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Nowadays, the performance analysis and evaluation of public transportation systems have great importance in traffic engineering science. So far, the bus system has not been very effective in some cities in Iran, and many management approaches such as the allocation of special lanes and regular bus scheduling, which are needed to increase the efficiency of this system, have not been sufficiently considered. The purpose of the present study is to optimize the delay of the signalized intersection of bus lane and investigate the factors affecting the urban bus usage by citizens in public transportation of Rasht city and especially their satisfaction. Therefore, the intersection delay was optimized by gathering the traffic volume data in peak hour time of a signalized intersection along the bus lane and using machine learning methods. In addition, by collecting two different questionnaires, taking 84 samples (first questionnaire) and 374 samples (second questionnaire), the satisfaction of citizens and business people on the boundary of the bus lane was considered. The results indicated that about 95% of the businesses around this route believe that the construction of the bus lane led to a decrease in the income of more than 110 dollars per month. Further to this, despite the lack of facilities, poorly designed routes, and lack of the bus system fleet, the bus lane of Imam Khomeini had a high degree of satisfaction among the citizens. The result of various models showed that the adaptive network-based fuzzy inference system (ANFIS) had the highest R2 and the lowest amount of root mean square error (RMSE). In fact, this model had a better performance to predict and optimize the delay of signalized intersection than the fuzzy model. The optimum amount of intersection delay was determined as 56 seconds. With this value, the delay of bus movements in the bus lane had a higher possibility of being reduced.

The best way to deal with the freezing of the road surfaces is to use deicers, especially in cold areas. The presence of moisture causes various stresses in the pavement and reduces the strength of mixtures. Using anti-stripping agents can decrease the moisture sensitivity of asphalt mixtures. Researchers have evaluated the impact of different deicers on the moisture sensitivity of asphalt mixtures. However, fewer studies have been conducted on the effect of these materials on fatigue failure and thermodynamic parameters of asphalt mixtures. Moreover, fewer studies have been performed to find the exact optimum amount of additives for maximizing the two objectives of tensile strength ratio (TSR) and fatigue life ratio (NFR) concurrently in moisture and fatigue damages. So in this research, the moisture sensitivity and fatigue failure of asphalt mixtures under the influence of different deicers, including calcium magnesium acetate (CMA), potassium acetate (PA), and sodium chloride (NaCl), were investigated using nanohydrated lime (NHL) as an anti-stripping agent. The surface free energy (SFE) of materials and the permeability of asphalt mixtures were examined, and a boiling water test was applied. Finally, the prediction models of multivariate regression (MVR), group method of data handling (GMDH), and genetic programming (GP) were provided to obtain optimum additive percentage with two objectives of TSR and NFR. The results showed that GP had a higher R-value than the 2 other methods such that the R-value of GP for TSR and NFR was 98.8 % and 99.8 %, respectively. The optimization results showed that 1.17 %, 1.34 %, 0.87 %, 1.21 %, and 1.06 % NHL, respectively, were the best optimum values to maximize the TSR and NFR simultaneously in all samples and samples saturated in water, CMA, NaCl, and PA solutions.