This work develops and tests a model that estimates the societal cost of travel by automotive vehicles in real time urban traffic conditions, as a function of mean velocity and acceleration. The research goes beyond the existing literature and studies the societal cost of travel in urban road networks to support real time urban traffic management via V2I systems. Travel time, traffic accidents, fuel consumption and pollutant emissions are the four factors that affect the societal cost; and thus environmental and economic facets of society. The societal cost reduction was studied and optimal values of mean velocity and acceleration of vehicles were estimated to minimize the cost. Experimental, field and simulation data were used. The methodology was applied to two traffic case studies at an urban coastal road in Patras city, Greece. The first case refers to the estimation of the mean velocity value that results in the minimum societal cost of travel, when controlling the network to achieve a constant-speed mode. The second case refers to the estimation of the deceleration values resulting in the minimum societal cost of travel, when a network is operating in a constant-speed mode and a red light is going to be imposed. Model integration into V2I system is tested via simulation.