Optimal Route Algorithm Considering Traffic Light and Energy Consumption

Vehicle speed trajectory and traffic signals significantly impact vehicle’s fuel consumption and travel time in the urban road network. In this paper, we proposed an optimal vehicle routing algorithm that takes the waiting time at signalized intersections and eco-driving model into consideration. First, the data of the floating car collected by the GPS are matched with the electronic map, and the average traveling speed of the vehicle in each road segment is calculated in real time. The position and timing information of the signal lights at each intersection are pre-acquired to establish an eco-driving model at signalized intersections with the support of cooperative vehicle infrastructure system technologies. The vehicle accelerates through the signalized intersection to reduce the waiting time in the case, where the headway is allowed, while the vehicle decelerates to the minimum speed to avoid idling. Based on the traffic lights red light, green light conversion probability, and vehicle energy-saving driving model, the signal light cycle duration is divided into four parts: the green light pass section, the red-light acceleration section, the red light idle section, and the red-light deceleration section. Combining the probability distribution and the fuel consumption model, the average fuel consumption at the intersection area can be calculated. Taking the optimal energy consumption as the goal, combined with the A* algorithm, an optimal path algorithm that considers the influence of traffic lights and energy consumption is proposed. Finally, two examples are tested, including a real-world road map in Changsha city to demonstrate the effectiveness of the proposed algorithm. And the results show that the proposed model has good performance on energy consumption reduction.