Travel time reliability has received much attention from researchers and practitioners because it not only affects driver route choice behavior butis also used in the assessment of a transportation system performance. The current state-of-the-art travel time reliability research assumes that roadway travel times follow a uni-modal distribution. Specifically, either a Weibull, exponential, lognormal, or normal distribution is used within the context of travel time reliability modeling. However, field observations demonstrate that roadway travel times are multi-modal especially during peak periods. This paper demonstrates that the multi modes observed in field data are a result of temporal variations in travel times both between and within days. The study evaluates the potential for generating multi-modal travel times using the INTEGRATION software and investigates the underlying traffic states that result in these multi modes. The study validates the two-state model proposed in an earlier study and demonstrates the robustness of model parameter estimates under varying stochastic traffic congestion levels and sampling techniques.
Abstract