Abstract:
Traffic incidents can occur due to both human errors and the inadequacy of road networks. These incidents can cause not only material damage but also loss of life. In case of an incident, it causes the vehicles in the traffic network to stay on the road longer and consume more fuel. The increase in fuel consumption increases the emission of CO2 (Carbon Dioxide), which is an effect of climate change. To reduce the negative effects of incidents on the environment, incident detection, and real-time traffic management methods are important. In this thesis, an uninterrupted road network was created utilizing SUMO traffic simulation to evaluate the environmental effects of incidents. This road network was evaluated over different scenarios with the integration of incident detection algorithms which are California and Standard Normal Deviation and real- time traffic management algorithms which are VSL and LCS. Environmental results were obtained by analyzing these different scenarios. Two types of vehicles were used: human- driven and connected autonomous vehicles. 11 different percentages of autonomous vehicles in increments of 10 from 0 to 100 were based on the research. It was seen that the increase in the use of connected autonomous vehicles in countries such as Turkey, which provide their electricity needs from non renewable energy sources, harms the environment. In the countries that provide their energy sources mostly from non-renewable sources, the scenario with the least CO2 emissions in the CAL-LCS and CAL-VSL scenarios was achieved in conditions with 40% connected autonomous vehicle traffic. Finally, a relationship of up to 80% was found between CO2 and speeds two by using KNN and Decision Tree Regressor models.
