Data Science for Transport by Charles Fox

Author:Charles Fox
Language: eng
Format: epub
Publisher: Springer International Publishing, Cham

6.4.2 Bayesian Network for Traffic Accidents

We will next consider a more complex model to see how Bayesian networks generalize easily where simple Frequentist statistics do not.

Suppose we have observed the number of accidents on a road per month for the last 110 months, which are,

[4, 5, 4, 0, 1, 4, 3, 4, 0, 6, 3, 3, 4, 0, 2, 6, 3, 3, 5, 4, 5, 3, 1, 4, 4, 1, 5, 5, 3, 4, 2, 5, 2, 2, 3, 4, 2, 1, 3, 2, 2, 1, 1, 1, 1, 3, 0, 0, 1, 0, 1, 1, 0, 0, 3, 1, 0, 3, 2, 2, 0, 1, 1, 1,0, 1, 0, 1, 0, 0, 0, 2, 1, 0, 0, 0, 1, 1, 0, 3, 3, 1, 1, 2, 1, 1, 1, 1, 2, 4, 2, 0, 0, 1, 4, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1].

Plotting this data suggests a theory that something changed part way through the time series which reduced the accident rate. Assume that under any fixed environmental state, the number of accidents, k, is Poisson distributed (i.e. the state has one parameter, , which makes accidents more or less probable; and the accidents within each month don’t affect each other),

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