Abstract
The importance of travel time reliability in traffic management, control, and network design has received a lot of attention in the past decade. In this paper, a network travel time distribution model based on the Johnson curve system is proposed. The model is applied to field travel time data collected by Automated Number Plate Recognition (ANPR) cameras. We further investigate the network-level travel time reliability by connecting the network reliability measures such as the weighted standard deviation of travel time rate and the weighted skewness of travel time rate distributions with network traffic characteristics (e.g., the network density). The weighting is done with respect to the number of signalized intersections on a trip. A clear linear relation between the weighted average travel time rate and the weighted standard deviation of travel time rate can be observed for different time periods with time-varying demand. Furthermore, both the weighted average travel time rate and the weighted standard deviation of travel time rate increase monotonically with network density. The empirical findings of the relation between network travel time reliability and network traffic characteristics can be possibly applied to assess traffic management and control measures to improve network travel time reliability.
Highlights
Urban travel times are well recognized to be variable due to time-varying demand, stochastic capacity, different control measures, and external factors
We calculated the value of parameters γ, η, ε, and λ in the Johnson curve system according to the steps described in the previous subsection
This paper proposes a network-level travel time distribution model by applying a flexible system of Johnson curves
Summary
Urban travel times are well recognized to be variable due to time-varying demand, stochastic capacity, different control measures (e.g., signal control at intersections), and external factors (e.g., weather conditions). The reliability analysis of link/route/network travel times can be conducted. In order to capture both the vehicleto-vehicle and day-to-day variability in travel time data, Kim and Mahmassani [9] proposed a Gamma-Gamma mixture distribution model. Kim et al [10] employed traffic simulation models to derive travel time distributions under different scenarios considering various demand and supply uncertainty factors, such as weather, traffic incidents, work zones, and traffic control. Chen and Zhou [11] proposed a simulation approach to derive network travel times by solving a traffic equilibrium problem considering demand uncertainty. Some discussion and conclusions are provided in the last section
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