## 2 Related Work
The authors in [10] developed a fuel efficient routing algorithm that computes the most fuel-saving route, taking into consideration the detailed mobility profiles of different road segments, the driving behavior, and the different vehicle types. The authors utilized the mobility profiles (including acceleration, deceleration, and idling time) of the road segments instead of assuming an average speed to better estimate the fuel consumption on each road segment. The authors concluded that their approach significantly reduced fuel consumption at the expense of a longer route or more travel time. The work conducted in [11] explored the effect of including the traffic signals into the dynamic traffic routing problem. The authors devised a 0-1 integer programming approach with a multi-objective function (travel time and energy consumption) to find the energy-efficient route for electric vehicles. The proposed method finds the optimal route that would allow the electric vehicle to go through the road network, avoiding stopping at traffic signals, and thus reducing energy consumption (in decelerating and accelerating). The authors assumed the vehicle receives all the needed information (traffic signal timing) without modeling the communication constraints (i.e., IDEAL communication). Most of the work conducted in the area of the eco-routing assume an IDEAL communication (i.e., no packet loss or delay) network and does not model its impact on the eco-routing application performance or even the other relation, the impact of the eco-routing application on the communication system performance. As far as we know, there is no work or tool in the literature that studies the mutual bi-directional impact between the communication and the transportation system at both the system performance and the application performance levels. The authors in [12] assumed wireless connectivity using WiFi. They used a bluetooth-enabled OBD adapter to transmit the fuel consumption data to a phone application on the driver's cell phone in the car. The application then combined the OBD data with other sensory data and transmits them to an application server upon the availability of WiFi Internet connectivity. The work in [13] considered the impact of communication on the eco-routing application and used a discrete event simulation to model communication in Vehicular Ad-hoc NETworks (VANET). However, the system was developed for small scale networks and does not scale well with large road networks. In [14], the authors developed a novel analytical model for the Dedicated Short Range Communication (DSRC) communication technology and incorporated it within a microscopic traffic simulator, the INTEGRATION software [2], to enable the modeling of communication and traffic mobility in large-scale transportation systems. In [15], the authors utilized the analytical model for the DSRC communication technology to conduct their sensitivity analysis of both the DSRC communication system and the penetration ratio of connected vehicles and their impacts on the eco-routing application on a city-wide large-scale road network. The authors in [16] reviewed the studies that analyzed the environmental and safety impact of the vehicle-to-everything (V2X) communication technology on the Intelligent Transportation systems (ITS) applications. In this work, we utilize industry provided data to model the most recent communication technology, cellular vehicle-to-everything (C-V2X), to analyze the impact of the communication system on the C-EEDR application at different market penetration ratios and different traffic demand levels. We unitize a highly accurate fuel consumption model [17] to estimate the fuel consumption of the vehicles in the road network.