Traffic and the risk of vehicle-related pedestrian injury: a decision analytic support tool

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Abstract

Background:

Pedestrian injuries are a leading cause of death and disability. Transport policy decisions have a major impact on the risk of pedestrian injury, but the effects cannot usually be quantified in controlled studies. However, mathematical modeling can help to establish the injury consequences of transport policy decisions.

Methods:

A stochastic mathematical model was developed to estimate the effect of alternative transport scenarios on pedestrian injury risk. The model is based on a mechanistic description of pedestrian injury causation and comprises four sub-models: vehicle dynamics, pedestrian dynamics, collision incidence, and injury severity.

Results:

The model was used to estimate the yearly pedestrian injury rate for a baseline scenario, corresponding to current traffic conditions in London, UK, and three alternative scenarios, comprising reductions in vehicle speed, traffic volume, and vehicle mass. The model simulated a baseline injury rate of 88 per 100 000. Compared with baseline, a 15% reduction in mean speed resulted in a 21% reduction in injury rate and a 75% reduction in fatality rate. A 15% reduction in traffic volume resulted in a 14% reduction in injury rate and a 25% reduction in fatality rate. Reducing vehicle mass by 15% did not reduce the number of injuries, but a 25% reduction resulted in less severe injuries.

Conclusions:

The model simulated well the rates and severity of pedestrian injury corresponding to the baseline scenario and made predictions for different transport policy scenarios. However, it is offered primarily as a generic decision support tool for the assessment of alternative policies by transport authorities.

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