Improving Precision and Reducing Runtime of Microscopic Traffic Simulators through Stratified Sampling

Khewal Bhupendra Kesur

doi:10.1155/2013/754204

Abstract

This paper examines the application of Latin Hypercube Sampling (LHS) and Antithetic Variables (AVs) to reduce the variance of estimated performance measures from microscopic traffic simulators. LHS and AV allow for a more representative coverage of input probability distributions through stratification, reducing the standard error of simulation outputs. Two methods of implementation are examined, one where stratification is applied to headways and routing decisions of individual vehicles and another where vehicle counts and entry times are more evenly sampled. The proposed methods have wider applicability in general queuing systems. LHS is found to outperform AV, and reductions of up to 71% in the standard error of estimates of traffic network performance relative to independent sampling are obtained. LHS allows for a reduction in the execution time of computationally expensive microscopic traffic simulators as fewer simulations are required to achieve a fixed level of precision with reductions of up to 84% in computing time noted on the test cases considered. The benefits of LHS are amplified for more congested networks and as the required level of precision increases.

Highlights

Simulation models have become established as invaluable tools for managing signalized road traffic networks
This paper considers the application of the variance reduction techniques Latin Hypercube Sampling (LHS) and Antithetic Variables (AVs) to improve the precision of estimators from microscopic traffic simulators
And 9, we find that LHS offers larger variance reductions relative to AV in all cases expect for when n = 2 in oversaturated flow conditions

Summary

Introduction

Simulation models have become established as invaluable tools for managing signalized road traffic networks. Microscopic traffic simulators consider the state of vehicles individually rather than modeling an entire stream of traffic in aggregate as is the case with macroscopic traffic simulators. This more detailed consideration allows for a more comprehensive consideration of the complex effects of vehicle interactions as well as catering for the stochastic nature of vehicle arrivals, vehicle routing, and driver behavior to be considered. The disadvantage of using a microscopic traffic simulator is the increased computing requirement due to the more detailed modeling. The following studies serve as examples to demonstrate the extensive model run-times which can be experienced

Methods

Results

Conclusion