An Adaptive Control Method of Traffic Signal-Timing under Emergency Situations for Smart Cities

Traffic management is one of the most challenging issues in smart cities. Many large cities are facing the traffic congestion problem. This congestion becomes critical when an emergency vehicle goes on a mission. In such a scenario, delays are not tolerable. Current methods only focus on emergency vehicles arriving at their destination with minimum delay. However, ordinary vehicles have to experience a significant trip delay in these scenarios. This paper presents a Fuzzy rule-based system for traffic signal-timing called STC that tackle the problem of trip delay for emergency vehicles. This method formulates the knowledge of an expert to rules and takes advantage of fuzzy sets to have linguistic parameters such as estimated arrival time and current traffic as inputs. The outcome is the signal-timing process that reduces traffic load along the emergency vehicles routes. Although this method highly focuses on emergency vehicles to pass intersections quickly due to critical conditions, ordinary vehicles will not experience large delays. Our experiment results show that STC has 12% reduction in delay for emergency vehicles compared with FLCGA when the number of emergency vehicles is increased and achieves up to 18.5% reduction in delay for ordinary vehicles compared with ATLC when the number of ordinary vehicles is increased.