Modeling the Complexity of Road Accidents Prevention: A System Dynamics Approach

Modeling the Complexity of Road Accidents Prevention: A System Dynamics Approach

Alex Kizito (Kyambogo University, Kampala, Uganda) and Agnes Rwashana Semwanga (Makerere University College of Computing and Information Sciences, Kampala, Uganda)
Copyright: © 2020 |Pages: 18
DOI: 10.4018/IJSDA.2020040102

Abstract

Simplistic representations of traffic safety disregard the dynamic interactions between the components of the road transport system (RTS). The resultant road accident (RA) preventive measures are consequently focused almost solely on individual/team failures at the sharp end of the RTS (mainly the road users). The RTS is complex and therefore cannot be easily understood by studying the system parts in isolation. The study modeled the occurrence of road accidents in Uganda using the dynamic synthesis methodology (DSM). This article presents the work done in the first three stages of the DSM. Data was collected from various stakeholders including road users, traffic police officers, road users, and road constructors. The study focused on RA prevention by considering the linear and non-linear interactions of the variables during the pre-crash phase. Qualitative models were developed and from these, key leverage points that could possibly lower the road accident incidences demonstrating the need for a shared system wide responsibility for road safety at all levels are suggested.
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Introduction

Computer simulation provides a powerful tool that can be used to model and understand complex systems from the macro systems level to the micro genetic level. The application of System Dynamics (SD) has grown extensively and the availability of a variety of more sophisticated simulation software has significantly expanded the role of simulation in research, policy making and operational decisions (Greasley, 2017; Abar et al., 2017; Maani & Cavana, 2007, Zelinka & Amadei, 2019). Azar (2012) describes System Dynamics as a powerful methodology and computer simulation modelling technique for framing, understanding and discussing complex issues and problems in business, ecology, medical and social systems, engineering to mention a few. Computer models are used extensively in many areas of systems management to provide insight into the working of a system. This is particularly useful when the system is complex and/or when experimentation is not possible such as the road transport system (Urban et al., 2017; Pierce et al., 2019). SD has been used in a number of public transportation systems (Bajracharya, 2016; Elkady et al., 2016; Spichkova, 2016).

Bajracharya (2016) in a study to understand the public transportation system in Dubai used the System Dynamics approach and found out that it was challenging to motivate individuals to change from private car transport to public transportation. Elkady et al. (2016), in a study to investigate the effect of vehicle dynamics on collision of vehicles used 2 models. The first model demonstrated vehicle body crash parameters and the second model aimed to predict the effect of vehicle dynamics control system (VDCS) which showed that the VDCS can positively affect the crash characteristics and improve occupant behavior. Spichkova (2016) in a study to understand the dynamic decision-making system for public transport routes which focused on environmental, societal, spatial planning and optimization for smarter cities and user satisfaction for passengers and drivers. Cruz-Cantillo (2014) built a system dynamics model for the forecasting, prioritization, and distribution of critical supplies during relief operations in case of a hurricane event, while integrating GIS information. The model was used to for decision making, simulation of the behavior of key variables, estimate the supplies needed and the routes to use for delivery of supplies.

SD has been used to analyze traffic management aspects including safety improvement (Shire et al., 2018), traffic safety policy (Goh & Love, 2012), the underlying causes of organizational accidents (Goh et al., 2010a; Goh et al., 2012), and combat vehicle accidents (Minami & Madnick, 2009). Sterman (2000), Cooke and Rohleder (2006) and latterly Goh et al. (2010a) have advocated for the introduction of Systems thinking to the analysis of major accidents.

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