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

dc.contributor.advisorPasindu HR
dc.contributor.authorJayaratne DND
dc.date.accept2020
dc.date.accessioned2020
dc.date.available2020
dc.date.issued2020
dc.description.abstractRoad 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.identifier.accnoTH4448en_US
dc.identifier.degreeMaster of Philosophyen_US
dc.identifier.departmentDepartment of Civil Engineeringen_US
dc.identifier.facultyEngineeringen_US
dc.identifier.urihttp://dl.lib.uom.lk/handle/123/16934
dc.language.isoenen_US
dc.subjectCIVIL ENGINEERING- Dissertationen_US
dc.subjectURBAN ROADS MULTI-LANE ROADSen_US
dc.subjectURBAN MULTI-LANE ROADS – Capacity – Sri Lankaen_US
dc.subjectROADWAY CHARACTERISTICS– Sri Lankaen_US
dc.subjectHETEROGENEOUS TRAFFICen_US
dc.titleDevelopment of a model to evaluate capacity of urban multi-lane roads under heterogeneous traffic conditionsen_US
dc.typeThesis-Full-texten_US

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