چكيده به لاتين
Driving anger and aggressive behavior of drivers is one of the most important issues that has been researched in the behavioral and social sciences in this field, but in transportation engineering, especially in the case of taxi drivers, it has been less addressed. The reason for the importance of this issue is its direct relation with the violations of drivers and the safety of users on the streets and roads. In addition, taxi drivers spend many hours a day on the inner city routs, which has led to more than other users being affected by factors that lead to anger and rage. This doubles the importance of examining their behavior. Analyzing the aggressive behavior of drivers requires identifying and evaluating the factors and conditions affecting their occurrence. So far, the information collected in determining these factors has been through valid questionnaires designed for this purpose and completed by the drivers themselves. However, the collection and completion of these questionnaires has been done in different and varied ways. This method itself has limitations on the accuracy of the information and answers. On the other hand, in Iran, modeling and predicting the factors affecting the aggressive behavior of taxi drivers using logistic regression statistical models has not been addressed as it should be. Therefore, first in this thesis, by designing an application, information related to the aggressive behavior of drivers, including factors and reactions performed by the driver, is tried to pick up by a passenger who is aware of the problem and from his point of view, without informing the driver about collecting this information, online during the trip. Then, by categorizing the occurrences caused by the anger of taxi drivers into 8 categories, which are; No aggressive and calm reaction, sudden lane change with increasing speed, sudden braking in not maintaining proper safety distance, driver behavioral and protesting reactions, sudden braking and lane changing by increasing speed, sudden lane change with behavioral reactions and protest, sudden braking with behavioral and protesting reactions and sudden braking and lane changing combined with behavioral and protesting reactions, to determine and predict the factors affecting the aggressive behavior of taxi drivers using multiple logistic regression method. Among the most important factors identified are traffic jams caused by double parking of other vehicles on the route, sudden change of direction or lane of motorcyclists and other types of vehicles, sudden turn and much slower movement of the front vehicle compared to the flow of traffic.
