Please use this identifier to cite or link to this item: http://localhost:8081/xmlui/handle/123456789/7720
Title: MACROSCOPIC TRAFFIC FLOW MODELING OF SIX LANE HIGHWAYS
Authors: Gorani, Harish
Keywords: CIVIL ENGINEERING;MACROSCOPIC TRAFFIC FLOW MODELING;SIX LANE HIGHWAYS;TRANSPORT SECTOR
Issue Date: 2010
Abstract: Transport sector plays a very significant role in improving the economic development of any nation. Speed- flow relationships are major research items for many researchers and still this research is going on to establish full fledged relationships and subsequent roadway capacities realistically. The Government of India during the last decades has drawn up huge road capacity augmentation measures through the implementation of various ongoing National Highway Development Program (NHDP) projects like Golden Quadrilateral, North-South, East-West and some Expressway corridors. These projects are principally aimed towards developing high speed multi-lane corridors to link major cities. These radical changes in road network and vehicle technology have resulted in variations in speed-flow characteristics and subsequently road user costs. In this regard, it is essential to establish more realistic speed-flow relationship for different vehicle type under different conditions of road and traffic especially multi-lane high speed corridors. In the present study, linear regression and neural network models have been developed for speed-flow equations for different vehicles on each lane of selected road sections of six-lane divided carriageway. Data collected at four sections of six- lane divided carriageway having different physical conditions and varying traffic composition were analyzed. Dynamic PCU factors of different vehicles at different sections were also found out. Subsequently the speed flow models were used to estimate roadway capacity. From this study, the estimated capacity of Six lane divided carriageway by linear speed flow relationship is 6675 PCU/hr/direction, when dynamic PCU values is used for speed flow relationship capacity is 6150 PCU/hr/direction and by neural network model the capacity is 7680 PCU/hr/direction.
URI: http://hdl.handle.net/123456789/7720
Other Identifiers: M.Tech
Research Supervisor/ Guide: Velmurugan, S.
Jain, S. S.
metadata.dc.type: M.Tech Dessertation
Appears in Collections:MASTERS' DISSERTATIONS (Civil Engg)

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