@inproceedings {pub3208,
	title = {Solomon Curve 2020: Relating Microscopic Risk Models to Accident Statistics},
	author = {Julian Eggert},
	year = {2016},
	month = {November},
	abstract = {Traffic accident statistics as functions of the involved
accident participant parameters are important sources
of information to investigate accident causes. However, since
traffic accidents are sparse events and the factors that lead
to an accident can be very diverse, it is difficult to establish
a direct link which relates microscopic risk models to the
empirical findings. One seminal and widely debated work on
accident statistics on multi-lane roadways is given by the socalled
{\textquotedblleft}Solomon curve{\textquotedblright} [?], which describes the collision rate of
automobiles as a function of their speed. While one particular
characteristic of the Solomon curve - its u-shape - has been
explained theoretically in terms of traffic flow of passing cars
in [?], a detailed derivation from microscopic risk models is
still missing. In this paper, we start from a first-principles
generalized risk model, and reconfirm the explanation of the
curve, revealing that its parameters can be fully mapped to
the parameters of the underlying microscopic risk model. In
addition, an unexplained effect of the Solomon curve - the
assymmetry of its minimum with respect to the average velocity
- is predicted and explained by the derivation. The result then
is two-fold: On one hand, we can now fully understand the
Solomon curve in terms of microscopic risk parameters, and
on the other hand, the empirical findings of the Solomon curve
serve as a validation of the risk model and can be used to gain
reasonable settings for microscopic risk parameters.},
	publisher = {IEEE},
	booktitle = {Intelligent Transportation Systems Conference (ITSC) 2016},
	pages = {2293-2300}
}