The efficient control of traffic on motorways or freeways can produce many benefits, including quicker journey times, fewer pollutant emissions, and reduced driver stress. If it were possible to accurately predict the future state of traffic on a motorway, active measures could be taken to forestall congestion and its attendant negative impacts. This paper presents an intuitive method of producing these forecasts using a pattern matching technique. The technique adopted is novel in that it is a multivariate extension of nonparametric regression that exploits the three-dimensional nature of the traffic state. The application and other facets of the technique are illustrated with actual data from the London orbital motorway. The technique is able to produce forecasts for two of the three traffic state variables with reasonable accuracy and is capable of application on site.

The probit-based stochastic user-equilibrium (SUE) model is widely recognized as one of the most intuitively robust traffic network assignment techniques. Its advantages include explicit consideration of random perceptual differences in utility across the driver population and the ability to take proper account of the correlations in these utilities between overlapping routes, which the simpler logit SUE is unable to do. Its main drawback is the potentially heavy computational demands of the method, but various efficient approximation methods have been observed to provide reasonable solutions. This computational complexity was previously, however, thought to preclude a consideration of the sensitivity analysis of probit-based SUE, whereby an approximation to changes in the equilibrium solution is deduced as its input parameters are perturbed. An efficient computational method for performing such an analysis in general networks is described. A simple network example is described in detail to illustrate the method, and it is followed by a larger network example that further demonstrates the practical nature of the method.