Term of Award

Spring 2023

Degree Name

Master of Science, Computer Science (M.S.C.S.)

Document Type and Release Option

Thesis (open access)

Copyright Statement / License for Reuse

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.


Department of Computer Science

Committee Chair

Ryan Florin

Committee Member 1

Ryan Florin

Committee Member 2

Andrew Allen

Committee Member 3

Lixin Li

Committee Member 3 Email



As vehicles become more modern, a large majority of vehicles on the road will have the required sensors to smoothly interact with other vehicles and infrastructure on the road. There will be many benefits of this new connectivity between vehicles on the road but one of the most profound improvements will be in the area of road accident prevention. Vehicles will be able to share information vital to road safety to oncoming vehicles and vehicles that are occluded so they do not have a direct line of sight to see a pedestrian or another vehicle on the road.

Another advantage of these modern connected vehicles is that different traffic parameters can be more easily estimated using the onboard sensors and technologies in the vehicles. For many decades traffic engineers have been able to estimate different traffic parameters like traffic flow, density, and velocity based on how many vehicles the primary vehicle passes and how many vehicles pass the primary vehicles. For much of the time that traffic engineers have been working on traffic estimation, it has been done using more manual and tedious methods. In this paper, a more novel approach of determining these traffic parameters is used.

Also, one of the problems with traffic parameter estimation is that sometimes the results are not accurate because of vehicles that might not have been counted because of occlusion. In this paper, a proposal is put forward on how this can be remedied utilizing the connected vehicle's framework.

Research Data and Supplementary Material