ERSA Congress, Dortmund, 2002
Abstract - In a multi-agent transportation simulation, each traveler is represented individually. Such a simulation consists of at least the following modules: (i) Activity generation. For each traveler in the simulation, a complete 24-hour day-plan is generated, with each major activity (sleep, eat, work, shop, drink beer), their times, and their locations. (ii) Modal and route choice. For each traveler in the simulation, the mode of transportation and the actual routes are computed. (iii) The Traffic simulation itself. In this module, the travelers are moved through the system, via the transportation modes they have chosen. (iv) Learning and feedback. In order to find solutions which are consistent between the modules, a relaxation technique is used. This technique has similarities to day-to-day human learning and can also be interpreted that way. - Besides, one needs input data, such data of the road network, or (synthetic) populations. In the future, further modules need to be added, such as for housing and land use, or for freight traffic.
Using advanced computational methods, in particular parallel computing, it is now possible to do this for large metropolitan areas with 10 million inhabitants or more. We are currently working on such a simulation of all of Switzerland. Our focus is on a computationally efficient implementation of the agent-based representation, which means that we in fact represent each agent with an individual set of plans as explained above. We use a database to store the agent's strategies, then load them into the simulation modules as required, and feed back individual performance measures into the database. This approach allows that additional modules can be coupled easily, and without destroying computational performance.