A study on gap acceptance in roundabouts in Sweden

Abstract

Traffic levels and congestion is rising both globally and in Sweden and is causing issues related to time loss, health problems and pollution. Traffic analysis software is a powerful tool to help planning and optimization of the road network to tackle these issues and the accuracy of their predictions is of a vital importance. This thesis aims to examine the use of a new method to collect data regarding gap acceptance parameters. The method consisted of recording video footage of three different roundabouts in the Greater Gothenburg area and implementing the AI-based video analysis software GoodVision to extract data from the recordings to then analyze the gap acceptance parameters. Raff’s method was used to calculate critical gap values at all roundabouts at 3.68 seconds, 3.81 seconds and 3.86 seconds respectively. Making up a combined critical gap value of 3.73 seconds. Comparative analysis was performed by using the traffic analysis software SIDRA Intersection and measuring the results of using the critical gap value based on the data collected at the location and the standardized critical gap value in the program. The results showed substantial differences, one scenario displayed an increased capacity reaching up to 25% when using critical gap values calculated from local data. Differences in results of this magnitude greatly affects the decision making in traffic planning and could ultimately be the difference maker between investing in new infrastructure or not. Parameters affecting why a gap is accepted or rejected were analyzed by implementing machine learning classification algorithms. The results showed that the individual driving behavior of each driver had a higher impact on the decision of accepting or rejecting a gap than roundabout geometry or vehicle type in this thesis.