Application of Zonal Crash Prediction Models in Traffic Safety Evaluation of a Fuel-Cost Increase Scenario Using an Activity-Based Transportation Model

Travel demand management (TDM) consists of a variety of policy measures that affect the transportation system’s effectiveness by changing travel behavior. The primary objective to implement such TDM strategies is not to improve traffic safety; however, their potential in providing traffic safety profits should not be neglected. The main purpose of this study is to evaluate the traffic safety impacts of conducting a fuel-cost scenario in Flanders, Belgium. While travel demand management strategies are usually conducted at an aggregated level, crash prediction models (CPMs) should also be developed at a more aggregated level. Therefore zonal crash prediction models (ZCPMs) are considered to present the association between observed crashes in each zone and a set of predictor variables. To this end, an activity-based transportation model framework is applied to produce exposure variables which will be used in prediction models. This allows the ability to conduct a more detailed and reliable assessment while TDM strategies are inherently modeled in the activity-based models unlike traditional models in which the impact of TDM strategies are assumed. Crash data used in this study consist of observed injury crashes between 2004 and 2007. Other network and socio-demographic variables are also collected from other sources. In this study, a selected zonal crash prediction model is developed to predict the number of injury crashes (NOICs) for both the null and a fuel-cost scenario. The results show a considerable traffic safety benefit of conducting the fuel-cost scenario besides its impact on reducing total vehicle kilometers travelled. The expected NOICs decreased by 4.17%, whilst a yearly 4.66 billion vehicle kilometers travelled reduction was observed after implementing the fuel-cost scenario; i.e. increasing the fuel cost by 20%.