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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]

The functional classification system (FCS) of roads means categorizing roads based on their service characteristics. The two primary considerations in classifying highway and street networks are accessibility and mobility, where by increasing the role of one, the other's role is reduced. In this paper, besides the conventional variables such as geometric design characteristics, parking lots, land use features, and accessibility; the Sydney Coordinated Adaptive Traffic System (SCATS) data following the real-time traffic flow and average speed of vehicles collected by Location-Based Services (LBS) are considered as new variables for estimating the FCS. Linear regression is used to model the importance of the variables. The chi-square test compared the observational and predicted speeds in the five categories of roads in Tehran, the capital of Iran. Results show that on-street parking has the highest impact and the land use variable has the lowest impact on speed that changes the FCS. Moreover, the presented classification was one to two categories compared with the conventional FCS presented in manuals in the case of Tehran's transportation network as a developing city. [ABSTRACT FROM AUTHOR]

Traffic congestion in urban areas is a challenging issue in transportation planning. Policy options have been proposed to evaluate the impacts of interventional action through change detection or before–after studies. In this research, low-cost traffic image data collected by smartphone-based application have been employed and the impact of new congestion charging scheme (CCS) upon congestion within congestion charging zone (CCZ) as well as the entire network in Tehran, the capital of Iran has been investigated. Applying statistical tests indicated the significance of change in congestion within CCZ by applying the new CCS. Differential Moran's I as spatial autocorrelation index specified the spatial patterns of congestion between the critical time of changing the scheme on weekdays (17:00–19:00) and weekend (6:00–13:00) after implementing the new CCS. The approach in this paper can be used with a low-cost appropriate instrument to monitor the probable change in traffic congestion by introducing any new scheme or sudden change. [ABSTRACT FROM AUTHOR]