Institutional-Repository, University of Moratuwa.  

Development of a model to evaluate capacity of urban multi-lane roads under heterogeneous traffic conditions

Show simple item record

dc.contributor.advisor Pasindu HR
dc.contributor.author Jayaratne DND
dc.date.accessioned 2020
dc.date.available 2020
dc.date.issued 2020
dc.identifier.uri http://dl.lib.uom.lk/handle/123/16934
dc.description.abstract Road capacity is defined as the maximum sustainable hourly flow rate at which vehicles can reasonably be expected to traverse a point or uniform section of a lane during a given time period under prevailing roadway and traffic conditions in the US Highway Capacity Manual. The knowledge of capacity of a given section of a road is an important input parameter for transport planning and traffic management studies. Presently, there aren’t any up-to-date guidelines for road capacity estimation in Sri Lanka. The use of foreign guidelines is not recommended as each country has unique factors that influence capacity. Since urban multi-lane roads are typically the busiest roads, this research study focuses on developing a capacity estimation model for urban multi-lane roads in Sri Lanka. Flow and speed data were collected using manual counting methods and Google Distance Matrix API (Application Program Interface) method respectively. The heterogeneous traffic flows were converted to Passenger Car Units (PCUs) using Chandra’s method. Greenshields’ traffic flow model was used to calibrate the empirical data. Capacity values were established from the developed flow-speed model. Using this method, the capacity values of all study locations were established. The average observed lane capacity was 1829 pcu/h/l. Regression models were developed to estimate capacity of four-lane and six-lane roads. It was observed that the four-lane road capacity was influenced by the effective lane width, access point density, built environment and median type whereas the six-lane capacity was influenced by the effective lane width and access point density. The four-lane capacity model had an R-squared value of 0.81 and the six-lane capacity model had an R-squared value of 0.86. The two models were combined to create a single model that predicts both 4-lane and 6-lane roads. In addition to the capacity models, a regression model was developed to estimate the Free Flow Speed (FFS) of roads. The predictor variables of the FFS model are lateral clearance, built environment and median type. Verification of developed models were done by surveying 10 road sections. It was observed that all three models accurately predicted flow and speed from the statistical tests done (Mean Absolute Percentage Error <10%). Important findings from the research study includes the development models to estimate four-lane and six-lane capacity values, and FFS. The typical base capacity for a 4-lane urban road was found to be 2044 pcu/h/l. The base capacity for a 6-lane sub-urban road section was estimated to be 2108 pcu/h/l. Even though the capacity values are comparable with capacity values in guidelines such as the HCM (1900-2200 pcu/h/l) since the speeds at capacity are in the range of 20km/h the traffic streams are susceptible to breakdown. The typical FFS of a rural road section with 2m lateral clearance and a center median was 50km/h. Sub-urban and urban road sections with similar conditions have 36km/h and 35km/h FFS speeds respectively. The findings of this research can be used for transport planning and traffic engineering studies in Sri Lanka as well as for further research in the area of capacity estimation. en_US
dc.language.iso en en_US
dc.subject CIVIL ENGINEERING- Dissertation en_US
dc.subject URBAN ROADS MULTI-LANE ROADS en_US
dc.subject URBAN MULTI-LANE ROADS – Capacity – Sri Lanka en_US
dc.subject ROADWAY CHARACTERISTICS– Sri Lanka en_US
dc.subject HETEROGENEOUS TRAFFIC en_US
dc.title Development of a model to evaluate capacity of urban multi-lane roads under heterogeneous traffic conditions en_US
dc.type Thesis-Full-text en_US
dc.identifier.faculty Engineering en_US
dc.identifier.degree Master of Philosophy en_US
dc.identifier.department Department of Civil Engineering en_US
dc.date.accept 2020
dc.identifier.accno TH4448 en_US


Files in this item

This item appears in the following Collection(s)

Show simple item record