Traffic information impact on the optimisation of fuel consumption and emissions in an urban driving scenario

Traffic jams are one of the main causes of city pollution and significantly impact the economic cost of transportation. Context awareness by the traffic players may be key to improving the current control strategies and optimising traffic flow. This study investigates the effect of information availability through vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) connectivity in an urban real-driving route. An optimal control problem (OCP) is formulated to create speed advisory profiles, and it is solved using dynamic programming (DP) to provide the global optimal solution. Experimental engine tests have been used to characterise the fuel consumption and emissions of the engine, while traffic sensors around the city of Valencia have been used to reproduce realistic urban mobility using the traffic simulation software SUMO. The paper quantifies the impact of traffic information on vehicle fuel consumption and emissions. Under normal traffic conditions and assuming total access to the traffic information, the DP algorithm can reduce almost 60% on average the fuel consumption compared to normal driving behaviour provided by the default car-following model of SUMO.