Mahmassani, Kim Win TRB Transportation Network Modeling Committee Honor

MahmassaniJiwon Kim

The Transportation Research Board has announced that NUTC director Hani Mahmassani and researcher Jiwon Kim were awarded the TRB Transportation Network Modeling Committee's Best Paper honor for 2014. The paper, entitled How Many Runs? Analytical Method for Optimal Scenario Sampling to Estimate the Variance of Travel Time Distributions in Vehicular Traffic Network, uses scenario-based approaches to provide an effective and practical approach for capturing the probabilistic nature of travel time in traffic networks.

The pair investigated important questions regarding travel time, including best methods to reliably estimate travel times when working with a limited budget. Using a simulation study, they found an analytical formula that can effectively estimate the answers to these questions using the fewest models possible. Read an abstract of the paper

Congratulations to our award winners!

TRB Best Paper


Scenario-based approaches provide an effective and practical approach for capturing the probabilistic nature of travel time in a traffic network. Scenarios that represent daily roadway conditions are generated by identifying various demand- and supply-side factors that affect travel time variability, and sampling a set of mutually consistent combinations of the associated events. The sampled scenarios are then evaluated using network simulation models to obtain travel time distributions that provide a basis for extracting a wide range of reliability performance metrics. A key question under this framework pertains to the number of input scenarios needed to achieve the best estimators of the reliability measures of interest given a limited computational budget. Given a stratification of the entire domain of daily scenarios into distinct scenario categories (or strata), the study addresses the optimal sample size allocation problem in connection with stratified sampling. Existing sample allocation schemes, e.g. Neyman’s, are optimized for estimation of the mean. However, dispersion measures such as variance or standard deviation are of greater concern for reliability analysis. Thus this study explicitly specifies the optimal allocation scheme for the estimation of the variance. Using a specific characteristic observed in travel time data, namely, a strong positive correlation between standard deviation and mean, an analytical formula that approximates the variance of the sample variance is developed and the optimal allocation solution for estimating the variance estimate is derived. The proposed method is validated using a simulation study and compared with other allocation methods in terms of the estimation of various reliability measures.