UBC Theses and Dissertations
Alternative approaches of identifying accident prone location Wang, Charlene
Despite the many advances in highway design, traffic engineering, automobile manufacturing, and police enforcement technologies, many countries around the world still suffer from an ever-increasing problem of traffic accidents. Therefore, there has been a keen interest throughout the world in developing Road Safety Improvement Programs (RSIPs) aimed at: 1) identifying accident-prone locations; 2) diagnosing their problems; and 3) suggesting proper countermeasures. While the success of these programs has varied considerably from one jurisdiction to another, the overall performance of these programs has been less than satisfactory in terms of number of accidents eliminated. The main reason for that is believed to relate to the inadequacy of procedures adopted in the execution of these programs. Most importantly, the faulty identification of accident-prone locations (i.e., identification of locations that are not really accident-prone) seems to be the primary reason behind the lack of success of these programs. This thesis discusses the problems encountered in developing and implementing RSIPs from an engineering perspective. It also describes the efforts in developing new techniques to make these programs more effective in identifying and treating accidentprone locations. Sayed (1995) has described two new techniques for the identification process. The first one is the Modified RSIP that alters the definition of accident-prone locations and introduces the concept of correctable accidents. The second one is the Countermeasure-Based RSIP that starts by identifying prevailing accident patterns and then suggest proper engineering countermeasures, thus effectively reversing the normal flow of procedures in traditional RSIPs. This thesis introduces further refinements to these new techniques. The classification process of accidents in the first method is refined using artificial neural networks and neuro-fuzzy models. Accident prediction models are used to identify over-represented accident patterns in the second method. These refinements significantly improve the results of the two methods. Examples of real-life applications are given and their results are discussed.
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