Better traffic and revenue forecasting / Luis G. Willumsen.

Demand and revenue forecasting for transport concessions has an inconsistent track record. There are several reasons for this: a possible optimism bias, the inherent uncertainty in any forecast, limited data and a poor choice of modelling tools. Therefore any better approach must acknowledge these constraints from the outset and be adapted to deal with revenue risk. This book addresses these issues on the basis of 20 years of international experience producing Traffic and Revenue projections for a range of transport concessions: Toll Roads, Managed Lanes, LRT, BRT, Metro and Rail projects with involvement of the private sector. It includes an additional chapter on Congestion Charging. The book is divided into three parts. Part I covers the context for the participation of the private sector and the requirements posed for demand forecasting. Part II is more technical and describes the strengths and limitations of the modelling tools used in both conventional and Traffic and Revenue forecasting models; it deals with issues like induced traffic, destination, mode and time of travel choice and, of course, discusses assignment in detail; willingness to pay is central to this part. Part III covers the practice of producing forecasts for different types of concessions: sound assumptions, modelling price and means of payment, growth models, treatment of congestion and travel time reliability, optimal pricing, annualisation, inflation and tariff escalation. This part also deals with the critical issue of future uncertainty with suggestions for peer reviews, sensitivity test, risk analysis and scenario planning. The book should be of interest to professionals working in procuring authorities preparing a transport concession, consortia bidding for such projects and financial specialists seeking to get a better understanding of the techniques used for Traffic and Revenue forecasting. Planners developing projects where price and revenue risk are important will also gain useful insights. The text contains a large numbe