Research on road safety has been of great interest to engineers and planners for decades. Regardless of modeling techniques, a serious factor of inaccuracy - in most past studies - has been data aggregation. Nowadays, most freeways are equipped with continuous surveillance systems making disaggregate traffic data readily available ; these have been used in few studies. In this context, the main objective of this dissertation is to capitalize highway traffic data collected on a real-time basis at the moment of accident occurrence in order to expand previous road safety work and to highlight potential further applications. To this end, we first examine the effects of various traffic parameters on type of road crash as well as on the injury level sustained by vehicle occupants involved in accidents, while controlling for environmental and geometric factors. Probit models are specified on 4-years of data from the A4-A86 highway section in the Ile-de -France region, France. Empirical findings indicate that crash type can almost exclusively be defined by the prevailing traffic conditions shortly before its occurrence. Increased traffic volume is found to have a consistently positive effect on severity, while speed has a differential effect on severity depending on flow conditions. We then establish a conceptual framework for incident management applications using real-time traffic data on urban freeways. We use dissertation previous findings to explore potential implications towards incident propensity detection and enhanced management
| Identifer | oai:union.ndltd.org:CCSD/oai:tel.archives-ouvertes.fr:tel-00626573 |
| Date | 01 December 2010 |
| Creators | Christoforou, Zoi |
| Publisher | Université Paris-Est |
| Source Sets | CCSD theses-EN-ligne, France |
| Language | English |
| Detected Language | English |
| Type | PhD thesis |
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