Application of hyperpath strategy and driving experience to risk‐averse navigation

Abstract

This study aims to find an experientially reliable path considering travel time uncertainty and driving experience of local probe vehicle drivers. Accordingly, a two‐stage route‐finding procedure is proposed. First, a set of candidate paths is built by using the hyperpath algorithm, where the choice probability is assigned to each link with uncertain travel time. Second, the shortest path algorithm is applied to find the experientially reliable path on the graph of hyperpath where the modified link cost is penalised based on the link choice probability derived from hyperpath algorithm and the driving experience of local drivers. Four kinds of optimal path in a real‐world network are compared with the observed one. It is found that the proposed path has the most similarity with the observed path and it has a higher degree of familiarity and reasonable time and distance.

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