Empirical data collected on-street was used to provide an insight into how real traffic systems are behaving. The data are fitted to statistical models and these models show how traffic systems respond when the capacity of the system is reduced. This research follows on from the general interest in highway capacity reduction. A great deal of effort has been spent on models that simulate the day-to-day behaviour of road traffic. Many of these models include parameters that adjust how quickly users will respond bychanging their behaviour. This paper provides a starting point for the calibration of such models by careful statistical analysis of real-life datacollected in the City of York, UK. The data were collected to track a real-life capacity-reducing event that occurred in the city (a partial road closure). The survey took place in the morning peak hour and collected licence plate data. It considers the effect of a road closure. Before and during data are available for several days at several sites to establish the ambient variability in the system and then to compare this with the response to the change in the system. Day to day variability in the traffic pattern are considered and recurrence rate of vehicles determined. A statistical model is fitted to the data showing that recurrence rate starts lower than many researchers might expect for a morning rush hour and falls off quickly for subsequent days. Recurrence is higher on the same day of the subsequent week. A change in the road system evoked a strong response on the first day but the effect became weaker in subsequent days. The effects of rerouting as seen in the data are considered and the route choice response to an incident is measured. For the covering abstract see ITRD E144727. Reprinted with permission of Elsevier.
Samenvatting