An ambulance location problem for covering inherently rare and random road crashes

Ambulance Location Problem (ALP) is studied in the field of covering road crashes, which are often rare and random in nature. On the account of rareness, crashes should be attributed to the network edges rather than nodes. Therefore, a covering problem with edge demand is the most compatible location problem. Accordingly, this paper proposes an edge maximal covering location problem with partial coverage of the facilities on the edges. Randomness is a feature associated with the frequency and severity of crashes. An Empirical Bayes (EB) method is applied to the observed frequency of crashes to reduce errors caused by the random feature of the crashes. The randomness associated with crash severity is investigated by adding the data derived from Property Damage Only (PDO) crashes to the network demand. As a result, an equivalent PDO measure is proposed to satisfy the demand of the network edges. The proposed method is applied to a real case study and some insights are presented.

Automated summary

The paper addresses the Ambulance Location Problem in the context of road crash coverage, focusing on the rare and random nature of crashes. It proposes an edge maximal covering location problem with partial facility coverage to address the rarity of crashes attributed to network edges. The study applies Empirical Bayes method and includes PDO crash data to mitigate errors caused by random features and investigate crash severity randomness.