Paper Info
Title
Fast-Forwarding Agent States to Accelerate Microscopic Traffic Simulations.
Abstract
Traditionally, the model time in agent-based simulations is advanced in fixed time steps. However, a purely time-stepped execution is inefficient in situations where the states of individual agents are independent of other agents and thus easily predictable far into the simulated future. In this work, we propose a method to accelerate microscopic traffic simulations based on identifying independence among agent state updates. Instead of iteratively updating an agent's state throughout a sequence of time steps, a computationally inexpensive "fast-forward" function advances the agent's state to the time of its earliest possible interaction with other agents. To demonstrate the approach in practice, we present an algorithm to efficiently determine intervals of independence in microscopic traffic simulations and derive a fast-forward function for the popular Intelligent Driver Model (IDM). In contrast to existing acceleration approaches based on reducing the level of model detail, our approach retains the microscopic nature of the simulation. A performance evaluation is performed in a synthetic scenario and on the road network of the city of Singapore. At low traffic densities, we achieved a speedup of up to 2.8, whereas at the highest considered densities, only few opportunities for fast-forwarding could be identified. The algorithm parameters can be tuned to control the overhead of the approach.
Year
DOI
Venue
2018
10.1145/3200921.3200923
SIGSIM-PADS '18: SIGSIM Principles of Advanced Discrete Simulation Rome Italy May, 2018
Field
DocType
ISBN
Fixed time,Computer science,Traffic simulation,Real-time computing,Intelligent driver model,Acceleration,Distributed computing,Speedup
Conference
978-1-4503-5092-1
Citations 
PageRank 
References 
1
0.34
13
Authors
5
Name
Order
Citations
PageRank
Philipp Andelfinger1458.15
Yadong Xu2636.49
Wentong Cai31928197.81
David Eckhoff418920.07
Alois Knoll Knoll51700271.32