Synergizing Roadway Infrastructure Investment with Digital Infrastructure for Infrastructure-Based Connected Vehicle Applications: Review of Current Status and Future Directions

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.The safety, mobility, environmental and economic benefits of Connected and Autonomous Vehicles (CAVs) are potentially dramatic. However, realization of these benefits largely hinges on the timely upgrading of the existing transportation system. CAVs must be enabled to send and receive data to and from other vehicles and drivers (V2V communication) and to and from infrastructure (V2I communication). Further, infrastructure and the transportation agencies that manage it must be able to collect, process, distribute and archive these data quickly, reliably, and securely. This paper focuses on current digital roadway infrastructure initiatives and highlights the importance of including digital infrastructure investment alongside more traditional infrastructure investment to keep up with the auto industry's push towards this real time communication and data processing capability. Agencies responsible for transportation infrastructure construction and management must collaborate, establishing national and international platforms to guide the planning, deployment and management of digital infrastructure in their jurisdictions. This will help create standardized interoperable national and international systems so that CAV technology is not deployed in a haphazard and uncoordinated manner

Development and Performance Evaluation of a Connected Vehicle Application Development Platform (CVDeP)

Connected vehicle (CV) application developers need a development platform to build, test and debug real-world CV applications, such as safety, mobility, and environmental applications, in edge-centric cyber-physical systems. Our study objective is to develop and evaluate a scalable and secure CV application development platform (CVDeP) that enables application developers to build, test and debug CV applications in realtime. CVDeP ensures that the functional requirements of the CV applications meet the corresponding requirements imposed by the specific applications. We evaluated the efficacy of CVDeP using two CV applications (one safety and one mobility application) and validated them through a field experiment at the Clemson University Connected Vehicle Testbed (CU-CVT). Analyses prove the efficacy of CVDeP, which satisfies the functional requirements (i.e., latency and throughput) of a CV application while maintaining scalability and security of the platform and applications

A Multi-Objective Decision-Making Framework for Transportation Investments

This paper presents a framework based on multi-objective optimization that can be used to generate and analyze the most desirable transportation investment options based on their objectives and constraints. The framework, which is based on the surrogate worth trade-off analysis, could be applied to both discrete or continuous decision-problem scenarios. In a discrete problem, a pre-defined set of alternatives is available, whereas continuous problems are not characterized by a pre-defined set of alternatives. This framework was applied with the data generated for a Capital Beltway Corridor investment study. The multi-objective decision-making framework was found to be adaptable to this typical investment case study

A Multi-Objective Decision-Making Framework for Transportation Investments

This paper presents a framework based on multi-objective optimization that can be used to generate and analyze the most desirable transportation investment options based on their objectives and constraints. The framework, which is based on the surrogate worth trade-off analysis, could be applied to both discrete or continuous decision-problem scenarios. In a discrete problem, a predefined set of alternatives is available, whereas continuous problems are not characterized by a predefined set of alternatives. This framework was applied with the data generated for a Capital Beltway Corridor investment study. The multi-objective decision-making framework was found to be adaptable to this typical investment case study

Current Practice of Design, Delivery and Maintenance of Online Training for Transportation Professionals at Public Agencies

Transportation officials need to address the day-to-day challenges of both traffic demands and infrastructure needs to maintain the sustainability of the existing transportation system. While participating in the decision making process, professionals from any public agency (i.e. State Department of Transportation or DOT) need diverse knowledge and dynamic skills to identify any specific points of concern. In order to make the transportation professionals efficient in their job, different public agencies offer online trainings along with the traditional instructor-led courses. These online capacity building courses augment contemporary knowledge and technical skills, which empower professionals to perform their tasks proficiently. Apart from an extensive literature review, this study compiles the outcome of the survey among 10 different state DOTs online training. In addition, this study also summarizes the results from interviewing 6 different state DOTs, of which more than 80 percent, design and develop online trainings for their employees. While outcome of the online survey has provided a general overview regarding online training practiced by state DOTs, the telephone interviews has helped to obtain detailed insight about the design considerations, delivery methods and adopted strategies for developing effective online trainings

A Multi-Objective Decision-Making Framework for Transportation Investments

This paper presents a framework based on multi-objective optimization that can be used to generate and analyze the most desirable transportation investment options based on their objectives and constraints. The framework, which is based on the surrogate worth trade-off analysis, could be applied to both discrete or continuous decision-problem scenarios. In a discrete problem, a predefined set of alternatives is available, whereas continuous problems are not characterized by a predefined set of alternatives. This framework was applied with the data generated for a Capital Beltway Corridor investment study. The multi-objective decision-making framework was found to be adaptable to this typical investment case study