The currently available release of the AASHTO Highway Safety Manual (HSM) does not include a model referred to freeways and interchanges. For this aim the NCHRP Project 17-45 has recently developed specific Crash Prediction Models for this type of infrastructures. A key issue to make the HSM a standard for road owners and managers world-wide is the transferability of the prediction models to different networks. An extensive study has been conducted on the primary Italian motorway network in order to evaluate the potential issues that occur applying this methodology to a network characterized by different environmental conditions, road characteristics, drivers’ attitude and crash reporting systems, as compared to the ones where the HSM models have been developed. The freeway network considered in this study, approximately 6700 km long, has been represented by 56 freeway sections characterized by an average length of about 12.5 km, covering 700 km of freeway distributed along all the country. Four different calibration factors were obtained for freeway segments depending on crash severity and type, and two calibration factors were obtained for speed-change lanes depending on crash severity. The results show a good transferability of the analyzed models to the Italian network and especially the freeway models for fatal and injury crashes even though a tendency to underestimate high crash frequency locations has been observed. Some improvements could be made considering variable calibration factors within the datasets or crash modification factors local calibrations. The need for an improved localization of the crash data on the Italian road network has also been highlighted, mainly for speed-change lanes.

Transferability of the Highway Safety Manual Freeway Model to the Italian Motorway Network / Francesca La Torre; Lorenzo Domenichini; Francesco Corsi; Francesco Fanfani. - In: TRANSPORTATION RESEARCH RECORD. - ISSN 0361-1981. - STAMPA. - 2435:(2014), pp. 61-71. [10.3141/2435-08]

Transferability of the Highway Safety Manual Freeway Model to the Italian Motorway Network

LA TORRE, FRANCESCA;DOMENICHINI, LORENZO;
2014

Abstract

The currently available release of the AASHTO Highway Safety Manual (HSM) does not include a model referred to freeways and interchanges. For this aim the NCHRP Project 17-45 has recently developed specific Crash Prediction Models for this type of infrastructures. A key issue to make the HSM a standard for road owners and managers world-wide is the transferability of the prediction models to different networks. An extensive study has been conducted on the primary Italian motorway network in order to evaluate the potential issues that occur applying this methodology to a network characterized by different environmental conditions, road characteristics, drivers’ attitude and crash reporting systems, as compared to the ones where the HSM models have been developed. The freeway network considered in this study, approximately 6700 km long, has been represented by 56 freeway sections characterized by an average length of about 12.5 km, covering 700 km of freeway distributed along all the country. Four different calibration factors were obtained for freeway segments depending on crash severity and type, and two calibration factors were obtained for speed-change lanes depending on crash severity. The results show a good transferability of the analyzed models to the Italian network and especially the freeway models for fatal and injury crashes even though a tendency to underestimate high crash frequency locations has been observed. Some improvements could be made considering variable calibration factors within the datasets or crash modification factors local calibrations. The need for an improved localization of the crash data on the Italian road network has also been highlighted, mainly for speed-change lanes.
2014
2435
61
71
Francesca La Torre; Lorenzo Domenichini; Francesco Corsi; Francesco Fanfani
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Utilizza questo identificatore per citare o creare un link a questa risorsa: https://hdl.handle.net/2158/930733
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