Dynamic Hyperpath Search for Transit and Traffic Assignment

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Path choices by transport network users depend in part on decisions made in response to foreseeable circumstances at the start of the trip and in part on choices made as the trip unfolds. In the case of transit networks, on-trip choices may depend on factors such as which line arrives first at a particular stop or platform, whereas in the case of traffic networks, on-trip choices may be influenced by factors such as an encountered queue, the state of traffic signals, or information received by the driver. In transit assignment, the effects of on-trip decisions are accommodated through the definition of a set of elemental paths that may be optimal (referred to as a hyperpath) and strategies for on-trip choices. In traffic assignment, the stochastic user equilibrium principle has been used to capture the effects of on-trip choices and different user preferences. However, the usual forms of stochastic assignment (C-logit, path size logit, cross-nested logit or probit) generally place few or ad hoc constraints on the set of feasible paths and assume that all feasible paths have a non-zero probability of use. Choice of path is assumed to be the result of random utility variation rather than the outcome of a choice strategy in the face of unfolding circumstances as the trip takes place. To this extent, the treatment of route choice behaviour is more sophisticated in transit than in traffic assignment. Where congestion and service disruptions are a prominent feature, it is necessary to take time-dependency into account in defining hyperpaths. The objective of this proposal is to extend the concepts of hyperpath and strategy-based decision-making to dynamic transit networks, traffic networks and multi-modal networks and then assess the benefits of so doing.