Crash prediction model on horizontal curves for two lane rural roads in the south of Iran by negative binomial model

Ali Aram (P42531)

Please use this identifier to cite or link to this item: https://ptsldigital.ukm.my/jspui/handle/123456789/487262

Full metadata record

DC Field	Value	Language
dc.contributor.advisor	Amiruddin Ismail, Professor Engr. Dr.	-
dc.contributor.author	Ali Aram (P42531)	-
dc.date.accessioned	2023-10-11T02:31:29Z	-
dc.date.available	2023-10-11T02:31:29Z	-
dc.date.issued	2013-06-20	-
dc.identifier.other	ukmvital:74766	-
dc.identifier.uri	https://ptsldigital.ukm.my/jspui/handle/123456789/487262	-
dc.description	Research carried out in Iran and some of the developing countries indicated that 50% to 60% of crashes in the suburban areas occurred on two lane rural roads. More than half of these crashes happened along the arches and 20% took place along the horizontal curves. Road crashes were the second-leading cause of death in the Kohgiluyeh Boyerahmad province in the South of Iran after infectious and non-infectious diseases (myocardial infection, cerebral vascular diseases, and cancers). The review of crashes data between 2008 to 2010 suggested the two lane rural roads leading to the Yasuj city of Kohgiluyeh Boyerahmad province were potentially crash prone area. The aim of this study is to identify factors caused vehicle crashes on horizontal curves in two lane rural roads. The selected routes were divided into seven road types based on road length, accident information, traffic conditions and geometric design of horizontal curves for access to related information. The data on these routes were classified into five different dataset categories namely field road inventory, traffic volume, horizontal curve, cross sections and related crash data for Kohgiluyeh Boyerahmad. Then the crash dataset on horizontal curves were abstracted and their effective variables were calculated using Statistical Package for Social Science (SPSS) and Microsoft Excel (MS EXCEL) software. The crash dataset provide comprehensive information about each crash recorded on horizontal curves of the Kohgiluyeh Boyerahmad's road network. By applying Data Analysis and Statistical Software (STATA) and SPSS to the crash dataset the most significant independent variables were identified namely radius horizontal curve (Rc), deflection angle curve (Δc), superelevation curve (Ec), and average daily traffic (ADT) and these independent variables were used for crash prediction modelling. Nine potential crash prediction models on horizontal curves (CPMHC) on two lane rural roads were developed and the optimum model is CPMHC6 = Exp [(-2.1760 - 0.000279 (Rc) + 0.010474 (Δc) + 0.101199 (Ec) + 0.000164 (ADT)]. The priority and relative importance of these variables were ADT, Ec, Δc, and Rc respectively. From optimum crash prediction model, study found the critical horizontal curve dimensions on two lane rural roads namely Δc and Rc were equal to 22.55º and 558 metres respectively. However, the design and crash rate reduction for horizontal curves on two lane rural roads were recommended the variable values for maximum Ec, Δc and minimum Rc would be equal to 5%, 20o and 600 metres respectively.,Ph.D	-
dc.language.iso	eng	-
dc.publisher	UKM, Bangi	-
dc.relation	Faculty of Engineering and Built Environment / Fakulti Kejuruteraan dan Alam Bina	-
dc.rights	UKM	-
dc.subject	Rural roads	-
dc.subject	Iran	-
dc.subject	Rural roads -- Iran	-
dc.title	Crash prediction model on horizontal curves for two lane rural roads in the south of Iran by negative binomial model	-
dc.type	Theses	-
dc.format.pages	263	-
dc.identifier.callno	TE229.A733 2013 3	-
dc.identifier.barcode	000305	-
Appears in Collections:	Faculty of Engineering and Built Environment / Fakulti Kejuruteraan dan Alam Bina

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