Measuring the benefits of civil systems connectivity and automation – a discussion in the context of highway transport

ABSTRACT

Connectivity refers to the ability of civil engineering system components to transmit/receive data for making strategic, tactical and operational decisions towards enhanced efficiency, effectiveness, and lower costs to the system stakeholders. Automation is the capability of a system or its component to carry out control functions or decisions that are traditionally done by humans. As the benefits of these two technologies become increasingly apparent in various civil engineering disciplines, it seems useful to measure such impacts to generate information for evaluating the feasibility of past or prospective future applications related to connectivity and automation (C/A) or for comparing alternative C/A applications. This paper discusses some constructs for measuring the benefits or effectiveness (MOEs) of C/A applications. An MOE expressed in terms of an appropriate system performance indicator (SPI) can help ascertain the extent to which a C/A application has accomplished or is expected to accomplish its specified goals or to compare the efficacy of alternative C/A applications. This paper identifies a number of SPIs, and establishes a number of MOEs for measuring the effectiveness of C/A applications in terms of the relevant SPIs. The paper provides illustrations to the concepts discussed in the context of highway transport.

KEYWORDS:

• Connectivity
• system performance
• automation benefits
• autonomous vehicles
• connected vehicles

Acknowledgments

The contents of this paper reflect the views of the author, who is responsible for the facts and the accuracy of the data presented herein, and do not necessarily reflect the official views or policies of the sponsoring organization. I herein acknowledge the contributions of CCAT staff including Dr. Mohammad Miralinaghi and Dr. Sikai Chen (post-doctoral fellows), and Bortiorkor Alabi, Jiqian Frank Dong, Paul Young Joun Ha, Runjia Rayne Du, Shuya Dong, and Yujie Li (graduate researchers).

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported by Purdue University’s Center for Connected and Automated Transportation (CCAT), a part of the larger CCAT consortium, a U.S. Department of Transportation Region 5 University Transportation Center funded by the [grant number: USDOT Award #69A3551747105].