Civil and Environmental Engineering


Date of this Version

Spring 5-8-2015


Wu, Z. 2015. Measuring Reliability In Dynamic And Stochastic Transportation Networks. PhD Dissertation. Department of Civil Engineering, University of Nebraska-Lincoln.


A DISSERTATION Presented to the Faculty of The Graduate College at the University of Nebraska In Partial Fulfillment of Requirements For the Degree of Doctor of Philosophy, Major: Civil Engineering (Transportation System Engineering), Under the Supervision of Professor Laurence R. Rilett. Lincoln, Nebraska: March, 2015

Copyright (c) 2015 Zifeng Wu


As the traffic demand levels continue to grow in cities, more and more transportation systems experience instability during recurrent and non-recurrent congestion periods. Therefore, reliability has taken on increasing emphases in performance evaluation for transportation agencies, and in performance communication between agencies and the public. Existing reliability-related studies in transportation engineering focus on the long-term reliability of day-to-day travel time variations. This dissertation expands the reliability research literature with studies on the short-term reliability which is valuable for both real-time management and real-time traffic information systems.

This dissertation proposes a level of service reliability metric for system evaluation. Instead of using an average measurement, the confidence interval of a point estimate of the performance measurement of interest is incorporated to evaluate the reliability of each level of service for traffic systems. Bootstrap methods are applied to generate confidence intervals.

A reliability interval based on the travel time standard deviation is defined to describe short-term travel time variability for drivers’ information. This dissertation investigates both estimation and prediction methodologies for the mean and reliability interval of travel time, using a five km arterial corridor consisting of three links as a test bed. Regarding the estimation methods, the first-order and second-order approximation methods show superiority compared with the naïve sum method, which is widely applied to freeway corridors in practice. In terms of the prediction methodologies, the nonlinear autoregressive with exogenous inputs (NARX) neural network is shown to be effective to generate accurate reliability intervals in both the overall condition and the unexpected incident condition.

Finally, the proposed reliability metrics and estimation methodologies are applied on a bimodal traffic network with highway-railway at-grade crossings in Lincoln to evaluate the impact of train traffic on the roadway travel time reliability.

Advisor: Laurence R. Rilett