<http://purl.org/dc/terms/description>
"Despite the many advances in highway design, traffic engineering, automobile\r\nmanufacturing, and police enforcement technologies, many countries around the world\r\nstill suffer from an ever-increasing problem of traffic accidents. Therefore, there has been\r\na keen interest throughout the world in developing Road Safety Improvement Programs\r\n(RSIPs) aimed at: 1) identifying accident-prone locations; 2) diagnosing their problems;\r\nand 3) suggesting proper countermeasures. While the success of these programs has\r\nvaried considerably from one jurisdiction to another, the overall performance of these\r\nprograms has been less than satisfactory in terms of number of accidents eliminated. The\r\nmain reason for that is believed to relate to the inadequacy of procedures adopted in the\r\nexecution of these programs. Most importantly, the faulty identification of accident-prone\r\nlocations (i.e., identification of locations that are not really accident-prone) seems to be\r\nthe primary reason behind the lack of success of these programs.\r\nThis thesis discusses the problems encountered in developing and implementing RSIPs\r\nfrom an engineering perspective. It also describes the efforts in developing new\r\ntechniques to make these programs more effective in identifying and treating accidentprone\r\nlocations. Sayed (1995) has described two new techniques for the identification\r\nprocess. The first one is the Modified RSIP that alters the definition of accident-prone\r\nlocations and introduces the concept of correctable accidents. The second one is the\r\nCountermeasure-Based RSIP that starts by identifying prevailing accident patterns and\r\nthen suggest proper engineering countermeasures, thus effectively reversing the normal\r\nflow 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\r\nusing artificial neural networks and neuro-fuzzy models. Accident prediction models are\r\nused to identify over-represented accident patterns in the second method. These\r\nrefinements significantly improve the results of the two methods. Examples of real-life\r\napplications are given and their results are discussed."@en
<https://open.library.ubc.ca/cIRcle/collections/ubctheses/831/items/1.0064067>