Abstract
This paper proposes a simplified network model which analyzes travel time reliability in a road network. A risk-averse driver is assumed in the simplified model. The risk-averse driver chooses a path by taking into account both a path travel time variance and a mean path travel time. The uncertainty addressed in this model is that of traffic flows (i.e., stochastic demand flows). In the simplified network model, the path travel time variance is not calculated by considering all travel time covariance between two links in the network. The path travel time variance is calculated by considering all travel time covariance between two adjacent links in the network. Numerical experiments are carried out to illustrate the applicability and validity of the proposed model. The experiments introduce the path choice behavior of a risk-neutral driver and several types of risk-averse drivers. It is shown that the mean link flows calculated by introducing the risk-neutral driver differ as a whole from those calculated by introducing several types of risk-averse drivers. It is also shown that the mean link flows calculated by the simplified network model are almost the same as the flows calculated by using the exact path travel time variance.
Highlights
Conventional frameworks for analyzing and modeling transportation systems have been confined to average representations of the network state
By presenting the path flows for cases 1–3 to which MSA was applied, it is easy to understand that the generalized travel times for the paths between each O-D pair that are used by the drivers are the same both the mean travel time and travel time variance of a path between the O-D pair can be different from
Since mean travel time of a path is calculated by using mean link flows, similar mean path travel times are obtained among the four cases
Summary
Conventional frameworks for analyzing and modeling transportation systems have been confined to average representations of the network state (e.g., average link flow or average travel flow). In the traditional traffic assignment model, one can obtain a deterministic prediction of a future flow on a certain link in the network based on average origin-destination (O-D) flows, link capacities, and a form of proportional path choice model (either deterministic user equilibrium (DUE) or stochastic user equilibrium (SUE)). This represents a deterministic view of the environment and the modeler’s postulation that the variability or uncertainty in the system is not influential in system design and evaluation. Travelers may experience excessive variability of travel time from day to day on the same trips [6,7,8]
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