Traffic congestion is a nuisance for urban centers. Traditional congestion management policies, like road pricing and road expansion, increase urban sprawl and inequity between road users. Recent developments in Information and Communication Technologies (ICT) enabled Advanced Travel Information Services (ATIS) that provide road users with real-time traffic information. Road users can benefit from this information by avoiding congestion. Travel information may hamper the traffic condition, as perfect information held by rational road users leads to a state known as User Equilibrium (UE), where travel times on all routes between an origin and a destination are equal. UE is not overlapping with the concept of System Optimum (SO)where the aggregate travel time of all road users is minimized. Achieving SO was long considered impossible, as certain road users must take a slower route than they would under UE in contradiction to the rational character of road users. Use of prescriptive travel information that leads to SO can theoretically work in repeated interactions-if road users know that in order to guarantee a shorter travel time on average, once in a while they have to sacrifice and experience a longer trip, they might agree to comply with the prescriptive travel information recommendation. We carry out a behavioral economic experiment that examines the efficiency of such prescriptive travel information in a binary road network. The participants interact over many rounds in which they are provided with basic travel information and with prescriptive travel information, and are rewarded for travel time savings. The experiment includes two treatments-one where the participants are only provided with prescriptive information, and one where participants are penalized for not complying with the recommendation of the prescriptive information, and rewarded for complying. Preliminary results show that the effect of punishment and rewards is significant, as opposed to having only pure information. The results are compared to a computer simulation that examines different decision making heuristics in the same scenario. This research highlights the necessary and sufficient conditions for using prescriptive travel information as a measure of reducing congestion.