Evaluation of impacts of congestion improvement scheme s on travel time reliability is very significant for road authorities since travel time reliability repr esents operational performance of expressway segments. In this paper, a methodology is presented to estimate travel tim e reliability prior to implementation of congestion relief schemes based on travel time variation modeling as a function of demand, capacity, weather conditions and road accident s. For subject expressway segmen ts, traffic conditions are modeled over a whole year considering demand and capacity as random variables. Patterns of demand and capacity are generated for each five minute interval by appl ying Monte-Carlo simulation technique, and accidents are randomly generated based on a model that links acci dent rate to traffic conditions. A whole year analysis is performed by comparing de mand and available capacity for each scenario and queue length is estimated through shockwave analysis for each time in terval. Travel times are estimated from refined speed-flow relationships developed for intercity expressways and buffer time index is estimated consequently as a measure of travel time reliability. For validation, estimated reliability indices are compared with measured values from empirical data, and it is shown that the proposed method is suitable for operational evaluation and planning purposes.
This paper presents a new approach for dynamic es timation of origin-destina tion (OD) matrices based on Unscented Kalman Filter (UKF). The new approach, di ffers from most of the conventional approaches, does not assume linearity of the original nonlinear relationship between OD parameters and measurements. Instead, the nonlinearity is retained by representing th is relationship using dynamic traffic simulation. This eliminates the need to compute the assignment matrix an d offers some degrees of flexibility in selecting the traffic simulation model and incorporating speed as additional measurement variables. Preliminary results demonstrate the potential of the proposed approach and the contribution from using speed data.