ABSTRACT
This chapter discusses the theory behind the demand model. It explores how reductions in incident probabilities and capacity expansion can affect system characteristics. Sample enumeration of the synthetic sample allows the demand model to forecast the probability of choosing each of eleven possible departure times, relative to the work start time for each individual. Travel times are only one component of total costs but are generally used for most evaluations of transportation projects and policies. Intelligent Transportation System (ITS) infrastructure is also generally promoted as a method for reducing travel times. The chapter addresses two components of ITS: management systems to detect and reduce the number of unexpected incidents and systems to provide commuters with more accurate travel information. The discrete choice demand model is applied using a synthetic sample of 5000 individuals, each with a randomly assigned 'work start time', tw.
