Investigating The Impacts of Adverse Road Weather Conditions on Free-Flow Speed and Lane Utilization

Simple item page
dc.contributor.advisorSharma, Satish
dc.contributor.advisorMehran, Babak
dc.contributor.authorRillagodage, Navoda Yasanthi
dc.contributor.committeememberJin, Yee-Chung
dc.contributor.committeememberNg, Kelvin Tsun Wai
dc.contributor.externalexaminerPeng, Wei
dc.date.accessioned2019-06-21T19:30:50Z
dc.date.available2019-06-21T19:30:50Z
dc.date.issued2018-11
dc.descriptionA Thesis Submitted to the Faculty of Graduate Studies and Research In Partial Fulfillment of the Requirements for the Degree of Master of Applied Science in Environmental Systems Engineering, University of Regina. ix, 106 p.en_US
dc.description.abstractWinter driving poses additional safety challenges to drivers and other road users due to reduced visibility, snow and ice accumulation on travel lanes, and variations in transportation infrastructure such as reduced lane width due to snow plow operations and coverage of lane markings by snow. On the other hand, road-weather related variations in transportation infrastructure characteristics and driver behavior negatively impact traffic operations in winter. Therefore, it is crucial to understand the anticipated consequences of extreme road-weather conditions on traffic flow parameters and driver behavior. Thus, in this study, the free-flow speed and the lane utilization patterns under adverse roadweather conditions were studied. Road-weather data were collected from a Road-Weather Information System (RWIS) and traffic data were collected from a Weigh-In-Motion (WIM) station located in Highway 16 in Alberta from October, 2014 to December, 2015. The first part of the study attempts to investigate drivers’ choice of desired speed under adverse road-weather conditions with two main objectives: i) to model the impacts of road-weather events on free-flow speed of light and heavy vehicles, and ii) to explore the variability in free-flow speed models caused by adopting different data analysis methods. Separate linear and nonlinear regression models were developed by adopting three distinct statistical approaches i.e. i) with dependent variable as individual vehicle speeds, ii) with dependent variable as 20 minute aggregate vehicle speeds, and iii) with sampled data. The study results revealed that slight, moderate and heavy snow will reduce the free-flow speed of light vehicles travelling in shoulder lane by 0.2%, 3.4% and 0.8% and the free-flow speed of heavy vehicles by 1.7%, 0.1% and 1.3% respectively. Further, it was unveiled that the drivers’ reaction to ice warning pavement surface conditions is maximum. Linear regression models with aggregated speeds as the dependent variable was concluded as the best fit for the data among the other statistical approaches tested. The second part of the study aims to model lane utilization patterns of light and heavy vehicles under different road-weather conditions in macroscopic and microscopic facets. Two main types of lane utilization models were developed exerting a linear and a logistic regression approach in modelling. Each main model contains three sub models with refined data corresponding to all light and heavy vehicles. Interestingly, moderate rain and nighttime cause the vehicles to prefer shoulder lane irrespective of the vehicle type as suggested by both models. The findings indicated that heavy vehicles tend to choose the median lane under icy pavement conditions as estimated by both model types. Light vehicles tend to utilize the median lane under ice watch and frost pavement conditions in macroscopic and microscopic contexts, respectively. Unlike the other similar studies conducted so far, this study estimates the free-flow speeds and lane utilization patterns of vehicles more accurately because of the unique location configuration of the data collectors. In addition, the study results direct the weather responsive traffic management systems to a new path by introducing more dimensions to be considered about, such as variable speed limits depending on different road-weather conditions and traffic composition, and dynamic lane utilization schemes.en_US
dc.description.authorstatusStudenten
dc.description.peerreviewyesen
dc.identifier.tcnumberTC-SRU-8858
dc.identifier.thesisurlhttps://ourspace.uregina.ca/bitstream/handle/10294/8858/Rillagodage_Navoda_MASC_EVSE_Spring2019.pdf
dc.identifier.urihttps://hdl.handle.net/10294/8858
dc.language.isoenen_US
dc.publisherFaculty of Graduate Studies and Research, University of Reginaen_US
dc.titleInvestigating The Impacts of Adverse Road Weather Conditions on Free-Flow Speed and Lane Utilizationen_US
dc.typeThesisen
thesis.degree.departmentFaculty of Engineering and Applied Scienceen_US
thesis.degree.disciplineEngineering - Environmental Systemsen_US
thesis.degree.grantorUniversity of Reginaen
thesis.degree.levelMaster'sen
thesis.degree.nameMaster of Applied Science (MASc)en_US
Files
Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
Rillagodage_Navoda_MASC_EVSE_Spring2019.pdf
Size:
1.75 MB
Format:
Adobe Portable Document Format
Description:
Download
License bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
license.txt
Size:
2.22 KB
Format:
Item-specific license agreed upon to submission
Description:
Download
Collections
Master's Theses