## 2 Related Work  
A previous study [3] developed a stochastic energy-efficient feedback routing system that builds on vehicle connectivity. This system selects vehicle routes based on the the energy consumption experiences of other vehicles within the same vehicle class. All drivers within a specific class are divided into five sub-populations, each comprising 20% of all drivers. The paths for each of these sub-populations are then updated every \(t\) seconds during the simulation based on real-time measurements of link (arc) energy usage for the specific vehicle class under consideration. The minimum path updates for each vehicle sub-population are staggered in time to avoid situations in which all vehicles select identical paths if the minimum and next minimum path are similar. This logic results in 20% of the driver paths being updated every \(t/5\) s. The selection of the next link that a vehicle should take is made using a vehicle-specific array that lists the entire sequence of links from a vehicle's current link to its destination. Upon the completion of any link, a vehicle simply submits its energy consumption on the link and then queries this array to determine which link it should utilize next to reach its ultimate destination in the most efficient manner. The vehicle only uses the experiences of other vehicles in the same class to update the energy consumption estimates on a link. This allows for a routing system that is multi-class (up to five classes; vehicles are only affected by experiences of other vehicles in the same class), stochastic (the system can introduce white noise in the link-specific cost function; the user specifies the coefficient of variation of the white noise distribution), and dynamic (based on the updating frequency specified by the user). The authors showed how the benefits varied between the Columbus and Cleveland road networks using the \"IDEAL\" communication framework. Based on those findings, we expect our proposed system \"Connected-Energy Efficient Dynamic Routing (C-EEDR)\" application to perform well on other urban networks. Figure 1: PRR of C-V2X communication data generated using a proprietary simulation software. The routing algorithm was further enhanced by developing an ant colony-based eco-routing technique (ACO-ECO) that reduces data latency (the time taken to obtain the link cost information) [4]. The ACO-ECO algorithm is also able to capture randomness in route selection, pheromone updating, and pheromone evaporation. The ACO-ECO algorithm reduced the network-wide fuel consumption and CO\({}_{2}\) emission levels by 2.3% to 6.0% relative to the feedback eco-routing system. Further enhancements were made using a linear programming technique to compute system-optimum routings [5]. This was the first system optimum eco-routing model that uses linear programming and stochastic route assignment to minimize the system-wide fuel consumption. The system optimum eco-routing reduced the fuel consumption by about 36%, compared to the user equilibrium model. This energy-efficient routing algorithm was first developed in [6] to select the most energy efficient route, thus minimizing CO\({}_{2}\) emissions. The algorithm was applied on the road network of the city of Lund, Sweden. This routing algorithm produced a 4% average saving in fuel consumption. Eco-routing finds the optimal routes based on the fuel consumption cost only. Authors in [7; 8] used the travel time and fuel consumption as a multi-objective for finding the best routes for connected and automated vehicles. The authors in [9] applied the concept of fuel savings not only in the routing context but also in the eco-driving application in the same road network simulations. Two ITS applications were activated in the simulations, namely, eco-driving and eco-routing. The application optimizes the routes for better fuel savings and also optimizes the speed profile to achieve better fuel consumption levels.