Mid-block crashes

5.2.1 Cyclist v motor vehicle crashes

Ten models were developed for this crash type before settling on a preferred model (see appendix A for the models calculated for this and all other crash types). Appendix B outlines the full set of

predictor variables and model parameters that were calculated for each of the ten models. Equation 5.1 presents the preferred model form, which includes the total two-way flow for both motor vehicles and cyclists, the length of the mid-block section and a covariate for the presence of a flush median.

MEDIAN FLUSH

UCMN Q C L

A ₀ 1.0510-2  0.25 0.16  0.45  (Equation 5.1)

where:

AUCMN0 = annual number of mid-block crashes involving cyclists only (subscript denotes

model type – see Appendix C);

Q = total two-way motor vehicle flow for the link C = total two-way cycle flow for the link

L = length of mid-block in kilometres

ФFLUSHMEDIAN = factor to multiply the crash prediction by if a flush median is present. This factor is

Equation 5.1 implies that the presence of a flush median mid-block can reduce cyclist crashes by 37%. The safety benefit provided by flush medians to cyclists may be caused by the extra width that flush medians provide to motorists to avoid cyclists travelling on the side of the carriageway.

Equation 5.1 has a p-value of 0.05, indicating a model with good fit (values below 0.05 indicate a poor model). The goodness of fit can be illustrated by comparing the predicted mean number of crashes and the reported number of crashes for ‘grouped’ (approach) data (as outlined in Wood (2002)). Figure 5.1 presents this comparison between ‘grouped’ reported and predicted crashes for the preferred model. A poor fit is illustrated by a group that has different predicted and reported numbers of crashes (where the plotted point is furthest from the 45 degree line). If we have no evidence of poor fit, this gives us valid grounds for increased confidence in the model. Figure 5.1 indicates a generally good fit for most approach groups. However, the model appears to underestimate crashes at sites with higher traffic volumes.

Figure 5.1 Relationship between predicted and reported crashes for AUCMN0

A number of other models were developed but were less than ideal. These included non-flow variables with significant relationships, such as:

 effective width of the kerbside lane, including the vehicle lane and cycle lane, where present

 the presence of a cycle lane

 mean motor vehicle speed along each mid-block section.

0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 0 1 2 3 4 5

Predicted grouped mean (crashes/five years)

Reporte d gro u ped me an (c ra shes /fi v e years)

The models show that crashes increase with increasing traffic volume, mid-block length, effective width and mean motor vehicle speed. One model suggests that the presence of a cycle lane increases crashes by 21%, as shown in equation 5.2, which includes a covariate for the presence of a cycle lane.

CYCLANE

UCMN Q C L

A ₀ 7.1110-3  0.25 0.19  0.38  (Equation 5.2)

where:

AUCMN0 = annual number of mid-block crashes involving cyclists only (subscript denotes

model type – see appendix C)

Q = total two-way motor vehicle flow for the link C = total two-way cycle flow for the link

L = length of mid-block in kilometres

ФCYCLANE = factor to multiply the crash prediction by if a cycle lane is present. This factor is

ФCYCLANE = 121.

The increase in the crash rate with the presence of a cycle lane is counter-intuitive and counter to other research. The data used in the crash prediction model, however, is biased, which complicates the outcome. High crash frequency sites have historically been a high-priority location for cycle lane construction, so it is unlikely that sites with cycle lanes and sites without cycle lanes will have the same background crash rate. The before-and-after studies using the same locations with and without cycle lanes, as discussed in section 5.9, remedy this problem.

5.2.2 All crashes

Ten models were developed for this crash type before settling on a preferred model. Appendix B outlines the full set of predictor variables and model parameters that were calculated for each of the 10 models. Equation 5.3 presents the preferred model form, which includes the total two-way flow for motor vehicles, the length of the mid-block section and a covariate for parking prohibition.

NOPARKING

UAMN Q L

A ₀ 2.3610-4  0.84  0.30  (Equation 5.3) where:

AUAMN0 = annual number of mid-block crashes involving all vehicle types (subscript denotes

model type – see appendix C)

Q = total two-way motor vehicle flow for the link L = length of mid-block in kilometres

ФNOPARKING = factor to multiply the crash prediction by if the mid-block length does not allow

parking. This factor is ФNOPARKING = 0.25.

Equation 5.3 implies that all crashes occurring in mid-blocks can be reduced by 75% by removing parking from mid-block sections. The reduction in crashes by removing parking may be caused by removing conflicting movements of the parking manoeuvre with the carriageway traffic.

Equation 5.3 has a p-value of 0.17, indicating a model that fits well. Figure 5.2 presents the

comparison between the predicted and reported number of crashes for the preferred model. Figure 5.2 indicates a generally good fit for most approach groups. However, the model appears to underestimate crashes at sites with higher traffic volumes.

Figure 5.2 Relationship between predicted and reported crashes for AUAMN0