Evaluating the road safety effects of a fuel cost increase measure by means of zonal crash prediction modeling

Abstract

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, although their impact on traffic safety should not be neglected. The main purpose of this study is to evaluate the traffic safety impact of conducting a fuel-cost increase scenario (i.e. increasing the fuel price by 20%) in Flanders, Belgium. Since TDM strategies are usually conducted at an aggregate level, Crash Prediction Models (CPMs) should also be developed at a geographically 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 activitybased transportation model framework is applied to produce exposure metrics which will be used in prediction models. This allows us 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. The crash data used in this study consist of fatal and injury crashes observed between 2004 and 2007. The network and socio-demographic variables are also collected from other sources. In this study, different ZCPMs are developed to predict the Number of Injury Crashes (NOCs) (disaggregated by different severity levels and crash types) for both the null and the fuel-cost increase scenario. The results show a considerable traffic safety benefit of conducting the fuel-cost increase scenario apart from its impact on the reduction of the total Vehicle Kilometers Travelled (VKT). A 20% increase in fuel price is predicted to reduce the annual VKT by 5.02 billion (11.57% of the total annual VKT in Flanders), which causes the total NOCs to decline by 2.83%.

Socioeconomic and sociodemographic inequalities and their association with road traffic injuries

“…Recently, crash analyses at a macroscopic level received more attention from the research community. Several studies examined the association of a collection of neighborhood-level factors (e.g., traffic patterns, socio-demographic and socioeconomic variables, land use patterns and weather conditions) with crashes, aggregated according to specific spatial scales (Aguero-Valverde and Jovanis, 2006;Hadayeghi et al, 2010aHadayeghi et al, , 2010bHuang et al, 2010;Lovegrove and Litman, 2008;Pirdavani et al, 2013Pirdavani et al, , 2012Wier et al, 2009). Neighborhood-level crash analyses can provide valuable information that would enable cross-sectional neighborhood comparisons, more accurate identification of neighborhood-specific safety problems, and subsequent implementation of appropriate safety interventions (Huang et al, 2010).…”

Spatial crash prediction models: an evaluation of the impacts of enriched information on model performance and the suitability of different spatial modeling approaches

“…In the model developed for motorized transport, reductions of proximately 20% and 25% were observed compared to the basic model, for AICc and MSPE respectively ( VKT. These results are consistent with findings in previous studies (such as Hadayeghi et al, 2003;Aguero-Valverde & Jovanis, 2006;Pirdavani et al, 2013aPirdavani et al, , 2013bShariat-Mohaymany et al, 2015;Xu et al, 2017).…”

“…Casualties were found to have a negative correlation with the number of children attending school and car ownership, in a large number of TAZs. This negative association with other social standing variables, e.g., income level, has been found in other previous studies (such as in Li, Wang, Liu, Bigham, & Ragland, 2013;Pirdavani et al, 2013aPirdavani et al, , 2013bPirdavani et al, 2014;Pirdavani et al, 2016). One could assume that, less casualties are expected to occur in more affluent areas (in this study, where car ownership is higher).…”

“…Traveled (VKT), commonly used in CPM (Miaou, Song, & Mallick, 2003;Fristrøm, Ifver, Ingebrigtsen, Kulmala, & Thomsen, 1995;Jovanis & Chang, 1986;Ahmed et al, 2012;Pirdavani, Brijs, Bellemans, Kochan, & Wets, 2012;Pirdavani et al, 2013a).…”

Development of Macro-Level Safety Performance Functions in the City of Naples

“…Transportation Demand Management (TDM) strategies have hardly ever been implemented to improve traffic safety. Their main objectives are usually the reduction of congestion and emission, as well as travel costs and energy by means of reducing travel demand and consequently vehicle distance traveled, although their impact on traffic safety should not be neglected [29][30][31]. However, different individual daily trips and land use are examined in numerous crash investigations.…”