Simple queueing model applied to the city of Portland

We use a simple traffic micro-simulation model based on queueing dynamics a s introduced by Gawron [IJMPC, 9(3):393, 1998] in order to simulate traffic in Portland/Oregon. Links have a flow capacity, that is, they do not relea se more vehicles per second than is possible according to their capacity. T his leads to queue built-up if demand exceeds capacity. Links also have a s torage capacity, which means that once a link is full, vehicles that want t o enter the link need to wait. This leads to queue spill-back through the n etwork. The model is compatible with route-plan-based approaches such as TR ANSIMS, where each vehicle attempts to follow its pre-computed path. Yet, b oth the data requirements and the computational requirements are considerab ly lower than for the full TRANSIMS microsimulation. Indeed, the model uses standard emme/2 network data, and runs about eight times faster than real time with more than 100 000 vehicles simultaneously in the simulation on a single Pentium-type CPU. We derive the model's fundamental diagrams and explain. it. The simulation is used to simulate traffic on the emme/2 network of the Portland (Oregon) metropolitan region (20 000 links). Demand is generated by a simplified hom e-to-work destination assignment which generates about half a million trips for the morning peak. Route assignment is done by iterative feedback betwe en micro-simulation and router. An iterative solution of the route assignme nt for the above problem can be achieved within about half a day of computi ng time on a desktop workstation. We compare results with field data and wi th results of traditional assignment runs by the Portland Metropolitan Plan ning Organization. Thus, with a model such as this one, it is possible to use a dynamic, activ ities-based approach to transportation simulation (such as in TRANSIMS) wit h affordable data and hardware. This should enable systematic research abou t the coupling of demand generation, route assignment, and micro-simulation output.