An Integrated Dynamic Traffic Assignment–Bayesian Belief Networks Methodology to Assess Traffic Safety

Abstract

Traffic conditions significantly affect drivers‟ behavior that constitutes one of the principal causes of road accidents. Being able to accurately predict traffic conditions significantly improves the assessment of accident risk. Traditional approaches to traffic flow analysis use static representations of the road system and as such have limited accuracy. Dynamic Traffic Assignment (DTA) approaches better utilize temporal aspects of such system (where and when to travel on the road network) to produce better predictions. The work presented herein integrates two mature methodologies namely simulation-based DTA and Bayesian Networks (BN) to address this problem. The former is widely used in transport modeling to predict driver‟s travel behavior while the later constitute a powerful artificial intelligence technique that predicts effects given prior knowledge and input evidence in uncertain settings. The integration of BN with the DTA-based simulator, Visual Interactive Systems for Transport Algorithms (VISTA), provides the framework for improved safety evaluation of road networks and future planning.