Estimate freeway travel time reliability under recurring and nonrecurring congestion
Travel time and its reliability are considered as intuitive measure of service quality by transportation agencies. Moreover, highly reliable travel times allow for arriving at work or other destinations on time in the context of personal travel and facilitate just-in-time logistics services in freight operations. Travel times are the result of the traffic congestion. By considering different impact factors and shortcoming of the sensing technologies, this dissertation proposed methods for travel time and its reliability estimation.
First of all, this dissertation presented a method to estimate corridor-level travel times based on data collected from roadside radar sensors, considering spatially correlated traffic conditions. Link-level and corridor-level travel time distributions are estimated using these travel time estimates and compared with the ones estimated based on probe vehicle data. The maximum likelihood estimation is used to estimate the parameters of Weibull, gamma, normal, and lognormal distributions. According to the log likelihood values, lognormal distribution is the best fit among all the tested distributions. Corridor-level travel time reliability measures are extracted from the travel time distributions. The proposed travel time estimation model can well capture the temporal pattern of travel time and its distribution.
Second, a travel time reliability measure estimation method is proposed by incorporating standstill distance and time headway distributions in car-following models. The method is based on simplified two-component travel time distribution. By using Monte Carlo simulation, the speed-density region under congested condition and the travel time reliability measures can be generated. The results shows that the speed-density region derived from the steady-state Pipes model encloses most of the field data. Moreover, the proposed method estimate travel time reliability measures more precisely and faster, compared with using VISSIM simulation.
Finally, a work zone travel time estimation approach is proposed in this dissertation. First, the impact of work zone on capacity is investigated. For the work zone capacity prediction framework, the predicted upper bound of capacity is close to the maximum 15-min flow rate. Moreover, based on the predicted capacity, density at capacity and free flow speed, work zone travel times are estimated by using the modified segment speed estimation model from the study of Newman. The estimated travel times roughly followed the pattern of the INRIX travel times. Moreover, the travel time reliability indices are estimated directly from the estimated travel times. The result shows that the travel time reliability indices based on estimated travel times are close to the indices based on INRIX travel times.