Contemporary research
indicates that the era of autonomous vehicles (AVs) is not only inevitable but
may be reached sooner than expected; however, not enough research has been done
to address road infrastructure readiness for supporting AV operations. Highway
agencies at all levels of governments seek to identify the needed
infrastructure changes to facilitate the successful integration of AVs into the
existing roadway system. Given multiple sources of uncertainty particularly the
market penetration of AVs, agencies find it difficult to justify the
substantial investments needed to make these infrastructure changes using
traditional value engineering approaches. It is needed to account for these
uncertainties by doing a phased retrofitting of road infrastructure to keep up
with the AV market penetration. This way, the agency can expand, defer, or
scale back the investments at a future time. This dissertation develops a real
options analysis (ROA) framework to address these issues while capturing the
monetary value of investment timing flexibility. Using key stakeholder feedback,
an extensive literature review, and discussions with experts, the needed
AV-motivated changes in road infrastructure were identified across two stages
of AV operations; the transition phase and the fully-autonomous phase. For a
project-level case study of a 66-mile stretch of Indiana’s four-six lane
Interstate corridor, two potential scenarios of infrastructure retrofitting
were established and evaluated using the net present value (NPV) and ROA
approaches. The results show that the NPV approach can lead to decisions at the
start of the evaluation period but does not address the uncertainty associated
with AV market penetration. In contrast, ROA was found to address uncertainty
by incorporating investment timing flexibility and capturing its monetary
value. Using the dissertation’s framework, agencies can identify and analyze a
wide range of possible scenarios of AV-oriented infrastructure retrofitting to
enhance readiness, at both the project and network levels.
| Identifer | oai:union.ndltd.org:purdue.edu/oai:figshare.com:article/8949011 |
| Date | 15 August 2019 |
| Creators | Tariq Usman Saeed (6992318) |
| Source Sets | Purdue University |
| Detected Language | English |
| Type | Text, Thesis |
| Rights | CC BY 4.0 |
| Relation | https://figshare.com/articles/Road_Infrastructure_Readiness_for_Autonomous_Vehicles/8949011 |
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