The number of vehicles on the road keeps rising every day. This increasing demand for mobility puts more and more stress on the supply side of traffic: the road infrastructure. When the available capacity of the road network can no longer meet the demand put on it by the road users, traffic jams occur and time is wasted. Information and Communication Technology already plays an important role in route guidance. By enabling fast information exchange modern day technology allows advanced traveler information systems to better inform thenbsp;users. This additional information should help road users make informed decisions when choosing their routes. Traffic systems, with the large number of vehicles and wide spread traffic network, pose a number of challenges. Providing all road users with uniquely tailored information regarding their travel plans is made difficult by the scale and dynamic nature of traffic. Delegate multiagent systems offer a set of patterns that can facilitate the design of multiagent systems. The patterns help by encapsulating the complex interactions needed to build coordination and control applications capable of coordinating large scale systems such as traffic. Delegate multiagent systems have been applied succesfully in other problem domainsnbsp;as manufacturing control and service composition. Traffic systems, with their scale and dynamism offer some challenges delegate multiagent systems is unable to deal with. The scale of the traffic network is too large to explore using delegate multiagent systems. The uncertainty and dynamism of traffic makes the reservation based mechanism used by delegate multiagent too rigid. These characteristics of traffic make it necessary tonbsp;the patterns provided bynbsp;multiagentnbsp;thesis describes a feasibility study of delegate multiagent systems for trafficnbsp;To test the applicability of delegate multiagent systems in traffic, AntTIS, an anticipatory traveler information systemnbsp;developed. The goal of AntTIS is to help road users make betternbsp;decisions that lead to shorter travel times,nbsp;also decisions that can delay congestion buildups. AntTIS relies on intention based traffic predictions to guide its users. It collects information about the route its users intend to follow and uses that information to predict the traffic conditions those users will experience while following that route, or an alternative route. To collect this intention information and to distribute the traffic predictions back to all relevant road users, the AntTIS system uses anbsp;called delegate multiagent systems. Delegate multiagent systems facilitatenbsp;design and development of a multiagent system by encapsulating certain interactions and removing the top agents from those responsibilities. The traffic predictions generated by AntTIS are based on the information shared by its users. This information from the community of users is combined with artificial neural networks to predictnbsp;time it takes anbsp;to traverse a certain link. The AntTIS system autonomously maintains both the community provided information and the artificial neuralnbsp;that help model the links in the traffic network. AntTIS is evaluated using extensive traffic simulations. Scenarios of both urban andnbsp;size are simulated to assess the quality of the routing advice given to the road users. The evaluation shows that AntTIS, can adapt to traffic patterns and cannbsp;road users find good routes, especially in conditions where the road users lacknbsp;knowledge about what trafficnbsp;to expect. Innbsp;of unusual traffic patterns or disturbances in the traffic network, routes provided by AntTIS are better than the routes users would choosenbsp;on prior knowledge.