applications such as Google Maps 2 and Waze $^{3}$, which provide real time traffic information on demand. A key element of real-time information systems consists of sensors [32, 40]. Once traffic states are detected, broadcasting or making them available is relatively straightforward. Since there are different types of sensors (fixed, mobile), sensor integration [109] is a relevant problem to obtain useful information. Intervehicle communication can provide useful real-time local information, which can be exploited to adapt to dynamic traffic states and improve traffic flow [86]. Real-time information for public transportation systems can also help passengers to adapt their routes more efficiently, and even their behavior [66]. In general, location-based services offer a broad application potential [110]. 3.2 Logistics Biologistics [73] notes that the organization, coordination and optimization of various mate- rial flows is not restricted to artificial systems, but that living systems also have to deal with material flows. Moreover, living systems can handle material flows efficiently, adaptively, robustly, and learn from past experiences. Thus, with biological inspiration, using princi- ples of modularity, self-assembly, self-organization, and decentralized coordination, artificial logistic systems can be designed that can adapt efficiently to changes of demand. A drawback to traditional approaches in logistics is that the supplies and demands for different goods are dynamic and unpredictable. This demands approaches where systems can adapt to changing demands at the same scale at which changes occur. For example, swarm intelligence [27, 85, 125] has been applied to several problems in logistics [122]. Computationally, algorithms inspired by swarms or neurons are equivalent [55], since they function at multiple scales, allowing them to compute solutions to problems at a faster $^{2}$http://maps.google.com $^{3}$http://www.waze.com 12