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., vehicle-centric, 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.

Vehicle-to-Vehicle (V2V) communication systems have an eminence potential to improve road safety and optimize traffic flow by broadcasting Basic Safety Messages (BSMs). Dedicated Short-Range Communication (DSRC) and LTE Vehicleto-Everything (V2X) are two candidate technologies to enable V2V communication. DSRC relies on the IEEE 802.11p standard for its PHY and MAC layer while LTE-V2X is based on 3GPP's Release 14 and operates in a distributed manner in the absence of cellular infrastructure. There has been considerable debate over the relative advantages and disadvantages of DSRC and LTE-V2X, aiming to answer the fundamental question of which technology is most effective in real-world scenarios for various road safety and traffic efficiency applications. In this paper, we present a comprehensive survey of these two technologies (i.e., DSRC and LTE-V2X) and related works. More specifically, we study the PHY and MAC layer of both technologies in the survey study and compare the PHY layer performance using a variety of field tests. First, we provide a summary of each technology and highlight the limitations of each in supporting V2X applications. Then, we examine their performance based on different metrics.

Connected vehicle (CV) technology has great potential to improve the performance of today's advanced driver assistance systems in terms of safety, energy efficiency, and driving comfort. The aim of this paper is to develop a specific CV application that assists with lane selection, i.e., finding the best travel lane in terms of travel time based on predicted lane-level traffic states. In this paper, a spatial-temporal model (ST-model) was developed, which utilizes spatial and temporal information of road cells to predict future traffic states. This information was used by the proposed lane selection assistance application to select an optimal lane sequence for the application-equipped vehicle. A comprehensive simulation-based evaluation was then conducted under various scenarios, e.g., with different traffic volumes, penetration rates of communication-capable vehicles, and information update cycles. The evaluation results reveal several interesting findings, including: 1) the proposed ST-model outperforms the basic estimation model in terms of traffic state prediction accuracy; 2) travel times of application-equipped vehicles can be reduced by up to 8% with the use of the proposed lane selection assistance application when compared with the baseline, under various traffic scenarios; 3) the application can be effective in the early deployment stage of CV technology, where the penetration rate of communication-capable vehicles is still low; and 4) the potential conflict risk of application-equipped vehicles is reduced, although the application is mainly designed for mobility benefits, due to the more strategic and informed lane changes suggested by the proposed application.

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