Options for enhancing network-wide annual average daily truck volume estimates
| dc.contributor.author | Zrobek, Cassidy | |
| dc.contributor.examiningcommittee | Mehran, Babak (Civil Engineering) | |
| dc.contributor.examiningcommittee | Mufti, Aftab (Civil Engineering) | |
| dc.contributor.supervisor | Regehr, Jonathan | |
| dc.date.accessioned | 2023-09-04T17:14:34Z | |
| dc.date.available | 2023-09-04T17:14:34Z | |
| dc.date.issued | 2023-08-22 | |
| dc.date.submitted | 2023-08-23T03:52:40Z | en_US |
| dc.degree.discipline | Civil Engineering | en_US |
| dc.degree.level | Master of Science (M.Sc.) | |
| dc.description.abstract | This research presents two projects that investigate options for enhancing network-wide annual average daily truck traffic volume estimates. Annual average daily traffic (AADT) and annual average daily truck traffic (AADTT) are valuable traffic statistics that are required for safety, planning, operation, design, and environmental applications. However, it is challenging to obtain estimates of AADT and AADTT across Canada’s vast highway network due to resource limitations. It is even more difficult to obtain annual average estimates of traffic volumes by specific classes of trucks, which are used for applications such as mechanistic-empirical pavement design, bridge evaluation, and asset management. The first research project investigates how annual average daily truck volumes can be enhanced by improving traffic count sampling strategies and technologies. To help determine the count duration needed to obtain a sufficient sample of vehicle volumes by class, the study uses continuous classification count data to simulate short-duration counts of 1 to 8 days. The change in the variability of the simulated count volumes with duration was used as an indicator of the accuracy of the AADTT estimates they would produce. The results showed that at most sites, a 7-day count of trucks could reach the same level of variability as a 1-day count of total traffic. In terms of reductions in count variability, there tended to be diminishing returns beyond a 2-day or 3-day count of total traffic and beyond a 6-day count of truck traffic. The second research project assesses the use of commercially available probe-based data products for truck volume estimation. The study evaluates the accuracy of total traffic and truck traffic estimates obtained from the third-party data provider StreetLight Data by comparing them to volume estimates from conventional traffic counts. This work contributes new knowledge by being the first evaluation of StreetLight Data’s medium-duty and heavy-duty truck indices in North America. The findings indicated that the probe-based AADT and heavy-duty AADTT estimates had the highest and lowest accuracy, respectively. Further, it was found that probe-based AADT and AADTT estimates were reasonably similar to the estimates obtained from short-duration counts with mean absolute percent differences of approximately 25%. | |
| dc.description.note | October 2023 | |
| dc.description.sponsorship | National Research Council Canada, International Road Dynamics, Inc., Institute of Transportation Engineers Canada, Transportation Association of Canada, Canadian Transportation Research Forum | |
| dc.identifier.uri | http://hdl.handle.net/1993/37559 | |
| dc.language.iso | eng | |
| dc.rights | open access | en_US |
| dc.subject | Truck Volumes | |
| dc.subject | AADTT | |
| dc.subject | Truck Traffic Monitoring | |
| dc.subject | Probe Data | |
| dc.subject | StreetLight Data | |
| dc.subject | Vehicle Classification | |
| dc.subject | Short-Duration Counts | |
| dc.title | Options for enhancing network-wide annual average daily truck volume estimates | |
| dc.type | master thesis | en_US |
| local.subject.manitoba | yes | |
| oaire.awardTitle | University of Manitoba Graduate Fellowship | |
| oaire.awardURI | https://umanitoba.ca/graduate-studies/funding-awards-and-financial-aid/university-manitoba-graduate-fellowship-umgf | |
| project.funder.identifier | https://doi.org/10.13039/100010318 | |
| project.funder.name | University of Manitoba |