Real time traffic management using connected vehicles: A case study on D100 highway

Traffic congestion is one of the most crucial issue that have a negative impact on the economy and the environment of a city. The negative impacts of congestion become stronger with the increasing population and vehicle usage. Here, the metropolitan areas are the most problematic and sensitive areas by considering that they contain most of the population within itself. Even traffic congestion in a local region may be felt throughout the network and; therefore, it might deteriorate the overall traffic conditions of the road network of a city which makes metropolitans quite sensitive. The scale of this impact gets especially grander in overpopulated cities; thus, it becomes even a more critical issue. In this thesis, a real time traffic management method using connected vehicles is tested in a simulation environment with synthetic and D 100 Highway real traffic data. To test this method, a 5.4 km length network with 3 lanes was built in the SUMO (Simulation of Urban Mobility) environment. An incident was generated in each run to observe its impact on the traffic network. By changing the incident and the parameters of the connected vehicles, 189 different scenarios were tested. The results of these 189 scenarios show that the introduction of connected vehicles causes an increase up to 20.35% in terms of mean speed, a decrease up to 32.54% in terms of mean density and an increase up to 2.45% in terms of mean flow for the local system before the incident. Based on the 189 scenarios, the connected vehicles provide the maximum mean speed increase and mean density decrease when they control traffic flow 1750 meters behind the incident location.