## 1 Introduction  
By providing real-time, highly reliable, and actionable information flows, cellular vehicle-to-everything (C-V2X) seeks to redefine transportation by enabling better informed decisions. C-V2X can enhance road safety, increase traffic flow efficiency, reduce vehicular environmental impacts, and provide travelers with new valuable communication services. C-V2X will serve as the platform to V2X providing 360\({}^{\circ}\) non-line-of-sight awareness and a higher level of predictability for enhanced road safety and autonomous driving. C-V2X technology could be the key that can finally unlock the promise of intelligent transportation systems. C-V2X systems are based on the widely deployed 3 GPP LTE cellular specifications [1]. Future communication models will take advantage of newer 5G technology to add features and to improve data rates and latency. There are two research objectives of this work. The first objective is to assess the environmental effects of a wide deployment of a C-V2X-enabled dynamic routing application. More specifically, we attempt to quantitatively determine how specific C-V2X-enabled applications can reduce CO\({}_{2}\) emissions and fuel consumption. This analysis will help various stakeholders improve their understanding of the societal benefits of C-V2X, and thereby accelerate the deployment of C-V2X across the US. The second objective is to study the impact of C-V2X communication on the performance of transportation applications. The proposed methodology entails developing and using a microscopic integrated traffic and C-V2X simulation tool to quantify the network-wide efficiency and environmental impacts of a Connected Energy-Efficient Dynamic Routing (C-EEDR) application. The C-EEDR application is an ITS application that attempts to minimize the vehicle's fuel consumption levels, by routing vehicles through the most environmentally friendly routes. It utilizes connected vehicle technology to collect real-time fuel consumption information from probe vehicles to compute the best routes. The C-EEDR navigation system assumes the capability of some vehicles (known as sensor vehicles or probe vehicles) to compute the fuel consumption on each traversed road segment and report the computed fuel consumption and the associated road segments to the cloud. The optimal routes are calculated at the cloud and reported back to all the connected vehicles. Our contributions are:  
* We developed a fully integrated traffic and C-V2X modeling framework and used this modeling framework to quantify the system-wide efficiency, energy, and environmental impacts of the C-EEDR application. The evaluation was conducted on a calibrated network of downtown Los Angeles (Figure 1), considering a peak hour of the weekday morning peak period (approximately 144,000 vehicles were modeled). The application testing considered different traffic demand levels and different levels of the market penetration of connected vehicles. * Our modeling framework also captures the bidirectional interaction of the communication and transportation systems. The traffic density affects the communication network performance. On the other hand, in the C-EEDR navigation application, the traffic mobility is directly affected by the performance of the communication system (i.e., packet reception rate, PRR). A low packet reception rate means reduced information for the application, leading to calculating sub-optimal routes, and resulting in higher fuel consumption levels. * The developed framework allows us to study and model such complex and challenging systems by capturing the mutual interactions between the communication and transportation systems [2] at a city-level road network scale. * We compare the C-EEDR application performance in two main cases:
* The IDEAL communication case, which assumes a perfect communication performance (i.e., no packet loss or delay); and
* The realistic communication modeling case, in which we utilize a C-V2X communication effectiveness model and study its impact on the system performance.