A future intelligent traffic system with mixed autonomous vehicles and human-driven vehicles

Abstract

Autonomous vehicles (AVs) being an essential component of the future smart city traffic system is a hot topic in recent years, even though it is still in its development stage. It is conceivable that in the near future, AVs and human-driven vehicles (HVs) will have to co-exist in traffic systems. This is the first paper to study a mixed traffic system from a micro perspective based on the cellular automation model. In this system, by the use of sensors or mutual information exchange, each AV will have a ‘foresight’ and will be able to know the speeds and positions of vehicles in front of it. In the circular road scenario, we studied how the traffic capacity is influenced by the degree of foresight, the ratio of AVs to HVs, vehicle density, and the probability of random deceleration of HVs. Then we came up with the below conclusions: (a) The optimal foresight is k=5. An AV only needs to gather the information of the 5 vehicles in front of it. (b) The two critical factors to measure the capacity of a traffic network are the critical vehicle density and the maximum average flow. When the ratio of AVs to HVs is increased, these two critical factors increase at an accelerating rate. (c) Even if a low ratio of HVs is running in the system, it will have an appreciable negative impact. An increased probability of random deceleration can expand the hysteresis loop range and reduce average flow. (d) Within a specific range of vehicle density, there is an optimal ratio of AVs at which the traffic system has the maximum average flow. This has implications in controlling the ratio of AVs. Finally, theoretical solutions of critical vehicle density are obtained by using the mean-field theory in physics. If the vehicle density is larger than these critical values, the traffic system will be in a deadlock state, and all vehicles cannot move. These formulas are crucial to controlling the density and the ratio of AVs and HVs in future intelligent traffic systems and will help to avoid large-scale traffic congestion. The above findings will have practical ramifications for precise traffic management and traffic control in a mixed AV and HVs system.