Dušan Teodorovic. Swarm Intelligence systems for transportation engineering: principles and applications. Transportation Research, 2008.
This is one of the most comprehensive articles I have found that attempts to connect swarm intelligence heuristics to transportation systems. Most of the article is dedicated to introducing four multi-agent systems that leverage information sharing models to optimize search techniques, and I will recap those here.
Ant Colony Optimization (ACO)
Ants leave pheromone trails, and an ant will use the strength of the signal to weight their choice of path, as well-trod paths traveled by ants heading to a food source have a stronger pheromone signal. Computational applications of this general approach can be observed in the ant system, ant colony system, and the max-min ant system. The ant system has been used to solve the traveling salesman problem (TSP), and the article includes details on the algorithm. The author also shares his research on a model that blends ACO with fuzzy logic, which he calls the Fuzzy Ant System (FAS). This approach takes into account gradations like visibility and pheromone intensity. FAS seems to give you more knobs to turn to sensitize your model. The author goes on to spell out applications of ACO in transportation.
Particle Swarm Optimization (PSO)
All the birds ("particles") start out flying randomly in search of food. They keep track of the best fitness value they have achieved thus far (pbest), while also memorizing the best fitness value of any other particle (gbest). In each moment, the particles adjust their flying to take into account pbest and gbest. Two promising transportation examples are given - PSO in highway incident detection (Srinivasan et al. 2003) and an application in a vehicle routing problem with time windows (Zhu et al. 2006).
Bee Colony Optimization (BCO)
"Bees incrementally add solution components to the current partial solution and communicate directly to generate feasible solutions". The artificial bees in this model either fly forward (exploration) or backward (back to the hive to participate in the decision-making process). They then go back out to reinforce viable partial paths.
Stochastic Diffusion Search (SDS)
I'm not sure why the author included this heuristic, since they didn't present the algorithm or any transportation applications. Maybe four seemed like a better number than three?