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Open Access Publications from the University of California

Analysis of Bottleneck Traffic Capacity Drop using Aimsun Simulation

  • Author(s): Chong, Aaron
  • Advisor(s): Jin, Wenlong
  • et al.

Capacity must be understood to mitigate congestion and improve conditions on constrained road networks. Analytical and empirical studies have been used to examine capacity drop, but there are weaknesses for both; empirical work suffers from insufficient data and non-variable parameters while analytical research stems from simplified assumptions on the aggregate level. Simulation is often considered inaccurate, but can model microscopic properties and modify factors not possible with the other two. Therefore, this research serves to bridge the gap between analytical and empirical work by analyzing factors that affect capacity drop through microscopic simulation.

A short review was presented on the Gipps car following model and on Aimsun modelling. Simulation data for an activated bottleneck was obtained and plot against the triangular fundamental diagram to find initial capacity. Cumulative count curves were transformed using an oblique coordinate system to accurately observe capacity and determine average discharge flow-rate. Furthermore, speed profiles were obtained to assist in verifying system properties.

Capacity drop features were reproduced in simulation: a reduction in flow-rate was observed when upstream demand was higher than downstream capacity and discharge flow-rate was identified as upstream demand when less than downstream capacity. The difference between the capacity and discharge rate yielded a capacity drop ratio of 5.52\%. Speed profiles were generated to observe near stationary patterns as vehicles approached the bottleneck. Analytical and simulated results demonstrated that acceleration affects capacity to a higher degree, but merge distance is closer in magnitude between the two cases.

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