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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]

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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 concentration of black carbon was measured in four sites of the industrial and high-traffic metropolis of Tehran with different land uses. Then, the contribution of biomass and fossil fuels in the emission of this pollutant was modeled using the Aethalometer model. The possible locations of important sources of black carbon dissemination were projected using PSCF and CWT models, and the results were compared in the two periods before and after the Covid-19 outbreak. Temporal variations of black carbon illustrated that BC concentration decreased in the period after the onset of the pandemic in all studied areas, and this decline was more explicit in the traffic intersection of the city. Diurnal changes of BC concentration indicated the significant impact of the application of the law banning night traffic of motor vehicles in reducing the BC concentration in this period, and probably the reduction of HDDV traffic has played the most important role in this reduction. The results related to the share of BC sources indicated that black carbon emissions are affected by an average of about 80% of fossil fuel combustion and wood combustion interferes with about 20% of BC emissions. Finally, speculations were made about the possible sources of BC emission and its urban scale transport using PSCF and CWT models, which indicated the superiority of the CWT model in terms of source segregation. The results of this analysis were further utilized to surmise black carbon emission sources based on the land use of receptor points. [ABSTRACT FROM AUTHOR]