An experimental exploration of route choice: Identifying drivers choices and choice patterns, and capturing network evolution

Driver route choice is typically modeled using mathematical programming approaches that assume that drivers choose their routes to minimize some objective function, and assume that drivers have perfect, or close to perfect, knowledge of their choice set, as well as the travel characteristics associated with each of the choice elements. It is, however, well documented in human psychological behavior that human perceptions are often different from actual reality, and that humans tend to minimize their cognitive efforts, and follow simple heuristics to reach their decisions; especially under uncertainty and time constraints. With this in mind, unlike most route choice research that is primarily focused on the end result of the route choice task, this research effort traces the evolution of route choices with driving experience and network knowledge. The research presented in this paper monitors and traces actual human route choice, and demonstrates that (a) drivers' route choice evolution varies; while some drivers do not evaluate the various alternative routes others do not decide on a specific route, (b) although there appears to be possible evidence to conclude that drivers learn the network conditions by experience, it appears that drivers perceptions over estimate the benefits, (c) drivers' route choice behavior differs between different driver groups, and (d) soliciting drivers' route choice based on observing choices over a period of time with reasonable accuracy is possible.