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.
Detecting and improving traffic accident hotspots have always been considered significantly important to ensure public transport safety. Additionally, the identification of accident-prone segments and hotspots can also assist in determining the most optimal station locations for rescue teams and emergency responses to reduce the overall casualties in urban public transport-related accidents. This article analyses the various effects of exclusive bus lanes in traffic accident hotspots in Rasht, the capital of Guilan province. The research included eight distinguished road networks with bus exclusive transit lanes, and the appointed traffic accident hotspots have been identified using IG (intensity index), EPDO (equivalent property damage only), and SI (severity index) criteria. Moreover, the obtained results of the current article indicate a higher frequency of accident-prone areas before 2015 (when the exclusive bus lanes were initially established) compared to the following years, concluding that the establishment of exclusive bus lanes can potentially reduce the overall traffic accident-prone segments.
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