Bryan Raney

Towards Truly Agent-Based Traffic and Mobility Simulations

Traveling is necessary and desirable; yet, it imposes external costs on other people. Quantitative methods help finding a balance. Multi-agent simulations seem an obvious possibility here. A real world traffic simulation consists of many modules, all requiring different expertise. The paper discusses how such modules can be coupled to a complete simulation system, how such a system can be made fast enough to deal with real-world sizes (several millions of travelers), and how agent memory can be introduced. A real-world case study is presented, which says that multi-agent methods for traffic are mature enough to be used alongside existing methods. Finally, some outlook into the near future is given.

Large-Scale Multi-Agent Simulations for Transportation Applications

In many transportation simulation applications including intelligent transportation systems (ITS), behavioral responses of individual travelers are important. This implies that simulating individual travelers directly may be useful. Such a microscopic simulation, consisting of many intelligent particles (= agents), is an example of a multi-agent simulation. For ITS applications, it would be useful to simulate large metropolitan areas, with ten million travelers or more. Indeed, when using parallel computing and efficient implementations, multi-agent simulations of transportation systems of that size are feasible, with computational speeds of up to 300 times faster than real time. It is also possible to efficiently implement the simulation of day-to-day agent-based learning, and it is possible to make this implementation modular and essentially “plug-and-play.” Unfortunately, these techniques are not immediately applicable for within-day replanning, which would be paramount for ITS. Alternative techniques, which allow within-day replanning also for large scenarios, are discussed.

Towards a Microscopic Traffic Simulation of All of Switzerland

Multi-agent transportation simulations are rule-based. The fact that such simulations do not vectorize means that the recent move to distributed computing architectures results in an explosion of computing capabilities of multiagent simulations. This paper describes the general modules which are necessary for transportation planning simulations, reports the status of an implementation of such a simulation for all of Switzerland, and gives computational performance numbers.

Complex Systems Applications for Transportation Planning

A possible goal for transportation and regional planning is to design the transportation or regional systems so that the people who use them will be happy. Yet, happiness is difficult to define in a way that is useful for engineering. For most people, a certain level of economic performance is probably a pre-requisite for happiness, and thus economic indicators are important factors in the design. Other important indicators might include noise, pollution, safety, access to a variety of destinations, or even something as intangible as beauty. These indicators may have different impacts on the happiness of different users of the above-mentioned systems, so the design of these systems should somehow account for the individuality of the users.