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Earthquake is one of the hazardous disasters in Iran and caused huge causalities in the last century. Among different effective criteria in earthquake management especially in the response phase, morphological components of the urban structure have the main role. This paper aims to assess and monitor the suitability of urban roads for emergency response after an earthquake. The main contribution is the definition of key morphological factors, assessment, and monitoring of urban road suitability using nonlinear autoregressive with external input (NARX) as a time series artificial neural network (ANN). In this framework, first, the effective criteria are detected and analyzed based on experts' opinions, then the suitability of urban roads for emergency earthquake response is assessed for three temporal datasets for the 2010, 2015, and 2020 years. The proposed method has been implemented in Tehran; the capital of Iran based on designed ANNs. The RMSE and R of time series ANNs are 0.97629 and 0.027 for 2010, 0.91479 and 0.13 for 2015, 0.93569 and 0.056 for 2020. The results show that the suitability of urban roads has been improved significantly between 2010 to 2015, while according to the new expansion of roads in the west of Tehran, the level of suitability has been decreased from 2015 to 2020. As this region is going to be populated recent so some new strategies should be improved for urban traffic network structure. [ABSTRACT FROM AUTHOR]