This paper describes one possible implementation of a large-scale agent-based simulation package for regional planning. As was repeatedly pointed out, the approach is modular and extensible. In order to test the modularity, replacing one or more modules by alternative ones is desirable. In the following, this is discussed on a module-by-module basis.
The queue simulation has its limitations, for example with respect to complicated intersections, inhomogeneous vehicle fleets, queue dissolution, interaction between different modes of transportation, etc. These limitations will be difficult or impossible to remove within the method of the queue simulation approach. Therefore it seems desirable to move beyond the queue simulation to a more realistic traffic simulation. Besides being more realistic, this simulation should fulfil the following criteria in order to be consistent with our approach: It should be able to process travelers with individual plans; and it should be computationally fast. There are currently few traffic simulations which fulfill these criteria simultaneously. The TRANSIMS microsimulation is one of them (transims.tsasa.lanl.gov; www.transims.net). We attempted to use it in the past years, and indeed some preliminary results were based on it (Voellmy et al., forthcoming; Raney et al., 2002). We however stopped using it because it turned out to be rather difficult to obtain the necessary input data, most importantly lane connectivities across intersections and signal plans. There are automatic generation methods for these attributes from static assignment networks, and we intend to evaluate those. Nevertheless, some aspects will take quite some time and considerable manual work.
Our current router computes car-only fastest paths, without regard for alternative cost functions (such as monetary cost, familiarity, scenic beauty, etc.), and without regard for alternative modes. Tests with the multi-modal TRANSIMS router were unsuccessful, because of at least one serious bug. (This refers to the router of TRANSIMS-1.0 from fall 1999. Earlier TRANSIMS results were based on a different router. Later versions of TRANSIMS supposedly will have that problem fixed, but are currently not available.) Some of our work investigates how individualized partial knowledge of the road network (mental map) influences route choice.
The above results use traditional origin-destination tables for demand generation. We intend to move our investigations to activity-based demand generation. One method will be based on discrete choice theory, one on genetic algorithms.
The use of the agent database in the feedback mechanism works well, but needs tuning. Both computational speed and the learning behavior of the system are an issue. The computational speed issues are addressed via a combimation of database performance tuning and consolidating the current script-based approach into one program. The methodological questions will be addressed via an examination of established learning methods (such as best reply or reinforcement learning).
In addition, a grave shortcoming or the current method is that replanning can happen only over night. Work is under way to improve this situation via an online coupling between modules, which will allow within-day replanning (Gloor, 2001). We explicitely want to avoid coupling the modules via standard subroutine/library calls, since this both violates the modular approach idea and efficiency considerations for parallel computing.