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The worst traffic jam known to man was probably the one that occurred last Au-gust in China. Dubbed the “Mother of All Traffic Jams” by the press, vehicles crawledin stop-and-go traffic for more than 9 days over a stretch of the Beijing Tibet Ex-pressway. It was anything but “express”—the queues of vehicles stretched to over 60miles. What is even more amazing was the quick appearance, and disappearance, ofthe congestion. It was as if someone had flipped a switch.

This cautionary tale should convince you that modeling traffic is important. It isimportant to understand what causes traffic jams, and how to best deal with themwhen they occur.

The first article in this issue’s Survey and Review section discusses in detail effortsto model traffic. In fact the article goes beyond traffic models, which are basically one-dimensional, to talk about modeling the movement of crowds and swarms, which aretwo- and three-dimensional. The authors describe three main approaches to modelingtraffic and crowds. Since the players in a traffic flow are vehicles, one could considerwriting down equations that govern the interactions of these individual “points.” Onthe other hand, it might be more efficient to view traffic as a “blob” and treat it asa continuum. This second approach uses concepts from gas dynamics. Yet anotherapproach is to incorporate the probabilistic nature of traffic flow at a macroscopicscale and apply ideas from kinetic theory.

The paper is up-to-date and self-contained. The authors, Nicola Bellomo andChristian Dogbe, make the material highly accessible. They also provide critical reviewsof the different modeling efforts.

The second article in this issue concerns robust optimization. This is an importantand rapidly developing research area in optimization. The basic premise of robustoptimization is to find the best solution to an optimization problem when you areuncertain about the parameters describing the problem. Such problems occur in manyapplications. For example, when designing a bridge, the uncertainties could be theloads from the vehicles going over it, the wind conditions, the added loads from heavysnow, and the rare occurrence of earthquakes. One is interested in maximizing thestrength of the bridge while minimizing the cost under these uncertain conditions.

The article, by Dimitris Bertsimas, David Brown, and Constantine Caramanis,offers a gentle introduction to the subject of robust optimization. It leads the readerinto the key ideas of the area through an example from financial portfolio optimization,where uncertainty abounds. From there, the paper goes deeper into the mathematicalstructure of robust optimization problems. Its relation to stochastic programming,where statistics of the uncertainties are available and used, is also explored.

The authors also provide an excellent exposition to robust adaptable optimizationwhere the problem involves multistage decisions. The paper contains a fine portfolioof applications of robust optimization. The list of examples is long, and the domains inwhich they arise very diverse. The paper ends by offering a survey of current researchdirections.

Both papers represent valuable resources to the mathematical sciences. Theycan be read in depth to gain a full understanding of the intricacies and mathematicalrichness of these two research areas. The casual reader can also quickly get a view ofthe research landscape of these fields.

Fadil SantosaSection Editor

santosa@math.umn.edu

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