Algorithms Used in Eco-Driving Studies 

One of the most important factors to implement eco-driving is in the development of efficient and sustainable model algorithms to evaluate the components of eco driving. Hence many studies have been conducted in this regard. Rakha and Kamalanathsharmain (2011) conducted a study which focused on eco-driving at signalized intersections using V2I communication. The authors developed a framework to enhance vehicle fuel consumption efficiency while approaching a signalized intersection through the provision of signal phase and timing information that may be available through vehicle-to-infrastructure communication.

The stand out factor in this study is that reducing fuel consumption is maintained as it is while alternative analysis is used to compare different velocity profiles that can be taken by a vehicle approaching an intersection.

Therefore, this study highlighted the importance of retaining microscopic fuel consumption models in the optimization function for enhanced eco-driving (15).Tec Yao et al. (2018) developed a Variable Speed Limits Method with Location Optimization (VSL-LC) with pre-fixed traffic signals which dynamically imposed speed limits on some identified Target Controlled Vehicles (TCVs) with Vehicle to Infrastructures (V2I) communication ability at two VSLs along an approaching lane.

The study is unique in such a way that it renders more flexibility to adjust smoothed trajectory shape to achieve the optimal traffic performance by optimizing the locations of VSLs while focusing on the optimal design of a system in both temporal and spatial dimensions. The study outcomes suggested that VSL-LC method can greatly increase traffic efficiency and reduce fuel consumption, and it improves as the compliance rate increases (16).

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Kundu et al. in 2013 presented a new approach of eco-speed control at a microscopic level using the concept of platoon of vehicles to reduce fuel consumption in a journey covering multiple intersections in a multiple vehicle setting. The study analysed three heuristic algorithms and found that fuel consumption was reduced by 10% in a journey for a single fixed platoon of vehicles using multiple algorithms. The applicability of the proposed models to all the vehicles approaching an intersection from all the directions is also discussed since traffic light periods are maintained as constant (17). Nunzio et al. in 2016 presented a new approach of eco-driving in urban traffic networks using traffic signals information. A pruning algorithm was developed to reduce the optimization domain by considering only the portions of the traffic light’s green phases that allowed to drive in compliance with the city speed limits. The study also focused on the possibility of further improvements when large scale information about many successive signalized intersections is available (18).

Mousa et al. (2018) in a recent study demonstrated a deep-reinforcement learning algorithm for eco-driving control at signalized intersections with prioritized experience replay, target network, and double learning. The main intent of the work was to overcome the complexity of non-linear models for fuel consumption (19). Chen et al. in 2015 developed an eco-driving speed control algorithm for a platoon of vehicles at a signalized intersection. The main considerations of the algorithm were running status of the target platoon and the impact of the downstream platoon. The results indicated that when the platoon needs to accelerate to pass the intersection, a smaller headway causes less fuel consumption; while a larger headway results in less fuel consumption if vehicles decelerate to pass the intersection. On the contrary, the consumption of fuel by the leading vehicle was found to be altered by the ability of the vehicle to obey the system’s advice (20). Chen et al. in 2014 developed an optimization model to determine the optimal eco-driving trajectory at a signalized intersection.

The core idea was the utilization of the information of signal phases and the queue-discharging time in eco-driving to optimize the speed trajectories for a vehicle approaching an intersection to reduce fuel consumption and emissions. A model simulator for Nitrogen Oxide emissions and the Genetic algorithm was selected to solve the developed optimization problem. The results highlighted that eco-driving could achieve satisfactory reduction in emissions by more than 50% and in travel time by about 7% compared with the normal driving strategy. Chen et al. in 2012 proposed an enhanced algorithm taking the driving style of preceding vehicle, host vehicle drivers and road information into account to enhance eco-driving system based on V2X communication. The simulation results inferred that the controller became more flexible and reduced the fuel consumption of the controlled vehicle in different working conditions with the help of the enhanced algorithm (21).

Kamalanathsharma (2014) presented an algorithmic development of an Eco-Cooperative Adaptive Cruise Control system within a dynamic programming framework to find near-optimal and near-real- time solutions to a complex non-linear programming problem that involves minimizing vehicle fuel consumption in the vicinity of signalized intersections. The results were found to generate significant savings of up to 30% in fuel consumption within the traffic signalized intersection vicinity. Further work was carried out to test the proposed system in an agent-based environment using the Rakha-Pasumarthy-Adjerid (RPA) car-following model as well as the Society of Automobile Engineers (SAE) for vehicle-to-infrastructure (V2I) and vehicle-to-vehicle (V2V) communication. The final goal was to develop an integrated framework for the control of autonomous vehicle movements through intersections using a multi-objective optimization algorithm.

