Investment and Financing of Roadway Digital Infrastructure for Automated Driving

Simple item page
dc.contributor.advisorMehdi Nourinejad
dc.contributor.authorMohammadAmir Ahmadian Shahreza
dc.date.accessioned2024-11-07T11:14:06Z
dc.date.available2024-11-07T11:14:06Z
dc.date.copyright2024-08-02
dc.date.issued2024-11-07
dc.date.updated2024-11-07T11:14:05Z
dc.degree.disciplineCivil Engineering
dc.degree.levelMaster's
dc.degree.nameMASc - Master of Applied Science
dc.description.abstractConnected automated vehicles (CAVs) rely on sensors to scan their environment, enabling efficient decision-making, though their limited range poses challenges. Enhancing CAV operations by leveraging cooperative sensing via vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications offers ways to improve autonomy. This study examines optimal investments in vehicular connectivity and stationary sensor deployment under different traffic conditions. Our results highlight the trade-off between roadside stationary sensors and CAV-mounted sensors. Results show that for low traffic flow and constrained budgets, infrastructure investment is preferable, while higher traffic flow favors connectivity among CAVs. Additionally, the analysis shows that an optimal toll cannot fully cover digital infrastructure costs, though if safety benefits are factored in, covering the costs of constructing such infrastructure becomes feasible. The self-financing theorem also holds for the case of digitalization of existing roads if their flow-capacity ratio exceeds a certain threshold.
dc.identifier.urihttps://hdl.handle.net/10315/42486
dc.languageen
dc.rightsAuthor owns copyright, except where explicitly noted. Please contact the author directly with licensing requests.
dc.subjectCivil engineering
dc.subjectTransportation planning
dc.subject.keywordsTransportation engineering
dc.subject.keywordsAutomated vehicles
dc.subject.keywordsConnected automated vehicles
dc.subject.keywordsCooperative sensing
dc.subject.keywordsVehicular connectivity
dc.subject.keywordsInvestment policy
dc.titleInvestment and Financing of Roadway Digital Infrastructure for Automated Driving
dc.typeElectronic Thesis or Dissertation

Files

Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
AhmadianShahreza_MohammadAmir_2024_Masters.pdf
Size:
3.52 MB
Format:
Adobe Portable Document Format
Download

Collections

ETD SWORD Deposit

All items in the YorkSpace institutional repository are protected by copyright, with all rights reserved except where explicitly noted.