Traffic light control (TSC) is an important and challenging real-world problem with the aim of reducing travel time as well as saving energy. Recent researches have numerous attempts to apply intelligent methods for TSC at four-way crossroads to solve the traffic light scheduling problem. However, there is the limitation of researches on efficient TSC at three-way crossroads. Therefore, this paper introduces a novel TSC solution for three-way crossroad environment (TW-TSC). The proposed TSC method is designed based on a deep reinforcement learning approach, namely Soft Actor-Critic (TWSAC). Firstly, we create a simulation environment for three-way crossroads which consists of numerous transportation and two parallel lanes using Unity framework. Secondly, to achieve practical movements of transportation in three-way crossroads, we carefully design agents which have a high impact to the transportation movement, notably the time to wait for traffic light, the velocity of transportation, and the number of transportation passing successfully. Finally, to achieve TW-TSC efficiency, we propose a novel reward function together with a design of TWSAC algorithm. Experimental results show that the proposed TWSAC in TW-TSC achieves higher performance than both fixed-time TSC methods and relevant RL algorithms.