Connected Vehicles (CVs) has the potential to serve as a valuable source of traffic data in some transportation research areas from traffic state monitoring to transportation management and control strategies, due to the low cost, wide coverage, and relative high accuracy. CV technology also enables a variety of CV-based applications which present an opportunity to provide vehicles and drivers with situational awareness and improve upon the limited behaviors of conventional vehicles. Increasing efforts in the development and deployment of CV-enabled applications are ongoing to improve traffic safety, mobility, environmental sustainability, efficiency, and driving comfort. CVs have been analyzed separately for safety, mobility, and environmental sustainability (SME), however, very few studies evaluate these three performance measures holistically. There are important co-benefits and tradeoffs among SME when evaluating impacts CVs bring.

In this dissertation research, an integrated holistic analysis framework has been developed to evaluate CV applications from SME impacts both qualitatively and quantitatively. Furthermore, a unique innovative use of “entropy” has been developed and applied in the transportation field to help evaluate CVs holistically. Under the developed qualitative analysis framework for SME, a series of innovative CV-based applications have been developed, taking the SME tradeoffs and co-benefits into consideration. These developed CV-based applications include Lane Speed Monitoring (LSM), Optimal Lane Selection (OLS) application, and Cooperative Smart Lane Selection (CSLS) application. Specifically, the LSM guides the vehicle driver to the fastest lane by utilizing short-range downstream vehicle information, helping the vehicle driver achieve mobility benefits. The OLS adopts longer-range connectivity and guides the application-equipped vehicle driver to go through a micro-route to reduce the travel time and the potential conflict risk of each individual application-equipped vehicle, obtaining mobility and safety co-benefits. The CSLS application is designed from the cooperative perspective which can be regarded as an evolved version on top of LSM, to increase mobility for the overall traffic system. These CV applications can be dynamically managed under various traffic conditions and users’ needs. As a future work, an ideal goal is to develop applications with all SME co-benefits considered by using strategies such as combining different applications.

In this whitepaper, the authors briefly describe the three major MOEs, followed by a categorization summary based on the most recent literature. Next, a number of typical CAV applications have been examined in depth, providing a detailed analysis of the different MOEs co-benefits and tradeoffs. Further, three representative CAV applications have been examined in detail in order to show the association between the application focus and tradeoffs/co-benefits of different performance measures. The CAV applications include High Speed Differential Warning (safety-focused), Lane Speed Monitoring (mobility-focused), and Eco-Speed Harmonization (environmental impacts-focused). The authors then highlight several future research directions, including the identification of key influential factors on system performance and how to obtain co-benefits across all key MOEs. The overall intent of this whitepaper is to inform practitioners and policy makers on the potential interactions between the safety, mobility, and environmental sustainability goals of implementing specific CAV applications as part of their ITS programs.

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A number of Connected and/or Automated Vehicle (CAV) applications have recently been designed to improve the performance of our transportation system. Safety, mobility and environmental sustainability are three cornerstone performance metrics when evaluating the benefits of CAV applications. These metrics can be quantified by various measures of effectiveness (MOEs). Most of the existing CAV research assesses the benefits of CAV applications on only one (e.g., safety) or two (e.g., mobility and environment) aspects, without holistically evaluating the interactions among the three types of MOEs. This paper first proposes a broad classification of CAV applications, i.e., vehiclecentric, infrastructure-centric, and traveler-centric. Based on a comprehensive literature review, a number of typical CAV applications have been examined in great detail, where a categorized analysis in terms of MOEs is performed. Finally, several conclusions are drawn, including the identification of influential factors on system performance, and suggested approaches for obtaining co-benefits across different types of MOEs.

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