The results showed that the eco-speed control algorithm was able to reduce the overall fuel-consumption of autonomous vehicles passing through an intersection by 15% while maintaining the 80% saving in delay when compared to a traditional signalized intersection control. These kind of studies open avenues to explore integrated optimization options to enhance environment friendly outcomes through eco-driving (22). What contributes to excessive fuel consumption at signalized intersections?  On urban roads, acceleration operation frequently happens at intersection where the drivers encounter many stop-and-go procedures. Traffic congestion and idling are two main reasons for energy consumption in signalized intersections. Although the delay time at intersections is a minor portion of entire commuting time, it may contribute up to about 25% of the total trip emissions (18). Such phenomenon has significant effect on instant emission, production of much pollution and excessive fuel consumption. Literature shows if all factors are constant, at signalized intersection, car emission rates are the function of speed and acceleration (23). Emissions in short term events (like acceleration and deceleration) are called micro-scale emissions.

The models related to these emissions are very instrumental in devising traffic control strategies at road sections where such short-term events are frequent (24). Using optimizing signal timing design and some applications such as real-time adaptive signal control, eco-driving can modify the speed trajectories (the speed profile) for a vehicle approaching a signalized intersection to reduce fuel consumption and emissions. In addition to such technologies, there are some other traffic control strategies relying on wireless communication. Specially, VII (Vehicle Infrastructure Integration), and V2I (Vehicle to Vehicle). In this section we discussed how these technologies can be helpful in implementing eco-driving at signalized intersections.

Eco-driving at signalized intersections using VII, V2I communications  Using Vehicle Infrastructure Integration (VII) was started by FHWA for the first time in 2003. They aimed to bring the benefit of vehicle and infrastructure together to increase the safety of the roads, reduce traffic congestion and mitigating the environmental impacts of transportation. (15). Then, many researchers investigated how VII can be helpful for implementing the eco-driving at signalized intersections. For example: Li t al. in 2009 conducted a study (25) to focus on providing drivers with advanced traffic signal status (TSS) information using communication capabilities between vehicle and roadside infrastructure enabled by VII for energy and emissions reduction.  Such information can alert the drivers to slow down when there is a little or no chance to pass the intersection before getting red. Therefore, this can prevent the drivers to have unnecessary accelerating and having hard braking near the stop line.

They defined two different cases: Case 1 is when driver do not have the signal information and have an unnecessary hard braking. Case 2 is the case in which the driver has TSS information in advance and realizes that the residual time of current signal phase will not allow for pass through the intersection and let the driver to slow down smoothly to stop. The following figure shows the vehicle trajectories with and without TSS information.Advanced Driving Alert System (ADAS) When the TSS information is available through communication enabled by VII to instrumented vehicle, the predicted red onset warning can be processed in conjunction with the vehicle estimated travel time. This information helps drivers to decelerate smoothly to have a safe stop back of a queue. While just small percentages of the vehicles are instrumented to get the TSS information, the other vehicles following the leading vehicle with the information have to decelerate in a similar manner.

ADAS use a signal’s time-to-red information and the estimated travel time to an intersection as the basis the advisory decisions. TTR is communicated to all instrumented vehicles as soon as the signal turns to green. The on-board vehicle system compares the signals TTR with the estimated travel time to intersection and decides whether to issue the alert. Assume that the vehicle is at distance d to the far end of the intersection traveling at speed v. The estimated travel time to intersection j is modeled by:= where  is the travel time of vehicle k to the intersection j. The probability of a vehicle being able to travel through intersection j before red is:  When 1-  is greater than the threshold, the vehicle is determined not to able to pass the intersection and the driver will receive alarm to stop due to the expected red signal downstream.It is important to know that TSS warning information has the largest benefit during under-saturated traffic congestion.

Because in this situation, decelerate-idle-accelerate and decelerate-accelerate sequence would be expected to occur close to intersection when traffic signal is red but when a over-saturated traffic is occurred, the influence area would be stretch between the links of the intersections.Then to assess the energy and emissions impact, they used the Comprehensive Modal Emissions Model CMEM) which is a microscopic emissions model that has been developed at the university of California ,Riverside and it is able to predict second-by-second fuel consumption and tailpipe emissions of CO2,CO.HC and NOX. According to this model, two major dynamic factors that contribute to the energy/emission rates of vehicles in intersections are instantaneous speed and acceleration. They conclude that under medium congestion saving of fuel consumption will reach to 8% and co2 emissions reduce by about 7% by using TSS. Eco-driving at signalized intersections using V2I communications V2I wireless technology also exchanges the critical dynamic traffic situation and operational data between vehicle and roadside infrastructure. Li et al.(2015) to experiment V2I system on vehicle emissions considered two different scenarios: vehicle driving approaching signalized intersection and 2.