Route flow estimation based on time-dependent route choice sets and historical travel times

Since congestion leads to variations in travel time which gives a variation in the traffic flow, it is interesting to estimate the traffic flow in larger cities where cars drive in a limited space. In order to estimate the traffic flow, different traffic models are usually used. These models often use volume-delay functions which calculate the travel times for each link depending on the current traffic flow. However, in these models, the process for reaching equilibrium can be time consuming and it is hard to calibrate the volume-delay functions for a road network with a large set of links. Instead, we assume that it is relatively simply to measure or collect historical time-dependent travel times on a large set of links. With this assumption, a method that uses time-dependent route choice sets and time-dependent travel times in order to estimate time-dependent route flows, is developed. In this thesis, the method was applied to Stockholm County where it is interesting to study the route choice since congestion occurs in the area which generate variations in travel time. In order to estimate time-dependent route flows, a time-sliced OD-matrix was created by dividing the matrix for the peak hour using two different time-slicing distributions. The time-dependent route choice set with time-dependent travel times was created by using an existing route planning tool. These routes were mapped to the links in a road network in order to estimate link flows. The mapping was done by using map matching and a shortest path algorithm. Route shares were decided by using a method that splits the demand equally among the routes in the route choice set for an OD-pair, and with a logit model that takes travel time into account with the assumption that the travel time can affect a traveler’s route choice. The evaluation of the resulting link flows was done by comparing these link flows with observed link flows using different time-slicing distributions and route share models. Furthermore, the method’s resulting link flows was evaluated against the resulting link flows from a scenario where all travelers are assumed to choose the shortest path, in terms of free flow travel time, between each OD-pair. The developed method can estimate link flows so that 27.9 % of the links have a GEH value less than 5, which can be compared to the commonly used acceptance criteria of 85 %. This shows that the method needs to be developed further in order to achieve link flow estimations that fulfills the acceptance criteria. Even though the overall result show that the developed method does not fulfill the acceptance criteria, the method works well on some individual links. Furthermore, the resulting link flows from the developed method match the observed link flows better than the resulting link flows from the scenario where all travelers are assumed to choose the shortest path