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On the variation of the crash risk with the total number of bicyclists

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Figure

Fig. 1 Schematic illustration of the memory model (2). Including a memory τ > 1 has the effect of an inert response [cf
Fig. 2 Synthesized crash data [in (a) without memory and in (b) with memory] and real crash data [in (c)]
Fig. 3 a Slowly varying average bicycle countotherwise) and the blue squares indicate the observed numbers P(t) obtained by local regression (solid black curve) and monthly bicycle counts (blue squares) for thelocation “Kreuzberg”; b Predictions of the bicycle volume model for the deviation W(tj) (red triangles at the monthly count dates and black crosses, wj during monthly bicycle counts
Fig. 6 a The normalized estimated risks (black crosses) per month. The total 14-year average per month is indicated by blue squares.estimated monthly risks with least squares fit for the same predictor as in ( b Linear fit(with intercept) to a negative bin
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