SIMPLE QUEUEING MODEL APPLIED TO THE CITY OF PORTLAND

Patrice M. Simon (), Jörg Esser () and Kai Nagel ()
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Patrice M. Simon: Los Alamos National Laboratory and Santa Fe Institute, 1399 Hyde Park Rd, Santa Fe NM 87501, USA
Jörg Esser: Los Alamos National Laboratory and Santa Fe Institute, 1399 Hyde Park Rd, Santa Fe NM 87501, USA
Kai Nagel: Los Alamos National Laboratory and Santa Fe Institute, 1399 Hyde Park Rd, Santa Fe NM 87501, USA

International Journal of Modern Physics C (IJMPC), 1999, vol. 10, issue 05, 941-960

Abstract: We use a simple traffic micro-simulation model based on queueing dynamics as 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 release more vehicles per second than is possible according to their capacity. This leads to queue built-up if demand exceeds capacity. Links also have a storage capacity, which means that once a link is full, vehicles that want to enter the link need to wait. This leads to queue spill-back through the network. The model is compatible with route-plan-based approaches such as TRANSIMS, where each vehicle attempts to follow its pre-computed path. Yet, both the data requirements and the computational requirements are considerably 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 home-to-work destination assignment which generates about half a million trips for the morning peak. Route assignment is done by iterative feedback between micro-simulation and router. An iterative solution of the route assignment for the above problem can be achieved within about half a day of computing time on a desktop workstation. We compare results with field data and with results of traditional assignment runs by the Portland Metropolitan Planning Organization.Thus, with a model such as this one, it is possible to use a dynamic, activities-based approach to transportation simulation (such as in TRANSIMS) with affordable data and hardware. This should enable systematic research about the coupling of demand generation, route assignment, and micro-simulation output.

Keywords: Large scale transportation simulations; Traffic simulations; Transportation planning; Queueing models (search for similar items in EconPapers)
Date: 1999
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DOI: 10.1142/S0129183199000747

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