关键词: 城市交通, 路径偏好, Dogit模型, 日常出行, 动态演化

Abstract:

Individuals’daily route choice results, is not only related to the perceived route travel time, but also the individuals’personal preference, travel inertia and other factors. In this paper, a dynamic-updating mechanism for route preference based on Dogit model is introduced into the traditional day- to- day route choice model. Three different learning strategies on perceived route travel time are proposed, and the effects of individual route preference on day- to- day route flow evolution track are analyzed and compared. The numerical results show that, in contrast with the static Dogit model, the Logit model always overestimates the route difference owing to ignoring individuals’route preference, to be specific, always overestimates the traffic flow on dominant routes and underestimates the traffic flow on inferior routes. When the individuals’ route preference is dynamically updated, the equilibrium flow always stays between that under Logit model and static Dogit model with same initial parameters.

Key words: urban traffic, route preference, Dogit model, day-to-day travel, dynamic evolution