The trend toward sustainable city development is associated with intelligent transportation systems (ITS). Automation, efficiency, safety, security, and cost-effectiveness are critical factors in establishing each aspect of a smart city. Real-time data obtained from ITS play an essential role in improving the level of service of road segments, enhancing road safety, and supporting road users with road circumstances information. Travel time information is applicable in travel time maps, decision makings for traffic congestion, dynamic pricing of the network, emergency relief services, traffic flow monitoring, traffic jams management, and air quality analysis. Travel time on a road segment highly depends on geometrical specifications, environmental and weather conditions, traffic flow, and driving behavior. Due to specific driving behavior and road conditions, the above parameters are not essentially applicable in another region. The present research uses the data collected from loop detectors and License Plate Recognition (LPR) systems to develop a Bureau of Public Roads (BPR) model for Iran’s freeway network (Tehran-Qom Freeway). Because of the large amount of data, the SQL server program was used for creating and organizing the database and the BPR model was calibrated using SPSS statistical software. The results of the BPR model were evaluated with an ANOVA test, indicating that the derived model can estimate the travel time at freeway sections with a %5.2 error for the volume-to-capacity ratio (V/C) of less than 0.8.

During the last decades, environmental crises through energy consumption and economic growth were noticed as a growing concern among researchers. The industrial sector is the main part of economic growth in each country using conservative energy and emitting carbon dioxide that causes global warming. The Paris agreement and Kyoto protocol were two agreements to prevent governments from emitting CO2 freely. Purpose of this research was to investigate the cement industry, steel industry, and automobile industry products’ effects on CO2 emissions in Iran and to rank them according to their measured effects on CO2 emissions. The methodology used in this study was to estimate equations with CO2 emissions as a dependent variable and cement, steel, and automobile industries’ products with ordinary least squares (OLS) and generalized moment method (GMM) approaches. Stationary, Johansen cointegration, Durbin-Watson, Breusch-Godfrey, Chow breakpoint, and normal residual tests were checked. In-sample forecasting was implemented to check the precision of the estimation and an updated ranking was reported as a final result to consider which industry has affected CO2 emissions more than the others per unit of production cost. In conclusion, the cement industry, steel industry, and automobile industry had the most positive effects on CO2 emissions, respectively. This result is suitable to prioritize the industries for enhancing green technology and optimizing industrial production for a more sustainable economy.