Cycle traffic in the regional transport model of Norway

Abstract

The regional transport model developed in Norway over the last 12 years will from now on be used also for urban transport. Can the transport model also be used to model changes in the demand for cycling in its current form? Is it enough to detail the coding of the transport network in more detail, or is it necessary to include bike‐specific variables in the demand model to achieve this? These two issues are addressed in this paper by going in depth in the modeled and actual travel patterns in an area south of Trondheim, where an infrastructure measure for cyclists is scheduled in 2014. The cycle measure is a bridge for pedestrians and cyclists, which will reduce the travel distance, and hilliness on the route between two districts.

Data sources for the actual travel patterns is the national travel survey conducted in 2009 and 2010, with an enhanced range of Trondheim. This is compared with model calculations at a paramount level, within areas of mode choice, trip length distribution for the various modes of transport, within the private travel purposes represented in the travel survey. Moreover, a short time registration of cycle traffic is carried out on roads in the near‐by area of the scheduled bridge. In addition, two small surveys are conducted in the same period aimed at households and workers. Interviewees are asked to give information about their travel patterns over the two previous days. Trips that could potentially to use the new bridge are labeled specifically. In addition, they were asked about how the bridge could affect their travel patterns.

The results suggest that the transport net must be coded in more detail than we have done up until now. Both experience reported in the literature and results from this study, indicates that hilliness should be included in the net coding and skimming of level of service data, both because it is an important explanation factor to the demand for cycle traffic, but also to improve the net assignment.