Calculating Expected Delays and Travel Time Variability in Cost-benefit Analyses
J Eliasson, Transek, SE
We develop a method to evaluate how different proposed measures or investments will affect delay risks and travel time variability. The method deals separately with delays caused by incidents and day-to-day travel time variability.
As congestion problems are growing more severe in urban regions, problems with delays and travel time variability are receiving more attention. Since travel time unreliability, risk for delays and the tendency to avoid driving in queues affect people?s utility and their travel behaviour, it seems natural to try to include these phenomena in cost-benefit analyses. The work presented here is a first step towards this. Neglecting to do this will introduce a bias in the CBA calculations, which will most likely underestimate the utility of investments aiming to reduce urban congestion, thus in general favouring e.g. rural or inter-urban investments.
Over the past few years, quite a lot of work has been carried out on the valuation of delays and travel time variability. It is reasonably safe to say that we how have a fairly good grasp about the magnitude of such valuations, and methodology to obtain them.
The next step consists of developing methods to evaluate how different proposed measures or investments will affect delay risks and travel time variability. This work intends to start filling that gap.
The project is split in two parts. The first part deals with incidents causing (major) temporary losses of capacity and the delays caused by this. Examples of such incidents can be car breakdowns, accidents etc. We estimate a model where the frequency and length of such incidents are a function of traffic flow and road characteristics. The following delays are then calculated using microsimulation methods, and the total delay (conditional upon the length and severity of the incident) is a function of the traffic flow and the remaining capacity. Combining the two functions, we can calculate the expected total incident-related delay per link in the network as a function of link and link characteristics.
The second part deals with travel time variability, i.e. the random variation in travel time that is due to (among other things) unpredictable day-to-day variation in traffic volumes. We use travel time data on individual vehicles, obtained from traffic cameras. Using this data, we estimate travel time variability as a function of average traffic volume.
Combining these two parts with valuation of delays and variability from previous projects, we develop functions that can be used in large-scale cost-benefit applications, using output from standard software such as Emme/2.
Association for European Transport