Analysis and Modelling of Road Traffic Using SUMO to Optimize the Arrival Time of Emergency Vehicles

Abstract

Traffic simulation tools are used by city planners and traffic professionals over the years for modelling and analysis of existing and future infrastructural or policy implementations. There are numerous studies on emergency vehicle (EV) prioritization in cities all over the world, but every area is unique and requires the data collection and simulation to be done separately. In this case, the focus area is the Mörfelder Landstraße in Frankfurt am Main, Germany, one of the busiest streets in this city. Thestudy illustrates demand modelling, simulation and evaluation of a traffic improvement strategy for EVs. Vehicular traffic such as passenger cars and trams are simulated microscopically. To perform accurate traffic simulation, input data quality assurance and cleansing of Master Data is required. Therefore, the data is adapted to reproduce the real-world scenario and transformed into the readable format for the simulation model. Vehicular demand is calibrated by traffic count data provided by the Frankfurt Traffic Department. To model road traffic and road network, origin destination matrices using the Gravity Mathematical Model and Open Street Maps are generated, respectively. This process is time-consuming and requires effort. However, this process is critical to get realistic results. In the next step, the road traffic is simulated using SUMO (Simulation of Urban mobility). Finally, EV relevant key performance indicators (KPIs): total trip time and total delay time are derived from simulations. The real-world scenario is compared with five alternative scenarios. The comparison of the KPIs revealed that the real-world scenario results in longer travel times compared to the EV-prioritization scenario. In the least case, the overall travel times for EV has decreased significantly and, as we know, in the case of EVs, even a few seconds saved could prove crucial for a person in need.