Estimating traffic demand risk - a multiscale analysis

This paper proposes a novel method for estimating the traffic demand risk associated with transportation. Using mathematical properties of wavelets, we develop a statistical measure of traffic demand sensitivity with respect to GDP. This measure can be adapted in a flexible way to capture risk levels relevant for different investment horizons. We demonstrate the time-scale decomposition of risk with Swedish traffic demand data for 1950-2005. In general, rail transports show a stronger co-movement with GDP than road transports. Moreover, we examine the volatility exhibited by traffic demand. Our findings suggest that rail investments are more risky than road investments. Since the findings can be used for optimal investment timing and for choice among public investment alternatives, they are deemed important for public policy in general.