliability of road travel time after earthquake was proposed. According to the example simulation and analysis, the proposed post-earthquake road travel time distribution estimation and its reliability are verified. The results show that when the threshold value is determined, the travel time of the path before the earthquake is the most dependable, and with the increase in the earthquake damage index, the travel time of this road section becomes increasingly unreliable. However, after the earthquake, the peak probability density of road travel time distribution weakens, and the overall probability shifts to the direction of long time. earthquake emergency; floating car technology; travel time after earthquake; reliability; probability distribution +
Footnote †: journal: Applied Sciences  
1
Footnote †: journal: Applied Sciences  
1
Footnote 1: College of Transportation Engineering, Nanjing Tech University, Nanjing 211816, China**
[PERSON] et al. [8] proposed a system framework for floating car traffic data extraction, which could realize travel time prediction and optimal route selection by taking taxi as floating car data acquisition carrier. [PERSON] [9] used high-quality floating car travel time data to detect traffic events on the urban road network. With the rapid development of data processing technology, a new type of extended floating car technology was taken into application, which means more sensors for the floating car. Image recognition equipment, which can identify the road information of the floating car, was widely used because its recognition of signal lights. [PERSON] [10] combined intelligent vehicle and floating vehicle technologies and purposed a road condition recognition system derived from image recognition, including the recognition of road signs and pavement. At present, the research level of travel time calculation gained from floating car data has been improved. According to the travel rules of travelers, [PERSON] et al. [11] divided the paths into the shortest path, the fastest path, and the preferred path, and suggested three preferred origin destination (OD) travel time prediction methods, believing that this method can significantly improve the prediction accuracy of OD travel time. To solve the problem of real-time floating car data sparse, [PERSON] et al. [12] used multi-layer neural network to conduct reinforcement learning on the relationship between feature and travel time of target road section and adjacent road section and obtained the predicted travel time of target road section. The results showed this method can also be used to solve the problem of partial missing of offline data. [PERSON] et al. [13] designed a travel time estimation model gained from road network similarity, which is derived from road attributes such as road length, road analysis, number of lanes, and similarity characteristics such as access degree of each road section and point of interest (POI) intensity. From the perspective of road network similarity, the feasibility of solving real-time data missing is verified. [PERSON] et al. [14] created a real-time city-wide model to estimate the travel time of any route in the city. Modeling the driving time of different drivers in different sections shows the effectiveness of the method.